Opportunity, Environmental Characteristics, and Crime: An Analysis of Auto Theft Patterns (Criminal Justice: Recent Scholarship)

Criminal Justice Recent Scholarship

Edited by Marilyn McShane and Frank P. Williams III

A Series from LFB Scholarly

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Opportunity, Environmental Characteristics and Crime An Analysis of Auto Theft Patterns

Marissa P. Levy

LFB Scholarly Publishing LLC El Paso 2009

Copyright © 2009 by LFB Scholarly Publishing LLC All rights reserved. Library of Congress Cataloging-in-Publication Data Levy, Marissa Potchak, 1978Opportunity, environmental characteristics, and crime : an analysis of auto theft patterns / Marissa P. Levy. p. cm. -- (Criminal justice : recent scholarship) Includes bibliographical references and index. ISBN 978-1-59332-327-1 (alk. paper) 1. Automobile theft--United States. 2. Theft from motor vehicles-United States. 3. Crime--United States. I. Title. HV6658.L48 2008 364.16'286292220973--dc22 2008034680

ISBN 978-1-59332-327-1 Printed on acid-free 250-year-life paper. Manufactured in the United States of America.

TABLE OF CONTENTS

1 – Introduction to the Research…………………………….. 1 Introduction.....................................................................1 Importance of Research………………………………...3 2 – Community Crime Patterns………………………………7 Introduction…………………………………………….7 Ecological Theory……………………………………...7 Community-level Scholarship………………………...12 3 – High Crime Areas & Opportunity Structures………….29 Introduction……………………………………………29 Opportunity Literature………………………………...29 Pattern Theory……………………………………….. 38 Hot Spots……………………………………………...41 Repeat Victimization…………………………...……..45 4 – Micro/Site-level Crime Patterns………………………....51 Introduction………………………………….………..51 Site-level Scholarship……………………………...….57 5 – A Multi-level Investigation of Auto Theft………………83 Community-level Research……………………………84 Site-level Research………………………………….....95 6 – A Community-level Investigation of Auto Theft…..….107 Introduction………………………………………..…107 The Models –Auto Theft in Lexington-Fayette………107 7 – A Site-level Investigation of Auto Theft…….…………125 Introduction…………………………………………..125 Database Description…………………………..…….125 8 – Discussion of the Relevance of the Environment on Auto Theft…………………………………………………155 Community-level Discussion…………………..……155 Site-level Discussion………………………………...164 v

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9 – Discussion of the Limitations to Studying the Effects of the Environment on Auto Theft……………………177 Introduction……………………………………….….177 City Selection……..…...…..…...………………….…177 Community-level Analysis……………………..……179 Site-level Analysis…………………………………...181 Review of Limitations……………………………….183 10 – Policy Implications: Studying the Effects of the Environment on Crime………………………….…185 Lessons……………………………………………....185 Practical Issues……………………………………….191 Using W.A.L.L.S. and the Opportunity Structure for other Crimes………………………………………195 The Future of Geographic Analyses………………….196 The Future of Crime Prevention…….………………..197 References…………………………………………………....199 Index…………………………….………………………...…219

ACKNOWLEDGEMENTS I would first like to thank Leslie Kennedy, George Kelling, and Marcus Felson for their guidance during the early writing and data collection for this book. I would also like to thank Gisela Bichler for the time, energy, and motivation she provided throughout. Much appreciation goes out to my colleagues at Richard Stockton College of NJ, especially Christine Tartaro for her insight and affability, and Alison Wray for her research and organizational assistance. Finally, I am grateful to my family for their understanding and patience, especially Zoe Rose for her inspiration and wisdom.

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CHAPTER 1

Introduction to the Research

INTRODUCTION It has been shown that crime is not randomly distributed across space or time (Brantingham & Brantingham, 1981, 1984; Harries, 1990; Rengert, 1989) but rather there are “patterns” of clustering referred to as hot spots; small places with a high concentration of crime over a certain period of time that is so prevalent it has become predictable (Sherman, 1995). This research explores the crime of auto theft by investigating the offender’s decision-making process and the impact of the environment on those decisions. The research combines relevant selections of theoretical and empirical research currently used in the field to develop an opportunity structure and victimization model for auto theft. To this end, several theories are utilized to examine how the offender makes decisions regarding crime. The Routine Activity Approach links the offender’s daily activities to criminal events (Clarke & Felson, 1993; Eck, 1995; Felson, 1994). Situational Crime Prevention offers a unique perspective on environmental characteristics that are present in crime (and crime-free) locations. Crime Pattern Theory provides a link between the two existing approaches since it examines how offenders find, select, and gain access to a target (Brantingham & Brantingham, 1981, 1984, 1993a). Together these theories explain why crime occurs at some addresses and not at others; why crime concentrates around certain types of facilities; and which factors influence offenders the most in terms of target selection. In order to examine the potential interaction of these theories more closely, the crime of auto theft has been chosen. The need to prevent auto theft has mandated the Lexington Police Department (population 270,179, according to U.S. Department of Justice, 2008) to study patterns in the number of reported auto thefts. In 2001, auto theft rose in cities of every size, including Lexington-Fayette, Kentucky where auto theft increased by 1.3 percent (U.S. Department of Justice, 2001). However, from 2003 to 2007, the auto theft rate in the United States 1

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has been on a steady decline (U.S. Department of Justice, 2008). Lexington-Fayette follows this trend with 773 reports in 2004, 745 in 2005, 703 in 2006 and 612 in 2007. Between the years 2006 and 2007, the greatest decline in auto theft took place in cities with populations between 50,000 to 99,999 (-9.5% change) and 250,000 to 499,999 persons (-9.4% change); Lexington falls into the latter category (U.S. Department of Justice, 2008). According to the Uniform Crime Reports (U.S. Department of Justice, 2006), clearance rates for auto theft for cities with a population greater than 1 million (7.9% cleared in 2006) are on average lower than those in cities with a population of less than 10,000 (24% cleared in 2006). Despite the decline in auto theft rates, the National Insurance Crime Bureau (NICB) indicated that “vehicle theft is the nation’s number one property crime, costing an estimated $7.6 billion each year” (2008, p. 2). Cities with major port facilities or those near international borders recorded the greatest number of thefts: Los Angeles – 65,243 stolen vehicles, New York – 46,709 stolen vehicles, and Philadelphia – 30,355 stolen vehicles occupied the top three slots (National Insurance Crime Bureau, 2000). In 2007, Las Vegas was ranked #1 (NICB, 2008). This indicates a shift in auto thefts from coastal regions to more inland cities. Perhaps this trend will shed some light on auto theft that occurs in rural and inland areas. The clearance rates may indicate that it is difficult for police to track down and arrest offenders. Cities provide outsiders with easier access to target vehicles via better road networks, more anonymity than they have in their own neighborhoods (Brantingham & Brantingham, 1981) and increased awareness of city routes and pathways during everyday travel (Brantingham & Brantingham, 1984; Rengert & Wasilchick, 1985). Because of the increased access and awareness space of offenders, and greater ease with which offenders can maneuver around a city, it is necessary for research to progress towards an analysis of relevant opportunities. In other words, since criminologists have established a method of identifying likely offenders and targets, it is now necessary and possible (with the increased use and acceptance of GIS technologies) to identify situations that present greater opportunities to offenders. Opportunities, like offenders, are crime-specific, and therefore must be identified based on characteristics and patterns of the crimes they represent. By identifying auto theft opportunities, an opportunity structure can be assembled to aid the crime analyst in recognizing potential auto theft locations.

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Through combining an opportunity structure with an intensive sitelevel approach (Rondeau, 2000; Bichler-Robertson & Potchak, 2002), this study will focus on the community-level environmental cues that facilitate crime, as well as site-level features that present offenders with attractive opportunities for auto theft. This two-tiered approach will identify areas that auto thieves see as conducive for crime and the specific situational aspects they encounter when selecting individual targets. IMPORTANCE OF RESEARCH This project contributes to our understanding of crime patterns in three capacities. Community-level Research: High Crime Areas & Opportunity Structures An opportunity structure drawn from theory to predict crime concentration for auto theft is developed. This opportunity structure is used to model the crime concentration in the site area, LexingtonFayette, Kentucky. Routine Activity Approach (Cohen & Felson, 1979; Felson, 1994) and crime pattern theory (Brantingham & Brantingham, 1981, 1993a) are drawn upon in order to link situations and offenders. In addition to these place-based theories, several studies of offender decision-making have been conducted where offenders are questioned about the factors they consider when choosing a target. Eck, Clarke, and Guerette (2007). These interviews and hypothetical scenarios also lend support to the idea that the Rational Choice perspective (Cornish & Clarke, 1986) should be included in the opportunity structure. The opportunity structure will be derived from a combination of theory and empirically tested variables such as: streets and major roadways that provide access and awareness (Beavon, Brantingham, & Brantingham, 1994; Cohen & Felson, 1979; Felson, 1998; Felson & Cohen, 1980), residential land use that provides parking in locations where individuals spend most of their time (Decker, Shichor, & O’Brien, 1982; Greenberg & Rohe, 1984; Greenberg, Rohe, & Williams, 1982; Jacobs, 1961; Kuo & Sullivan, 2001; Park & Burgess, 1925; Rengert, 1981; Sampson & Wooldredge, 1987), public housing, or in this case, Section 8 housing, which fosters an increased opportunity for crime (Fagan & Davies, 2000; Popkin, Gwiasda,

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Rosenbaum, Amendolia, Johnson, & Olson, 1999; Jeffery, 1971; Mayhew, 1979; Newman, 1972, 1996; Roncek & Faggiani, 1985), apartment complexes, which are thought to act similar to public housing since their design and upkeep are similar, parking facilities, which provide a large number of targets and few capable guardians (Cohen & Felson, 1979; Felson, 1998; Felson & Cohen, 1980; Miethe & McCorkle, 2001; Newman, 1972; Poyner, 1997; Poyner & Fawcett, 1995) and rarely utilize target hardening devices (Clarke, 1997; Smith, 1996; Webb, Brown, & Bennett, 1992), convenience stores and gas stations that provide increased opportunities for drive-offs (Duffala, 1976; Graham, 2001; Hunter & Jeffrey, 1992; LaVigne, 1994; Petrosino & Brensilber, 1997; Schiebler, Crotts, & Hollinger, 1996; Smith, Frazee, & Davison, 2000; Swanson, 1986) and auto theft, transportation hubs that provide unsupervised targets for hours at a time (Brantingham & Brantingham, 1999; Brantingham, Brantingham, & Wong, 1991; Levine & Wachs, 1986; Loukaitou-Sideris, 1999; McCord & Ratcliffe, 2007); schools (Wilcox et al., 2006) where a condensed amount of unsupervised teens are released at the same time (Fox & Newman, 1997) and residences located on the same block as schools which have been found to have a higher crime rates (Jensen & Brownsfield, 1986; Roncek & Faggiani, 1985; Roncek & Lobosco, 1983; Sampson & Wooldredge, 1987), fast food establishments (Ford & Beveridge, 2004) and bars, since they draw people into the area (Kumar & Waylor, undated; Loukaitou-Sideris, 1999; Roncek & Bell, 1981; Rossmo, 1994; Rossmo & Fisher, 1993; McCord & Ratcliffe, 2007; Britt et al., 2005; Gruenewald et al., 2006) are known to generate crime (Roncek & Bell, 1981; McCord, Ratcliffe, Garcia, & Taylor, 2007; Eck, Clarke, & Guerette, 2007) and are activity nodes (Brantingham & Brantingham, 1999), accommodations such as motels and hotels, that provide increased opportunities when valuables are left in cars (Cook, Merlo, & McHugh, 1993; Fennelly, 1992; Huang, Kwag, & Streib, 1998; Smith, Frazee, & Davison, 2000), and auto parts and repair shops that create an increased number of targets (Gant & Grabosky, 2002; La Vigne, Fluery, & Szakas, 2000). Together, these theories and other empirical works suggest a number of geographic factors that should be included in the framework to account for crime concentration. Based on these works, several models are developed and compared to the density map of all auto thefts in Lexington-Fayette, Kentucky for a period of two years. A density map containing well-traveled roadways, government-subsidized housing, parking lots, convenience stores/gas stations, transportation

Introduction to the Research hubs, and schools is used as the base model. Several variables combined with the base model, one at a time, and compared to density of auto theft. A Full Model (with all variables) and Alternate Model (with the best variables) are developed as opportunity structure for auto theft in Lexington, Kentucky.

5 are the an the

Micro/Site-level Crime Patterns The second aspect of this project identifies environmental factors that may contribute to higher auto theft victimization. A site survey is conducted of 75 randomly chosen auto theft locations that are matched to 75 single victimization locations. When arriving at the locations, data on the Watchers, Activity nodes, Location, Lighting, and Security (W.A.L.L.S.) variables was collected. The W.A.L.L.S. variables were selected based on a number of published works used to identify target attractiveness (Rhodes & Conly, 1981), environmental cues (Taylor & Nee, 1988; Wright & Logie, 1988), selection tactics (Bennett & Wright, 1984), and insight (Walsh, 1986) of convicted or known offenders. Studies of offender decision-making typically involve questioning known offenders (Bennett & Wright, 1984; Walsh, 1986) —usually burglars, robbers, or motor vehicle thieves—about factors they consider when choosing a target. In these types of research studies, interviews are conducted after the crime has occurred and offenders are usually questioned about hypothetical situations. In addition to these works, hot spot research is explored to link the conceptual work with concrete examples of research and findings currently used in the field. Ratcliffe and McCullagh (1998) suggest potential problems and issues locating hot addresses and Verma and Lodha (2002) suggest developing multidimensional models to identify “burning times” within the dimensions of time and space. Even GIS technology has been used to predict the potential for crime (Groff and La Vigne, 2002; Boba, 2005) after being combined with theory (Eck, 1995; 1998) and adjusted for proper units of analysis (Bichler, 2004). These works enable crime analysts to study hot spots and compare their environmental characteristics to locations that experience single or no victimization.

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Blended Research: Combining Opportunity and Environmental Factors to Identify Repeat Victimization The third contribution offered to the field is a model that identifies areas with increased criminal opportunity and provides a detailed, sitelevel analysis of locations that have suffered repeat victimization. Information that is learned here can offer valuable insight to police and crime analysts about the target selection processes of offenders. By using this combination of community and site-level examination, several variables that significantly increase the likelihood of victimization can be identified. Though this model has been specified using the example of auto theft, the variables can be adjusted to fit most other property crimes. These models, which are driven by theory, provide crime analysts with a methodologically sound tool to study crime. Some crime analysts have adopted the practice of crime mapping without adopting the theory necessary for meaningful crime analysis (Bruce, 2002; Eck, 1998), casting geographically-based crime analysis as a data-driven process that simply describes crime patterns and neglects the importance of criminology and the vast literature that sheds light on criminal behavior. Understanding the theoretical underpinnings of particular spatial patterns allows the researcher to identify important clusters of events and factors that produce crime (Boba & Price, 2002). Presenting a guide based on theory reduces possible confounding and extraneous factors that may mislead the interpretation of crime maps. One of the principal reasons for the use of ineffective strategies is that police agencies often do not have the resources or inclination to engage in detailed examinations of the targeted crime problem, nor are they cognizant of the empirical research that has addressed similar crime issues (Eck & Wartell, 1999; Schmerler & Velasco, 2001). This project explores the use of theory to guide the crime analysis process. By doing so, the research develops an auto theft opportunity structure for Lexington-Fayette, Kentucky that identifies locations of high crime opportunity and provides a framework for the study of repeat and single victimization locations.

CHAPTER 2

Community Crime Patterns

INTRODUCTION For the last seventy-five years sociologists have been exploring the role of the environment in crime. From the first recognition of patterns in plant ecology that resembled patterns in human life, sociologists, and then later, criminologists, acknowledged a distinct similarity between plant and human survival. From these studies, the evolution of the environment in which people live has become the main focus of environmental criminologists. The organization of human surroundings has been analyzed to identify and compare locations where crime occurs often to those where crime rarely occurs. Today, when academics think of crime prevention, they focus on the situational aspects of reducing crime. Such crime prevention techniques as mounting video cameras, building fences, and installing locks are used to thwart crime and change the environment into one in which crime is absent. The purpose of this chapter is to connect current crime patterns with their roots in human ecology. ECOLOGICAL THEORY Ecological theory is based on the survival, competition, and succession of plant ecologies. The same model of survival, competition, and succession is then applied to society, hence social ecology. Park and Burgess (1925) recognized the importance of the location of a resident’s home in relation to other ecological aspects of the city. The City, written by Park and Burgess (1925), is one of the first pieces that was taken from ecology and applied to criminology. The ideas borrowed from ecological theory recognize the importance of the environment as a reflection of its people. The citizens in a particular area (a city or a neighborhood) are the key to understanding how the community is organized and whether or not the police can rely on the citizens to control crime. In addition, the location of trains, buildings, elevators etc. will have an effect on the way in which people move and communicate. Understanding traditional ecological theories, such as 7

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Park and Burgess, will allow researchers to understand that they must look at the full picture in order to understand specific situations of crime. Often researchers become focused on such a small crime phenomenon, a specific crime or a specific crime-generating location, and they forget to look at the structure of the environment, the city’s layout, and the people who live in the area for cues regarding crime. Ecological theory has served not only as the foundation for environmental criminology but has also contributed to the theoretical body of the discipline. In 1936, Park discussed the topic of “economic competition” (p. 10), an individual’s struggle for power and prestige in an attempt to find his/her niche in society. According to Park, an individual will use his/her surroundings, including interactions with other people, to develop a niche in order to succeed. Humans will strive to find a “balance of social equilibrium” (Park, 1936, p. 13), including the use and interaction of populations, artifacts, customs, and natural resources in order to succeed. Without the idea that land, land-use, physical features (mountains, roads, train tracks), and city layout may have an effect on humans (e.g. encourage or discourage opportunities for crime), criminologists today would have never been linked to geographers. This essential tie has enabled the two disciplines to work well together and to maintain a symbiotic relationship. Human Ecology Hawley (1950) identified the community as an organization of human relationships that occur in time and space. He identified rhythm, tempo, and timing as the three important components of the community organization. Rhythm is the regular periodicity with which events occur. Tempo is the number of events over time. Timing is the synchronization of events with regard to the occurrence of the event. Hawley used these three concepts to explain the interaction of humans in the environment; but rhythm, tempo, and timing can also be applied to the criminal event with regard to occurrence, frequency, and presence of repeat victimization. Human Ecology Joins Other Emerging Theories Decker, Shichor, and O’Brien (1982) examined the relationship between urban structure and victimization. Using National Crime Survey (NCS) data, they looked at 26 cities across the Unted States

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using a scale of property crimes with contact, property crimes without contact, and non-property crimes. Motor vehicle theft, considered a property crime without contact, was one of the only variables positively correlated with both population density and percentage on public assistance. More importantly, Decker et al. (1982) found that all types of crimes should not be approached with a single criminal justice policy. Instead, the development of policy must take into account each particular type of crime as well as consider the relationships between crime, population, and environment. In 1986, Byrne and Sampson merged ecological theory with social disorganization theory and continued to lead scholars in the direction of environmental criminology. Byrne and Sampson (1986) state that ecologists believe it is the characteristics of the city who cause crime while non-ecologists think it is the people in the city that are responsible for criminal activity. Byrne and Sampson believe that it is an interaction between the two entities. Consistent with a view of human ecology, the study of the relationship of persons to the environment and the interaction of persons within the environment, are important factors that determine a person’s role in everyday activities; one such activity is crime. Sampson (1985) used approximately 400,000 interviews with household respondents to determine the effects of income inequality, unemployment, racial composition, residential mobility, structural density, and family structure on crime. Analysis of these indicators found that divorce, family disruption, and family dissolution also have significant positive effects on victimization. These results, in conjunction with the earlier findings, suggest that many of the previous models in ecological research may be misspecified (Sampson, 1985, p. 39). Sampson (1985) concludes that an interaction takes place between ecological and social disorganization theories. This interaction was the spark that led to many other influential works in the field. These influential works studied both the ecological and social perspectives, and eventually the two perspectives together, most directly, Taylor and Covington (1988). Taylor and Covington (1988) found that in the lowest status, least stable, minority neighborhoods, further slippage in status and stability levels, relative to the other neighborhoods in the city, were associated with increasing levels of serious disorder. “As a neighborhood’s role in the larger urban fabric undergoes redefinition – as its position vis-à-vis other neighborhoods changes – disorder increases” (Taylor & Covington, 1988, p. 582). It appears, therefore, that social disorganization and relative deprivation processes support ecological

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change – violence changes relationships. Taylor and Covington (1993) found that recent increased attention to the social disorganization theory stemmed from the soundness of its core proposition: community structure has an effect on the ability of residents to informally control their streets and to fend off crime and fear (p. 390). Britt, Carlin, Toomey, and Wagenaar (2005) and McCord and Ratcliffe (2007) found this to be true with regard to alcohol outlet density, as did McCleary (2008) with regard to strip clubs. These findings again acknowledge the need for an integration of social disorganization and ecological theories. Rengert (1989) introduces the idea of spatial justice in terms of social disorganization. The idea of spatial justice or spatial equality is based on the idea that there may not be equal risk of victimization in all locations. This may be the result of criminal justice practices (police patrol or allocation of resources) or it may be the effect of economics (those in the lower class tend to live in areas that are not as safe as other classes). Either way, criminologists must study crime and place and its impact on the ecology of crime. Pattern Theory Brantingham and Brantingham (1993a) suggest that each criminal event is the result of the law, offender motivation, and target characteristics that take place on an environmental backcloth at a particular point in time (p. 259). All of these events are influenced by the previous experiences of both the offender and the victim and are affected by both parties’ daily patterns. Pattern theory suggests that opportunities are uncovered by offenders either during their normal course of daily activities or during search periods when they are looking to commit a crime. Opportunities are not uniformly distributed through space and time, since they tend to mimic the location of crime specific targets (Brantingham & Brantingham, 1993a). Even though opportunities are available only when targets are available, targets (and therefore opportunities) may be seen as suitable at times and unsuitable at other times. For example, a laptop being carried by a student on campus while walking with a large group of friends may seem like an unsuitable target, but the same laptop becomes a suitable target when the owner steps away from his study area in the library to make a phone call and leaves his computer on the desk. This example clearly illustrates that time and space can have an enormous effect on the attractiveness of a target.

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Brantingham and Brantingham (1993a) express the importance of the backcloth in the offender’s decision to commit the crime. The setting of the environment is always changing, this produces a dynamic and unique backcloth for each crime location. The change in the backcloth can be predictable or erratic and fast or slow (Brantingham & Brantingham, 1993a). The decision of an offender to wait until late night in order to use the cover of darkness is based upon one predictable aspect of the backcloth; the offender anticipates his acts to be obscured by nightfall and he can predict that nightfall will come each evening. However, the backcloth for a residential burglary may prove to be erratic when a family must leave town suddenly, or may change slowly if a family member becomes ill and rarely leaves the residence. Ironically, the only static quality of the backcloth is that it will continuously change. The criminal event occurs, according to Brantingham and Brantingham (1993a), when the readiness of the offender is high and s/he encounters a favorable opportunity placed in a proper backcloth. Since all of these attributes are changing at different points in time, they must meet and be agreeable to continue the criminal act. At the time that the offender’s readiness is triggered, the offender will either be in, or travel towards, a place that is in his awareness space. The suddenness of the trigger may depend on the location of the offender at the time. Often, offenders report that they have planned out their crimes (these are situations in which the trigger is followed by a trip to the location). However, other offenders have indicated that seeing the opportunity has triggered their readiness (these are situations in which the offender is already in the location and there is no “journey to crime”). Either way, offenders build their awareness space when traveling through these locations on their way to work or when engaging in social activities – these locations are familiar to offenders because they encounter them during their daily routine activities, when they are not involved in illegal activities. Offenders rarely target locations that are not situated in their awareness space since they are unfamiliar with these areas and can not anticipate the consequences associated with their criminal actions at these locations. Such targets are labeled as “bad” targets and are those that offenders avoid (Brantingham & Brantingham, 1993a). Instead, offenders will go where they are familiar, to locations where “good” targets are to be found (Brantingham & Brantingham, 1993a). Pattern theory is derived from the ideas of the Routine Activity Approach, environmental criminology, and Rational Choice. This allows guidance of existing theory but also permits further exploration

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of the concept of offender-decision making. Pattern theory bridges the gap between the locations where crimes take place and the reasons why criminals choose those particular locations. Pattern theory suggests that it is a combination of both a suitable environment (referred to as a backcloth) and the decisions made by the offender (both the type of crime and where to look for the specific target). Community-level opportunity has evolved from ecological theories and has been enhanced by the Routine Activity Approach and Pattern Theory. Without these contributions to the ecological foundation, the concept of opportunity would not have advanced. Ecological theory provides the framework to which other researchers have been able to add essential elements. Hawley (1950) makes the first important leap from plant ecology to human ecology. Without this, researchers would never have continued along this route. Routine Activity Approach adds the very specific situational context of likely offender, suitable target, and absence of capable guardian. Without this contribution scholars would be left without very specific guidelines from which to calculate offender opportunities and victim risks. Pattern theory is based on the premise that a single location with reduced guardianship and an influx of likely offenders is likely to produce a suitable target. These likely offenders find suitable targets while going about their normal daily activities, and thus encounter a backcloth within the environment that is either suitable or unsuitable for crime at a particular time. These elements of the backcloth change over time and location and, when the timing is right, prove to be excellent targets for offenders. Each of these contributions is unique in its perspective, but together they provide strong support for the study of community-level crime patterns. COMMUNITY-LEVEL SCHOLARSHIP Cohen and Felson (1979) and Clarke (1997) state that opportunity is crime specific. In other words, if the opportunity exists for a particular type of crime it will be that type of crime that occurs in that location. Therefore, criminal opportunities may differ from crime to crime. It is best for researchers to look at the opportunity for a crime to occur within a framework specific to the attributes of that crime. However, researchers must learn about criminal opportunity in related crimes in order to enhance the opportunity structure for the crime they are studying. For example, the crime of auto theft is proven to have a greater likelihood of occurrence in a location with poor lighting (Painter, 1994; Smith, 1996). If a researcher is studying the crime of

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burglary, s/he may benefit from the lighting research conducted with regard to auto theft. Though scholars must be wary of exact replicas of opportunity structures from crime to crime, they must not forget that several factors included in an opportunity structure for a similar crime may be applicable. Locations may not only provide opportunities for specific crimes, but some locations may pose increased risk for such crime-specific victimization. Eck et al. (2007) and Clarke and Eck (2007) expand on the location of crime with the concept of “risky facilities”. Facilities are defined as “places with specific public or private functions” (Clarke & Eck, 2007, p. 3). Risky facilities include convenience stores, bars and taverns, gas stations, schools, payphones, bus routes and bus stops, and shops. Clarke and Eck (2007) believe that “a small proportion of any specific type of facility will account for the majority of crime and disorder problems experienced or produced by the group of facilities as a whole” (p. 4). With this in mind, we must study opportunity structures for crimes related to auto theft. Identification of facilities in the community that may increase auto theft “risk” is essential. The supporting theories discussed above provide a framework for the scholarship in this section. Without one or more of these theories, the following researchers would not have been able to provide methodologically sound and theoretically supported empirical research. The following academic works indicate the advancement of community-level research and, more specifically, the development of the community-level opportunity structure. The research is organized first by the crime type under study and then discusses the impact each type of location has on the community. Literature presented in Chapter 4 will provide a similar look at each crime generator, but the research will focus on the site level. The remaining part of the chapter will focus on the identification of opportunities within the community and community-level attributes that effect crime. Street Layers Streets are absolutely essential for motor vehicles to exist. Without them, there would be no location from which cars could be stolen and the ability for thieves to leave the crime scene would be hampered. Streets aid thieves by providing the opportunity for the thief to steal the car and providing the ease with which to do it.

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Major Roadways Routine Activity Approach (Cohen & Felson, 1979; Felson, 1998; Felson & Cohen, 1980) indicates that major roadways provide one way for motivated offenders to come into contact with, and become aware of, suitable targets. With that in mind, auto theft should be higher around well-traveled roadways. Beavon, Brantingham, and Brantingham (1994) and La Vigne (1996) investigated the idea of increased crime in areas with well-traveled roadways and found that property crimes are more likely to occur if they are on accessible and frequently traveled streets. Sampson (2004) studied auto theft in Chula Vista, CA, 10 minutes from the Mexico border. She determined that parking lots that were 1/10 mile from a freeway had the highest victimization risk (Sampson, 2004). According to the Uniform Crime Report, roughly 18 percent of auto thefts occurred on Highways/Road/Alleys, 35.31 percent of auto thefts occurred in Residential/Home locations, and 22.75 percent occurred in Parking Lots and Garages. Together, these three locations accounted for 76.02 percent of all auto theft crime locations (U.S. Department of Justice, 2000, p. 285) indicating that roughly three of every four motor vehicles will be stolen from one of these locations. Due to these high percentages, it is imperative that researchers consider the significance of well-traveled roadways in connection with auto theft. Residential Areas According to the Uniform Crime Report, in 2005, 54.8 percent of auto thefts occurred in residential locations (U.S. Department of Justice, 2006). This means about half of auto thefts occurred in residential areas. Park and Burgess (1925) theorized, and later confirmed, that one reason city centers had more crime was because there were more people living in a smaller space. Greenberg, Rohe, and Williams (1982) and Greenberg and Rohe (1984) agreed that residential areas had high amounts of crime, but that criminal opportunities differed in those areas. Keister (2007) discusses the impact of lighting on auto theft in residential areas. Keister suggests that most residents have their cars at home during the evening; therefore, residential areas are at increased risk for auto theft due to the cover of darkness and improper lighting in residential areas (2007). Lighting is just one of the factors thought to increase auto theft in residential areas.

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In 1982, Decker, Shichor, and O’Brien examined the relationship between urban structure and victimization. Using National Crime Survey (NCS) data, they looked at 26 cities across the United States using a scale of property crimes with contact, property crimes without contact, and non-property crimes. Motor vehicle theft, considered a property crime without contact, was one of the only variables positively correlated with both population density and percentage on public assistance. Rengert (1980, 1981) and Kuo and Sullivan (2001) discussed the characteristics of the environment and why some crimes are committed in certain locations and other locations are rarely victimized. Kuo and Sullivan (2001) indicate that the presence of certain environmental aspects, such as vegetation, may entice people to use the streets more. This increased use will lead to increased surveillance by residents and, perhaps, deter criminals from committing crime. “Theft from parked cars is one of the most common complaints received by police in residential neighborhoods” (Keister, 2007, p. 2) Clarke (2004) argues that closing neighborhood streets may deter crime. He suggests that offenders find targets in familiar territory, therefore, if they are not permitted access to an area, it will not become familiar (Clarke, 2004). Furthermore, closing off the streets will permit residents to recognize who belongs and who does not, thus enabling them to more effectively watch over the neighborhood for suspicious activity. Sampson and Wooldredge (1987) also found an increase in crime in areas with more “street activity”. The concepts of both measuring street activity, in terms of pedestrians as well as motor vehicle use, and identifying streets as travel patterns (to and from schools, work, etc.) are direct results of this and other research based on these crucial concepts. Regardless of the purpose of the research, residential areas have proven to be criminogenic. Public Housing Newman (1972, 1996) found an increased opportunity for crime in public housing complexes. In Defensible Space, Newman (1972) acknowledges the heightened opportunity for crime to occur in public housing areas and suggests ways in which public housing buildings can be designed to prevent crime. Despite tardy attempts of city planners to help control crime by adding extra police forces to high volume public housing, crime skyrocketed in many public housing complexes when they were first built. Other suggestions by Newman include the use of

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many entrances and elevators with clear view in and out of these locations, open parking lots so that residents can see in the parking lot and those parking cars can see out (1972). The building of stores, parking, and other public facilities adjacent to public housing to provide surveillance by pedestrians and patrons was suggested by Jacobs (1961) and later adopted in the context of public housing. Newman (1972) also suggested the use of symbolic and real barriers to decrease crime. Symbolic barriers are such things as creating open gateways, building a short section of steps, and planting trees and flowers to change the texture of the walking area. Real barriers include constructing L-shaped buildings, creating high walls and fences, and installing locked gates and doors. Both real and symbolic barriers form boundary lines when defining areas of safety since they show an outsider s/he has entered semiprivate domain. The inclusion of common landscaping and recreational areas as semiprivate grounds helps to protect the area from outsiders and leads to a reduction in property crime. Newer public housing complexes are integrating these ideas into construction plans to avoid costly corrections and prevent high crime locations. Mayhew (1979) suggests that even defensible space can be problematic since once offenders are in private areas they may be mistaken for residents and, acting under that guise, may continue to commit crimes unnoticed. To guard against this, patrol officers should become acquainted with neighbors and be aware of both residents and strangers entering semiprivate and private areas. Tijerino (1998) suggests that the civil behavior should be the focus of crime prevention efforts. “Civil space” should represent “a setting where feelings of security and/or insecurity and the physical nature of the built environment intersect…it welcomes all means necessary for the maintenance of a civil space…” (Tijerino, 1998, p. 324). This civil space should focus on neighborhood and community cohesiveness and keep incivilities, like abandoned properties to a minimum (Tijerino, 1998). Residential areas were also considered by Roncek and Francik (1981) in a study that examined 4,000 residential city blocks in Cleveland to determine the effects of public housing complexes on surrounding residential areas. Roncek and Francik (1981) examined crime in public housing using the number of public housing units on city blocks, the distances of blocks from particular public housing projects, and the distance of blocks from all public housing projects. Researchers found that project blocks have higher levels of crime than non-project blocks. This reinforced Newman’s claim to higher crime rates in public housing and public housing areas. Roncek and Francik

Community Crime Patterns

17

(1981) also found that houses in the vicinity of public housing, but not adjacent to it, do not have significantly higher incidences of violent or property crimes. The size of housing projects has an effect on the distribution of crime across all city blocks. These spillover effects on crime are small and seem to be affected primarily by the size of the projects. Due to these findings, Roncek and Francik suggest keeping each public housing area small (1981). Wilcox, Madensen, and Tillyer (2007) agree that properties with more defensible space were less likely to experience burglaries, reinforcing Newman’s ideas. More recent crime prevention research, on the topic of public housing, has focused on police intervention (Barlow, 1990), locations with high concentration of crime, better known as “hotspots” (Block & Block, 1995; Olligschlaeger, 1998), utilization of new analysis techniques such as Geographic Information Systems (Hyatt & Holzman, 1999; Joelson & Fishbine, 1980), and combinations of social control and guardianship in public housing “microneighborhoods” (Fagan & Davies, 2000). These and many other types of research have been conducted based on the work of Jacobs (1961), Newman (1972, 1996), and Jeffery (1971). Some of the most important findings have indicated that solutions to crime in public housing must be simple and must be easily maintained. Without maintenance, the effect of situational or environmental crime prevention techniques is short-term, at best. This was best explained by Popkin et al. (1999) when studying the Chicago Housing Authority. Shortly after the crime prevention efforts were in place, the strategies were abandoned due to legal and financial issues. The crime rate was again high and residents were fearful. Parking Facilities Jacobs (1961) identified five areas that are harmful unless their locations are controlled: parking lots, trucking depos, gas stations, outdoor advertising, and things with wrong scale. Since parking lots can reduce the effects of capable guardianship (Cohen & Felson, 1979; Felson, 1998; Felson & Cohen, 1980) and rarely utilize target hardening devices (Clarke, 1997), parking lots are considered a greater risk for auto theft than street parking within view of pedestrians and residents (Poyner & Fawcett, 1995). In 1972 Newman, in conjunction with his public housing literature, identified the use of parking lots, if properly built, as a method of surveillance. Newman simultaneously indicated that if these lots were closed, or located too far from

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Opportunity, Environmental Characteristics and Crime

residences, they would become criminogenic (1972). Poyner (1997) found that parking garages are much more susceptible to crime than are open parking lots. In open parking lots it is much easier to maintain surveillance than in a closed lot (Poyner, 1997; Clarke & Goldstein, 2007). Sampson (2004) interviewed 17 apprehended auto thieves and found that offenders liked to target parking lots since they provided a condensed number of vehicles in unguarded settings. These research findings support the idea that parking lots should be created with increased surveillance both inside and outside the lots. Parking lots may also contribute to crime when they are considered with regard to city planning and land use. Nichols (1980) found that robberies occur in streets, parking lots, highways, and vacant lots that are relatively abundant and at other locations that conform to an orderly pattern of commercial or some other type of land use. Greenberg and Rohe (1984) found that low-crime neighborhoods had smaller streets, lower rates of nonresidential land use, fewer public parking lots, and more single-family dwellings. These areas were more isolated from outsiders and contained fewer public activities and parking facilities, thus decreasing the supply of potential offenders to the area. Both Nichols (1980) and Greenberg and Rohe (1984) found correlations between the way in which the environment was manipulated and crime rates. Webb, Brown, and Bennett (1992) discussed the layouts of the parking lots or garage to be important. Webb et al. (1992) found levels of lighting, the presence of closed circuit television (CCTV), and controlled vehicular access to be important factors utilized to enhance surveillance and on-site security. In comparison to lots and garages with ‘pay and display’ methods of payment, lots with on-site security and personnel to monitor exits have better surveillance. Webb et al. (1992) also note the importance of comparing similar types of lots/garages when comparing crime rates. Lots open in the evening and those with ‘pay and display’ methods of payment are more likely to be victimized and should not be compared to those open only during the day or those with a different method of payment. Locations that park the vehicles in the block method and keep the keys for owners tend to be very safe. An offender could only steal the cars located on the end of the lots since all other vehicles would be blocked. These techniques help reduce the opportunity for auto theft in some locations. Some research indicates that opportunities can remain blocked if certain crime prevention tactics are used. Smith (1996) found that the combination of good lighting and CCTV cameras with loudspeakers produced a more secure atmosphere. Smith (1996) found that “in the

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period immediately following the installation of the CCTV system the level of thefts had been dramatically reduced, with many months having no reported incidents” (p. 163). Clarke & Goldstein (2007) also report a connection between CCTV and lower rates of auto theft in parking facilities. Despite these crime prevention tactics, Fleming (1999) notes that “roughly a third of the offenders thought underground parking lots the best place to steal vehicles” (p. 76). The Uniform Crime Report concurs, 30.8% of auto thefts occurred in parking lots or garages in 2005 (U.S. Department of Justice, 2006). While this is large proportion of auto thefts, Miethe and McCorkle (2001) note that if onethird of all auto thefts take place in these parking structures, two-thirds take place at private residences and on public streets. Convenience Stores and Gas Stations Convenience stores and gas stations provide an increased opportunity for crimes such as drive-offs (La Vigne, 1994) and both robberies (Duffala, 1976; Petrosino & Brensilber, 1997; Schiebler, Crotts, & Hollinger, 1996; Smith, Frazee, & Davison, 2000) and homicides (Petrosino & Brensilber, 1997). In 1961 Jacobs identified gas stations as one of the five locations that are harmful if not controlled. Fifteen years later, Duffala (1976) hypothesized that “convenience stores would be more vulnerable when they were located: (1) within two blocks of a major street, (2) on streets with light amounts of traffic, (3) in a residential and/or vacant land use area, and, (4) in an area with few surrounding commercial activities” (p. 193). Nichols (1980) added to these factors the idea that a mental map is drawn by the offender to find a location to offend in relation to some known point. Nichols (1980) also found that older offenders show a preference for open space sites such as convenience stores and younger offenders preferred both convenience stores and gas stations. Both Duffala (1976) and Nichols (1980) suggest that there is an environmental aspect to the site selection process of offenders when they are targeting convenience stores. According to Pattern Theory (Brantingham & Brantingham, 1993a, 1993b), the offender’s selection process of the convenience store is important because it becomes attractive to the offender when it becomes an activity node or is located in the path in which the offender routinely takes. As the offender’s awareness space around the location of the convenience store is enhanced, the store is more likely to become a target. Moreover, if the particular store has other enticing qualities such as poor surveillance, poor lighting, and/or attractive targets, it

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becomes an even more likely target. La Vigne (1994) suggests removing signs from windows, installing brighter lights, and instituting a pay-first policy to eliminate convenience store crimes and gas station drive-offs. La Vigne further indicates that a criminal measures the potential risk and reward of an offense through observation of the physical and social environment as well as the severity of potential punishments. The natural surveillance of a location, including obstructions, fences, bushes, and poor maintenance can encourage offenders (La Vigne, 1994). Swanson (1986) found that remote areas were desirable for convenience store robberies. Prior research on convenience store crimes suggests that they are a function of both the external and internal environment. Many researchers who conducted empirical studies on the internal convenience store robbery identified some site specific factors that may affect offenders targeting certain convenience stores and gas stations before others. The number of employees per shift (Bellamy, 1996; Crow & Bull, 1975; Hunter & Jeffery, 1992; La Vigne, 1994), cash handling techniques (Hunter & Jeffery, 1992), access control (Hunter & Jeffery, 1992), natural surveillance (Graham, 2001; Hunter & Jeffery, 1992) including lighting (La Vigne, 1994), and elimination of escape routes (Crow & Bull, 1975; Graham, 2001) are a few of the situational crime prevention techniques whose effectiveness are hotly debated. These factors will be discussed in the site-level section of this book. Transportation Hubs Brantingham and Brantingham (1999) and Levine and Wachs (1986) identified transportation hubs as types of environmental gatherings that present a greater risk for victimization. Levy (1994) and Poister (1996) found a great deal of victimization in subways and nearby parking lots. This is related to both the concentration of people and the expectation on the part of criminals that the victim will be away from the vehicle for a length of time while watching a movie, shopping, or taking public transportation. Patterson (1985) found that bus stops generated fear leaving elderly residents scared to use public transportation. These hubs should be considered crime generators because they bring potential offenders and victims together, even if it is for reasons unrelated to crime. Cohen, Felson, and Land (1980) noted that with all other things being equal, “an increase in the numbers of persons in transit locations produces an increase in criminal opportunity and hence an increase in the rate of occurrence of property crime violations” (p. 99). In addition to crime within the transportation hub, the car parked

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in a lot, without residents or interested consumers to play the role of capable guardian (Barcley, Buckley, Brantingham, Brantingham & Whin-Yates, 1997; Hollinger & Dabney, 1999; Loukaitou-Sideris, 1999; Loukaitou-Sideris, Liggett, Iseki, & Thurlow, 2001; Poister, 1996), is at great risk. Researchers typically think of the majority of transportation crime occurring in hubs. But other locations, such as bus stops, have recorded problems with crime. Patterson (1985) conducted a survey of elderly transit users in Philadelphia. The survey was administered at eleven senior citizens centers. The two most frequently cited problems were concerns with the physical condition of the bus (68.2 percent stated that the windows were so dirty that they could not see out) and the social environment (68 percent stated that the buses were so crowded that they were afraid of being robbed or assaulted) (Patterson, 1985, p. 282). Fear while waiting for the bus was a problem for 77.3 percent of the respondents. Levine and Wachs (1986) found approximately 105,000 reported bus stop Part I crimes for 1983. Brantingham, Brantingham, and Wong (1991) believe that transportation affects crime by introducing potential offenders to potential targets, shaping travel times and destinations, determining travel paths, and influencing the types of crimes that occur at a location. This research furthers the idea that offenders and victims meet while conducting their normal daily routines. Brantingham and Brantingham (1993a) suggest that “urbanization, mass transit, and new highways alter movement patterns, routine activities, and awareness spaces” (p. 269) and the residents' knowledge of surrounding areas grow. Similarly, those who drive will form different patterns that those who walk between destinations (Beavon, Brantingham, & Brantingham, 1994). Besides patterns in those who use transportation, the locations of hubs and transit stops have another effect on crime. Residential and commercial locations near transit stops and hubs experience an increased amount of crime as compared to similar establishments not located near transportation. Huang, Kwag, and Streib (1998) found that hotels with easy access to public transportation tend to have a greater amount of auto thefts than hotels without transportation stops. Hollinger and Dabney (1999) established similar results with motor vehicle theft. Offenses increased as loitering young people, public transportation, and street gangs increased. Loukaitou-Sideris (1999) studied situational variables such as lighting at bus stops, businesses able to see bus stops from workstations, distance from police sub-station, presence of empty or abandoned lots near bus stop, and location and distance of liquor stores

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and bars from bus stops to determine their correlations with crime. Loukaitou-Sideris et al. (2001) found that crimes rates were higher at intersections with alleys, mid-block passages, multi-family housing, undesirable establishments, vacant buildings, and graffiti and litter. Similarly, bus stops with good visibility and bus shelters seemed to produce a positive impact on crime (Loukaitou-Sideris et. al, 2001). Rhodes and Conly (1981) explain that both “target attractiveness” and “spatial attractiveness” are considered by offenders before they commit a crime. If applied to Loukaitou-Sideris’ (1999) study of bus stop crime, offenders will look at both the characteristics of those using the bus and the location and atmosphere of the passengers. These site-level factors are investigated further in subsequent chapters, as land use that surrounds a bus stop appears to be quite critical to bus stop safety. Schools National Center for Education Statistics reports that thefts at school are underreported and rarely reported to the police (2007). Most criminologists focus on what juveniles do after school, not during school hours. Fox and Newman (1997) suggest that juveniles commit the most crime between the hours of 3 pm and 8 pm. This occurs since the students are released from school in the afternoon and are unsupervised. Since students often live in the vicinity of the schools, they may walk home, which increases their access to cars parked on the street. Research has indicated that residences located on the same block as schools are at an increased risk for property crime victimization (Roncek & Faggiani, 1985; Roncek & Lobosco, 1983; Sampson & Wooldredge, 1987). In 1980, Phillips studied the length of journey to crime for different types of offenders. Phillips concluded that white juveniles had longer journeys to crime than black juveniles by about 700 feet per average trip (1980, p. 175). Phillips (1980) also suggested that women on average had a 40 percent longer journey to crime than men. As interesting as these findings were, the most fascinating finding was that type of offense was the greatest predictor of length of journey. “Mean journey length varied nearly four-fold with the type of offense, from 3,694 feet for assault to 12,995 feet for petty larceny” (Phillips, 1980, p. 177). Assault had by far the shortest journey length which supported previous research by Pyle (1976), who concluded that crimes against persons are characterized by short journey lengths. With regard to severity, Phillips (1980) found no difference between the severity of

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crime and journey to crime. This contrasts previous literature by Capone and Nichols (1976) suggesting that the length of the journey to crime (in this case robbery) increases in a positive relationship with larger dollar amounts. In 1983, Roncek and Lobosco studied crime in relation to the location to public and private high schools. Blocks which were immediately adjacent to public high schools had an increased crime rate but there was no effect on blocks that were more than one block away from the public high school (Roncek & Lobosco, 1983). Private high schools showed no significant relationship to crime in surrounding areas. In addition to these findings, the more use a block receives, the more crime, especially burglary and auto theft, was found in the areas surrounding the school (Roncek & Lobosco, 1983). However, with regard to commercial burglary, Hakim and Shachnurove (1996) determined that the location to schools deters commercial burglars because of the increased chance of being witnessed during the act. This opposes the idea suggested by Roncek and Lobosco (1983) that with regard to auto theft and residential burglary, crime is increased in close proximity to schools. Hakim and Shachnurove (1996) found that large malls were the most attractive targets and that newly opened establishments are the most vulnerable. In 1985, Roncek and Faggiani studied other characteristics of the schools to detect potential correlations with crime. Roncek and Faggiani found that those areas within a one block proximity to the public high school had a greater likelihood of crime (1985). The number of students attending the public high school and the land use on the block surrounding the public high school did not have a significant effect on crime rates (Roncek & Faggiani, 1985). In 1986, Jensen and Brownfield studied data from a national survey of high school seniors and students in a Tucson, Arizona high school. Jensen and Brownfield (1986) determined that delinquent activity has a positive relationship to victimization; there is a positive relationship between participating in delinquent activity and becoming a victim of crime. Jensen and Brownsfield (1986) also discuss that the greater the accessibility to unprotected targets, the greater the opportunity for crime. This conclusion lends support to all the previous data regarding opportunities and criminal targets and the Brantingham and Brantingham (1993b) finding that “school” is one of the nodes in the pathways in which youth form their awareness space. Lodha and Verma (1999) suggest that one possible solution to the effects of high school students on crime in the surrounding area is to distribute traffic flows, bus schedules and sporting events with the opening and closing

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Opportunity, Environmental Characteristics and Crime

hours of schools and malls to better regulate the flow of activities. Hendrix (2000) suggests using raster based data models to study crime over a continual surface but many police departments are not yet equipped to handle that sort of technology (Mamalian & La Vigne, 1999). Fast Food Locations and Bars Although past research indicated that bars, stores and restaurants keep streets safer (Jacobs, 1961), late night food establishments (Brantingham & Brantingham, 1982; Ford & Beveridge, 2004) and bars (Roncek & Bell, 1981; Rossmo, 1994; Rossmo & Fisher, 1993; see also Kumar & Waylor, undated) are now thought to draw offenders and victims into similar areas. More recent research has shown that crime is more likely to occur on blocks that have bars than on blocks that don’t (Roncek & Bell, 1981; McCord et al., 2007). According to Brantingham and Brantingham (1982) in studies of commercial burglary, locations where fast food restaurants, traditional restaurants, supermarkets and department stores, and pubs were located, supermarket and department store blocks had commercial burglary rates comparable to blocks without these businesses, but fast food restaurants, traditional restaurants, and pubs had commercial burglary rates of more than two times higher than blocks without these businesses (Brantingham & Brantingham, 1982). Late night food establishments are thought to draw people similar to the way bars draw people into the area. These activity nodes (Brantingham & Brantingham, 1993b, 1999) present a greater risk for victimization because these locations of high activity tend to attract both offenders and victims. Thus, bars and late-night fast food restaurants act as “crime generators”, locations that draw a high volume of activity (Brantingham & Brantingham, 1993b; Ford & Beveridge, 2004) and serve offenders with criminal opportunities. Since patrons of fast food restaurants are permitted to stay at these locations once they have purchased an item from the establishment, offenders are often given legitimate cover for illegal activities such as scouting out opportunities or suitable targets. Graham, La Rocque, Yetman, Ross and Guistra (1980) concluded that bar patrons tend to be in and out of the bar all day and use the bar as a “home base” for social and other activities. Offenders tend to use these legitimate activities to disguise illegitimate ones. Even those who don’t use bars as their “home base” but are frequenting bars, going to

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movies, and spending time out of the house walking or driving around are more vulnerable to assaults and robberies (Kennedy & Forde, 1990a, 1990b) especially when their activity nodes and/or paths intersect those of offenders (Brantingham & Brantingham, 1993b). Roncek and Bell (1981) not only found that blocks with bars have more crime than blocks without bars, but that the number of bars on a block is an important factor in explaining where crimes occur. Specifically, grand theft and auto theft were found to be more likely on blocks with bars than those without (Roncek & Bell, 1981). For all personal crimes except rapes, blocks with bars on them have significantly more crimes than those without bars (Roncek & Bell, 1981). Each additional bar on a residential block increases the incidence of index crimes by approximately four crimes (Roncek & Bell, 1981). Both of these findings completely contradict the ideas proposed by Jacobs (1961) that having more bars, restaurants, and evening activities in the area will provide more constant surveillance and, therefore, less crime. In fact, Roncek and Bell (1981) suggest that the effects of concentrations of bars are so great that increased surveillance on bar blocks is suggested. Regardless of the viewpoint of bar patrons, management, or bar neighborhoods, bars in bad neighborhoods will generate more crime than those in good neighborhoods (Sherman, 1995) and bars in close proximity to each other with simultaneous closing times will add to the “potentiation” of crime occurrences (Rossmo, 1995, 1994; Rossmo & Fisher, 1993, see also Engstad, 1975; Roncek & Maier, 1991; Roncek & Pravatiner, 1989). Though bars in any location provide a “permissive environment” (Sacco & Kennedy, 2002) for crime and liquor-related violence to occur, there isn’t always a direct correlation between a location with a liquor license and liquor-related crime (Block & Block, 1995). Clearly some bars are attractors of aggression; those with high numbers of felonious incidents are clearly criminogenic. However, Block and Block (1995) found that a liquor store located within a hot spot is also likely to be located near a rapid transit station. Loukaitou-Sideris (1999) found a correlation between liquor stores and bus stops while studying bus stop crime. Smith, Frazee, and Davison (2000) found correlations between bars and street robberies. Despite these possible correlations, Block and Block (1995) found that even when areas are located within a hotspot, low-crime places tend to be surrounded by fewer places with liquor licenses and experience much less crime in their immediate areas than high-crime places. This indicates that researchers need to look at many attributes of the environment

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surrounding the location under investigation, not only the one business that is being studied. Homel and Clark (1994) found that the physical environment had an effect on the level of crime in their study of 45 pub sites in Sydney, Australia. These correlations could possibly indicate a more complex crime-location relationship between liquor licenses and the contextual backdrop of the environment (Block & Block, 1995). Accommodations For at least the last fifteen years researchers have been studying the effects of hotels and motels on crime. Hotels and motels create an increased risk for all types of crime (Jones & Groenenboom, 2002; Rice & Smith, 2002), especially auto theft since they provide a great number of opportunities for auto thieves. People who are staying at hotels/motels are often transient individuals, if not all of the time, certainly at the time in which they are staying at the hotel/motel (Schmerler, 2005). Often these individuals have out of state, or at least out of town, cars. When out of state cars are stolen local police have a more difficult time tracking down the owners and maintaining contact. Travelers also tend to have more items in their possession since they are going to be away from home for longer periods (Huang, Kwag, & Streib, 1998). They don’t see the harm in leaving items in the car instead of carrying them in and out of the hotel/motel room. When these items are visible, they increase the chance that the vehicle will be targeted for both motor vehicle theft and theft from the motor vehicle (Fennelly, 1992). Sherman (1989) has indicated that the amount of property crime in a given place is heavily dependant upon the number of opportunities that are available. Recent accounts of crime at budget lodging establishments have further frightened patrons, especially women, when staying at these locations (Cook, Merlo, & McHugh, 1993). Tourists are also at high risk of victimization at these locations (Schiebler, Crotts, & Hollinger, 1996; Harper, 2001) especially when carrying high priced items such as cameras, video equipment and computers. Huang, Kwag, and Streib (1998) found auto theft to account for more than 12 percent (103 auto thefts out of 820 incidents) of all incidents. Zhao and Ho (2006) reviewed data from the MiamiDade Police Department. The authors found that out of 564 criminal offenses committed at the hotel, 50% of offenses were burglary, 37% were theft (some included theft from vehicles) and 13% were robbery. Cars create an increasing amount of opportunity. Most people who travel long distances in the United States travel by car. As individuals

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travel they end up pulling into hotel or motels for evening accommodations. Offenders anticipate that owners will not return to their vehicles until at least the early morning hours; thus providing offenders with hours of uninterrupted time to steal vehicles or items from them. Interestingly, hotels and motels also have an effect on the environment around them. For unexplained reasons, Smith, Frazee, and Davison (2000) found that motels and hotels have a 24 percent increase in the number of street robberies as compared to locations without these businesses. Hotels/motels create opportunities for crime in and around their locations. Auto Repairs and Auto Parts Locations Though the body of literature linking auto repair and auto parts locations with increased levels of crime is virtually non-existent, researchers could use existing literature focused on other crimes to make predictions about auto theft. Gant and Grabosky (2002) interviewed a snowball sample of motor vehicle thieves. Thieves in this sample indicated that professionals in search of parts to repair vehicles actually steal vehicles (especially older vehicles) and dismantle the vehicle for parts to sell them to auto repair businesses. Due to this finding, auto repair shops and auto parts locations may present an increased likelihood for victimization since many cars are present in one location; providing a great opportunity for victimization. In 1999, Fleming interviewed thirty-one incarcerated offenders who indicated that autos are typically stolen for a number of reasons, such as, profit, transportation, or recreation. Those who steal cars to profit are thought to be more “professional offenders” and don’t steal for reasons such as quick transportation and/or recreation. These professional offenders are often driven by the demand for certain makes and models of cars and their parts. Professional offenders may spend more time searching than offenders who are simply looking to joyride or find transportation home. Phillips (1980) indicated that journeys to crime are longer for crimes against property compared to crimes against the person. This may be even truer for those hunting specific makes or models of vehicles since Capone and Nichols (1976) found wide differences in different types of robbery and determined that armed robberies involved longer journeys to crime than strong arm robberies; crimes that required a greater degree of planning usually involved longer journeys to crime.

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Opportunity, Environmental Characteristics and Crime

With regard to planning of crimes, La Vigne, Fleury, and Szakas (2000) applied the distance-decay and rational choice theories to the patterns of auto thieves and their travel patterns to chop shop locations. The idea of distance decay, when applied to auto theft, would indicate that criminals come in contact with things and people that are close to their homes and other locations within their routine travel patterns, such as work, school, and recreation areas. The research conducted by La Vigne et al. (2000) indicates that if police or researchers can get estimates of journeys to crime from offenders, they can estimate a average distance from the location of the theft to the location of the chop shop or other location the car was taken to. For instance, La Vigne et al. (2000) found that in study area #1, thieves traveled an average of 3.4 miles. A car that is stolen in study area #1 should be located at a chop shop or location in a 1.9 to 4.7 mile radius from the location of the theft. Lu (2003) studied journey-after-crime rates for auto theft in Buffalo, New York. Lu found that 47.9% of stolen vehicles were found 1.5 miles from the location of the theft (2003). Both LaVigne et al. (2000) and Lu (2003) add another facet to recent criminological literature, the concept of offender search patterns, which will be discussed in more detail in the next chapter. Summary This chapter discussed all of the variables to be included in the opportunity structure. Many of the variables have been studied and a relationship to auto theft has been established (e.g. streets, major roadways, residential areas, public housing, parking lots, and transportation hubs). Other variables are relevant additions to opportunity for crimes similar to auto theft and their significance to the crime of auto theft will be tested in this book (e.g. fast food and bars, convenience stores and gas stations, accommodations, schools, and auto repair and auto parts locations). The articles presented here provide a strong foundation from which researchers can draw in order to assemble an opportunity structure for auto theft and related property crimes. The supporting theories provide a framework while the community-level scholarship delves into the specific crimes and digs deeper to uncover unique aspects of the environment that influence crime occurrence and pattern formation. Both theoretical and empirical works are necessary in order to form an opportunity structure strong enough to predict auto theft.

CHAPTER 3

High Crime Areas & Opportunity Structures

INTRODUCTION For the last seventy-five years, criminologists have been exploring the role of the environment in crime. There are salient factors associated with both sociological and ecological causes of crime. Today, when academics think of crime prevention, they focus on the situational aspects of reducing crime, adding video cameras, fences, locks, etc. However, crime tends to occur in only a small percentage of places (Sherman, 1995); since some areas naturally attract, and other areas naturally repel, crime. In order for criminologists to better understand these areas or hot spots (Grubesic, 2006), it seems as if one should be looking at both areas that do and do not attract criminals. In addition, it is fairly obvious that researchers must integrate theories instead of testing them separately. Empirical tests to date indicate that ecological and organizational aspects of communities can not provide researchers with all of the answers. For this reason, it is necessary to move towards a more integrated approach to crime analysis. Geographic analysis, guided by theory, will allow researchers to better understand the mechanisms driving crime. Using geographic analysis techniques to uncover locations of increased opportunity, and therefore, to uncover areas that will become targets for crime, will aid in the development of crime prevention policies. OPPORTUNITY LITERATURE Merton (1938) discussed crime in terms of the social structure. Merton believed that people resort to crime when they are placed under a type of strain which leaves them no other option. This strain, according to Merton, is usually correlated with social class, primarily the lower class. The lower class does not have the resources necessary to succeed in the world and, thus, must resort to illegal means in order to survive. 29

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Merton believed that individuals first consider legal opportunities, but when these opportunities become blocked, individuals turn to illegal opportunities in order to achieve success (1938). Mansfield, Gould, and Namenwirth (1974) explore the role of opportunity in crime and determine that while illegal opportunities are those that criminologists focus on, researchers must first study all opportunities in order to determine the importance, significance, and prevalence of illegal ones. Knox (1981) suggests that experiencing a barrier in a legitimate opportunity structure (a blocked opportunity) is a primary factor that leads individuals to commit unlawful behavior. It is not necessarily the presence of criminal opportunity but also the “blocked opportunity” of legitimate sources (Knox, 1981). In the last decade, research in several areas has demonstrated links between the structure of the environment and crime. Baron, Forde, and Kay (2007) determined that opportunities “…can mediate and shape the effects of self-control and propensity for crime” (p. 134) while studying self-control and risky behaviors of street youths. Brownlow (2006) discussed the impact of landscaping and other environmental factors on fear of crime, finding that the perception of criminal opportunity exists in areas that appear to be decayed. Wilcox, Augustine, and Clayton (2006) utilized data collected from schools in Kentucky to understand the opportunities present in the physical environment and how they impact victimization. These and many other studies suggest that understanding criminal opportunity will aid police and community members in crime prevention efforts. Opportunity Based on Numbers of Locations Key opportunity literature started with the idea that opportunities should be determined by looking at the number of crime-specific occurrences divided by the total possible number of crime-specific victims. Boggs (1965) suggests that if safes are the primary target for commercial burglaries, researchers can calculate rates of commercial burglary by dividing the total number of safes that were burgled by the total number of safes in the reference area. These become the total possible opportunities for the burglar (see also Hollinger & Dabney, 1999 with regard to auto theft). The opportunities that are available in one location may differ in another location; therefore, this denominator of possible occurrences should change based on location. Cohen, Kaufman, and Gottfredson (1985) conducted a study of both traditional data collection methods

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(dividing the number of crimes occurred by the census numbers) and “alternative” data collection methods (dividing the number of crimes occurred by a more realistic number of targets for that crime). They determined that the forecasts of the “traditional” rates are consistently, but only slightly, more accurate than those of the “alternative” rates. This suggests that perhaps the problems with data collected by police departments are slightly exaggerated. Engstad suggested the use of “opportunity indices” (1975, p. 184), opportunity-based offense rates, in order to determine all possible opportunities in a location. Subsequent research has used a series of opportunity indices in order to better understand hotel theft and shopping center crime (Engstad, 1975). Land and Felson (1976) explain the need to go beyond the use of opportunity indices by suggesting the use of an opportunity-based structure for social indicators. Social indicators should be used to provide a theoretical framework in order to better understand societal changes and thus better predict an opportunity structure and potential changes to the opportunity structure. Along the lines of social indicators, Sparks (1980) discusses the need to consider the number of potential offenders in the area. Research from Land and Felson (1976) and Sparks (1980) indicate that there may be a more complex formula than simply counting the number of opportunities or the number of offenders. Cohen (1981) also suggests that since opportunities are constantly changing, researchers must look at trend patterns fluidly to determine how social change influences opportunity, and, perhaps, crime rates at specific points in time. Rattner (1990) adds to the relationship between opportunity and social trends when finding that as unemployment increases, homicide, property crime, and robbery also increase (with a one year lag) (See also Edmark, 2005). Rattner’s (1990) dynamic model is set in a time series format and accounts for a large percentage change in the above crimes. Pezzin (1995), while studying the termination of criminal careers, also found that economic incentives (changes in societal patterns) that change opportunities exert a powerful influence on criminal career duration. Rengert (1981) suggests that rather than computing a crime occurrence rate with respect to opportunities for crime, it might be preferable to analyze the absolute number of occurrences within an area with respect to both defined targets and accessibility of these targets to potential criminals. In effect, site characteristics of an area are weighted by their relative accessibility to criminals in determining the opportunity structure of the region, not simply the fact that they present an opportunity to an offender. Rengert (1981) cautions that researchers

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must consider the fact that an area that seems to be a suitable target may not be considered an opportunity if there is no way for offenders to reach the area under study. More recently, the discussion of proper denominators has extended beyond the rate of reported crime. Brantingham and Brantingham (1998) have elaborated on the need for proper denominators when calculating location quotients, counts and rates. Tartaro and Levy (2007) have discussed using more accurate denominators for calculating prison statistics. These and many more research projects highlight the importance of accurate figures when discussing opportunity and repeat victimization. Opportunity Based on Offenders’ Decision Processes In 1971, Jeffery, while studying the effects of situational crime prevention, determined that offenders follow a decision model of sorts while engaging in a criminal act. The offender considers many factors such as previous experiences, the immediate opportunities available for the crime, and the chances of apprehension or injury. The decision is made in terms of the potential payoff versus potential loss, following in Bentham’s (1948) footsteps. Bentham posits that an individual’s decisions are based on a calculation of potential gains and harms and s/he will make a decision that produces the greatest potential gain (1948). In 1976, Capone and Nichols discuss the idea of criminal mobility. Criminal mobility is based on urban structure; more specifically, the length of the journey to crime is based on patterns, distance, morphology, and relative location of offenders and targets in the “urban system” (Capone & Nichols, 1976, p. 200). Next, the criminal’s travel is viewed in relation to all potential crime locations and their attractiveness with regard to the criminal event, the objective spatial structure in the criminal’s action space and the urban locations that the offender is both familiar with and in which he prefers to conduct daily activities. Capone and Nichols concluded that the differences between the lengths of journeys to crime are apparent; “liquor stores, supermarkets, and loan companies show quite lengthy average trip distances, while residences, gas stations, and grocery stores exhibit relatively short average distances” (1976, p. 210). Kent, Leitner, & Curtis (2006) found an approximate distance of 12-15 miles is the maximum distance an offender is willing to travel to commit a homicide. As the distance an offender travels increases, the likelihood of criminal activity decreases (Kent et al., 2006). Duwe, Donnay, and Tewksbury (2008)

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found that this theory does not hold for violent offenders. According to their study, residential proximity to the crime scene proved to be only moderately relevant in sex offenses (Duwe et al., 2008). Clearly, decision-making processes are crime and offender-specific. With regard to an offender’s journey, a new phenomenon, “journey to associates” has recently been identified (Malm, Kinney, & Pollard, 2008). Malm et al. (2008) studied drug networks in Vancouver, British Columbia and found that 31 percent of the sample travelled a distance of zero to their associates (co-offenders lived together). On average, criminals traveled nearly four miles on their journey to associates. The work of Malm et al. (2008) suggests that drug networks have spatial constraints, which may help to shape the policies of police departments when they investigate drug-related crimes. Distance is only one factor in target selection; target choice is also linked to offender decision-making. Cook (1986) concluded that criminals tend to be somewhat selective in choosing a crime target and are more attracted to targets that offer a high payoff with little risk of police intervention. Sampson and Wooldredge (1987) determined that the closer ecological proximity of potential targets to motivated offenders, the greater the opportunity – and therefore – the greater the risk of victimization. Sampson and Wooldredge (1987) also found that individuals living in a community with low guardianship and surveillance may increase victimization risk since this is an attractor for criminals. Continuing the idea of high pay off, Felson and Clarke (1998) believe that as technology advances, crimes will change. Targets become easier to carry off (they weigh less due to new, lighter materials) and will change (as technology produces new and better products older products become less desired). Albanese (2000) found that new criminal opportunities provide motivation for individuals who were not formerly involved in illegal activity. If not for the advances in these technological areas, these crimes would not exist. Part of the decision process that offenders exercise before committing a crime includes some ideas about the location. GeorgesAbeyie and Harries (1980) suggest that motor vehicle theft, being a crime of opportunity, is designated by spatial and temporal locations of targets that dictate the occurrence of the crime. These spatial and temporal locations help the offender determine if, when, and where to commit a crime. Ekblom and Tilley (2000) suggest that crime can be stopped, to some extent, by removing offenders from the streets; however, if a person really wants to reduce crime s/he must change the environment (niche) so that it can not be used by another offender (p. 389).

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In a study of socially disorganized areas, Moriarty and Williams (1996) studied high crime zones (the five census tracts with the most crime) and low crime zones (the five census tracts with the least crime). The surveys inquired about variables such as crime against homes, theft of property, destruction of property, and total property victimization. Independent variables included questions regarding motivated offenders, target suitability, and guardianship (Moriarty & Williams, 1996). The authors found that the Routine Activity Approach explained more of the property crime victimization variance in the socially disorganized areas than those described as socially organized (Moriarty & Williams, 1996). The findings are based on the authors’ conceptions of motivated offenders, target suitability, and guardianship. Not all the measures of Routine Activity Approach used in this study are necessarily the same indicators that other researchers would use; however, the results lend support to the theory in their own way. Using more traditional measures Groff (2007) conducted a study on street robbery that also confirmed the predictions of the Routine Activity Approach. As the time spent away from home increases, so does the number of street robberies. Similarly, as the time spent away from home increases the opportunity for street robbery (convergence in time and space of the likely offender, suitable target and lack of a capable guardian) also increases. These results also confirm the findings of Cohen and Felson (1979) and Moriarty and Williams (1996). Regardless of the crime to be committed, Brantingham and Brantingham (1993a) suggest that each criminal event is “an opportune cross-product of law, offender motivation, and target characteristic arrayed on an environmental backcloth at a particular point in spacetime” (p. 259). Each of these elements of the event has been shaped by past experience and future intention within the limits of the location and the environment. The opportunities for criminal events may be discovered either in the course of ordinary noncriminal activities or through search patterns. Perhaps the most important contribution of Brantingham and Brantingham (1993a) is the idea that “neither motivated offenders nor opportunities for crime are uniformly distributed in space and time” (p. 262). Locations designated as commercial districts typically have few residences. This indicates a good chance that residential burglaries in this area will be relatively non-existent – simply because there are no residences to burgle. Likewise, time can contribute to the distribution of targets since business districts may experience a large volume of people during the day and fewer people in the evening. Due to this distribution,

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muggings and pick-pockets are more prevalent during daytime hours than nighttime hours. Opportunity Based on Target Location Duffala (1976) hypothesized that stores would be more vulnerable when they were located within two blocks of a major street, on streets with light amounts of traffic, in a residential and/or vacant land use area, and in an area with few surrounding commercial activities. Duffala’s research suggested that all four hypotheses are significant predictors of target locations. Cohen and Felson (1979) continued to investigate characteristics of crime generating locations when they hypothesized that the dispersion of activities away from households and families increases the opportunity for crime in residential locations. Greenberg and Rohe (1984) found that low crime neighborhoods had smaller streets and low rates of nonresidential land use; in other words, low crime areas were more isolated from outsiders and contained fewer public activities and parking facilities, thus decreasing the supply of potential offenders to the area. Streets can be designed in order to limit crime by blocking unnecessary exits and entrances (Donnelly & Kimble, 1997) to prevent easy getaway (Graham, 2001), eliminating or blocking proximity to open rural areas or freeways (Graham, 2001), and manipulating streets to be smaller and more private (Greenberg & Rohe, 1984). Graham (2001) found three environmental factors that held the most promise for reducing the potential for convenience store robbery: 1.) having two or more clerks on duty at a time; 2.) cash handling procedures which limit available cash; and 3.) elimination of concealed access to the target, which includes escape routes, the most influential factor discussed in this section. Beavon, Brantingham, and Brantingham (1994) continued work on street designs and noted that street networks have the greatest impact on crimes committed by people who learn areas by traveling through them in motor vehicles. Those individuals who drive during their daily routines have different street use than those who walk during their routines. The street network for a pedestrian is larger than the physical street network available to someone in a motor vehicle since a pedestrian can use sidewalks, back paths, etc. Beavon et al. (1994) studied street accessibility by determining the number of turnings into each street segment. The authors found that blocks with both high accessibility (a greater number of intersections to turn on to a street) and high street flow have greater amounts of crime. These two

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characteristics, turns and flow, account for almost 15 percent of the variance in crime. These findings support earlier research suggesting that property offenders target locations in their routine activity spaces. Fortunately, city planners and police agencies are able to “design out” crime by structuring buildings to have fewer crime aspects on the exterior. In 1987, Sampson demonstrated the importance of linking the community and site-level characteristics of locations with regard to opportunity. Sampson (1987) found that living in areas characterized by a high proportion of “primary individual households” significantly increases burglary risk, independent of individual household configuration, and living in a “primary individual household” increases burglary risk regardless of the surrounding neighbors. Cohen and Felson (1979) believe that “structural changes in routine activity patterns can influence crime rates by affecting the convergence in space and time of the three minimal elements of directcontact predatory violations: 1) motivated offenders, 2) suitable targets, 3) the absence of a capable guardian against a violation” (p. 589, see also Cohen, Felson, & Land, 1980). A change in any one of these elements (including suitable targets) could change the crime rate in a given location. A decrease in the population (people acting as capable guardians) should lead to an increase in opportunities (Cohen, 1981; Cohen et al., 1980). While Cohen et al. (1980) and Cohen (1981) suggest that decreases in the population could increase property crime, Roncek (1981) suggests that increases in the population could increase personal crime. Increases in the population may be due to the sheer increase in people or to the increase or type of housing purchased or rented by the new population. Roncek’s findings suggest that the change in both the social and physical environment has an effect on crime rates (1981). Building on the research of Duffala (1976) and Cohen and Felson (1979), Brantingham and Brantingham (1981) suggest that the development of major transportation arteries leads to a concentration of criminal events close to the highways, especially near major intersections and areas with grid networks. Since individuals are traveling by car or public transportation, they are in these areas more often, and these areas become part of their awareness space. Once individuals are aware of the locations, these areas have a higher potential for crime. The study of awareness space also effects research on looting. Muhlin et al. (1981) set out to predict that crime will occur when and where opportunities exist, using the example of looting. They found

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that less than three percent of the variance in offending is attributed to looting opportunity. While the findings of Muhlin et al. were discouraging, the researchers suggest that perhaps it is not opportunity that predicts crime but rather opportunities to sell looted goods that would predict looting in areas (1981). Taylor (1999) further clarified this view of opportunity with regard to repeat commercial burglary. Taylor (1999) concludes that it is not necessarily the victim who presents the opportunity but rather the quality of the opportunity that victim represents. Both sets of research indicate that offenders are motivated by more than simply an unguarded target; both an outlet for stolen goods and the quality of stolen goods should be considered in the model to predict certain property crimes. Target selection for burglary was further examined by Bernasco and Nieuwbeerta (2005) when studying single-offender burglaries in The Hague. The authors analyzed 548 burglaries occurring in a 5 year time period. According to their findings, neighborhoods with single family dwellings are at increased risk for burglary. Burglary risk is also increased by a factor of 1.67 for every kilometer it is closer to the burglar’s home. Houses located in neighborhoods with more residential units are more likely to be victimized, by a factor of 1.35 (Bernasco & Nieuwbeerta, 2005). In a study of 50 residential burglars incarcerated in the United Kingdom, Nee and Meenaghan (2006) found that the burglars’ primary motivating factor was money. With regard to target attractiveness there were three factors most often considered by burglars while selecting a target, general upkeep and décor, visible, expensive items, and type of car parked outside. In terms of layout, burglars considered the degree of cover, access and ‘get-away’ routes (Nee & Meenaghan, 2006). Groff and La Vigne (2001) also used opportunity theory to identify likely variables associated with “desirable and undesirable targets for residential burglaries” (p. 258). Groff and La Vigne (2001) used a raster-based surface with variables of equal weight to create an opportunity surface to predict repeat burglary locations. Those areas that appear to have a low opportunity surface are those that have the least amount of opportunity. If offenders chose to commit crimes in these areas they would have to put forth great effort and endure a large amount of risk in order to succeed at committing a crime. Areas in which the surface indicated high opportunity would pose less of a threat for offenders. There is a reduced amount of risk in these areas and offenders would expend less effort to commit a crime. Groff and La Vigne were able to use this model to predict repeat burglary.

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Potchak, McGloin, and Zgoba (2002) used a similar structure to study the occurrence of auto theft in Newark, New Jersey with regard to criminal effort and opportunity. The crime dataset consisted of auto theft incidents in Newark, New Jersey. The opportunity structure consisted of four maps layered over one another to form a pseudo density map, indicating the areas with differential opportunity. A coding scheme consisted of four grid layers (land-use, public housing, major roadways, and Penn Station), which were then summed for an overall opportunity structure. This research concluded that the opportunity structure created did predict opportunities for auto theft, despite the inclusion of only a few variables. Felson and Clarke (1998) continued to advance crime prevention techniques by studying shoplifting. Felson and Clarke (1998) determine that stores can reduce the loss of values by following the rules of VIVA. VIVA stands for Value, Inertia (weight), Visibility (exposure to theft), and Access (items near the door or on the way home in sight) (Felson & Clarke, 1998). Targets with VIVA qualities are more likely to be stolen. Felson and Clarke (1998) utilize the Rational Choice perspective to better understand how offenders consider different aspects, such as time and effort, before they make the decision to commit a crime. These considerations were expanded by Felson and Clarke (1998) into the ten principles of opportunity and crime, which helped explain the reasons why some locations present a greater opportunity for crime than others. The work of Groff and La Vigne (2001) and Potchak et al. (2002) followed the model of opportunity presented by Clarke (1997). Clarke (1997) discussed several situational crime prevention techniques that seek to reduce opportunities for crime instead of trying to control offender behavior or impulses. The sixteen opportunity reducing techniques, which are crime-specific, involve the systematic design of the environment so as to make crime more difficult to commit. Further discussion will continue in Chapter 5 and 6 with regard to applications of these techniques in this research. PATTERN THEORY Patterns have existed in this world since the beginning of time. But, within the last century, scholars have identified specific patterns that relate to human interaction. Communication and transportation patterns within cities were discussed by Burgess (1916). Burgess believed that these patters were crucial to shaping human existence and events,

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including crime related events (1916). White (1932) identified a pattern that impacted felonies in Indianapolis. White suggests that felonies decrease from the central city to the areas located just outside of the city. White suggests that social factors such as the economy and government action are related to the locations in which crime is occurring (1932). Regardless of the reason, a pattern was identified. Recent Pattern Theory Literature Related to Crime More recent crime pattern theory suggests that offenders develop a spatial preference (Capone & Nichols, 1976) and spatial awareness of targets by observing their surroundings as they engage in daily activities (Brantingham & Brantingham, 1981, 1984, 1993a; Appiahene-Gyamfi, 2002). Capone and Nichols suggest that the offender is present at a location (his/her residence) and the offender views possible locations of crime with regard to that residence (1976). The offenses that he commits are located in his action or activity space (Horton & Reynolds, 1971). The locations in his action space are those that he has information about and those locations with which he is familiar. Central concepts of pattern theory include personal activity nodes (i.e. home, work, school, and entertainment), paths between nodes (i.e. frequently traveled roads and walkways) and edges (i.e. boundaries that separate land use). These factors combine to form the backcloth from which decisions are made (Brantingham & Brantingham, 1993b). Target selection is influenced by this spatial awareness; decisions are based on many things including: the location of the target or victim, the distance one must travel to the target or victim (Brantingham & Brantingham, 1981), environmental characteristics of the crime location (Jeffery, 1971), the readiness of offenders and the amount of risk they are willing to take (Cornish & Clarke, 1986), but they are certainly influenced by the patterns or routes that offenders take during their everyday, non-criminogenic travels. Brantingham and Brantingham (1993a) view the criminal event as any other event; it is shaped by what has happened before it, it exists because it is a reflection or result of its surroundings and it follows the rhythms and patterns of life. Just like other occurrences, crime is not “uniformly distributed in space and time” (Brantingham & Brantingham, 1993a; Appiahene-Gyamfi, 2002) and most “good” crime targets are not “good” targets at every point in the day. Because of this ever-changing course, Brantingham and Brantingham note that there is

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an “interconnectiveness of objects, processes, or ideas” (1993a, p. 264). This interconnectiveness is constantly changing and continues to modify everything it is connected to, including crime. When the crime is triggered, according to Brantingham and Brantingham (1993a), the offender’s awareness space is recalled and possible targets are recollected. These possible targets are found in locations that the offender is familiar with, one of the nodes, paths, or edges that he has encountered (Brantingham & Brantingham, 1993a). All of the locations that the offender has visited, or seen while visiting another location, are recalled. The paths or streets used to get to those locations are evaluated and a target and travel path is decided upon. At one point in time these locations were not considered by the offender to be targets, in fact, the offender probably encountered these locations while engaged in legal activities. Another significant pattern that has been identified is simultaneous closing times of bars or other evening establishments. Potentiation, as identified by Rossmo (1995), occurs when bars and/or nightclubs in an area close simultaneously dumping a large volume of people onto the street at the same time. These large crowds can create disturbances that lead to violence (Engstad, 1975; Roncek & Maier, 1991; Roncek & Pravatiner, 1989; Rossmo & Fisher, 1993). These disturbances, if identified in advance, can be avoided by staggering closing times or strategically placing similar locations a safe distance apart. Similar patterns may also be handled by ‘controllers’ (Jochelson, 1997) and ‘place managers’ (Eck, 1994), if others means cannot be used to control potentiation. The identification of patterns is an important contribution to criminological literature. Patterns such as closing times of bars, use of activity nodes, and routine travel paths of both victims and offenders can help researchers uncover locations, times, and likely targets. This information can also help profilers to work backwards; to start with the crime and determine the location of the offender using knowledge of activity nodes, travel patterns, and well traveled routes. This knowledge should also be used to make citizens become more aware of their actions and the potential consequences and victimization that could likely result as they go about their day. Study and investigation into these patterns must continue diligently and in connection with current technology to advance prevention and aid in speedy apprehension of offenders.

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HOT SPOTS Overwhelming evidence suggests that certain victims are targeted repeatedly (Anderson, Chenery, & Pease, 1995; Farrell, 1995; Sherman, Gartin, & Buerger, 1989). For example, Sherman et al. (1989) determined by using calls for service data that crime is not distributed evenly throughout time and space (Blumstein, Cohen, Roth, & Visher, 1986). In fact, just over 50 percent of calls were attributed to three percent of locations (Sherman et al., 1989). But this also means that most places do not experience any crime and many locations that do experience crime, experience very little. While this is interesting, it is much more interesting to determine what makes some locations experience very heavy repeat victimization, only one victimization, or never experience a single victimization at all. Where Hot Spots Started A hot spot is an area that experiences an intense number of victimizations in the surrounding locations. The term, “hot spots” was first used to identify locations that experienced high rates of death due to cancer (Mason, McKay, Hoover, Blot, & Fraumeni, 1985). With regard to crime, “hot spots” could consist of anything from one block including the intersections on one or both ends, the distance of a couple blocks, an entire apartment complex, or an entire shopping center. There is no preset size of a hot spot. According to the work of Eck (1994; 1998) and Felson (1986), hot spots may exist if and when locations do not have “place managers” (Eck, 1994) or “handlers” (Felson, 1986). This lack of guardianship that both place managers and handlers provide would cause an area to be more inviting to offenders and could become an area with a high level of victimization. Sherman (1995) suggests that there are many reasons why a hot spot could flareup – high-crime people could congregate in high-crime locations, poor management of these high-crime locations, or a combination of poor supervision and poor management. For whatever reason, hot spots do exist and many police departments have taken proactive steps to control these locations while researchers seek to investigate the relationship between hot spots and the environment in order to recommend ways in which police could prevent future crime. Koper (1995) defined a hot spot as “a cluster of addresses which together produced 20 or more hard crime calls (e.g. robbery, rape, burglary) and 20 or more soft crime calls (e.g. disturbances,

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prostitution) over a one-year period (the selection year dated from June 1987 to June 1988) and showed a stable number of calls over a twoyear period” (p. 654). The criterion for a hot spot was that it was no longer than one street block, there were not two hot spots within one block of each other, and the entire hot spot had to be viewed from the epicenter (Koper, 1995). Each hot spot was randomly assigned to a treatment and control group. Koper (1995) found that preventive patrol decreases non-criminal activities and that each additional minute of patrol time increases survival by 23 percent (Koper, 1995, p. 663). The findings suggest that increased patrol is one possible method of decreasing crime in hot spot areas and that preventive patrol also decreases non-criminal disorderly behavior. Weisburd and Green (1995) studied hot spots in Jersey City, New Jersey. In this study, hot spots were ranked into four groups of very high call and arrest activity (10 hot spots), high activity (8 hot spots), medium activity (26 hot spots), and low activity (12 hot spots). The findings indicated three things police can be more effective when they take a more specific approach to crime and disorder, the specific actions of police are important when they are targeting problems or problematic places, and enforcement efforts on specific places do not necessarily cause displacement to surrounding areas (Weisburd & Green, 1995). Weisburd et al. (2006) also found no evidence of displacement in Jersey City. Jacobson (1999) conducted a study in New Bedford, MA of the effectiveness and efficiency of patrol in areas that were identified as hot spots. It was determined that it was more effective to patrol hot spot areas since locations with five or more calls for service had a probability of another incident equal to .87. Though the research indicates that it is targeting drug hot spots, it seems that much of the analysis and targeted intervention is dependent upon whether or not an individual location was targeted on five or more occasions (indicating repeat victimization of a location as the unit of analysis). Whatever the unit under investigation, Jacobson (1999) found that surveillance of all forms, natural, formal, and informal contributed to the success of decreasing calls for service in the hot spots areas. But that surveillance, specifically natural surveillance, had little deterrent effect on drugdealing if dealers believe that members of the general public were unlikely to intervene. Brantingham and Brantingham (1999) discuss the formation of hot spots and the importance of the backcloth and crime generators in the environment. Brantingham and Brantingham (1999) suggest three ways to identify hot spots: visual inspection, statistical identification,

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and prediction. Brantingham and Brantingham (1999) also suggest that movement is a critical component of hot spot formation. The ways in which people move through the area – the flow of traffic in vehicles or on foot – creates the paths with which people become familiar. These awareness spaces are more likely to be targeted than other locations because individuals are more familiar with the areas that they travel on a daily basis. Brantingham and Brantingham (1999) also suggest that hot spot formations can be used to evaluate crime prevention techniques and their effectiveness in specific settings. Weisburd and Green-Mazerolle (2000) researched the amount of arrests and calls for service for drug related crime as well as calls related to disorder in drug hot spots. Drug hot spot locations were more likely to experience crime and disorder problems when compared to non-drug hot spots locations in the city. Confirmation of these findings was found in a meta-analysis conducted by Braga (2001). This research indicated that seven out of nine studies using experimental designs to study hot spots found noteworthy crime reductions in their studies with some level of social disorder at ten of eleven sites (Braga, 2001). Most importantly, there was no significant displacement or diffusion found during or shortly after the experimental and control groups were undergoing the research experiment (Braga, 2001). In a further analysis of the Jersey City study, Weisburd et al. (2006) concurred with Braga that in Jersey City, New Jersey, crime prevention efforts resulted in a diffusion of benefits, not crime displacement. With regard to auto theft, Lu (2006) studied the city of Buffalo, New York and found that major roads and roads directly connected to major roads had a disproportionate number of auto thefts. Lu attributes this to the number of activity nodes on these streets (2006). The activity nodes become crime generators and/or crime attractors and create an area that is attractive to auto thieves. Most importantly, Lu indicates that thieves do not pick streets randomly. According to this research study, 3,179 auto theft offenses were concentrated in 1,526 census blocks, leaving 1,731 blocks auto theft-free (Lu, 2006). This research indicates that crime prevention tactics utilized in these areas may be more effective than city-wide dispersal of resources. McCleary (2008) also discussed the importance of crime attractors and crime generators in the study of a new strip club in a rural area. Using a comparison of pre-strip club and post-strip club crime rates, McCleary found that crime rose when the Lion’s Den opened and fell when the club was closed (2008). This finding suggests that alterations to the environment play a role in victimization. New locations increase

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roadway usage, expose victims to criminal activity, disrupt the routine activities of the town and provide cover to offenders who may be scouting an area for criminal opportunities. Ratcliffe and Taniguchi (in press) further elaborate on the importance of crime attractors and crime generators in their study of drug-gang street corners. These authors found that street corners with more than one gang had the highest crime rates; higher crime rates than those corners with a single gang. Corners with single gangs had significantly more crime than those without gang affiliation (Ratcliffe & Taniguchi, in press). Ratcliffe and Taniguchi (in press) caution the reader about the time order of these events. One shouldn’t suggest that gang corners predict high crime rates, it may be that high crime rates paved the way for gangs to sell drugs on specific corners. Causality, in either direction, was not explored in this study. What is clear from hot spot research is that more specific measurements must be utilized to draw more firm conclusions about how hot spots are born and evolve. There are many techniques used for hot spot identification (National Institute of Justice, 2008). Kernel Density Estimation (KDE) is becoming one of the most popular tools in identifying criminal hot spots (Chainey, Tompson, & Uhlig, 2008). Many research projects have found that mapping techniques of all kinds are better at predicting where street crime will occur than other crime types. Areas with high crime occurrences and neighborhoods that suffer from high rates of both violent and non-violent victimization do have options to reduce the occurrence of crime. The first task that police must undertake is identification of crime locations. If entire areas (blocks, street segments, neighborhoods) appear to be criminogenic, perhaps beautification or other environmental changes can be made to clean the environment and change the offenders’ view of the area. Crime prevention techniques such as lighting and security devices can also be added in order to suggest that offenders will not be able to successfully commit crimes in these locations. The studies mentioned in the last section indicate that patrol and surveillance techniques seem to reduce crime and make citizens feel safer. Perhaps a combination of these techniques (Ratcliffe & McCullagh, 2001) can be implemented to alter high crime areas where expensive routine patrol is not an option.

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REPEAT VICTIMIZATION Repeat victimization is a term used to describe a victimization that occurs at the same location or to the same person more than once. The term ‘repeat victimization’, unlike the term ‘hot spot’, indicates that the same address has been re-victimized, and, most often, the same crime has been committed both times (Reiss, 1980). Repeat victimization locations can indicate a high level of crime in an area and drive the formation of hot spots or they can occur by themselves in the middle of a relatively “cold” area. A hot spot can also be derived of several repeat victimization locations that are in the same general area. Regardless of their composition, the terms ‘hot spot’ and ‘repeat victimization’ are not interchangeable. One is a specific location or address that experiences a high amount of crime (repeat victimization) and one is an area with a high concentration of crime (hot spot). Gottfredson (1981) posited that victimization could be explained by the ‘exposure model’, the concept that the amount and kind of interaction that people have in high risk areas (their lifestyle) can predict the types of victimizations they may suffer (Gottfredson, 1981, 1986). Gottfredson (1986) suggested that perhaps victims are reluctant to call the police because of the nature of the crime that was committed against them, not the characteristics of the victim. For instance, according to police records, men are more likely to be victimized than women; perhaps men do not call the police because the crimes committed against them (a bar fight) are not as serious as the crimes committed against women (a rape). Lasley and Rosenbaum (1988) studied the activity patterns of those who experienced repeat victimization and those who experienced single victimization. Lasley and Rosenbaum (1988) believed that if those who were victimized repeatedly could prove “random” victimization then they were in possession of “bad luck” instead of a characteristic that made them more likely to be victimized. If these repeat victims were victimized non-randomly then it would be concluded that they possess some trait that predisposes them to victimization. Lasley and Rosenbaum (1988) concluded that the chance of suffering a repeat personal crime increases with the reduction in workforce activity and the number of weekend nights spent away from home. More specifically, victimization increases as routine activities that take place outside of the home increase. Hindelang, Gottfredson, and Garofalo (1978) and Sparks, Genn, and Dodd (1977) also found that some individuals are more likely to suffer victimization than others, but did

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not attribute that proneness to the same activities. Both Gottfredson’s ‘exposure model’ (1981) and Lasley and Rosenbaum’s routine activities theory (1988) explains crime on a local or neighborhood level while other researchers have studied victimization on the national or international levels. Block (1984) attempts to explain differences in victimization based on the differences of traditions and norms in different countries. Block (1984) discusses the idea that when comparing crime rates of several countries, one notices that that the crime rates are very similar but the pattern of the crime and the types of crimes committed are very different. Block (1984) believes this is due to the variation in history and culture of different countries and the people within them. One difference is the availability of targets for crimes in different countries. Many countries do not have the same economic systems nor the same goods and services and, therefore, crimes can not be committed against those goods and services if they do not exist (Block, 1984). Sherman et al. (1989) found that places that citizens determine to be ‘very safe’ experience some of the highest victimization. Sherman, Gartin, and Buerger (1989) found that 24 department stores generated 2,444 calls for service in one year. This research study also indicates that most places (95%) of locations are crime-free. However, once an offense occurs, the likelihood of repeat victimization is 26 percent; after three victimizations the likelihood of another offense increases to over 50 percent. Trickett, Osborn, Seymour, and Pease (1992) also find repeat victimization to dramatically increase, including both prevalence and vulnerability, as the crime rate itself increases. Despite these findings, Farrell and Pease (1993) report that police systems are not good at detecting repeat victimization. This occurs when reports or files are not entered in the exact same fashion each time they are entered into the system. If a burglary took place at the address of One Winding Way on November 2nd and 1 Winding Way on November 3rd, and the addresses were recorded differently (“one” versus “1”), the second may not appear to be a repeat location. This, and other police system problems, dramatically underestimate the number of repeat victimizations. Farrell and Pease (1993) also suggest that those who are frequently victims of crime are those who live in bad areas and have chaotic lifestyles. These individuals are less likely to report crime to the police in the first place and may move around so frequently that a repeat victimization listed by address would never be found. Besides police error, other factors may decrease the incidence of reported repeat victimizations (Farrell, 1995). Farrell (1995) suggests that rapid response by police may scare off offenders in the

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middle of committing the crime and the attempted break-in may not be reported as such. Sometimes, repeat victimization is not the same type of victimization (Hope, Bryan, Trickett, & Osborn, 2001) and often multiple victimizations stemming out of several different types of crime will never be identified as repeats. Lauritsen and Quinet (1995) suggest that prior victimization does have an effect on subsequent victimization, but not directly through the actions of the offender. Lauritsen and Quinet (1995) propose that prior victimization alters something about the individual victim which increases the victim’s likelihood for future victimization. Perhaps this initial victimization occurs because this particular victim exhibits proneness for crime, and therefore is anticipated to be repeatedly victimized. In either case, if any victimization indicates an increased chance of additional victimization, this attribute can be used to identify people or locations in need of crime prevention techniques (Mukherjee & Carcach, 1998). With regard to burglary, young men and women and single women were the most likely candidates for repeat victimization (Mukherjee & Carcach, 1998). Anderson, Chenery, and Pease (1995) examined repeat victimization in West Yorkshire, England for an 11-month period. This research found that a considerable number of targets suffered repeat victimization: between 16 and 23.5 percent of domestic burglaries targeted the same address; between 28 and 69.5 percent of commercial burglary victims were victimized again; and, between 6 and 49 percent of motor vehicle thefts were repeated. Bennett and Durie (1999) and Polvi and Pease (1991) conclude that there are three options for locations that are repeatedly victimized in a short period of time: the same offender returns, the offender tells another offender and he returns, or the home is an attractive target for all burglars. Both of these works reaffirm that crime is concentrated more among targets than among offenders; thereby highlighting the need to examine the location of crime occurrence and the reasons why crime clusters in particular places. In 1996, these repeat locations became known as hot dots (Pease & Laycock). ‘Hot dots’ are locations within a hot spot that are known to have a high incidence of victimization – a repeat victimization location. Pease and Laycock put forth five indicators of repeat victimization: past victimization is a good predictor of subsequent victimization, the greater the number of prior victimization the higher the chances of suffering another victimization, repeat victimization tends to happen shortly after the past victimization, a small number of offenders are responsible for a large number of repeated offenses, and the extent of

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the prevalence of repeat victimization is masked by the police department’s inability to properly record offenses (1996). Despite the fact that police data is criticized for its inability to capture repeat offenses (Townsley, Homel, & Chaseling, 2000), many researchers have used these data in an attempt to better understand the phenomenon. LeBeau and Vincent (1997) use graduated circle maps to compare the spatial distributions of alarm calls and burglary incidents across Charlotte, North Carolina. Groff (1998) used police department data to identify the places that accounted for the highest proportion of crime. This technique, Minimum Plotting Density (MPD) allows researchers to identify problem places in a specific area in order to target police efforts at specific high-volume problems. Pease (1998) indicates that there are advantages for police departments to concentrate on repeat victimization locations. Some of these advantages include: an automatic focus on highest crime areas, a focus on high risk citizens/locations, the ability to focus on temporal as well as spatial patterns, the ability to combine victim support and crime prevention, an increased likelihood of targeting repeat perpetrators, and an unarguable way of focusing on the worst offenders. Though advancements in crime mapping technologies have aided police departments, Bichler (2004) cautions researchers about the use of the software products. The default settings may not be appropriate for all levels of crime analysis. If these are not adjusted, the findings can be meaningless, resulting in wasting time, money and resources for researchers and police. Another way to view repeat offenses is through the time window. Taylor (1999) discusses the problem of the time window. If an individual is looking at the period from January 1-December 31, 2002 as the year within which a crime is committed, the following one year period, from January 1-December 31, 2003 is the period for which most people will look for a repeat victimization (Farrell et al., 2002). Farrell, Sousa, and Weisel (2002) suggest that a one-year time window captures 42% more repeats than a six-month window. According to a one-year time window, those locations that were victimized on January 1, 2002 have almost a full 2-year time span within which the opportunity for a repeat victimization can occur. Locations that were victimized on December 31, 2002 have half as much time with which to identify a repeat victimization. More importantly, if the location that is victimized on December 31, 2002 is re-victimized on January 1, 2004, that victimization is out of the “time window” and will not be seen as a repeat victimization, despite the fact that it occurred within less time than the possible re-victimization of the first house victimized.

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Because of this problem it is suggested that a window of the same time period be used for all victimization, so that the last victimization has the same number of days in its “window” as the first victimization. Townsley et al. (2000) indicate that a survival analysis would also be a good statistical test to use in these situations and would provide interesting results for victimization studies. Very often the impact of repeat offenses is limited to the location of the event or the number of times that the person or property was victimized. Rarely do researchers look at the impact repeat victimization has on the likelihood of the victim becoming an offender. Chang, Chen, and Brownson (2003) studied delinquency rates and patterns of high school seniors. “Seniors who were repeatedly victimized were 1.85 times more likely to initiate a delinquent act” (Chang et al., 2003, p. 277) than those who were not repeat victims. Being male, earning bad grades, exhibiting risk-taking behaviors and fighting with parents were strongly linked to delinquent recidivism (Chang et al., 2003). Perhaps those studying the cycle of violence must consider the possibility that repeat victimization may play a role in the perpetration of future criminal acts. One common example of victim-becoming-offender is gang retaliation. Ratcliffe and Rengert (2008) identified a pattern in property crime and shooting events in Philadelphia using the term “near repeat”. “The near-repeat phenomenon states that if a location is the target of a crime such as burglary, the homes within a relatively short distance have an increased chance of being burgled for a limited number of weeks” (Ratcliffe & Rengert, 2008, p. 58). In terms of a near-repeat shooting, the dimensions of time and space must be considered. In order for a near-repeat pattern to be established, the second shooting must occur in the same city block (400 feet in Philadelphia) within a week or two of the previous shooting. In Philadelphia, Ratcliffe and Rengert found the risk of a shooting to be elevated by 33 per cent, given these parameters (2008). This suggests that increased attention should be given to these locations for at least two weeks following a shooting. Two other research projects found support for the near-repeat theory. Townsley et al. (2003) found similar results with residential burglary. They suggest that houses in the same neighborhood of those that have been recently victimized are at greater risk for burglary because of their similar layout to the recently burgled homes (Townsley et al., 2003). Johnson et al. found that houses “within 200 m of a burgled home were at an elevated risk of burglary for a period of at least two weeks” (2007, p. 201).

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Along these lines, other aspects of the event should be studied to better understand the repeat victimization phenomenon. One such concept is the “unit of analysis” for the initial criminal event. Some crimes are unable to experience repeat victimizations when studied under specific units of analysis. Using auto theft for an example, if the unit of analysis is the car, and it is never recovered, the chance of repeat victimization does not exit. However, if the unit of analysis is the location that the car is stolen from, there is a chance for repeat victimization. Moreover, the location may be repeatedly victimized, but the owner may not be. If the location is a parking lot, many cars can be stolen from the lot, but they may not belong to the same owner. An owner can be yet another type of repeat victim. S/he could have a different car stolen at different points in time, from the same or different locations. Though the same car has not been victimized again, the owner has still been victimized repeatedly. Shaw and Pease (2000) found that 19 percent of victims had two or more vehicles taken during the course of a year. Despite these potential problems, identification of hot spots, repeat victimization locations, and even hot dots can aid researchers and police departments in identifying locations that need police attention. Whether this attention comes in the form of crime prevention tactics or patrols, these locations can be identified with user-friendly mapping software. Once hot spots and repeat victimization locations can be identified and the initial crime threat is over, preventive interventions can be used in addition to police concentration at these locations. In order to combine a community-level and site-level approach to crime prevention, community-level problems (hot spots) must be identified and then site-level (repeat victimization) locations can be targeted.

CHAPTER 4

Micro/Site-level Crime Patterns

INTRODUCTION Site-level research is conducted in order to get a better understanding of the physical characteristics of both the specific locations, and the environment surrounding the locations, that are targeted by offenders. Some research has even tried to compare locations with crime to those without crime to determine the environmental differences that may have led to the victimization of one location but not the other. Routine Activity Approach (Cohen & Felson, 1979) suggests that the absence of a capable guardian intersects with a suitable target to form a good mark for an offender. The definitions of a “capable guardian” and a “suitable target” are somewhat debated. Different research studies have defined a “capable guardian” strictly as a security guard or at least as the presence of “security personnel”. Other research projects consider guardians to be equipment such as video cameras, voice recognition systems, gates, etc. Regardless of the nature of the definitions, there is a wide acceptance of Routine Activity Approach in the field of criminal justice. Rational Choice Approach suggests that there is more than strictly a physical component to the commission of a crime. Rational Choice is based on the offender’s decision process in which s/he makes a rational calculation of the costs and benefits of committing a particular crime at a particular time (Cornish & Clarke, 1986). The offender may consider such things as potential gains (monetary gains as well as those which must be liquidated), potential costs (punishment for getting caught, jail time, conviction), the best time of day to conduct the crime, the risk of certain locations (with regard to apprehension, confrontation etc.) and finally make a decision based on the net gain or loss associated with all of these factors (Cornish & Clarke, 1986). Situational crime prevention identifies the physical components and structures that can facilitate a criminal act. Researchers have experimented with manipulating the environment in order to prevent and eliminate certain types of crime. Jeffery (1971) believes that crime 51

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can be prevented by creating invisible boundaries that people recognize to be areas that are off-limits, even if they aren’t physical barriers preventing access and egress. The lessons learned from Routine Activity Approach, Rational Choice Approach, and Situation Crime Prevention are all applied to locations to either identify why crime has occurred at these locations or learn what about these locations has repelled crime. The Routine Activity Approach Drawing from the work of Hawley (1950), the occurrence of criminal events is a routine activity socially organized in time and space. The Routine Activity Approach (Felson & Cohen, 1980) draws from human ecology research and emphasizes the context in which offenders choose to commit crimes. This approach concentrates upon the circumstances and environmental characteristics in which offenders carry out criminal acts. In particular, Cohen and Felson (1979) and Felson and Cohen (1980) originally hypothesize that the dispersion of activities away from households and families would increase the opportunity for crime and thus generate higher crime rates. The authors argue that changes in routine activity patterns can change crime rates by affecting the convergence in space and time of the three elements of a crime: motivated offenders, suitable targets, and the absence of a capable guardian. A lack of any one of these elements is enough to prevent the completion of a crime. If the number of motivated offenders or suitable targets were to remain the same, changes in routine activities could modify the likelihood of their convergence in space and time, creating more opportunities for crimes to occur. Groff (2008) tested Routine Activity Approach with regard to street robbery. She found that street robbery did increase with the time spent away from the home. Groff (2008) also found support for Ratcliffe’s hypothesis about temporal effects. “Spatio-temporal constrained schedules significantly increase the incidence of street robbery as compared to temporally constrained ones and radically change the distribution of street robbery events” (Groff, 2008, p. 111). The combination of space and time can better predict criminal events then either alone.

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Concepts of Routine Activity Approach The Routine Activity Approach introduces the concepts of a motivated offender, suitable target, and lack of a capable guardian. These three concepts have made a significant contribution to the field and, specifically, to the practice of environmental criminology. The idea that a crime could be pinpointed to a specific spot (a house within a neighborhood) instead of an entire neighborhood, dramatically changed the way criminologists think about crime. In addition to these three concepts, Cohen and Felson (1979) contribute the temporal components of rhythm, tempo, and timing into criminology as Hawley (1950) acknowledged their importance in social ecology. The addition of these three components added yet another aspect to the criminal event. The idea that within a particular area, and related to a particular event, there must be a coordination of activities with the offense (timing) as well as number of times offenses would occur in a day (tempo) and the periodicity of the events (rhythm). This provided a new way of looking at crime; it encouraged scholars to view crime as any other activity, just as one could view the number of cars that drive by a particular street corner. The importance of the criminal event is furthered by the introduction of place managers (Eck, 1994). Place managers are normal citizens who act as supervisors to tenants, other residents, neighborhoods etc. and indirectly police locations by acting as witnesses to any potential criminal activity (Eck, 1994). Eck’s proposal was tested in the early 1990s when he determined that drug markets were more likely to be productive when there was a weak place manager, physical security and customer access. Eck and Wartell (1999) found similar results in San Diego when rental properties were notified of criminal activity and letters were issued to landlords of the residences. More recently Culley, Conkline, Emshoff, Blakely, and Gorman (2006) called for the use of an ecological approach in schools in order to better understand the dynamic of the school system. Fox and Sobol (2000) discuss the responsibility of bar owners in controlling or restricting the level of disorder on the streets when patrons leave the bar. Perhaps if more landlords, schools, and bars utilized the concept of place managers, the amount of criminal activity associated with these locations would decrease. Likewise, Felson (1995, 1998) revises the Routine Activity Approach to include the importance of “handlers”. Felson (1995) adds

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that “handlers” supervise offenders and further develops the concept of supervision by discussing differential levels of responsibility: personal, assigned, diffuse, and general. This body of work suggests that crime opportunities concentrate where targets remain unsupervised for predictable periods of time and in areas that are well known to large numbers of people. Felson (1998) implies that criminals commit crime to work less. Therefore, if you make the crime harder to commit, the offender will be less likely to offend. These changes to the approach were made after the introduction of the concepts of effort in the Rational Choice Approach (Cornish & Clarke, 1986), offender opportunities presented in distance decay models (Brantingham & Brantingham, 1981; Rossmo, 1993, 1995), and offender awareness space (Brantingham & Brantingham, 1984; Rengert & Wasilchick, 1985) were introduced into the field. The shift in criminological philosophy influenced by the above concepts change the methods with which researchers study crime and the way in which they interact with police. Other criminologists also studied the concept of a criminal event, namely, Brantingham and Brantingham (1981) with regard to the spatial aspect of crime, Cornish and Clarke (1986) with regard to the choice structuring process of offenders, Kennedy and Forde (1999) with regard to situational aspects influencing conflict, Meier, Kennedy, and Sacco (2001) with regard to the criminal event perspective and Holtfreter, Reisig, and Pratt (2008) with regard to fraud. Situational crime prevention techniques utilize the results of these analyses to determine what factors encourage and discourage criminal activity. The Rational Choice Approach Specifically, Cornish and Clarke (1986) state, in Rational Choice theory, that a utilitarian cognitive process precedes and informs an offender's decision of whether or not to commit a particular crime at a particular time and place. This idea originates from Bentham (1948) and suggests that if the net result of an act is anticipated to produce a positive consequence, then the individual engages in the act. If the anticipated consequence of the criminal act is negative, then the individual refrains from such action. The factors that inform this decision, namely the associated risks, benefits, and costs, are generally only examined empirically in studies assessing individuals’ perceptions of potential costs and benefits associated with their perceived

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likelihood of committing particular criminal acts (Nagin, 1998; Nagin & Paternoster, 1993; Paternoster, 1987; Piquerro & Tibbetts, 1996). Less empirical attention has been paid to evaluating the underlying variables that enter into the decision process with more specificity. Clarke and Cornish (1985) indicate that there is a simpler cognitive process performed by the offender. First, the offender has to be receptive to the idea of committing a crime. Furthermore, s/he has to have already established the intention to commit a specific type of crime (Clarke, 1997). Specification is necessary because the offender must decide if this crime is one in which opportunities are present, or one in which the offender will have to search for these criminal opportunities. Those offenders who target available opportunities rationalize crime differently than those who are willing to seek out opportunities. Rational Choice Approach, as discussed by Cornish and Clarke (1986) is applied in this study when researchers attempt to capture cues in the environment that make the target an attractive one (e.g. a situation where the offender is less likely to be caught and a greater reward or payoff is expected) or an unattractive one (e.g. a crime in which a greater risk of being caught and/or a smaller reward or payoff exists). Information that the offender processes regarding lighting and location also play a role in his or her decision to commit a crime. Each small factor that is considered has an effect on the overall decisionmaking process of the offender and, whether consciously or subconsciously, is captured by the Rational Choice Approach. Situational Crime Prevention Jeffery (1971) indicates that crime cannot be controlled through measures designed for the individual offender, but can only be controlled through the manipulation of the environment where crimes occur (p. 19). Jeffery introduces the concept of crime prevention, the idea that police can stop crime before it occurs, by changing the structure of the environment. This concept seems obvious now, but prevention wasn’t always a tactic practiced by police. Jeffery continues the ideas of traditional ecological criminology by indicating that environments can influence criminal behavior in two ways – physically, by providing the physical surroundings to which individuals respond; and socially, by providing the social relationships to which individuals respond (1971, p. 215).

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Jacobs (1961, 1993) also suggests that spaces, especially public spaces, play a role in crime prevention. Her suggestions are based on the residential environment; by keeping block segments short and personable – clean and friendly and full of children playing – Jacobs indicates that they will remain crime free. She also believes that there needs to be “secondary diversity” in primary use areas. Areas that are designed for only one purpose will be unoccupied by local residents when they are not able to use those resources (e.g. a playground in winter). This invites outsiders to use these vacated areas for criminal purposes (Jacobs, 1961). Jacobs also identifies three places that are harmful unless the location is controlled: parking lots, trucking depos, and gas stations (1961). Clarke and Homel (1997) propose four strategies of crime prevention: 1. increasing perceived effort (includes target hardening, access control, deflecting offender and controlling facilitators); 2. increasing perceived risks (includes Entry/exit screening, formal surveillance, surveillance by employees, and natural surveillance); 3. reducing anticipated rewards (includes target removal, identifying property, reducing temptation, and denying benefits; 4. removing excuses (includes rule setting, stimulating conscience, controlling disinhibitions, and facilitating compliance). In 2003 Cornish and Clarke expanded the list to five strategies by introducing another category, reducing provocations (includes reducing frustration/stress, avoiding disputes, reducing emotional arousal, neutralizing peer pressure, and discouraging imitation). Since Clarke and Cornish (1985) stated that each crime is to be treated differently due to the type of offenders it draws and the unique nature of crime, not all of the opportunity reducing techniques listed above will apply to each crime. Similarly, several of the techniques could pertain to each crime, but the actual application used to prevent the crime may change from crime to crime. For example, both auto theft and commercial burglary may use the eleventh technique, Reducing Temptation, but the application to auto theft may be to park in off-street parking, while the technique applied to commercial burglary may be to remove displays from windows in the evening. These techniques are utilized when measuring variables in the site-level analysis. Routine Activity, Rational Choice and Situational Crime Prevention provide a framework from which other environmental criminologists can draw. These concepts, part drawn from theory and

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part learned from practice, aid researchers in identification and categorization of crime-specific and event-appropriate crime prevention measures. Site-level analysis is an important aspect in crime prevention. Researchers cannot provide accurate crime prevention guidance without acknowledging the specific problems that exist at locations that have been victimized. Just as opportunities are crime specific; crime prevention is crime specific and is best applied to crimes in which it has been tested. It is essential to use both confirmed crime prevention techniques and to test for new methods that may reduce or prevent crime. SITE-LEVEL SCHOLARSHIP For each type of crime, researchers have identified several environmental factors that may be related to the occurrence of crime in particular areas. See Table 1 at the end of this chapter. These environmental factors can be concretely measured using site-survey techniques to determine if locations where crime occurs have similar environmental features to those where crime is absent. Using burglary as an example, Buck, Hakim, and Rengert (1993) noted that unalarmed houses located on cul-de-sacs were more likely to be burglarized than unalarmed houses not located on cul-de-sacs. Weisel (2002) found that houses located near major thoroughfares were more likely to be burgled than those not located near major roads. Other environmental factors, like surveillance, can be more difficult to measure. Continuing with the burglary example, Bennett and Wright (1984) have identified that neighborhood surveillance is an important factor which determines whether or not individual houses get targeted for burglary. Regardless of how researchers measure these factors, some environmental factors are crime-specific while other factors are similar for related types of crime. Watchers, Activity Nodes, Location, Lighting, and Security (W.A.L.L.S.) are the five factors that are tested for significance in this book. These five factors have either been identified and empirically tested in prior auto theft research projects, or have been tested with regard to another crime and will be tested for significance to auto theft herein. The “Watcher” variable represents the presence or absence of capable guardians in the auto theft location, “Activity Nodes” refers to the locations of crime generators and attractors near the auto theft. “Location” refers to landscape and cover that may be accessible for

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offenders to use to hide their acts while stealing an automobile, “Lighting” refers to the quality and quantity of lighting near auto theft locations, and “Security” refers to the environmental cues meant to deter offenders. Watchers Cohen and Felson (1979) and Felson and Cohen (1980) discuss the importance of “capable guardians” in their research. These “capable guardians” can provide both informal and formal surveillance over an area or location. These guardians, or “watchers” as noted here, help to discourage offenders from committing crimes in the location they are guarding. Often offenders will chose locations where obvious guardians are not present since they believe that they are less likely to be apprehended for crimes that don’t have witnesses. Most subsequent research in this area was drawn from Cohen and Felson (1979) and Felson and Cohen (1980) for theoretical support. Bennett and Wright (1984) interviewed burglars to ascertain the thought processes of these offenders before they committed their crimes. The burglars were given photographs of houses to determine whether or not they would target each house for burglary. Burglars mentioned the following things to be important considerations in target selection: the openness of the area including roads, the class of occupants, use of security devices (dogs, alarms, etc.), surveillability, and occupancy. Burglars discussed the importance of being watched in terms of openness, surveillability, and occupancy. Locations that are enclosed are less likely to be seen from the street or neighboring home. These locations are considered favorable by burglars (Bennett & Wright, 1984). Keister (2007) and Tseng, Duane, and Hadipriono (2004) also suggested that tall shrubbery and other brush can provide cover and be inviting for thieves. Buck et al. (1993) found that burglars consider seclusion as a factor in the target selection process. Due to the concealed access, burglars preferred cul-de-sacs. Properties located on a cul-de-sac “were 1.5 times more likely to be burgled when unalarmed than the average probability for unalarmed properties in the community” (1993; p. 501). Buck et al. (1993) found that burglars tend to target locations where the turnover rate of homes was high. A high turnover rate can be linked to a decrease in available “watchers” since new home owners are less likely to recognize other owners, and therefore, are less likely to

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recognize those who do not belong. Wilcox, Madensen and Tillyer (2007) found that individual target hardening strategies work better when they are used in neighborhoods that have more natural surveillance, also reinforcing the importance of “watchers”. In another article on burglary, Brown and Bentley (1993) studied target selection of homes by 72 burglars. Photographs of ten houses from a sample of 1000 in a middle class area were given to the burglars. Houses were judged on the basis of those that were occupied vs. empty, degree of difficulty to enter, neighbor surveillance, and territorial concern. Brown and Bentley (1993) found that there may be a systematic relationship between perceptions of a house’s characteristics and victimization vulnerability. Houses that were perceived to have difficult entry were associated with those that were judged to be non-burglarized. The belief that neighbors would react to a burglar’s presence was also associated with a non-burglarized judgment. Both of these findings support the idea that the more likely the location is to be “watched” the more likely the burglar is to skip this location when choosing a target. Fisher (1991) also discussed the importance of neighborhood characteristics, social interaction, and the perception of social control. Locations that are deemed unsafe tend to use more target hardening devices such a grills and shutters that protect businesses and homes from burglary. These devices send a signal to offenders that residences and businesses owners are aware of dangers in the area. This signal also brings awareness to consumers who then see the devices as an indication that the areas are unsafe. If this happens, consumers may decide to shop in environments they deem safer, according to Fisher (1991). After conducting research in Minnesota, Fisher (1991) found that commercial areas that were surrounded by protective security measures may have invoked fear in consumers who, because of their perception of crime, may have decided to shop elsewhere. This fear may have affected employees as well, since some people may be reluctant to work in an area deemed unsafe (Fisher, 1991, see also Perkins, Meeks, & Taylor, 1992). Clarke (2003) suggests a few options instead of grills and shutters, these are: employing more security guards after hours, improving street lighting so that people feel safe in the area and will travel through these areas after hours and therefore increase surveillance, and promoting mixed use areas where residences can be located above commercial establishments in these areas.

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Neighborhood characteristics were also introduced in the work of Perkins et al. (1992). Perkins et al. (1992) discuss the ideas of territorial functioning. Territorial functioning is the use of the outdoor environment to send non-verbal cues to non-residents about the care and protection of residential areas. In addition to items previously noted to have an effect on territorial functioning (Taylor & Covington, 1988; Taylor & Brower, 1985), Perkins et al. (1992) found private outdoor lighting and plantings to contribute significantly to the model. Outdoor lighting and plantings contributed to the beautification and made respondents less likely to identify physical decay in the built environment. This is also a good option for those who feel CCTV may be too intrusive in public spaces (Goold, 2006). The built environment was also discussed by Webb, Brown, and Bennett (1992), in their research of parking lots and garages. Webb et al. (1992) found that with regard to surveillance, the layout of the parking structure is important. The ability to use Closed Circuit Television (CCTV) as a means of surveillance is a factor salient to reducing crime (see also Tilley, 1993). The ability for outside surveillance, or watchers, also depends on the level of lighting and the ability for vehicular access and egress. Parking structures with “pay and display” tickets do not require formal surveillance since they don’t require a person to validate tickets. Parking structures with manned exits that require seeing a person before exiting have a much higher perception of surveillance. Webb et al. (1992) found that manned exit surfaces and the “pay on foot” car parks show the lowest risk for theft (p. 15). In addition, car parks with manned exits also have a lower risk of theft from cars since attendants in the kiosks provide additional surveillance and protection (Webb et al., 1992). In another auto theft study, Poyner (1997) considered the amount of crime that occurs with regard to the type of parking structure. Poyner found that parking garages are much more susceptible to crime than are open parking lots (1997). Most of the discrepancy in crime between parking lots and parking garages has to do with the lack of surveillance in parking structures with many different levels. Since the parking garages have many levels located above the street level, surveillance from passing consumers and residents was minimal on any level other than the one visible from the street. Poyner (1997) found that Closed Circuit Television (CCTV) cameras were used in parking garages with diminished sight from the street in order to control surveillance. Often, cameras with loudspeaker capabilities were used

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to provide directions and verbal surveillance over the areas. Poyner (1997) found dramatic results with the CCTV systems. Thefts had been reduced and there were many months with no reported incidents whatsoever. However, not all crime reductions can be directly contributed to the use of CCTV only. Welsh and Farrington (2004) conducted a meta-analysis of 19 CCTV evaluations. Based on these evaluations, the authors found that CCTV does have an effect on crime. Crimes decreased by 21 percent in areas with CCTV when compared to areas without. More than 50 percent of studies in the meta-analysis showed evidence of the desired effect of CCTV on crime. Five of the 19 evaluations used lighting in conjunction with CCTV and the results of the meta-analysis indicate that the combination of CCTV and lighting is more effective than CCTV alone (Welsh & Farrington, 2004). With regard to surveillance, Hunter and Jeffery (1992), in a study of high risk of robbery victimization in convenience stores, found that locations that have two or more clerks on duty, especially at night, and more natural surveillance, have a lower chance of victimization (see also Graham, 2001 for a discussion on the need for multiple clerks for surveillance). Bellamy (1996) has suggested that with regard to repeat robbery, having more than two clerks on duty may bring the greatest decrease in victimization for stores that have already been victimized. The results generated by Hunter and Jeffery (1992) support the concept that robbers select their targets and that the decision to commit a crime is often based on physical and behavioral attributes of the environment. Though much research focuses on the physical environment, Felson (1986) introduced the term “handler” as a person who exerts control over a likely offender in order to keep the person from offending or re-offending within that environment. This concept is different from a guardian since a guardian keeps watch over targets, not offenders. A security detail at a parking lot is considered a guardian. The security officer’s job is to protect the vehicles parked in the lot. However, a parent, teacher, coach, employer or friends is considered a “handler” since s/he “handles” the offender to keep him/her out of crime (Felson, 1995, 1986; Jochelson, 1997). Eck (1994) and Eck and Wartell (1999) give a specific name to guardians who are in control of locations, “place managers”. These “place managers” are responsible for controlling crime and situations in places like hotels/motels, apartment complexes, and public housing. Since these places don’t have a fixed population (hotels and motels turn over their population on

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a daily basis, and public housing and large apartment complexes tend to have quick turnovers as well) those who would normally be providing informal surveillance are less familiar with the other residents and staff in the area. It is the place manager’s responsibility to regulate behavior in these locations (Eck, 1994; Eck & Wartell, 1999; see also Trickett, Osborn and Ellingsworth, 1995 with regard to length of residence and guardianship). Watchers are individuals who exert a presence in the community and provide informal surveillance in these locations. Watchers are residents, local storeowners, consumers, and pedestrians. They can be the same people every day or can consist of different people each day, based on the individual’s daily activity patterns and the land use in the area. Watchers are an important part of the landscape of crime, they can deter offenders simply by their presence and are often not required to do any more than go about their daily routine in order to be effective. Activity Nodes Beavon, Brantingham, and Brantingham (1994) introduced the concepts of street networks and their relationships to targets. The authors noted that crime was higher in areas that were more accessible. Beavon et al. (1994) noted that areas that had greater accessibility were areas that have more well-traveled roadways. Locations that were not accessible did not have well traveled roadways, had fewer targets known to offenders, and therefore less crime. Since Beavon et al. (1994) discuss road networks, their implications are sound only for those who travel by vehicle to commit their crimes, not those who walk to areas to offend. Blocks with high accessibility and high street flow were those that had a greater amount of crime (Beavon et al., 1994). This led to the idea that property offenders must engage in a patterned search before they decide on a target for a particular crime. These locations must fall in the offender’s routine activity space, or else the offender would not know that the target existed. If these roadways lead individuals to locations where they decide to commit crime, they can also lead offenders to places of high activity. These high activity locations are more likely than low activity locations to attract people, both those with criminal and non-criminal intentions. This line of thinking furthered the idea that some locations simply have more crime than other locations (Brantingham & Brantingham, 1982). Similarly, some locations simply attract people. Locations known as crime

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generators attract people for reasons unrelated to crime (Brantingham & Brantingham, 1999) and produce opportunities for victimization when offenders and victims are in the same location (Kumar & Waylor, undated). Residents closer to crime-generating locations viewed their neighborhood as more crime ridden than those farther from such crimegenerating locations (McCord, Ratcliffe, Garcia & Taylor, 2007). Due to the criminal element associated with some locations, Eck, Clarke and Guerette (2007) coined the term “risky facilities.” Risky facilities are the small proportion of the same type of facility that experience more crime than the other similar facilities. Investigating this specific location may help identify the criminogenic aspects of the environment that are unique, those that cause criminals to choose to offend there and not somewhere else. Eck et al. argue that focusing on this small proportion of facilities allows police and place mangers to be more effective (2007). Activity nodes also produce opportunities for surveillance. Individuals milling around at night after a bar or restaurant closes become involved in informal surveillance in the areas where they walk home or walk to their cars. Most of these locations have people arriving and leaving at all hours through the night, these patrons are not only potential targets, but they are also adding to the number of eyes on the street that could potentially witness a crime. Hours of operation of activity nodes are important since these hours determine when most people will be arriving and leaving the activity node. Some common activity nodes are: ATMs and payphones, bars, convenience stores, gas stations, fast food locations, hotels, schools, shopping centers, and transportation hubs. ATMs and Payphones ATMs (Automatic Teller Machines) and payphones are locations that may generate “watchers” or targets. ATMs are open 24 hours, are often attached to a bank (which could be the crime target), and are used during all hours by patrons. Payphones can be used at all hours and can be used in conjunction with drug sales and street markets. ATMs and payphone patrons frequently leave their cars running and unoccupied while they are conducting transactions. Due to these potential criminogenic factors, Scott (2002) suggests that patrons use ATMs with adequate lighting, good visibility (well maintained shrubs and landscaping), and visible mirrors (so patrons can

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see individuals approaching behind them). If at all possible, Scott suggests that patrons use ATMs in police substations, especially in the evening (2002). Scott (2002) suggests that ATM owners relocate ATMs that are in high-risk sites or limit hours of operation at these locations, install surveillance cameras, and possibly hire private surveillance teams if locations are in high risk areas. Police can also aid in ATM related crime by controlling street drug markets, targeting repeat offenders, and controlling loitering and panhandling near ATMs (Scott, 2002). An empirical study by Hendricks, Landsittel, Amandus, Malcan and Bell found that in convenience stores, “employee training, security systems, bullet-resistant shielding, ATM machines, and good cash handling policies were significantly (P<0.05) associated with a decreased risk of robbery” (1999, p. 1000). Bars Cavan’s (1966) study of bar behavior was one of the first ethnographic research projects conducted on the barroom atmosphere. In 1980, Graham, La Rocque, Yetman, Ross and Guistra found that many patrons tend to use the bar as a “home base” and are in and out of it at all hours of the day as one would utilize their home. This furthers the notion of the bar as a crime generator. Roncek and Bell (1981) studied the number of bars on a block to determine the importance of bars in explaining the occurrence of crime. Roncek and Bell (1981) found a statistically significant relationship between bars and auto theft. In fact, Roncek and Bell (1981) found that for all crimes except rape, blocks with bars on them had significantly more crime than those without bars. In 1994, Homel and Clark suggested that the physical environment that people are in, when in a bar, has a significant effect on the level of crime. Bars are open late and draw excited crowds and then close with drunken patrons filling the streets at all hours. These patrons may act as informal surveillance while they walk home or arrive at their next destination. Rossmo (1995) also studied bar patrons, he suggests that neighborhood blocks that have several bars on them are at increased risk for victimization since the bars close at the same time and then patrons are forced out onto the streets to find their way home. This potentiation (large numbers of people in the streets at late hours) may cause disturbances and commotion to the surrounding areas (Rossmo, 1995; Rossmo & Fisher, 1993). These patrons may cause problems or

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start fights or they may leave their cars at the bar (for fear of the repercussions of drunk driving) and find them stolen when they return for them the next day. Either way, these bars are known as attractors of aggression (Block & Block, 1995) and bring offenders and victims together (Kumar and Waylor, undated). Britt, Carlin, Toomey and Wagenaar (2005) found that there was a positive relationship between alcohol outlets and violent crime. For every additional alcohol establishment in the neighborhood, an increase in five violent crimes per 1,000 individuals is expected (Britt et al., 2005, See also Lipton & Gruenewald, 2002; Gruenewald, Freisthler, Remer, LaScala & Treno, 2006). McCord and Ratcliffe (2007) also found a relationship between alcohol outlets and crime, specifically drug markets in Philadelphia. The more alcohol outlets a location had, the more likely drug markets were to be present. Bromley and Nelson (2002) found a similar relationship between alcohol-related crime in urban areas. They suggest that alcohol related crimes occur in the late evening and early morning (similar to the pattern seen at gas stations and convenience stores). These studies suggest that the Routine Activity Approach and Pattern Theory can help explain how offenders come into contact with potential victims. Convenience Stores and Gas Stations Convenience stores and gas stations are activity nodes since most are open 24-hours a day. Due to their hours, both convenience stores and gas stations also attract offenders who are out looking for locations to target during the evening. Crow and Bull (1975) suggest that convenience stores are good robbery targets since they are open late (therefore avoiding the need to break and enter) and have cash readily available. Studies conducted in Florida indicated that convenience stores were less likely to be robbed if the cashier was located in the center of the store, if there was more than one clerk on duty, and if there was good visibility within the store (Jeffery, Hunter, & Griswold, 1987). Chakraborti, Gill, Willis, Hart and Smith (2002) studied gas stations in the U.K. The authors found that gas stations are most vulnerable in the early evening between 6:00 and 9:00 pm and during the late night/ early morning hours (Chakraborti et.al., 2002).

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Fast Food Locations Late night food establishments lure people into areas in a similar fashion as bars. Fast food locations offer a late night option when other restaurants would normally be closed. This unique situation may tempt offenders and victims alike. Activity nodes (Brantingham & Brantingham, 1993b, 1999) of this kind present a greater risk for victimization because these locations of high activity tend to attract both offenders and victims. Since patrons of fast food restaurants are permitted to stay at these locations once they have purchased an item from the establishment, offenders are often given legitimate cover for illegal activities such as scouting out opportunities or identifying suitable targets of crime. Ford and Beveridge (2004) used a phone survey via random-digit dialing in 12 mid-sized cities across the United States. Their goal was to ascertain the level of visibility of drug sales in residential communities. Locations that were identified by residents as having highly visible drug sales had three times as many fast food establishments as those considered to have low levels of visible drug sales (Ford & Beveridge, 2004). Other businesses that were positively related to highly visible drug sales were supermarkets, bars, checkcashing services, and liquor stores. Areas in which the residents perceived the visibility of drug sales to be ‘medium’ had the most pawnshops and used car lots. Movie theatres, bookstores, clothing stores, sporting good stores and fitness centers were more concentrated in areas viewed as having little visible drug sales. There was no difference in drug sales with locations of coin laundries, trash removal companies or warehouses (Ford & Beveridge, 2004). These findings are preliminary, and are specific to drug prevalence, but should be further tested with regard to drug crimes and other, similar crime types. Hotels Hotels are seen as activity nodes because they frequently are open 24hours a day and provide services to people at any hour. People come and go at hotels, particularly at hotels that are located on heavily traveled roadways. Individuals arriving at these locations are both potential targets and “watchers”. Because of the transient nature of hotels, victims are plentiful. Most people staying at a hotel are a long way from home and have traveled by car or public transportation to the

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hotel. Frequently, traveling individuals leave belongings in the car instead of carrying them into the hotel for the evening. This is a prime target for thieves since they are relatively certain that owners will not come out to check on their property after they have retired into their hotel room for the evening. Restaurants and lounges attached to the hotels are also crime generators. Individuals usually check into rooms and then leave to go eat dinner at a restaurant. During this time their unattended possessions are vulnerable. Fifteen percent of individuals surveyed by the American Society of Industrial Security reported fear of theft when they stayed at a hotel on business travel (Cook, Merlo, & McHugh, 1993). Huang, Kwag, & Streib (1998) noted auto theft to be the second most frequent crime occurring at hotels, as reported in a mail survey of business executives. Rice and Smith studied auto theft in a southeastern U.S. city with a population of about 250,000 residents (2002). Rice and Smith blended social disorganization and Routine Activity Approach to analyze auto theft patterns. The findings indicate that the presence of one hotel or motel on the block increased the risk of auto theft by 32 percent. Rice and Smith (2002) determined that social disorganization factors brought the offender to the block but routine activities introduced them to the opportunity as they were making the “choice” to commit the crime. Schools Roncek and Lobosco (1983) set out to study the effects of the location of schools on street crime. Knowing that many students walk home from school, Roncek and Lobosco studied the correlation between the locations of the public schools and the crime rate. The authors noted that blocks adjacent to high schools had an increased crime rate (Roncek & Lobosco, 1983), but the school had no effect on locations that were more than one block away. There was no correlation between private high schools and increased crime rates (Roncek & Lobosco, 1983). Two years later, Roncek and Faggiani (1985) tested a number of factors about schools and their relationship with the crime rate. The authors found that there was a greater incidence of crime in areas within one block of school property (Roncek & Faggiani, 1985). Enrollment numbers and other non-residential land use in the area did

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not produce a significant result when the variables were tested with the crime rate. Wilcox, Augustine, and Clayton (2006) found that in Kentucky schools, few features of the environment at the school or in the neighborhood were associated with student measures of school crime or misconduct. Teachers, however, did have a different perception of school crime based on these characteristics. This research should provide a prompt to other scholars to continue to investigate schoolrelated crime. Shopping Centers Shopping centers provide thieves with a large number of targets in a condensed area. Thieves also have easy access to the vehicles and are fairly certain that individuals will be away from their cars for a few hours when they leave them parked in shopping center lots. Engstad (1975) introduced the finding that parking lots provide opportunities for theft from auto and auto theft. It was determined that shopping center parking lots were more attractive to auto thieves. Engstad (1975) attributed this finding to the notion that individuals are more likely to return to their vehicles for frequent use when they are parked in hotel parking lots then when they have parked in the shopping center lot. Furthermore, massive parking lots with thousands of spots present increased opportunity for auto theft. Gibbs and Shelly (1982) indicated that supermarkets located in shopping centers with “wooded areas on three sides and adjacent to a major highway are considered ideal targets” (1982, p. 309). This type of store setup provides easy exit from the store premises and blocks the view of onlookers from three sides. The threat of getting caught is then minimized, which maximizes the potential payoff. Locations in which shopping centers are organized in strips provide a challenge to offenders since the easy getaway may be hampered by driving around several stores instead of driving around one store for access to the back of the building. In both crime situations, auto theft and commercial robbery, locations that are open late hours and those with little guardianship are easier targets.

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Transportation Levine and Wachs (1986) found that elderly and Hispanic women were the most vulnerable to bus stop crime in Los Angeles, according to a phone survey. Most crimes that were linked to the bus as a means of transportation were crimes that occurred at the bus stops while passengers were waiting to be picked up by buses. Levy (1994) also found extraordinary amounts of crime in the New York subways in the early 1990s. Loukaitou-Sideris (1999) reported that “ten bus stops during the 2-year period under study accounted for 18% of the total crime incidents reported at all bus stops” (p. 400). Eight of the stops were not visible from shops located on the same street, had poor lighting, and were not near police substations, which could have affected the number of crime incidents. In addition, seven of the locations were in the same area as a liquor store. McCord and Ratcliffe (2007) suggest that transportation hubs such as subway stops and bus terminals increase access for customers of a drug market. These stops act as crime generators/crime attractors, bringing people into the area by providing cheap and easy access to drug markets. Transportation hubs also provide offenders with more targets as people enter and exit the facilities. Smith (2008) addressed the risk of victimization when studying the “whole journey” for women passengers using public transportation. Smith suggests it is more important to look at the use of public transportation as one part of the journey (2008). Many travelers must plan the routes to and from this transportation to avoid victimization. Activity nodes are locations with multiple uses that draw heavy use by both offenders and those involved in lawful activities at similar times. Activity nodes, especially those open late (fast food establishments, bars, ATMs) bring people who are looking to commit a crime and those who are simply going about their day (or evening) into the same location at the same time. This puts individuals at these activity nodes, who are involved in legitimate activities, at greater risk for victimization than those who are not at these locations. However, individuals who are engaged in legal activities do not always take on the role of the victim. Often these individuals provide informal surveillance over these locations. It is this added function of surveillance that is most important to the W.A.L.L.S. characteristics.

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Locations The location of the potential target is an important factor that the offender considers before s/he offends. The characteristics of the location help the offender to determine if it is a “good” target to select. Offenders will use landscape and design features to determine if their crime will be concealed and if the layout of the area will impede their escape after the crime is committed. Jeffery (1971) noted that there is a decision model that offenders use during the actual commission of a criminal act. “The decision to commit a crime involves the past experience of the subject, the immediate opportunity for a crime, plus the chances of apprehension or injury.” (Jeffery, 1971, p. 251) The last part of the decision, the likelihood of apprehension, is discussed with regard to the layout of the location to be targeted. Offenders consider the design of a location before they commit the crime. All other things being equal, offenders will choose locations that provide an easy get away and enhance their chance of escaping apprehension. Nichols (1980) suggests that after an offender decides to commit a crime s/he engages in a search pattern of potential sites. This is conducted by using a “mental map” (p. 156) of known locations to help the offender evaluate his or her options. To continue the discussion of the search pattern, Brown (1985) adds the territorial model and territorial surveillance. With regard to residential burglary, territorial surveillance is determined by the offender when s/he ascertains whether the “target” residence is considered to be a public, open territory or if it is a private territory closed to strangers. According to Brown (1985) burglars also tend to consider symbolic barriers (such as landscaping, hedging, welcome mats) as well as actual barriers (locks, alarms, and/or guards) before they make the decision to burgle. “Nonburglarized houses in the Salt Lake study were more likely to have boundary barriers to access such as fences or hedges.” (Brown, 1985, p. 236) and burglars avoided locations that allowed visibility from an adjacent house. Wright and Logie (1988) agreed that victimized houses were less surveillable and added that houses with an edge or a fence are more attractive since both objects provide a cover for the burglary entrance. Brown and Bentley (1993) found that homes in a neighborhood where residents expressed territorial concern were less likely to be victims of burglary. All three findings indicate that burglars contemplate the location and characteristics of the surrounding area previous to their offending.

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Perkins et al. (1992) further the concept of territorial functioning by discussing the importance of actual barriers in addition to the importance of symbolic barriers. Outdoor property maintenance and beautification (such as yard decorations and gardens) as well as signs of personalization (nameplates and address signs) are objects that signify symbolic barriers, if they are present. Perkins et al. (1992) found that blocks with more private plantings were perceived to have less physical decay and were less likely to be targeted by residential burglars. Michael, Hull, and Zahm (2001) also noted that bushes and trees offer concealment and can lead to a decision to commit a crime in one location over a location without such concealment. Kuo and Sullivan (2001), in their study of vegetation in residential locations, also indicated that a correlation was found between green vegetation and crime; the greener the vegetation in the area the fewer crimes that were reported. Tseng, Duane, and Hadipriono (2004) suggesting trimming shrubs as a crime prevention tactic aimed at controlling access to a campus parking facility. Clearly, the existence of plantings and vegetation are considered by offenders. Perhaps it is because this vegetation signals to the offender that residents care about the neighborhood or perhaps because it signals a more individual territorial functioning – either consequence is apt to reduce the likelihood of crime. Buck et al. (1993) also studied the location of targeted burglary sites. Buck et al. (1993) concluded that burglars target well secluded streets including cul-de-sacs, which typically are surrounded by woods and provide cover and concealed access. Properties located on a culde-sac were 50% more likely to be burgled when unalarmed than similar properties not located on a cul-de-sac. Seventy-nine percent of these homes that were burgled were entered through the ground-level; 70 percent were entered through the front or back door. Wright, Logie, and Decker (1995) concluded that houses that were unoccupied as well as easily accessed led to an increased likelihood of being selected as a “good” location for residential burglary. Townsley et al. (2003) found that burglars will target homes within the same neighborhoods because the layout of many homes is similar to those they have burgled previously. Since they now know their way around these homes, they feel more comfortable targeting them and, thus, homes with similar structures become more attractive. With regard to bank robbery, Camp (1968) found that the physical layout of the bank was a target selection factor for 11 percent of the

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subjects. The subjects indicated that banks with poor visibility from outside and banks with low teller counters were better targets than those with opposite characteristics. In addition, some robbers indicated that more doors provided them with more possible escape routes while other robbers indicated that more doors were a sign of more potential witnesses from the street. Tiffany and Ketchel (1979) add that pillars between tellers also add to target attractiveness since they impair visibility between tellers and between tellers and the rest of the bank. Dickey (1980) found that robbers were particularly wary of being watched, and especially concerned that one-way mirrored glass may be used to aid in identification. Graham (2001) found a similar pattern in convenience stores. Graham also noted that a number of considerations are made by convenience store robbers, such as easy getaway, proximity to freeways, fences or walls, and the ability of store patrons or general public to view the robbery (2001). The design of the store such as location of clerks, locations of tills, and positioning of aisles are considerations robbers make prior to entry. Convenience store locations with blocked escape routes or obstacles are less likely to be victimized than locations with easily accessible escape routes and no obstacles. Lu (2006) also addressed the importance of escape routes for auto thieves. Major roads and those connected to major roads had disproportionately more auto thefts than less traveled roadways. Webb et al. (1992) found that in addition to lighting and surveillance, the parking lot layout was an important factor in the decision to steal a car. Parking lots, referred to as “surface car parks” (1992, p. 15) have better attendant visibility; attendants can be seen from the entire lot and, also, can see all of the cars in the lot. In addition, these single level lots have a lower risk of theft from cars as well as auto theft. Lots that have attendants at all times can also “block park” the cars and keep the keys, which prohibits cars from being stolen since the offender would have to move several cars in order to steal one in the middle. This can pose enough of an annoyance to auto thieves that they would choose other locations with fewer obstacles. Street parking is one such location. Street parking provides reduced surveillance and, in high density locations, a similarly plentiful group of targets. Clarke and Mayhew found that “parking in a domestic garage at night is safer by a factor of 20 than parking in a driveway or other private place, and safer by a factor of 50 than parking in a street near home” (1994, p. 91). This indicates an increased risk

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for victimization when a person parks in the street instead of in a personal garage located at the residence. Smith (1996) and Tseng et al. (2004) studied the effects of parking garages over parking lots with regard to crime. Many locations prefer garage parking since it takes up less ground space and is more cost effective for those who own the facilities. However, due to decreased surveillance opportunities, parking garages are less safe than parking lots. Smith suggests that parking garages that are either partially or fully enclosed and elevated above ground offer less natural surveillance (1996). This problem is enhanced by sloping ramps and multiple floors which further cut surveillance from other individuals inside the garages. High risk facilities require access control as well as well maintained grounds and surveillance (Smith, 1996) due to increased attractiveness to auto thieves (Tseng et al., 2004). These heightened requirements can be fulfilled by security personnel; or by security cameras, if a lower cost alternative is necessary. Location is an important contribution to the W.A.L.L.S. characteristics because it discusses a small part of the decision process that an offender makes in order to determine whether or not to commit the crime. Inside the location, the design features of the establishment, such as the position of tellers in the bank and the location of clerks in the convenience store affect the decision of the offender. Outside of the location, the positioning of the building on a cul-de-sac or the presence of the symbolic barrier are important factors in the decision to offend. The existence of adjacent freeways and open escape routes also help offenders choose “good” targets for crime. Finally, auto theft locations such as parking lots and parking garages offer different advantages and disadvantages for auto thieves than do cars parked on the street or in personal garages. All of these factors have been researched by scholars and deemed important contributions to the environmental aspects of the decision-making process of offenders. Lighting Lighting is an important factor in the offender’s decision making process with regard to a specific location (Keister, 2007). Poor lighting can provide cover for offenders. Just as offenders use hedges and walls to prevent their detection when breaking into a home or business, dark alleys and dimly lit streets hinder residents and other watchers from their important involvement in informal surveillance. “Lighting is

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often installed with little thought to its functional ability to illuminate specific areas.” (Bopp, 1982, p. 79). Furthermore, it is critical that lighting is both provided in adequate levels and is aimed in the proper direction (Bopp, 1982). Lighting has been a problem with regard to safety in public transit locations. In a survey of elderly transit users in Philadelphia, roughly 40 percent of senior citizens indicated that their bus stop had inadequate lighting (Patterson, 1985). Ramsay (1991) suggests that upgrading street lighting for the purpose of crime prevention requires a “multi-agency collaboration” (p. 1). Government organizations (such as police) as well as private organizations (such as residents’ associations) should get together and increase lighting and safety in areas in order to bolster the quality of life problems that poor lighting evokes (Ramsay, 1991). In addition, Ramsay (1991) suggests that even if lighting doesn’t deter crime itself, it reduces the public’s fear of crime. Lighting is important at locations like ATMs (Scott, 2002) and parking lots and garages (Smith, 1996; Webb et al., 1992). At locations such as these it is important to have proper, bright, directed lighting to increase the levels of surveillance. Parking garages pose an even more dangerous problem; garages are either partially or fully enclosed, which cuts off natural light from entering and prohibits natural surveillance from the street (Tseng et al., 2004; Poyner, 1997; Smith, 1996). Smith (1996) suggests that even though it is rather easy to install crime prevention techniques, maintenance can be difficult. Lighting, however, is one of the easier crime prevention techniques to maintain and is fairly inexpensive to install (Tseng et al., 2004; Smith, 1996). Willis, Powe, and Garrod (2005) replaced orange/yellow sodium lights with white sodium lights in Britain. Most respondents they surveyed felt safer with the new lighting. Willis et al. determined that the improvements in street lighting considerably outweighed the costs involved, by a factor of at least three to one (2005). Smith (1996) notes that illuminance (the intensity of light) and vertical illuminance are important traits that must be considered when installing lighting. White stain on the ceiling can reflect light and increase uniformity in the parking garages (La Vigne, 1997; Painter, 1994; Tseng et al., 2004). Garages that can minimize ramps and utilize flat surfaces for parking areas will maximize the positive effects of light and enhances natural surveillance from outside of the garage. Painter (1994) also concluded that an increase in lighting can lead to a reduction in auto theft.

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A number of convenience store (Crow & Bull, 1975; Graham, 2001; Jeffery et al., 1987; La Vigne, 1994) and small business (Fisher & Looye, 2000) studies have found poor lighting to be associated with criminogenic locations. In Florida, lighting was seen as one of three main causes of offenders’ choosing particular locations for convenience store robbery (Jeffery, Hunter, & Griswold, 1987). It was concluded that enhanced lighting at the rear and sides of the stores aid in the prevention of robberies. Crow and Bull (1975) found convenience stores with a clear view from outside to inside, bright lighting, signs posted indicating small amounts of cash, and employee training to be useful methods of reducing robberies. La Vigne (1994) also suggested installing brighter lights at convenience stores in order to deter convenience store crime. Despite the importance of lighting at individual locations, inadequate street lighting can lead to increases in fear of crime as well as contribute to crime at specific locations. Painter (1994) studied the improvements in lighting of pedestrian footpaths in London. Three study areas were selected and data from each project indicated that “properly designed and focused street lighting improvements can lead to reductions in crime, disorder, and fear of crime” (Painter, 1994, p. 116) and can also increase street activity. Enhanced street lighting improves visibility, reduces fear and encourages street usage (which increases surveillance) (Pain, MacFarlane, Turner, & Gill, 2006; Painter, 1994; see also Grabosky & James, 1995). Painter and Farrington (1997) remind academics that if improved street lighting reduces fear and encourages street use, it may also increase reporting of crimes. Therefore, calls for service or police reports should not be used to measure changes in crime rates; perhaps victim surveys or structured observations would be a better method. In order to assess the effect of street lighting on crime, Farrington and Welsh (2002) conducted a systematic review of street lighting. Eight lighting evaluations in the United States were included in the study while several were not included because they did not use a control group, the number of crimes reported was too small, or because they did not include crime as an outcome measure of the research project. Of these eight studies, four deemed lighting to effectively reduce crime and four found no effect. Five British evaluation studies met the criteria for the systematic review. The results of these five evaluations indicated that improved lighting led to decreases in crime. Farrington and Welsh (2002) also determined that “the financial

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savings from reduced crimes greatly exceeded the financial costs of the improve lighting” (p. 39). Welsh and Farrington (2004) found that utilizing lighting in combination with CCTVs provided a more effective crime prevention measure than either alone. However, the cost of such a combination can be prohibitive. The presence and quality of lighting is an important site-level factor considered before the commission of a crime. Both the amount of lighting that is properly working and the intensity of that lighting is important in the overall lighting of streets at night. Street lighting is just one part of the lighting requirement of specific sites. Most businesses and residential areas obtain evening lighting from the street lights and also from lights located on the store or residential property. Both the number of lights on the street and the property, and the intensity of the light in these locations, is needed to determine if the lighting is adequate. Recent literature indicates that the cost of street lighting is far less than the cost of crime in the area. Security Security devices are utilized in every facet of life. Businesses use security devices such as ink and magnetic tags to protect their merchandise and locks, alarms and shutters to protect the premise from vandalism and theft. Residents use security devices such as alarms, trip wires, and bars on windows to protect their residences and alarm and lock vehicles, and remove radio faces from their cars, to prevent auto theft and theft from auto. Almost every different type of location employs some method of security in order to protect their goods. For example, in order to combat fare evaders on toll roads, police threaten to issue tickets to vehicles that speed through tolls too fast to register payment. A photograph is taken of their license plate and the owner of the car is mailed a ticket. In order to reduce the number of individuals killed while jaywalking, major cities post signs that indicate a fine will be imposed on individuals who are caught crossing the street in the middle instead of at the intersections. “Conventional wisdom holds that crime prevention needs to be based on a thorough understanding of the causes of crime.” (Clarke, 1980, p. 136). Though that comment seems like common sense, researchers must actually look at each individual crime, and its causes, in order to come up with a “crime-specific” prevention method (Clarke, 1997).

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For locations such as convenience stores and parking lots, controlling entrances and exits is an important security feature (Hunter & Jeffery, 1992). Webb et al. (1992) noted that the layout of the parking structure is a factor considered in auto theft. Parking structures with manned exits have the lowest risk for both auto theft and theft from autos (Webb et al., 1992). Manned exits not only provide increased surveillance, but bars or mechanical arms on the exits provide a physical barrier to prevent cars from leaving the structures. The physical structure of banks (Camp, 1968) and convenience stores (Graham, 2001; La Vigne, 1994) also affect the decision processes of offenders. Offenders are less likely to rob a bank when one-way mirrors are present and when the teller has a clear sight of the offender’s escape route (Camp, 1968). For commercial burglaries, Clarke (2003) and Weisel (2002) suggest installing burglar alarms and marking property to aid in the prevention of thefts. Residences pose slightly different security factors. Brown and Bentley (1983) found that locations that appeared difficult to enter were less likely to be burgled. This difficulty could be based on the structure of locks and the offender’s level of skill, the presence or absence of an alarm, or the physical structure of the house (ranch, split-level, twostory) and the ease (or lack thereof) with which an offender could permeate the structure. Buck et al. (1993) found that in 40 percent of alarmed and burgled homes, no other security measures had been taken. Wright and Logie (1988) indicated that the presence of an alarm or a dog can make the house “unattractive” to burglars. Wright and Logie (1988) also concluded that often burglars see an alarm as an indication that there is something worth stealing. In this situation, offenders must determine if the potential risk is worth the assumed reward. Residential burglaries can be affected by neighborhood-level security measures as well. Beavon et al. (1994) found that neighborhoods with high accessibility and high street flow experience a greater amount of crime, accounting for nearly 15 percent of the increase in crime. Auto theft has its own set of precautions and security devices. Alarms are frequently installed by the manufacturer and are often installed by the owner if the vehicle does not have an alarm. Light, Nee, and Ingram (1993) found that 35 percent of auto thieves interviewed indicated that they would be deterred by alarms, 18 percent said it depended on the make of the alarm, while 9 percent said it depended on the model of the alarm (p. 50). Many thieves also indicated that audio equipment “pull outs”, radio faces that are

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designed to be detached and taken with the driver, are usually left in the vehicle and therefore are not a major deterrent to thieves. Fleming, Brantingham, and Brantingham (1994) found that nearly 75 percent of offenders would be deterred by an alarm, while over 60 percent indicated that they would avoid a car whose steering wheel was locked. Webb (1994) concludes that steering column locks are more beneficial in the long term to deter car theft. Brown and Billing (1996) found that devices such as immobilizers and other mechanical devices have been tested but have not been used on all vehicles. LoJack, however, has been a very effective tool used to locate cars after they are stolen, but it has not prevented these crimes from occurring. Ayres and Levitt (1998) suggest that the arrest rate for vehicles with LoJack is three times greater than for cars without the device. The LoJack company states that 90 percent of vehicles with LoJack are recovered (LoJack, 2003). Presence of security hardware, especially in the case of auto theft, is only effective if the hardware is installed and maintained properly. Individuals leaving the radio face intact or stashing it under the seat are not going to deter a criminal from stealing the car, or even the radio. Parking lots with gated access and egress are most effective in preventing auto theft. These structures require a toll collector (who dubs as a surveillance figure) to permit entry and exit. The Club has been somewhat ineffective at preventing vehicle theft, but can persuade thieves to steal a different car without the club, if one is nearby. LoJack has been the most effective tool available to the mainstream to date, but often it does not prevent cars from being stolen, it only aids in their recovery. Parking garages and parking lots should have as many crime prevention features as possible. Street parking locations, especially those that are physically incapable of meeting the crime prevention techniques that can be used in parking lots, should rely on other factors to keep auto thefts to a minimum. Summary Clarke (1997) indicated that crime events are situation-specific and effective interventions require a micro-level approach that reflects this quality. Traditionally, criminologists understood there to be inherent differences between personal and property crimes. Personal crimes are thought to be much more serious and harmful than their counterparts. In addition, offenders commit property crimes at different locations and

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for different reasons than they would commit a personal crime. Because of these salient differences, additional information is needed to understand why crimes occur at particular sites. Criminologists cannot identify these micro-level differences when examining the data at an aggregated level. To develop a deeper understanding of target selection and the important situational factors contributing to crime, some researchers have moved toward a place-intensive method of studying crime (Bichler-Robertson & Johnson, 2001; Bichler-Robertson & Potchak, 2002; Boba, 2001; Boesch, 2001; Woods, 2001). This research seeks to uncover the specific factors present at locations in which crimes occur by conducting site-level research of both repeat and single victimization locations. This research draws on empirical studies in order to identify factors that are important to the commission of auto theft and will also examine the salience of new factors that have been previously untested.

Table 1. Key Situational Factors Affecting Target Selection Preferences of Offenders Factor

Conceptual Definition

Justification – Relevant Sources

Watchers

Presence of capable guardians within site of parking areas, including: residents, shop owners, consumers, or pedestrians en route to other locations.

Activity Nodes

Places that draw heavy use with common temporal patterns. - ATMs

Bennett & Wright, 1984; Brown & Bentley, 1993; Buck et.al., 1993; Clarke, 2003; Cohen & Felson, 1979; Eck, 1994; Eck & Wartell, 1999; Felson, 1986; Felson & Cohen, 1980; Fisher, 1991; Hunter & Jeffery, 1992; Keister, 2007; Perkins et. al., 1992; Poyner, 1997; Tseng et al., 2004; Webb, et al., 1992; Welsh & Farrington, 2004; Wilcox et al., 2007 Beavon et al., 1994; Brantingham & Brantingham, 1982; Eck et al., 2007; Kumar & Waylor, undated; McCord et al., 2007 Hendricks et al., 1999; Scott, 2002

- Bars

- Convenience Store/Gas Station

Block & Block, 1995; Britt et. al., 2008; Bromley & Nelson, 2002; Cavan, 1966; Graham et al., 1980; Homel & Clark, 1994; McCord & Ratcliffe, 2007; Roncek & Bell, 1981; Rossmo, 1995; Rossmo & Fisher, 1993 Chaikraborti et al., 2002; Crow & Bull, 1975; Jeffery et al., 1987

- Fast Food Locations

Brantingham & Brantingham, 1993b, 1999; Ford & Beveridge, 2004

- Hotels

Cook et al., 1993; Huang et al., 1998; Rice & Smith, 2002

- Schools

Roncek & Faggiani, 1985; Roncek & Lobosco, 1983; Wilcox et al., 2006

- Shopping Centers

Engstad, 1975; Gibbs & Shelly, 1982

-Transportation

Levine & Wachs, 1986; Levy, 1994; Loukaitou-Sideris, 1999; McCord & Ratcliffe, 2007; Smith, 2008

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Table 1. Key Situational Factors Affecting Target Selection Preferences of Offenders (Continued) Factor

Conceptual Definition

Justification – Relevant Sources

Location

Landscape/design features that adequately cover offenders’ when initiating the crime; prevent offenders from being seen during the crime, and/or aid in a speedy departure from the crime scene.

Lighting

Presence and quality of lighting on different levels of parking structures and at entrances and exits and, the proportion of working lamps and light quality in parking structures or on streets.

Security

Presence of security hardware for the parking location and the vehicle such as gates, alarms, alarm signs, the Club, and tracking devises such as LoJack; as well as territorial cues indicating a cohesive and caring residential or commercial environment.

Brown, 1985; Brown & Bentley, 1993; Buck et. al., 1993; Camp, 1968; Clarke & Mayhew, 1994; Dickey, 1980; Jeffery, 1971; Graham, 2001; Kuo & Sullivan, 2001; Lu, 2006; Michael et al., 2001; Nichols, 1980; Perkins et al., 1992; Smith, 1996; Tiffany & Ketchel, 1979; Townsley et al., 2003; Tseng et al., 2004; Webb et al., 1992; Wright & Logie, 1988; Wright et al., 1995 Bopp, 1982; Crow & Bull, 1975; Farrington & Welsh, 2002; Fisher & Looye, 2000; Graham, 2001; Jeffery et al., 1987; Keister, 2007; La Vigne, 1994, 1997; Pain et al., 2006; Painter, 1994; Painter & Farrington, 1997; Patterson, 1985; Poyner, 1997; Ramsay, 1991; Scott, 2002; Smith, 1996; Tseng et al., 2004; Webb et al., 1992; Welsh & Farrington, 2004; Willis et al., 2005 Ayres & Levitt, 1998; Brown & Billing, 1996; Camp, 1968; Clarke, 2003; Fleming et al., 1994; Graham, 2001; Hunter & Jeffery, 1992; La Vigne, 1994; Light et al., 1993; Webb, 1994; Webb, et al., 1992; Weisel, 2002 Beavon et al., 1994; Brown & Bentley, 1983; Buck et al., 1993; Wright & Logie, 1988

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CHAPTER 5

A Multi-level Investigation of Auto Theft

INTRODUCTION This research has two general components; the community-level research and the site-level field research. This chapter first discusses the community-level methodology which examines whether the Opportunity Structure for auto theft mimics true auto theft opportunities. More specifically, are “high” opportunity locations (as defined in this research) those areas that experience more crime (hot spots)? The research provides crime analysts with a practical method of identifying crime-prone areas using the example of auto theft in Lexington-Fayette, Kentucky. Next, the site-level research component is introduced as well as the hypotheses, variables, and methods proposed for that part of the study. The site-level analysis involves the use of a site survey to capture environmental factors which may influence offenders’ decisions when they choose specific crime locations. The research indicates that there are physical or environmental differences between locations where crime occurs repeatedly and locations where crime does not. When used together, the community-level analysis and the sitelevel analysis provide a useful tool for both identification of potential high crime locations (hot spots) and suggestions for crime prevention techniques that could be used to thwart crime at the site level. Maps are produced to compare the opportunity structure to the actual auto theft data from police records.

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COMMUNITY-LEVEL RESEARCH Introduction Community-level research, as discussed in Chapter 2, started from the ideas of ecological theory. Ecological theory notes the importance of the organization of a city in determining whether or not crime will occur. In addition to the study of city design, the study of human ecology helped researchers to better understand the ways in which people move and live within a community. Human patterns and activities are based partially on the structure of the environment, and partially on the way people have learned to interact and move around that environment. As the study of different ecologies continues, concepts such as Social Disorganization, Routine Activity Approach, and Pattern Theory emerge. These theories suggest that human interaction (or lack thereof) influences the locations that people are aware of and the decisions that they make on a daily basis, including the decision to commit crime. In order to better understand the reasons why offenders’ choose specific locations, researchers must first study the effects of the environment on an individual’s decision to commit a crime. With this information an “opportunity structure” can be assembled. This “opportunity structure” indicates the different levels of opportunity present to commit a crime in a given area. Once this structure is developed and verified to be an accurate reflection of crime and criminal opportunities; researchers can compare the environmental characteristics that exist in high crime areas to those in low crime areas. This information can be used to identify the steps that need to be taken to alter the environment from one of high crime opportunity to one in which the opportunity for crime is absent or at least lessened. Community-level (Opportunity Structure) Hypotheses: • The base model, components of the opportunity structure that have been tested previously and found to be associated with increased levels of property crime (street layout, well-traveled roadways, government subsidized housing, parking lots, convenience stores/gas stations, transportation hubs, and schools), is more likely to predict opportunities for auto theft than any of the components individually.

A Multi-level Investigation of Auto Theft •

•

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It is also hypothesized that the base model and each of the components (apartments, fast food and bars, accommodations, auto repair/auto parts) is a better predictor of opportunities for auto theft than the base model alone. The full model (base model, apartments, fast food and bars, accommodations, and auto repair/auto parts components) is a better predictor of opportunities for auto theft than the base model alone.

Variables for the Opportunity Structure In order to construct the opportunity structure for auto theft in Lexington-Fayette, Kentucky, the following data layers must be assembled. 1. Street layer – The street layer was obtained in electronic form from the 2000 Census File Street Map. The street layer is essential to the project since it is the primary method of matching addresses for calls for service to the map of Lexington-Fayette, Kentucky. In addition, the street layer is not only necessary to plot points, but it also serves as the primary layer for the opportunity structure. The two most common places from which cars are stolen are from the street or from parking lots located directly off of the street. Streets provide access to vehicles. Therefore it is essential to include streets as one of the components of the opportunity structure. Since streets create an increased opportunity for crime, all streets receive a value of 1, and any other areas receive a 0 for this layer. 2. The major Roadways layer was extracted from the streets layer and major roadways are given a higher value in the opportunity structure. In Lexington-Fayette, the structure of the streets is different than that of other cities; Lexington-Fayette is laid out in a radial fashion, different than the grid models researchers typically see in cities of this size. Each of the radial streets in Lexington serves as a physical barrier and edge to the city itself. These roads are well-traveled and provide direct access from the downtown area to New Circle Road. New Circle Road forms a wide loop around the downtown and surrounding areas. Each of the roads (Newtown Pike, Georgetown Road, Leestown Road, Old Frankfort Pike, Versailles Road, Harrodsburg Road, Nicholasville Road, Tates Creek Road, Richmond Road, Winchester Road, Bryan

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Station Road, and Broadway) serve as arterial access from the downtown to New Circle Road and beyond. The distance from downtown to the loop (New Circle Road) varies depending on the radial roadway that is chosen. The smallest distance from downtown to New Circle Road is Newtown Pike or Broadway. Both roads can be traveled for an average of 1.2 miles from the downtown before hitting New Circle Road. Tates Creek Road is the longest radii measuring approximately 5.4 miles from downtown to New Circle Road. Beavon, Brantingham, and Brantingham (1994) and La Vigne (1996) found that crime was higher in areas that were more accessible and lower in areas more difficult to access. The Uniform Crime Report indicated that roughly 18 percent of stolen vehicles are stolen from highways and high volume roadways (United States Department of Justice, 2002). Since these radial roadways and New Circle are the most well traveled roads in Lexington-Fayette, they receive a value of .5 and all other roadways are coded with a value of 0 in this layer. A value of .5 is given to these major roadways since they are already receiving a 1 in the street layer. 3. Residential land-use layer – Since motor vehicles are most likely to be stolen in areas where they can be driven, and more specifically, in areas where they are usually parked, this layer allows the researcher to distinguish between areas where motor vehicle thefts are most likely to occur (residential locations with high population density) and locations where they are less likely to occur (areas with lower population density and areas not zoned for residences). The United States Department of Justice calculated that more than 35 percent of vehicles were stolen from residential locations (2000). Decker et al. (1982) found a correlation between urban structure and victimization – auto theft was positively correlated with population density. Sampson and Wooldredge (1987) found that crime tends to increase with street activity. Following the research studies of Decker, Shichor, and O’Brien (1982) and Sampson and Wooldredge (1987), residential areas are coded based on population density. Using polygons, each census tract polygon indicates the number of residents (population density) living in that area. Then, using standard deviations, census tracts with the highest population density are coded to reflect the greatest opportunity for auto theft. The census tracts are coded as follows:

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Table 2. Coding scheme for Residential Land-use based on the Standard Deviation (SD) of the population density Standard Deviations

Value

> 2 SD below the mean 1 – 2 SD below the mean -1 to 1 SD around the mean 1 – 2 SD above mean > 2 SD above mean

0.00 0.25 0.50 0.75 1.00

This coding ensures that locations with the highest population density are coded to reflect the locations with the greatest amount of opportunity. 4. Government Subsidized Housing – Section 8 housing – Police and city planners now use techniques suggested by Newman (1972, 1996) and other environmental criminologists (Jeffery, 1971; Roncek & Francik, 1981) when policing and building public housing. LexingtonFayette, Kentucky does not have any areas known as “public housing”; but, there are areas that have government subsidized housing known as Section 8 housing. The Section 8 housing locations were obtained by the Department of Public Housing and verified on site. Areas surrounding Section 8 housing in Lexington-Fayette are considered a greater opportunity for auto theft since these locations are characterized by higher levels of crime. Therefore, parking structures, lots, garages and street parking in these areas reflect higher opportunity in the overall opportunity structure. Each Section 8 housing location was identified from the Department of Public Housing and was located on the parcels for Lexington-Fayette. The parcel containing Section 8 housing was identified and a buffer of 100 feet was placed around the Section 8 housing parcel. This buffer received a value of .5 while all other locations, including the Section 8 housing itself, received a value of 0 since cars cannot be stolen from the complex itself, only from the streets and parking structures located around these areas. This coding allows the researcher to capture only the parking areas and streets located directly next to and around the Section 8 housing. 5. Parking facilities layer – Public parking lots and parking garages are considered a location of increased opportunity for auto theft. Poyner and Fawcett (1995) and Poyner (1997) indicate that surveillance is an

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important factor when looking to prevent or reduce crime in parking facilities. Parking garages are more dangerous than parking lots because of the decreased amount of surveillance from people on the streets and those in nearby businesses. In commercial areas the parking lots and garages are condensed into tight spaces and often stacked high; making small areas contain a large number of targets for auto theft. Parking lot data were gathered from the transportation authority as well as from the management system of the parking structures. Since parking garages offer the least amount of surveillance and the greatest volume of cars, they are coded with a value of 1. Parking lots that are enclosed are coded with a value of .75 due to their decreased surveillance capability, and parking lots that are not enclosed received a value of .5. All other areas were given a value of 0. 6. Convenience store/gas station layer – Convenience stores and gas stations are open all hours of the night and draw victims and offenders into the same locations (Brantingham & Brantingham, 1993b). Convenience stores and gas stations provide an increased opportunity for crimes such as drive-offs and robberies. In Lexington-Fayette, most of the convenience stores are attached to gas stations. This makes them both easier and more difficult to identify. The business licenses may be used to identify a few of the gas stations, but many gas stations are covered under a broad business license for all gas stations of that chain in Lexington-Fayette. In addition, the convenience stores are sometimes listed separately in the phone book, but depending on the size of the convenience store and its chain, it may not be listed. Since many of the convenience stores sell liquor, and liquor license numbers are required by law to be posted on all storefronts, many convenience stores can be cross-referenced with a liquor license list to obtain proper addresses. This layer was constructed through the phone book and verified during site visits using both business and liquor license lists. A buffer around the convenience store and gas station of 50 feet was coded with a value of .5 so it carries the same weight as an open parking lot in the parking facilities layer. All other areas were given a value of 0. 7. Transportation hubs – The presence of transportation and transportation hubs introduces an increased risk for victimization (Brantingham & Brantingham, 1999; Levine & Wachs, 1986). Transportation also increases awareness space for offenders and

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parking areas adjacent to transportation may experience increased victimization due to a high volume of targets which are parked unsupervised for long periods of time. These locations are identified by the Department of Transportation, Lexington-Fayette Urban County Government. Transportation lines, bus stops, and parking structures surrounding transportation are identified. Transportation lines were coded with a value of .5 since they increase offender awareness space. Bus stops and surrounding streets within a 1,312 foot (approximately 1/4 mile or 400 meters) radius were be given a value of 1 since these locations are in the average walking distance of a person taking public transportation (Wanderlöf, 1995). All other areas were given a value of 0 for this layer. 8. School layer – Research has indicated that residences located on the same block as schools are at an increased risk for property crime victimization (Roncek & Faggiani, 1985; Roncek & Lobosco, 1983). School addresses and data were collected from the Lexington-Fayette Urban County Government website and verified by telephone book. Middle schools and high schools were used to construct this layer of the opportunity structure since students’ ages range from 12-18, a similar age range to that of juvenile auto thieves. A 528 foot (roughly two blocks) radius was drawn around the schools. The areas between the school and 264 feet receive a score of 1 and the areas between 265 feet and 528 feet from a school receive a score of .5. All other locations receive a score of 0. 9. Apartment complexes – During the pilot study for the project it was found that apartment complexes seem to be built very much like Section 8 housing. The structures indicate poor visibility, a lack of territoriality over shared spaces, and a great amount of disrepair often associated with federally subsidized or public housing. Mukherjee and Carcach (1998) found rented dwellings to consistently report more crime than those that were owned. This finding was due to residents who were both less likely to be provided with security measures and less likely to invest in them. Due to these problems, structurally, the atmosphere at both the Section 8 housing complexes and large apartment complexes are similar. Apartment complexes also offer the additional feature of large parking lots. Most apartment complexes in Lexington-Fayette have at least 200 individual apartments in one complex. The sheer size of the complex indicates that there are going

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to be at least 400 parking spots to support enough parking for all residents and guests. With such a large amount of potential targets in one area, it would be irresponsible to exclude these types of parking areas from the opportunity structure. Due to this finding, apartment complexes were studied in addition to the Section 8 housing component of the opportunity structure. Since there is no theoretical reason to include this in the base model, this layer was tested alone and with the other components of the model to determine the magnitude of its significance. These areas were captured by extending a buffer for 100 feet around the parcel with the apartments and received a value of .5. All other areas received a value of 0 since cars cannot be stolen from the apartment complex itself, only from the streets and parking structures located around these areas. This coding allows the researcher to capture only the parking areas and streets located directly next to and around the apartment complex. 10. Fast food establishments and Bars – Late night food establishments and bars are thought to draw offenders and victims into similar areas. Research has shown that crime is more likely to occur on blocks that have bars than on blocks that don’t (Roncek & Bell, 1981), and according to Brantingham and Brantingham (1982), locations where fast food restaurants, traditional restaurants, and pubs were located had commercial burglary rates of more than two times higher than blocks without these businesses. These locations attract offenders into the area. Streets within approximately one-quarter mile (1,320 feet) of a fast food establishment or bar are coded with a value of 1 while all other streets are coded with a value of 0. Both the fast food establishments and bars are permitted a buffer of 100 feet since often on-site parking is found directly surrounding the establishment. This buffer is coded as a .5 and all other areas are coded as a 0. The importance of this variable is also being tested in this study so the layer was tested individually as well as with the base model to determine its significance in the overall opportunity structure. 11. Accommodation locations – Because of their transient nature, accommodations such as hotels and motels attract crime to their premises. Smith, Frazee, and Davison (2000) found that locations with motels and hotels in the area have a 24 percent increase in the number of street robberies as compared to locations without these businesses. A buffer of 200 feet is placed around these locations; the buffered area

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is given a value of 1 while all other locations are given a value of 0. As with the apartments and fast food and bars components, the relevance of hotels and motels to auto theft opportunities is being tested in this research project. Therefore, this component is tested both with the base model and independent of the other components. 12. Auto parts and auto repair shops – Gant and Grabosky (2002) found that professional thieves in search of parts to repair vehicles actually steal vehicles (especially older vehicles) and dismantle the vehicle for parts to sell them to other auto repair businesses or to repair vehicles in their shops. Due to this finding, auto repair shops and auto parts locations may present an increased likelihood for victimization since many cars are present in one location; providing a great opportunity for auto thieves. A 200-foot buffer is created around those locations identified as auto parts businesses and auto repair shops. Auto parts locations use only the buffered areas since vehicles are not typically parked inside the parts shops. The location of the auto repairs shops and the buffer are included in the opportunity structure since vehicles are typically parked inside the repair shops overnight and during daytime repairs. These locations received a value of 1 while all other locations received a value of 0. Community-level Research Models All of these components mentioned above are combined into an opportunity structure which predicts opportunities for auto theft offenders. The first eight components, streets, major roadways, Section 8 housing, parking facilities, convenience stores and gas stations, transportation hubs, and schools have been tested in conjunction with some sort of environmental factors which are thought to influence offenders’ decision making. This research uses these factors as a base model to determine if they aid in the prediction of auto theft opportunities. In addition to this base model, four components are being tested here for the first time (with regard to auto theft), apartments, fast food and bar locations, accommodations, and auto repair/auto parts locations. These components have either been tested indirectly in other studies or have been tested with regard to crimes other than auto theft. It is expected that these four components contribute significantly to the overall opportunity structure model. Table 3 outlines all of the components and their coded values.

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Measures of Crime Once the opportunity structure is composed, it is compared to two years of crime data to determine if the opportunities, as operationalized in the opportunity structure, mimic the actual occurrence of crime in these locations. Crime data were collected in the form of motor vehicle theft reports from the Lexington-Fayette Police Department. Reported crime offers fewer cases than calls for service but is thought to be a bit more accurate since false calls and claims are sorted out before an official report is recorded. The number, location, and description of auto thefts were collected directly from the Lexington-Fayette Police Department. Other studies (Bichler-Robertson & Potchak, 2002) have used data from the newspaper, and while that data seem to be reliable, access may be limited to printing space and seasonal publications. The police department provided data directly from the official data management system of the department and the auto theft database constructed by the auto theft unit. Data were cross-referenced for accuracy and mapped into a point theme in Arc View. Table 3. Explanation of Variables and Coding Scheme for Opportunity Structure – Data Collected in Lexington, Kentucky Variable

Reason for inclusion

Coding

Street Layers

Streets provide access and awareness of motor vehicle opportunities Increases access and opportunity during daily activities Provides parking for vehicles in locations where people spend time; indicates high “street activity” Encourages opportunities for crime Provides a large number of targets and few capable guardians Rarely utilizes target hardening devices

Streets = All other areas =

1.00 0.00

Major roads = All other areas =

0.50 0.00

Major Roadways

Residential land use

Section 8 Housing Parking Facilities

>2 SD below p.d.* = 0.00 1-2 SD below p.d. = 0.25 -1 to 1 SD p.d. = 0.50 1-2 SD above p.d. = 0.75 >2 SD above p.d. = 1.00 100 foot buffer = 0.50 All other areas = 0.00 Parking garages= 1.00 Covered lots= 0.75 Uncovered lots = 0.50

A Multi-level Investigation of Auto Theft Layered variable Convenience Stores and Gas Stations Transportation Hubs

Schools

Reason for inclusion Provide increased opportunities for driveoffs and other crimes Provides an intense # of targets for theft Lack of residents or interested consumers to play the capable guardian Increases awareness space Large number of unsupervised teens are released simultaneously

93 Coding 50 foot buffer = All other areas =

0.50 0.00

1,320 ft radius = All other areas =

1.00 0.00

0-264 ft (w/i 1 block) = 265-528 ft (1-2 blocks) =

Empirically tested effects of higher property crime rates at residences located on the same block as schools Journey to Crime Apartment Provides increased 100 ft buffer = Complex opportunity for crime All other areas = Fast Food and Bars Draw offenders and 1,320 ft radius = victims into locations 100 foot buffer= together All other areas = Accommodations Provide increased and 200 foot buffer= easy targets for auto All other areas= related crime Auto Repair and Provides concentrated 200 foot buffer= Auto Parts number of targets All other areas = *p.d.=population density: high density areas coded as high opportunity.

1.00 0.50

0.50 0.00 1.00 1.00 0.00 1.00 0.00 1.00 0.00

Using these data, durable hot spots of motor vehicle theft were identified through a Kernel Density function with a 1000-foot radius for motor vehicle theft reports. The intensity classification categories were determined through natural breaks algorithm to sustain internal homogeneity within categories and heterogeneity between categories (Harries, 1999). A continuous surface model was generated and crime intensity scores for each point in the selected area were produced. Estimates were created for all locations and a smooth surface created.

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This smooth surface permits a more accurate picture of the criminal environment as it blends from one location to the next and allows the researcher to visually compare high levels of opportunity on the opportunity structure to hot spots of reported crime for auto theft. Data Analysis The summation of the opportunity layers form the “opportunity structure”, which is calculated using the Raster Calculator in ArcView. The layers were first converted to raster format so that ArcView could add each of the grids together with the grid above it. The entire city of Lexington-Fayette was divided into 25 foot grids and each grid was assigned a value based on the total calculation of all the layers. As all of the values were calculated, each value was assigned a graduated color that indicated the magnitude of opportunity for motor vehicle theft in that particular location and a continuous surface model was created for the opportunity structure. Testing of Hypotheses All hypotheses are tested by using a visual comparison of the model to the hot spot locations on the map containing auto theft reports. A visual analysis is used since this is the most common method employed by police departments. The opportunity structure for each model is compared to the map indicating the concentration of the motor vehicle thefts records. If there are no significant differences between the opportunity structure and the motor vehicle theft reports, the opportunity structure accurately reflects the crime records, and is an accurate representation of opportunity for auto theft in LexingtonFayette, Kentucky. If the opportunity structure map appears significantly different, the opportunity structure is not a proper indicator of opportunity for motor vehicle thefts in Lexington-Fayette, Kentucky. Contributions of the Community-level Research This research suggests that one way to effectively study communitylevel data is to present these variables and investigate the ways in which they are connected to other community-level factors with regard to crime. By assembling an opportunity structure which seeks to

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determine this interconnectedness; researchers can study these factors and their interaction patterns to better predict locations that are likely to be criminogenic. By conducting a visual analysis, these findings can be shared with the police department and help the department to construct other crime-specific opportunity structures to focus police patrol and facilitate crime prevention. As criminogenic locations are identified, crime prevention tactics can be used to prevent crime before it has a chance to occur on its own. These proactive strategies enable both businesses and police to save time and money by preventing victimization using crime-specific opportunity structures as quasiprediction models. Simple community-level data collection and analysis is only the first part of the crime prevention process. These data muct be combined with site-level data to enhance the richness and effectiveness of data collected at the community level. Previous literature seeks to use only data from one level in each analysis, thereby excluding valuable data simply because it was collected with a different unit in mind. As important as the individual features of site-level data are, researchers must also understand the environment in which the data are collected. To use site or community-level data in exclusion of other data produces an unfocused picture with less vibrant detail and decreased accuracy. SITE-LEVEL RESEARCH Introduction The second part of the research project seeks to compare the site-level characteristics that are present at locations of repeat auto theft and those characteristics that are present at locations where repeat victimization does not exist. This part of the research consists of site visits to randomly selected repeat victimization sites (locations experiencing more than two auto thefts in a two year period) and matched comparison sites (locations experiencing one victimization in a two year period) to administer a site survey instrument and collect data on the variables present at each location.

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Site-level Hypotheses: Watchers • Locations with poor surveillance/guardianship (places with fewer watchers) have more auto theft than those with good surveillability (more watchers). • Addresses experiencing repeat victimization during the study period have lower scores on the watcher index than locations with single victimizations. Activity Nodes • Autos parked in locations with no activity nodes in the area have a greater likelihood of motor vehicle theft than those parked in areas with activity nodes. • Addresses experiencing repeat victimization during the study period have lower scores on the activity nodes index than locations with single thefts. Location • Locations that have landscape and design features that provide cover for offenders (location) have more auto theft than those without these features. • Addresses experiencing repeat victimization during the study period have lower scores on the location index than locations with single thefts. Lighting • Locations that are located in areas with poor lighting have more auto thefts than those with good lighting. • Addresses experiencing repeat victimization during the study period have lower scores on the lighting index than locations with single thefts. Security • Locations that have less security and cues indicating security have more auto thefts than those with more security or more security cues. • Addresses experiencing repeat victimization during the study period have lower scores on the security index than locations with single thefts.

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Identification of Research Sites A comparison of locations suffering repeat victimization and locations suffering single victimization during the study period are identified. Due to evidence suggesting that the “time-window effect” is an important factor influencing the number of events that are identified as chronic locations of crime, two years of data are used in this analysis (Farrell, Sousa, & Weisel, 2002). Repeat victimization locations for motor vehicle theft are operationalized as any location that has reported more than one motor vehicle theft in the two year study period. The comparison group is defined as any location that has reported one motor vehicle theft victimization in the two year study period. There are well over 2,000 calls for service regarding stolen vehicles, per year, in Lexington-Fayette, Kentucky. Of those 2,000 initial calls for service, 768 autos were reported stolen with actual crime reports filled out, in the year 2000. In 2001, there was an increase in auto theft reports, but the calls for service numbers remained approximately the same. In order to study these locations, 75 repeat locations (locations with more than two reported auto thefts in the two-year study period) were randomly selected and 75 comparison locations were selected to match the study group. The matched comparison group consists of locations that had one motor vehicle theft in the two-year study period. The comparison group locations were not randomly selected, but matched to the repeat locations based on proximity, zoning, associated building structure, and target density. Proximity was chosen as a criterion for the matched comparison group since the research study is seeking to understand the site-level characteristics of both repeat and single victimization locations. Proximity is necessary because each of the locations is influenced, at least in part, by the structure of the environment surrounding it. This environment forms a backcloth (Brantingham & Brantingham, 1999) for which locations are selected and rejected as potential targets. Choosing repeat and single victimization locations at random would not enable the researcher to take into consideration the effects of the individual backcloth surrounding the repeat location. Zoning was chosen as the second criterion for the matched comparison group since the study of auto theft itself differs in residential and commercial areas. Auto theft in commercial areas focuses on the street parking and public parking lots and garages, while

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auto theft in residential locations is mainly a driveway and residential garage phenomenon. Mixed land-use is an uncommon occurrence in Lexington-Fayette, Kentucky. Because of these distinct differences between commercial and residential auto theft, it would be nonsensical to compare a repeat auto theft location such as a public parking garage with a single victimization location such as personal garage. To help ensure that the repeat and single victimization locations are similar, each pair is matched on zoning. Building structure is the third criteria for matching repeat and single victimization locations. Since the number of motor vehicles, and hence the number of targets, varies depending on the size of the location in which the car is parked, the structure of the location is considered when matching single to repeat victimizations. A car thief looking for a target in a three-story lot is very different from a car thief looking for a target parked on the street. To compare a three-story lot to street parking would not be beneficial because the amount of capable watchers, signs of security, and location attributes would be too different. Target density is an important factor to consider during the matching process. Just as one would not pair a street location and a public parking lot due to lighting and security issues, one would not pair street parking and a public parking lot simply due to the sheer difference in size and potential for targets. A car thief looking to steal a car has a better chance of finding a make and model of his or her preference when entering a public lot than looking through the cars parked on the street. The public lot has many more targets in a condensed area than cars parked on the street. Similarly, visibility is limited inside of the public lot giving more control to the offender whereas s/he has less control in the open environment of the street. Once the repeat locations were randomly selected and the single victimization locations were matched, these locations were plotted on a map of Lexington-Fayette, Kentucky. Since there are a total of 150 locations, 75 repeat locations and 75 matched comparison locations; it is easiest to break up the locations into sections so that the motor vehicle thefts within the city are organized in the simplest way possible. In order to organize the route, an individual map of each section of the city was created. Each map consisted of one section of the city and identified street names and addresses for ease of finding each location. A more detailed street map and phone book were taken on the data collection run to locate difficult to find addresses.

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Site-level Data Collection A survey instrument was completed at each location. The survey instrument includes: taking the longitude and latitude of the area by Global Positioning System (GPS), photographing the area at the location, and completing the written survey instrument. This information is captured at the back of the survey instrument in the notes section. Photographing the area serves two purposes: first, photographs document and illustrate certain phenomenon that may not be able to be captured by word alone. For example, the detail or style of graffiti that otherwise could not be captured. Second, photographs give the reader a better understanding of unique phenomenon in the area. For example, Lexington-Fayette, Kentucky changes land-use and zoning very quickly, going from downtown to sprawled-out countryside in a matter of a couple miles. Finally, photographs allow the researcher to record any forgotten information or find discrepancies that may exist. Variables for the Site Survey The factors mentioned above can be condensed into five factors described by the acronym W.A.L.L.S.: watchers, activity nodes, location, lighting, and security devices. See Chapter 4 for conceptual definitions of these factors and their link to current theory and empirical research. Currently, crime analysis tends to focus on two types of available data: police generated crime measures (i.e. calls for service and crime records) and community data (i.e. census, land use, and zoning). This project provides a research model that may enhance the scientific rigor of crime analysis by developing a crime-specific opportunity model in conjunction with a site-level data collection survey. These site-level data can be used to identify situational crime prevention characteristics of crime-prone locations and support crime prevention strategies that may be introduced to these locations. Watchers consist of capable guardians within sight of the parking areas. These people may include residents, local storeowners, consumers, or pedestrians. These individuals are likely to unknowingly prevent crimes such as auto theft just by being on the streets and creating a highly visible presence. Watchers in commercial areas are typically consumers running errands, those out to lunch, or simply those passing by. Watchers in residential areas are likely to be residents in the neighborhood. Residential watchers may pay more

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attention to the surroundings in their own environment since they are likely to have more at risk in the commission of a crime then a commercial watcher. The amount of pedestrian traffic and vehicle traffic was also measured. See Table 4 for coding of “Watchers”. Table 4. Coding for Watchers Variables Conceptual Definition

Operationalization

Coding

Presence of capable guardians within site of parking areas, including: residents, shop owners, consumer, or pedestrians en route to other locations

-Dwellings/pedestrian can see car location -Landscaping obstructs sight from street -House/car set back from road -Ped/car traffic (per 3 min)

Y=0, N=1

-Emps working in Garage/Lot* -Emps w/ guard jobs* -Businesses w/ view of G/L* -Ped can see G/L * Data collected for Commercial locations

Y=1, N=0 Y=1, N=0 L=1, M=.5 H=0 Raw # Raw # Raw # L=0, M=.5, H=1

Activity nodes are another way to measure the concept of surveillance. Activity nodes draw individuals into an area for a variety of purposes. Activity nodes include Automatic Teller Machines, bars, gas stations, payphones and restaurants. If these establishments are open all night, or at least late into the night, they can provide a steady stream of people entering, exiting, or walking along the streets. These individuals provide extra surveillance to the area and can prevent crimes like auto theft. Hours of operation for activity nodes are extremely important since thieves may take advantages of cars left by drunken bar patrons who have walked or taken other transportation home due to high levels of intoxication. See Table 5 for coding of “Activity Nodes”. The location of cars with regard to landscaping and cover is an essential variable that should be considered by offenders and police alike. Cars in parking lots or parked on streets that are near an intersection are more visible than cars parked behind many other cars in lots or parked between many cars on the street. Other variables such as public or private ownership of a lot may tell offenders about potential security devices. The number or entrances and exits and methods of

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entry and exit may determine the ease with which offenders could remove the car unnoticed. A swipe card egress may permit an offender with a car parked in the lot to use his swipe card to easily remove the vehicle from the premises after breaking into it. In addition, if a car owner leaves the swipe card in view, an offender may see this car as more accessible than another car without a swipe card to permit egress. Table 5. Coding for Activity Nodes Variables Conceptual Definition

Operationalization

Coding

Places that draw heavy use with common temporal patterns; increase awareness

-Activity node open all night -ATM or payphone in sight -Gas station in sight -Bar in sight -Bus stop in sight -People hanging around after 8:00pm

Y=1, N=0 Y=1, N=0 Y=1, N=0 Y=1, N=0 Y=1, N=0 Y=1, N=0

Dimensions of the lot such as size of the overall lot, size of parking spaces, and cost may attract or repel a potential offender. If s/he would have to pay a substantial amount of money to remove the car, s/he may be deterred. If the lot is small, and a stranger’s presence would be easily noted, an offender may be discouraged from stealing from this lot. Similarly, lots with close parking spots and lots with a sturdy boundary, such as a concrete or brick wall tend to conceal offenders more so than lots with wide spaces or a thin fence exterior. Finally, accessibility may also affect an offender’s decision. Lots without easy pedestrian access would require the thief to find an accomplice or a ride to the location of the theft. See Table 6 for coding of “Location”. Lighting, especially in the evening, is an essential variable to study with regard to the location of the targeted car. Simply stated, poor lighting provides cover for offenders. Bright lighting highlights the acts of the offender and may reveal his/her identity. Lighting has been used to study situational aspects of the criminal event with regard to commercial and residential burglary and with regard to crime prevention when designing new homes and commercial establishments equipped with proper lighting to deter crime. See Table 7.

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Table 6. Coding for Location Variables Conceptual Definition Landscape/design features that adequately cover offenders’ when initiating the crime, prevent offenders from being seen during the crime, and/or aid in a speedy departure from the scene.

Operationalization

Coding

Residential -Type of Parking

Attached Garage= 2.5 Detached Garage= 2.0 Carport= 1.5 Driveway= 1.0 Lot= 0.5 Street= 0.0 Y=1, N=0 Y=0, N=1 Apt/Condo= 1 Single home=2 L=0, M=.5 H=1 1-way=1, 2-way=2 Y=1, N=0 _ Garage=0 Lot=1 Street=2 24 hrs=0 All others=1 Public=0 Private=1 Y=0, N=1 >500=0.0 200-500=1.0 100-199=1.5 <50=2.0 Raw number Swipe=2 Ticket=1 Nothing=0 Y=1, N=0 Wall=3 Fence=2 Rope=1 Y=0, N=1 Y=0, N=1 Y=0, N=1

-Alternative Parking -Covered Alley in Back -Type of Residence -Volume of car/pedestrian traffic (per 3 min) -Type of Street -Presence of median Commercial -Parking -Hours of operation -Ownership if G/L -More than one level in G/L -Number of slips

-Number of entrances/exits -Method of entry/exit

-Enclosed G/L -Type of Enclosure

-Pedestrian access in front -Covered alley in back -Open/unlock G/L entrance

_

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Table 7. Coding for Lighting Variables Conceptual Definition

Operationalization

Coding

Presence and quality of lighting and the number of working lamps

-Lighting at night (for commercial both front and back lighting is used) -Lights

Poor= 0 Average=1 Good= 2 Y=0, N= 1

Security devices such as alarm signs, alarms, guards, cameras, and gated access may prove to be simple and cost effective methods of reducing auto theft and crimes in general in public parking lots. Often the threat of a security device alone (i.e. stickers for a security alarm even when an alarm does not exist) is enough deterrence for an offender. However, other offenders are not scared by the threat of technology or the technology itself. Once an offender can figure a way around a device, no car with that device is safe. An example of this occurred shortly after The Club was introduced. Offenders began avoiding The Club altogether by removing the steering wheel and driving the car without the steering wheel. Other thieves carried a spare wheel and replaced the one with the club attached with their own. Residents can also send subliminal cues with regard to security to offenders. Territoriality cues such as whether or not the dweller’s name is identifiable on the home, whether or not the address is clearly visible from the street, whether piled mail is visible, and if the yard is tidy may also help the offender decide if s/he wants to steal a car from this location. See Table 8 for coding of “Security”. Data Analysis One index is created for each of the Watchers, Activity Nodes, Location, Lighting and Security Devices factors (total of five indices). Each index is created by combining the variables that measure each construct in an additive fashion. Two Pearson’s Correlation matrices and a difference of means test are run for each set of factors, one for residential locations and one for commercial locations. Finally, the factor indices for both types of locations are combined and a difference of means test is run with all the data. An independent samples t-test is used to determine if these variables are present in equal amounts in both repeat victimization locations and single victimization locations.

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The difference of means test is used to evaluate the hypotheses mentioned above and each of the hypotheses are retained or rejected. A factor analysis is not conducted since combining all factors into a statistic removes the tangible variables from the discussion. This makes it difficult for the police department to identify the exact measures that can be changed or fixed and, in so doing, makes the research less valuable. Because these models are designed to help the police department, factor analysis seems impractical. Table 8. Coding for Security Devices Variables Conceptual Definition

Operationalization

Presence of security hardware for the location and vehicle such as gates, alarm/ alarm signs, tracking devices and territorial cues.

Residential -Security dogs -Security System visible - > 1 garage entrance -Dweller’s name on the home -Address clearly visible from street -Piled mail visible -Tidy yard -Garage door open Commercial -Security signs/stickers -Security guard at exit or location -Security Cameras -Gate locks after hours -Guards present after hours -Keys held by attendant

Presence of security hardware for the location and vehicle such as gates, alarm/ alarm signs, tracking devices and territorial cues.

Coding Y=1, N=0 Y=1, N=0 Y=0, N=1 Y=1, N=0 Y=1, N=0 Y=0, N=1 Y=1, N=0 Y=0, N=1 Y=1, N=0 Y=1, N=0 Y=1, N=0 Y=1, N=0 Y=0, N=1 Y=0, N=1

Contributions of the Site-level Research Clarke and Homel (1997) and Cornish and Clarke (2003) have suggested opportunity-reducing techniques that can be applied to crimes to prevent their occurrence. These were discussed in Chapter 4. Many of these have been applied to the survey instrument and demonstrated here. Two that were not utilized, controlling facilitators and facilitating compliance were not included in the survey. In order to facilitate compliance with regard to auto theft, the parking facilitator

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would be required to enhance the transportation system or reduce the cost of public transit. Both of which are not feasible for those managing parking facilities. Testing the relationship between the presence of these factors and the occurrence of repeat victimization can help both police and academics to better understand the dynamics of the crime. Specifically, this site-level data analysis seeks to study, at a very detailed level, the locations offenders target and the factors that are associated with offender choices. The variables studied here have all been tested empirically with regard to one crime or another, but have not necessarily been studied with regard to motor vehicle theft. For instance, security cues such as uncollected mail, a tidy yard, and well identified home are usually studied with regard to residential burglary. However, many of these cues may be used by offenders when they determine whether or not to steal property from these locations, not just in a decision to commit a residential burglary. Including factors such as these in the study of motor vehicle theft adds new insight into the study of this crime. This research should also enhance the understanding of motor vehicle theft and the environmental cues associated with this crime.

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CHAPTER 6

A Community-Level Investigation of Auto Theft

INTRODUCTION This chapter presents the data analysis for the community-level research involving the opportunity structure hypotheses. The qualitative nature of the community-level data will enhance the sitelevel analysis by creating the context within which the site-level data can be interpreted and the results implemented in the police department. Human patterns and activities are shaped by the environment in which they move. If researchers can identify locations that restrict behaviors (such as crimes) and those that encourage deviant behavior, neighborhoods can design out crime. The models discussed in this chapter, while not as mathematical in nature as the W.A.L.L.S. variables, are used to enhance the collection of those variables and to provide a community approach to understanding their relationship to auto theft. THE MODELS - AUTO THEFT IN LEXINGTON-FAYETTE The first map created is a density map of all auto theft locations in Lexington during the years 2000-2001 (see Figure 1). Figure 1 indicates the highest concentration of auto thefts in the downtown area. High concentrations of auto theft are also located near New Circle and some of the roads that radiate from the center of town. The hot spot located by Versailles and New Circle is mainly a residential area with many apartment buildings condensed in that location. The location indicated by the hot spot at Winchester is mainly residential with several apartment complexes as well as three motels located within this hotspot. The largest concentration of convenience stores is located in the hotspot that runs from Bryan Station to Broadway, just north of the city-center. At the center of the hotspot on Nicholasville is a convenience store surrounded by three apartment complexes. The final 107

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area of high auto theft concentration is located on Georgetown. Within this concentration there are several apartment complexes and a string of hotels. Most of the areas surrounding downtown and almost all of the areas outside of New Circle, to the South, are residential. These areas indicate a low auto theft concentration. The area to the south of downtown, heading toward Harrodsburg, has experienced moderate levels of auto theft. This location has a few apartments and accommodations but is not nearly as condensed as locations experiencing much higher auto theft such as Versailles and Winchester. Streets and transportation lines seem to be related to locations for auto theft. The areas of few-moderate auto theft concentrations that are located on the outskirts of New Circle are not located near bus lines or transportation stops but most areas of high concentration of auto theft are located along transportation lines and the main arteries that run from the city-center and radiate outward. ‘Warm’ hotspots are found off Broadway, Versailles, Nicholasville, Richmond, and Winchester; all of which are major transportation arteries. Model 1 (Base Model) The first model is a map created by summing all parts of the base model (well-traveled roadways, government subsidized housing, parking lots, convenience stores/gas stations, transportation hubs, and schools). Figure 2 is the resulting map for the base model. This map indicates a high concentration of opportunity scattered throughout the city of Lexington. Again, the streets seem to be related to the locations of highest opportunity. Many of the areas that indicate high and highest opportunities fall on major arteries. Not one school is located directly in the middle of the highest opportunity areas. Schools are present on the fringe of the highest opportunity area located at Paris & New Circle, Bryan Station & New Circle, the area directly west of Bryan Station, and the spot between Winchester and Richmond. Gas Stations and conveniences stores create a very different picture. Where there are no schools at the center of highest opportunity areas, there are gas stations and/or convenience stores at the center of every one. In fact, there are seven gas stations in the highest opportunity spot located on the outskirts of town on Nicholasville. The Bryan Station high opportunity area has four gas stations in its center and the Richmond location has three. There are two gas stations at

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each of the highest opportunity areas located on Nicholasville (near the town center), Versailles (near the town center), and between Winchester and Richmond. One gas station is located at the highest opportunity area on Versailles (closer to New Circle).

Figure 1. Density of All Auto Thefts – Lexington, KY

*See text for Opportunity Structure calculation.

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In Lexington, Kentucky, many gas stations also have convenience stores attached to the business. For this reason, many of the convenience stores appear to be located at the same address as the gas stations. The exception to this finding is that there are several convenience stores that are not located at the same business as the gas stations in the highest opportunity area on Paris. The Paris location has three convenience stores but the gas stations are located on the border of the opportunity area. The Versailles location nearest to New Circle also has a convenience store in its opportunity area in addition the stores located at the two gas stations. Most of the large parking lots are located in the center of town. Two of the parking facilities fall in the opportunity area located in the center town on Nicholasville, one in the center and one on the fringe. The area of high opportunity directly in the center of town incorporates two facilities in the center and one on the edge. It should be noted that this area is not one characterized as having highest opportunity so it appears that the many lots in the small space do not, necessarily, present the highest opportunity. Model 2 (Base Model + Apartments) The second model is a map created by summing all parts of the base model (well-traveled roadways, government subsidized housing, parking lots, convenience stores/gas stations, transportation hubs, and schools) and adding the apartments layer. Figure 3 is the resulting map for this model. This map, like Model 1, indicates a number of scattered areas where the highest opportunity exists. In this model, areas with “highest” opportunity are replaced with areas of “moderate” and “high” opportunity, creating the first difference between Mode1 and Model 2. Similar to the base model, many of these areas of high and highest opportunity fall on major street arteries. In fact, two areas of high condensed opportunity fall on major arteries: one on Paris and a string on Nicholasville. This is the second difference between this model and Model 1. Model 2 indicates that the highest area of opportunity is located outside of the city-center while the base model shows highest opportunity in the city-center. Clearly, this shift in the opportunity structure is due largely to the location of apartments in the southeastern and northern outskirts of the city. With this model, opportunity on Richmond, Paris, and between Tates Creek & Nicholasville increases. The area of low opportunity on Paris in the

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base model now depicts an area of highest opportunity in this model. All of these locations are dense with apartment complexes.

Figure 2. Model 1 (Base Model) for Opportunity Structure* - Lexington, KY

*See text for Opportunity Structure calculation.

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Areas of low and moderate opportunity have shifted as well from the base model to this model. Gone is the high opportunity along Paris (near downtown) and Versailles. It is replaced with low-moderate opportunity. The highest opportunity area in the base model was in the city-center. In this model, the city-center has a smaller area for high opportunity surrounded by low and moderate opportunity. Despite a cluster of apartment buildings between Georgetown and Leestown, this area remains of low auto theft opportunity. The area between Winchester and Bryan Station follows a similar pattern. Model 3 (Base Model + Fast Food Locations and Bars) The third model is a map created by summing all parts of the base model (well-traveled roadways, government subsidized housing, parking lots, convenience stores/gas stations, transportation hubs, and schools) and adding the fast food and bars layers. Figure 4 is the resulting map for this model. Unlike the base model, Model 3 shows fewer areas of highest auto theft opportunity. In this map, there are five areas where the highest opportunity exists. As with the base map, the largest area of opportunity is present in the downtown area. The other four areas of highest auto theft opportunity are located on Broadway, Richmond, Nicholasville and Tates Creek. All of these locations are situated on or directly off the main roads. As with most cities, many bars and restaurants are located in the city-center in Lexington, Kentucky. This accounts for the change in opportunity concentration in this model. More than half of all the bars in Lexington are found in the area that indicates both high and highest opportunity for auto theft, the city-center. Though at first glance the locations of restaurants seem to fit this pattern, many restaurants are located around the entire city on New Circle. Many people living in the residential areas outside of the city-center travel New Circle to get to other destinations. Perhaps the location of these restaurants is related to the shape and position of the four highest opportunity areas outside of the city-center. Areas of low or moderate opportunity have changed when compared to the base model. According to this model there is very little auto theft opportunity to the west of the city-center. In the base model there was an area of highest opportunity on Versailles. In this model, that opportunity has decreased despite a large number of fast food locations in the area. Harrodsburg also has a very large number of

A Community-level Investigation of Auto Theft

Figure 3. Model 2 (Base + Apartments) Opportunity Structure* - Lexington, KY

*See text for Opportunity Structure calculation.

113

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fast food restaurants and does not seem to have increased opportunity due to their presence. This seems to be true for the restaurants on Paris as well. However, the very small area of high opportunity indicated on the base model at Tates Creek seems to have increased dramatically in this model. That is probably due, in part, to the high volume of gas stations in the area. An increase in opportunity was expected on Newtown (between Broadway and Georgetown). In the base model there was an area of moderate to high auto theft opportunity. Since there are many restaurants and bars in that area, the researcher expected to find an increase in auto theft opportunity. However, this was not the case. In fact, the area of high opportunity was reduced to one of low opportunity. The same is true of the highest opportunity area on Paris, which was indicated as an area of highest opportunity in Model 1 and reduced to low opportunity in Model 3. Model 4 (Base Model + Accommodations) The fourth model is a map created by summing all parts of the base model (well-traveled roadways, government subsidized housing, parking lots, convenience stores/gas stations, transportation hubs, and schools) and adding the accommodations layer. Figure 5 is the resulting map for this model. This model is strikingly similar to the base model. After careful consideration this is due to the fact that many accommodations are located in the same areas as gas stations and convenience stores, two of the strongest variables in Model 1, the base model. Very few differences exit between these two models; a few areas are increased from areas of moderate-high opportunity to areas with highest opportunity. A few areas that were identified as high opportunity in the base model are identified in this model as having moderate opportunity. Four areas were identified in the base model (Model 1) as having moderate and high opportunity. All of these locations are north or east of the city-center. One on Winchester, Broadway (near downtown), and Newtown (past New Circle) all increased from high to highest opportunity for auto theft. One area that spans from Georgetown to Newtown increased in the shape of its density on Newtown in Model 4. One final area located south-east of downtown also increased in size, signifying more moderate opportunity than was indicated in the base

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model. All of these are consistent with a dense distribution of hotels and motels in these same areas.

Figure 4. Model 3 (Base + Fast Food/Bars) Opportunity Structure* - Lexington, KY

*See text for Opportunity Structure calculation.

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Figure 5. Model 4 (Base + Accommodations) Opportunity Structure* - Lexington, KY

*See text for Opportunity Structure calculation.

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To the south of the city-center there are three locations that have reduced opportunity for auto theft in this model. These locations run between Harrodsburg and Nicholasville and Nicholasville and Tates Creek, outside of New Circle. There are no hotels in this area other than one that is located in the area with the highest opportunity, on Nicholasville. The decrease in opportunity in these areas is most likely due to the absence of accommodations. All areas of high-highest opportunity are still located on major roadways. The addition of this information does not significantly contribute to the base model. Model 5 (Base Model + Auto Parts and Repair Shops) The fifth model is a map created by summing all parts of the base model (well-traveled roadways, government subsidized housing, parking lots, convenience stores/gas stations, transportation hubs, and schools) and adding the auto parts and auto repair shops layer. Figure 6 is the resulting map for this model. This model is very different from the base map. Areas of highest opportunity are both fewer and more intense. There is no doubt that adding the auto parts and repair shops creates this change in shape and opportunity. In Model 5, there are three areas of highest opportunity, the area between Richmond and Bryan Station (most intense) and two other areas, one between Harrodsburg and Nicholasville and one downtown. All three of these areas displayed as areas of highest auto theft opportunity in Model 5, do not have a single convenience store and only two gas stations in their center. However, in two of the models, both convenience stores and gas stations are located in the periphery of these areas. Though there are schools dispersed throughout the city, they do not appear to add significantly to Model 5 and only one is found in the highest opportunity area located between Bryan Station and Richmond. There are no schools found in the other two areas that represent highest opportunities for auto theft in Model 5. Several areas that appeared to have high opportunity in the base model do not appear to create opportunity in Model 5. Two of significance, those on Nicholasville and Richmond, appear to present less opportunity in Model 5 than in Model 1. Similarly, areas north and northwest of city-center, on Georgetown and Broadway, also decreased in opportunity, according to Model 5. All areas west and south-west of the city-center decreased in opportunity with exception of the highest opportunity area on

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Nicholasville. Like the base model, all three of these highest opportunity areas overlap with well-traveled roadways.

Figure 6. Model 5 (Base + Auto Repair/Parts Shops) Opportunity Structure* - Lexington, KY

*See text for Opportunity Structure calculation.

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Model 6 (Full Model) The sixth (and final) model is a map created by summing all parts of the base model (well-traveled roadways, government subsidized housing, parking lots, convenience stores/gas stations, transportation hubs, and schools) and adding the apartment, fast food and bars, accommodations, and auto parts and auto repair shops layers. Figure 7 is the resulting map for this model. The map for Model 6 indicates highest opportunity for auto theft in seven areas. The highest concentration for opportunity is in the downtown area where two spots indicate opportunity at the highest level. All other areas with this level of auto theft opportunity are located on major roadways: Broadway, Winchester, Richmond, Tates Creek, and Nicholasville. Several areas containing high-highest opportunity for auto theft now indicate low to moderate opportunity in Model 6. One of the most notable is on Versailles, which indicates moderate-high opportunity in several of the models. Others on Newtown and between Harrodsburg and Nicholasville appear to be reduced to low opportunity in Model 6 as well. According to the full model, there are only areas with lowmoderate auto theft opportunities both west and north-west of the citycenter. The largest area for highest auto theft opportunity is located in the city-center. This area has more bars than any other variable considered in Model 6. This model suggests that bars draw the greatest opportunity in this area. The second largest area of highest opportunity, directly north east of downtown on Winchester and stretching north to Bryan Station, contains the largest concentration of auto repair and parts shops in all of Lexington. Auto repair and parts shops are located in this area more so than any other variable measured in Model 6. Gas stations and convenience stores are nearly the only variables present in the Nicholasville area of highest opportunity. This area is surrounded by apartments, but none of these apartment complexes fall in the highest opportunity zone. Apartment complexes, however, drive the opportunity that is present on Richmond. This area contains mostly apartments along with a few gas stations and a convenience store on the border. Accommodations, apartments, auto repair and parts shops, and bars are all present in the area of highest opportunity on Broadway. The last area with highest opportunity for auto theft is on Tates Creek.

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This location has a gas station in the center and is surrounded by an auto repair shop, apartments, and a convenience store on the periphery.

Figure 7. Model 6 (Full Model) Opportunity Structure* Lexington, KY

*See text for Opportunity Structure calculation.

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Review of Findings The base model provides an adequate understanding of the problems of auto theft in Lexington, Kentucky. It demonstrates the salience of major roadways and their importance in target selection choices of offenders. Model 1 also suggests that gas stations and convenience stores are important variables in the opportunity structure. Parking facilities are also considered in Model 1, despite the small effect they had on the overall structure. Schools are also included in this model but contributed very little to the locations where high opportunity was displayed. Model 2 supplemented the base model with the apartment complex layer. This model reinforced the importance of the well-traveled roads providing offenders with access to automobiles from all over the city. The apartment complex layer added significant locations of high opportunity to the residential areas on Versailles, Paris, and Nicholasville. It also focused opportunity in the residential areas, where the largest opportunity exists for Model 2. The apartment complex layer enhances the base model to look more similar to that of the density of actual auto thefts. Model 3 enhanced the base model with the fast food and bars layer. This model decreases the quantity of highest opportunity areas but spreads these areas out, focusing in both the downtown area and the areas away from the city-center. Most importantly, it adds areas of high opportunity to Broadway, Richmond, and Nicholasville, which were missing in previous models and appear on the density map of actual auto thefts in Lexington. This layer also shifts the opportunity structure away from the southwest of the city-center, as does the density of auto theft reports. This layer also improves the base model to look more similar to that of reported auto thefts. Model 4 presented the accommodations in addition to the base model. This model looks very similar to the base model and does not add anything significant to the contour of the opportunity structure. In fact, the outline of this model looks less similar to the actual density of auto thefts than the base model. Model 5 supplemented the base model with auto parts and repair shops. This model adds a significant intensity to the base model. The location of these shops, around New Circle and clustered between Richmond and Bryan Station, significantly contribute to an overall opportunity structure that would mimic actual auto theft in Lexington,

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Kentucky. This model presents schools and parking as being unimportant in the overall opportunity structure and also demonstrates the importance of auto parts and repair shops. Model 6, the full model, includes all layers. This model indicates an opportunity structure that proficiently captures opportunity. When Model 6 is compared to Figure 1, the density of all auto thefts in Lexington, Kentucky, 2000-2001, the patterns are similar. Two differences seem evident; the first is the lack of consistency in the contour of the downtown area when comparing the full model to actual auto thefts. The second difference is the density of the auto thefts that are located in the span between Winchester and Broadway on New Circle. Model 6 does not adequately predict the auto thefts that have occurred in this area in 2000-2001. To enhance these deficiencies, an Alternate Model is proposed. This Alternate Model includes the following layers: well-traveled roadways, gas stations, convenience stores, apartments, bars, and auto repair and parts shops (see Figure 8). It does not include schools and parking lots from the base layer, nor fast food locations from Model 3 or accommodations from Model 4. This model is superior to Model 6, the full model, because it provides a better curve for the downtown area and also indicates increased opportunity in the span between Winchester and Broadway on New Circle, the two flaws with Model 6. Neither this model or the full model provide a proper opportunity scale for the hot spot indicated on Versailles in the density map of auto thefts. No single model of opportunity has provided a perfect prediction model for auto theft. However, several of the models have provided valuable information about the variables that are located in areas where auto thefts are high (and low). The Alternate Model could be used to assist police departments in identification of locations that may generate auto theft. In addition, departments may use these auto theft opportunity structures to develop crime-specific models to investigate other problems.

A Community-level Investigation of Auto Theft

Figure 8. Alternate Model for Opportunity Structure* Lexington, KY

*See text for Opportunity Structure calculation.

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CHAPTER 7

A Site-level Investigation of Auto Theft

INTRODUCTION This chapter presents the data analysis for the site-level research involving the comparison of randomly selected repeat victimization locations (locations experiencing more than two auto thefts in a two year period) and matched comparison of single victimization locations (locations experiencing one incident of victimization in a two year period). Descriptive statistics for both repeat and single victimization locations will be presented, followed by the results of difference of means tests for each variable. DATABASE DESCRIPTION The dataset started with 150 locations containing 75 randomly picked repeat victimization locations and 75 single victimization locations matched to the repeats. Of these 150 locations, 132 are included in the study.1 Auto theft locations are nearly evenly divided with 51.5 percent in residential areas and 48.5 percent in commercial areas (See Table 9). Of the 64 commercial locations, roughly one-third of these locations are related to automobiles. Dealerships made up 14.1 percent of commercial locations and six percent of all locations in the sample. Businesses such as gas stations, parking lots/garages, towing, rental and parts and repair shops also are included, accounting for roughly 20 percent of all commercial locations in the sample. Small businesses including bookstores, print services, and a florist are combined with retail stores and two hotels to represent one-third of commercial locations. There are 68 residential locations in the sample. These 1

The reason for the removal of 18 locations is due to the inability of the researcher to locate one or both of the paired addresses during the site survey. This is discussed in more detail in Chapter 8.

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Opportunity, Environmental Characteristics and Crime

Table 9. Description of Auto Theft Locations Selected for Sample in Lexington, Kentucky. Location Description

N

Commercial Areas Repeat Victimization Single Victimization Residential Areas Repeat Victimization Single Victimization

64 (48.5%) 32 32 68 (51.5%) 34 34

Table 10. Description of Commercial and Residential Locations Selected for Sample in Lexington, KY Location Description

N

Commercial Areas Auto Rental Parts and Repair Dealer Gas Station Parking Garage/Lot Towing Food & Beverage Bar/Liquor Fast Food Other Restaurant Entertainment Strip mall Movies Businesses Small Business Retail Accommodations Wholesale Other Correctional Facility Medical Facility House of Worship School Residential Areas Apartment/Condominium Single Family Home

64 2 5 9 9 2 3 1 2 2 2 3 3 13 5 2 2 1 5 1 1 68 29 39

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locations are categorized into two groups: single-family homes (57.4 percent) and apartment/condominium (42.6 percent). There is no distinction made between apartment complexes and condominium complexes since parking facilities were similar in both types of locations (see Table 10). The commercial auto theft locations are mostly parking lots (76.6 percent) and rarely garages (3.1 percent). More than half of these locations hold fewer than 50 parking slips, while 32.8 percent have over 100 slips. When possible, the average length and width of the parking spaces were measured. Using data that are collected in 37 locations, the average length of parking slips is 201.4 inches and average width is 107.1 inches (see Table 11). Table 11. Characteristics of Commercial Auto Theft Locations Selected for Sample in Lexington, KY Variable Type of Parking Spaces Lot Garage Number of Parking Slips 1 - 20 21 - 50 51 - 100 ≥ 101 Length of Parking Slips* Width of Parking Slips*

Valid % (64) 20.3 76.6 3.1 (64) 23.4 31.3 12.5 32.8 201.4 107.1

(37) 31.07 sd (37) 8.67 sd

* The mean and standard deviation are reported for this item in inches.

Most residential parking locations have driveways only (33.8 percent) or parking lots (35.3 percent). Very few locations have attached garages (10.3 percent) or street parking (10.3 percent). Carports and detached garages combined include roughly 10 percent of residential locations. Sixty percent of locations do not have access to alternate parking arrangements, while nearly 40 percent do have such access (see Table 12).

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Opportunity, Environmental Characteristics and Crime

Table 12. Characteristics of Residential Auto Theft Locations Selected for Sample in Lexington, KY Variable Type of Parking Attached Garage Carport Driveway only Detached Garage Parking Lot Street Parking Alternate Parking No Yes

Valid % (68) 10.3 1.5 33.8 8.8 35.3 10.3 (68) 60.3 39.7

Watchers Descriptions of the Watcher variables are presented in Table 13 and 14. These variables are categorical but since they are utilized in an index are treated as continuous data. The Watcher Index ranges from 0-5 with a higher number indicating fewer Watchers in the area. The Watcher Index is the only index created with reverse coding. Most residential locations indicate that parking can be seen from the dwelling (58.8 percent) and that pedestrians can see parking (60.3 percent). The landscaping variable follows suit as 64.7 percent of locations had landscaping that did not obstruct view of the parking location. In most locations, pedestrian traffic and vehicle traffic were reported as light (82.4 and 67.6 percent, respectively). See Table 13. For commercial locations, the Watcher Index includes more questions, and scores range from 0-16. Few locations had guards or employees who provided surveillance. There were significantly more businesses with view of the front (M=2.020) than those with view of the back (M=.340). More locations had pedestrian view of the front (M=.540) than those with view of the back (M=.410). Despite this, pedestrian traffic was still light in most locations (89.1 percent). The Watcher Index for residential locations has a mean of 3.547 and standard deviation of 2.958. See Table 14.

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Residential Locations - Correlations Correlations among Watcher variables in the residential locations show an interesting relationship. Car and pedestrian traffic are significantly related with a positive, strong relationship (r=.625). Pedestrian traffic is also significantly related to Pedestrian sight (r=.260), though this relationship is a positive, moderate one. As logic dictates, pedestrian sight is significantly related to whether or not the house is set back from the road (r=.561) with a positive, moderate relationship (see Table 15). Table 13. Description of Watcher Variables in Residential Locations Selected for Sample in Lexington, Kentucky. Variables Dwelling can see car location Yes Partial No Pedestrian can see car location Yes Partial No Landscaping obstructs view Yes Partial No House and car set back Yes Partial No Pedestrian Traffic in Front Heavy Moderate Light Volume Car Traffic in Front Heavy Moderate Light Watcher Index

N 40 8 20 41 3 24 44 6 18 51 2 15 9 3 56 14 8 46 2.927 (mean) 1.282 (sd)

(N=68)

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Opportunity, Environmental Characteristics and Crime

Table 14. Description of Watcher Variables in Commercial Locations Selected for Sample in Lexington, Kentucky. Variables

(N)

Mean

Number of Employees .110 w/ View of Locationa Number of Employees .060 w/ Guard Jobs Businesses w/ View of 2.020 Front of Location Businesses w/ View of .340 Back of Location Pedestrians can see 35 Frontb Pedestrians can see Back 17 Pedestrian Traffic in Front Heavy 2 Moderate 5 Light 57 Watcher Index (64) 3.547 a Mean and SD noted for continuous data. b Number of locations given for discrete data.

SD .567 .302 2.171 .761

2.958

The Watcher Index is positively correlated with all components at a significance level of at least p<.05. The weakest relationships are with dwelling sight and landscape obstruction which both have a positive, weak relationship (r=.272 and r=.358 respectively). The Watcher Index has the strongest relationship with pedestrian traffic, pedestrian sight, and whether or not the house is set back from the road (r=.601, r=.695, and r=.638, respectively). The Watcher Index and car traffic are also significantly related (r=.424), but the relationship is positive and moderate in nature. See Table 15. Residential Locations – Difference of Means Test An independent-samples t-test was conducted to determine the difference between repeat and single victimization in residential locations using the Watcher Index. The Watcher Index is reverse coded; a higher score indicates fewer Watchers. The level of significance was set at p<.05. A statistical significance was found

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t(66)= -2.559, p=.013 between locations with repeat victimizations (M = 3.309, S.D. = 1.393) and single victimization (M = 2.544, S.D. = 1.047). The null hypothesis was rejected; locations with repeat victimization score higher on the Watcher Index in residential locations than those locations that were victimized once.2 See Table 33. Table 15. Inter-item Pearson Correlations for Watchers in Residential Locations Selected for Sample in Lexington, KY Watchers Characteristics 1. Car Traffic

1

2

3

4

.625** -.134

.019

5

6

.046 -.001

-.076 .260* .048 2. Pedestrian Traffic .091 .125 3. Dwelling Sight .012 4. Pedestrian Sight 5. Landscape Obstruction 6. House Set Back 7. Watcher Index ** Correlation is significant at the .01 level (2-tailed). * Correlation is significant at the .05 level (2-tailed).

7 .424**

.253

.601**

.088

.272*

.561** .695** .045

.358** .638**

Commercial Locations - Correlations There were fewer correlations between Watcher variables in commercial locations than residential locations. Pedestrian view of the front of the location was significantly correlated with both businesses who had a view of the back of the location (r=.257) and pedestrian view of the back of the location (r=.270). The relationship between both of these correlations is positive and moderate. Another significant relationship exists between businesses with employees and guards that

2

Variables in the Watcher index were coded to indicate that a higher score was indicative of the absence of the six Watcher variables and a low score indicates more “Watchers”.

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Opportunity, Environmental Characteristics and Crime

could see the location. This relationship is positive and weak to moderate (r=.423). The Watcher Index for commercial locations is significantly correlated with business view of the front and back and pedestrian view of the front and back. Business view of the back of the location and pedestrian view of the front are positively and moderately correlated with the Watcher Index (r=.342 and r=.354 respectively) for commercial locations. The strongest relationship exists between the Watcher Index and businesses with view of the front of the location (r=.781), with a strong to very strong, positive relationship. The Watcher Index and pedestrian view of the back are also significantly related with a positive, moderate relationship (r=.577). See Table 16. Table 16. Inter-item Pearson Correlations for Watchers in Commercial Locations Selected for Sample in Lexington, KY Watchers Characteristics

1. Employees 2. Guards 3. Business View Front 4. Business View Back 5. Pedestrians Front 6. Pedestrians Back 7. Pedestrian Traffic 8. Watcher Index

1

2

3

4

5

6

.423**-.169 -.052 -.043 -.044 .047 -.026

.194 -.028

.026 -.015

7

8

-.064 .068 .175 .245

.165

.065 .781**

.257* .090

-.005 .342**

.270* .159 .354** .011 .577** .156

** Correlation is significant at the .01 level (2-tailed). * Correlation is significant at the .05 level (2-tailed).

Commercial Locations - Difference of Means Test An independent-samples t-test was conducted to determine the difference between repeat and single victimization in commercial

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locations using the Watcher Index. The Watcher Index for commercial locations is also reverse coded; a higher score indicates fewer Watchers. The level of significance was set at p<.05. A statistical significance was not found t(62)= .382, p=.704 between locations with repeat victimizations (M = 3.409, S.D. = 2.983) and single victimization (M = 3.694, S.D. = 2.974) in commercial locations. The null hypothesis was retained. See Table 33. Watcher Index for Full Model – Difference of Means Test The final independent-samples t-test was conducted using the Watchers Index to determine the difference between repeat and single victimization in all sample locations. Again, the Watcher Index is reverse coded so that a higher score indicates fewer Watchers. The level of significance was set at p<.05. A statistical significance was not found t(130)= -.672, p=.503 between locations with repeat victimizations (M = 3.358, S.D. = 2.300) and single victimization (M = 3.092, S.D. = 2.246) in sample locations. The null hypothesis was retained. See Table 33. Activity Nodes Descriptions of the Activity Nodes variables are presented in Table 17 and 18. These variables are also categorical but since they are utilized in an index are treated as continuous data. The activity node index ranges from 0-3 with a higher score indicating more activity nodes. Most residential locations do not have activity nodes. Furthermore, 95.6 percent of all residential locations do not have activity nodes that are open all night. According to the sample, 13.2 percent of all residential locations have a bus stop in sight of the location and a little more than a quarter (26.5 percent) of locations recorded people loitering after dark. See Table 17. Commercial locations had far more activity nodes in sight, the Activity Nodes Index for commercial locations ranges from 0-6 with a higher number indicating more activity nodes. In fact, 60.9 percent of all locations had an activity node open all night. Almost one-third of commercial locations (31.3 percent) have an ATM in sight while almost half have a gas station (43.7 percent) in sight. Roughly onequarter of all locations had a bar in sight (26.6 percent) or a bus stop (25 percent) in sight. Surprisingly, more commercial locations (43.8

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Opportunity, Environmental Characteristics and Crime

percent) recorded people loitering after dark than in residential locations (26.5 percent). See Table 18. Table 17. Description of Activity Nodes Variables in Residential Locations Selected for Sample in Lexington, KY Variables Activity Node open all night Yes ATM in Sight Yes Gas Station in Sight Yes Bar in Sight Yes Bus Stop in Sight Yes Loitering after dark Yes Activity Nodes Index

Valid % (N)

Mean

SD

.581

.741

4.4 2.9 2.9 5.9 13.2 26.5 (68)

Table 18. Description of Activity Nodes Variables in Commercial Locations Selected for Sample in Lexington, KY Variables Activity Node open all night Yes ATM in Sight Yes Gas Station in Sight Yes Bar in Sight Yes Bus Stop in Sight Yes Loitering after dark Yes Activity Nodes Index

Valid % (N)

Mean

SD

2.313

2.000

60.9 31.3 43.7 26.6 25.0 43.8 (64)

Residential Locations – Correlations Correlations among Activity Nodes variables in the residential locations were weak, with the exception of those correlated to the

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Activity Nodes Index. Locations with a bar in sight and a bus stop in sight show a positive, weak correlation (r=.253). The Activity Nodes Index has a positive and weak to moderate correlation with an ATM in sight of locations (r=.336). Three variables show a significant and a positive, moderate relationship with the Activity Nodes Index: locations that have activity nodes open all night, locations with a bus stop in sight, and locations where loitering occurred on the block at night (r=.414, r=.458, and r=.588 respectively). The significant variable with the highest correlation to the Watcher Index is locations with a bar in sight. Bar in sight has a strong, positive relationship with the Activity Nodes Index (r=.675). See Table 19. Table 19. Inter-item Pearson Correlations for Activity Nodes in Residential Locations Selected for Sample in Lexington, KY Activity Node Characteristics 1. Activity Node open all night 2. ATM in Sight 3. Gas Station in Sight 4. Bar in Sight

1

2

3

-.037 -.037 -.030

4

5

.238 -.084 .313

6

7

.189

.414**

.189 -.110

.336**

-.048 -.068 -.110

.099

.253* .172

.675**

-.149

.458**

5. Bus Stop in Sight 6. Loitering on Block at Night 7. Activity Nodes Index ** Correlation is significant at the .01 level (2-tailed). * Correlation is significant at the .05 level (2-tailed).

.588**

Residential Locations – Difference of Means Test An independent-samples t-test was conducted to determine the difference between repeat and single victimization in residential locations using the Activity Nodes Index. A higher score on the Activity Nodes Index indicates more activity nodes are present in the area. The level of significance was set at p<.05. A statistical significance was not found t(66)= -1.232, p=.222 between locations

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Opportunity, Environmental Characteristics and Crime

with repeat victimizations (M = .691, S.D. = .844) and single victimization (M = .471, S.D. = .615) in residential locations. The null hypothesis was retained. See Table 33. Commercial Locations - Correlations There are many more correlations for the commercial locations than residential locations with regard to Activity Nodes. The variable measuring activity nodes open all night has a significant and at least moderate correlation with every variable (ATM r=.540, gas station r=.642, bar r=.482, bus stop r=.462 and loitering r=.448). Many activity nodes are, by the nature of being activity nodes, open all night. ATMs have a significant and weak to moderate relationship with gas stations (r=.357), bars (r=.434) and loitering at night (r=.289). Locations of bars, bus stops, and loitering at night are all significantly correlated to locations of gas stations. Every variable has a significant and strong to very strong correlation with the Activity Node Index in commercial locations. See Table 20. Table 20. Inter-item Pearson Correlations for Activity Nodes in Commercial Locations Selected for Sample in Lexington, KY Activity Node Characteristics 1. Activity Node open all night 2. ATM in Sight 3. Gas Station in Sight 4. Bar in Sight

1

2

3

4

5

6

7

.540** .642** .482** .462** .448** .853** .357** .434** .234

.289*

.676**

.325** .509** .429** .782** .061

5. Bus Stop in Sight 6. Loitering on Block at Night 7. Activity Nodes Index ** Correlation is significant at the .01 level (2-tailed). * Correlation is significant at the .05 level (2-tailed).

.111

.565**

.509** .655** .671**

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Commercial Locations – Difference of Means Test An independent-samples t-test was conducted to determine the difference between repeat and single victimization in commercial locations using the Activity Nodes Index. A higher score on this index indicates more activity nodes in the area. The level of significance was set at p<.05. A statistical significance was not found t(62)= .287, p=.775 between locations with repeat victimizations (M = 2.242, S.D. = 1.904) and single victimization (M = 2.387, S.D. = 2.124) in commercial locations. The null hypothesis was retained. See Table 33. Activity Nodes Index for Full Model – Difference of Means Test The final independent-samples t-test was conducted using the Activity Nodes Index to determine the difference between repeat and single victimization in all sample locations. The level of significance was set at p<.05. A statistical significance was not found t(130)= -.235, p=.815 between locations with repeat victimizations (M = 1.455, S.D. = 1.651) and single victimization (M = 1.385, S.D. = 1.800) in sample locations. The null hypothesis was retained. See Table 33. Location Descriptions of the Location variables are presented in Table 21 and 22. These variables are also categorical but since they are utilized in an index are treated as continuous data. The scores on the Location Index in residential areas range from 2.5-8.5, with the higher number indicating a safer location. Roughly two-thirds of residential locations had parking in either a lot (35.3 percent) or driveway (33.8 percent). Ten percent of locations had street parking as their primary parking option while 19.1 percent had a garage. Most locations had street parking as an alternative (67.6 percent). Approximately one-fourth of both residential and commercial locations had covered access. Apartments and single homes were split almost evenly with 51.5 percent and 48.5 percent, respectively. Pedestrian traffic was surprisingly heavy in residential areas with 82.4 percent of locations recorded as having heavy pedestrian traffic. Car traffic followed a similar pattern with 67.6 percent of locations indicating heavy vehicle

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Opportunity, Environmental Characteristics and Crime

traffic. In residential areas, two-thirds of streets had two-way traffic. See Table 21. In commercial areas the Location Index ranged from 3-15.5, with the higher number indication a safer location. In commercial areas, most parking is located in lots (76.6 percent) with only 20.3 percent on the street. Almost half of the lots are open 24 hours (43.7 percent) and are publically owned (78.1 percent). Almost all lots are one level (96.9 percent). Surprisingly, 21.9 percent of lots had more than 500 parking Table 21. Description of Location Variables in Residential Locations Selected for Sample in Lexington, KY Variables Type of Parking Street Lot Driveway Carport Garage Alternate Parking Yes No Covered Access in Back Yes No Type of Residence Apartment/Condo Single Home Pedestrian Traffic in Front Heavy Moderate Light Car Traffic in Front Heavy Moderate Light Type of Street 1-way 2-way Presence of Median Yes No Location Index

Valid % (N)

Mean

SD

10.3 35.3 33.8 1.5 19.1 67.6 32.4 25.0 75.0 51.5 48.5 82.4 4.4 13.2 67.6 11.8 20.6 32.4 67.6 1.5 98.5 (68)

5.493

1.520

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Table 22. Description of Location Variables in Commercial Locations Selected for Sample in Lexington, KY Variables Type of Parking Garage Lot Spaces on Street Open 24 Hours Yes No Ownership of Garage/Lot Yes No More than one level Yes No # of slips in Garage/Lot >500 200-500 100-199 50-99 <50 Number of Entrances Number of Exits Method of Entry/Exits Nothing Ticket Swipe Enclosed Yes No Type of Enclosure None Rope Fence Wall Pedestrian Access on Foot Yes No Covered Access/Alley in Back Yes No Open/Unlocked Entrance Yes No Location Index

Valid % (N)

Mean

SD

3.1 76.6 20.3 43.7 56.3 21.9 78.1 3.1 96.9 21.9 3.1 10.9 14.1 50.0 2.391 2.360

.828 .843

95.3 3.1 1.6 12.5 87.5 89.0 1.6 6.3 3.1 85.9 14.1 28.1 71.9 95.3 4.7 (64)

10.875

2.563

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Opportunity, Environmental Characteristics and Crime

slips while half had fewer than 50 slips. Most lots have two entrances (M=2.391) and exits (M=2.360) and do not use tickets (3.1 percent) or swipe cards (1.6 percent) to regulate access. Most lots are not enclosed (87.5 percent) and only a few use rope (1.6 percent), fence (6.3 percent) or walls (3.1 percent) to keep vehicles enclosed. Most (95.3 percent) do not have a locked entrance. See Table 22. Residential Locations – Correlations Correlations among location variables in residential locations vary greatly. Type of parking and availability of alternate parking are significantly related and indicate a negative, moderate relationship (r=.470). This relationship indicates that as the type of parking becomes more private, there are fewer alternatives for parking. Locations with less private parking (lots as opposed to personal garages) have other options for parking (more street parking). Alternate parking and type of residence also show significance and also have a negative, moderate relationship (r=-.546). The negative relationship between alternate parking and type of residence indicates that those who live apartments have more alternatives for parking. Type of residence and type of street are significantly correlated with a positive, weak relationship (r=.272). The Location Index is significantly correlated with all variables except alternate parking and presence of a median, at a level of at least p<.05. There is significant correlation and positive and moderate relationship between the Location Index and covered alley in back of the residence (r=.335), type of street (.410), and pedestrian traffic in front (r=.477). The strongest relationship exists between the Watcher Index and automobile traffic in front (r=.578), type of residence (r=.619), and type of garage parking (r=.694). See Table 23. Residential Locations – Difference of Means Test An independent-samples t-test was conducted to determine the difference between repeat and single victimization in residential locations. A lower score on the Location Index indicates a fewer safe features. The level of significance was set at p<.05. A statistical significance was found t(66)= 3.804, p=.000 between locations with repeat victimizations (M = 4.853, S.D. = 1.288) and single victimization (M = 6.182, S.D. = 1.479). The null hypothesis was

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rejected; locations with repeat victimization score lower on the Locations Index than those locations that were victimized once. See Table 33. Table 23. Inter-item Pearson Correlations for Location in Residential Locations Selected for Sample in Lexington, KY Location 1 2 3 4 5 6 Characteristics -.470** .118 .596** .076 .218 1. Type of Garage/Parking -.327 -.546** .218 -.052 2. Alternate Parking .221 -.012 .084 3. Covered Alley in Back -.077 .089 4. Type of Residence .625** 5. Pedestrian Traffic (front) 6. Automobile Traffic (front) 7. Type of Street 8. Presence of Median 9. Location Index ** Correlation is significant at the .01 level (2-tailed) * Correlation is significant at the .05 level (2-tailed

7

8

.047

-.080

.694**

-.075

.177

-.184

-.036

.071

.335**

.272* -.119

.619**

-.009

.055

.477**

.102

-.221

.578**

-.084

.410**

9

.001

Commercial Locations - Correlations There is a significant correlation and a negative relationship between type of parking and ownership of lot (r=-.300), method of entry/exit (r=-.248), and type of enclosure (-.422). Private lots and garages often dictate the need for increased levels of security and methods of monitoring vehicles entering and exiting. Likewise, ownership (public or private) is significantly correlated to both number of entrances (r=.300) and number of exits (r=-.315). The negative relationship indicates that as the level of ownership becomes private, there are fewer exits. The strongest significant correlation exits between the

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number of entrances and the number of exits (r=.978) as almost all parking locations have equal numbers of each. See Table 24. The Location Index shows a significant relationship with a number of variables. Variables such as pedestrian access (r=.261), method of entry/exit (r=.344) and number of slips (r=.335) have a positive, weak relationship with the Location Index. The correlations of these variables are insightful as the relationship between where cars are stolen from and why they are selected unfolds. Whether the lot is enclosed and the type of enclosure are positively and moderately related to Location Index (r=.487 and r=.402 respectively). The strongest correlation with the Location Index is found between the number of entrances (r=.818) and the number of exits (r=.824). See Table 24. Commercial Locations – Difference of Means Test An independent-samples t-test was conducted to determine the difference between repeat and single victimization in commercial locations using the Location Index. A lower score on the Location Index indicates fewer safe features. The level of significance was set at p<.05. A statistical significance was found t(62)= 2.497, p=.015 between locations with repeat victimizations (M = 10.121, S.D. = 2.781) and single victimization (M = 11.677, S.D. = 2.139). The null hypothesis was rejected; locations with repeat victimization score lower on the Locations Index in commercial locations than those locations that were victimized once. See Table 33. Location Index for Full Model – Difference of Means Test The final independent-samples t-test was conducted using the Location Index to determine the difference between repeat and single victimization in all sample locations. Again, a lower score indicates a less safe location. The level of significance was set at p<.05. A statistical significance was found t(130)= 2.266, p=.025 between locations with repeat victimizations (M = 7.448, S.D. = 3.409) and single victimization (M = 8.777, S.D. = 3.326). The null hypothesis was rejected; locations with repeat victimization score lower on the Locations Index in all sample locations than those locations that were victimized once. See Table 33.

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Opportunity, Environmental Characteristics and Crime

Lighting Descriptions of the Lighting variables are presented in Table 25 and 26. These variables are also categorical but since they are utilized in an index are treated as continuous data. In residential areas, the Lighting Index is based on a scale of 0-3 with a higher score indicate more lighting. In residential locations, almost two-thirds of locations received a poor score for lighting (63.2 percent) compared to 46.8 percent of commercial locations that received a poor score. Because of the nature of commercial locations, lighting at night was also considered in the rear of commercial locations. The Lighting Index for commercial locations ranges from 2-7 with a higher score indicating better lighting. Lighting was generally worse in the back of the commercial location (23.4 percent was rated good) than in the front (29.7 percent was rated good). Residential locations have far fewer lights out (41.2 percent) than commercial locations (84.5 percent). See Tables 25 and 26. Table 25. Description of Lighting Variables in Residential Locations Selected for Sample in Lexington, KY Variables Lighting at Night Good Average Poor Lights Out Yes No Lighting Index

Valid % (N)

Mean

SD

30.9 5.9 63.2 41.2 58.8 (68)

1.265

1.167

Residential Locations – Correlations Lighting at night and lights missing were significantly correlated with the relationship being positive and weak in nature (r=.292). The Lighting Index is significantly correlated to both lighting at night and number of lights missing. The relationship between the index and these variables is positive and strong for number of lights missing (r=.656) and positive and very strong for lighting at night (r=.914). The amount and adequacy of lighting at night is related to the number of lights missing in the residential locations. See Table 27.

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Table 26. Description of Lighting Variables in Commercial Locations Selected for Sample in Lexington, KY Variables Lighting at Night Good Average Poor Lighting at Night Good Average Poor Lights Out Yes No Lighting Index

Valid % (N)

Mean

SD

3.705

1.843

(Front) 29.7 23.5 46.8 (Back) 23.4 21.9 54.7 84.5 15.5 (64)

Table 27. Inter-item Pearson Correlations for Lighting in Residential Locations Selected for Sample in Lexington, KY Lighting Characteristics

1

2

1. Lighting at Night .292* 2. Lights Missing 3. Lighting Index ** Correlation is significant at the .01 level (2-tailed) * Correlation is significant at the .05 level (2-tailed)

3 .914** .656**

Residential Locations – Difference of Means Test An independent-samples t-test was conducted to determine the difference between repeat and single victimization in residential locations using the Lighting Index. A higher score on the Lighting Index indicates better lighting. The level of significance was set at p<.05. A statistical significance was found t(66)= 4.475, p=.000 between locations with repeat victimizations (M =.706, S.D. = .719) and single victimization (M = 1.823, S.D. = 1.266). The null hypothesis was rejected; locations with repeat victimization score lower on the Lighting Index in residential locations than those locations that were victimized once. See Table 33.

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Opportunity, Environmental Characteristics and Crime

Commercial Locations – Correlations All Lighting variables have significant correlations in the commercial locations. Lighting in front and lighting in back show a positive, strong relationship (r=.835). A more moderate, yet still positive, relationship exists between the number of lights missing and both lighting in front of the location (r=.447) and lighting in the back of the location (r=.545). An almost perfect positive relationship exists between the Lighting Index and both lighting in front (r=.935) and lighting in back (r=.952). The magnitude of these two relationships indicates that the Lighting Index is based strongly on these two variables. There is also a positive, strong relationship between the Lighting Index and the number of lights missing (r=.657). See Table 28. Table 28. Inter-item Pearson Correlations for Lighting in Commercial Locations Selected for Sample in Lexington, KY Lighting Characteristics 1. Lighting at Night (Front) 2. Lighting at Night (Back) 3. Lights Missing

1

2 .835*

3

4

.447**

.935**

.545**

.952** .657**

4. Lighting Index ** Correlation is significant at the .01 level (2-tailed). * Correlation is significant at the .05 level (2-tailed).

Commercial Locations – Difference of Means Test An independent-samples t-test was conducted to determine the difference between repeat and single victimization in commercial locations using the Lighting Index. A higher score on the Lighting Index indicates better lighting. The level of significance was set at p<.05. A statistical significance was found t(62)= 2.587, p=.012 between locations with repeat victimizations (M =3.152, S.D. = 1.497) and single victimization (M = 4.294, S.D. = 2.012). The null hypothesis was rejected; locations with repeat victimization score lower

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on the Lighting Index in commercial locations than those locations that were victimized once. See Table 33. Lighting Index for Full Model – Difference of Mean Test The final independent-samples t-test was conducted using the Lighting Index to determine the difference between repeat and single victimization in all sample locations. Again, a higher score on the Index indicates better lighting. The level of significance was set at p<.05. A statistical significance was found t(130)= 3.323, p=.001 between locations with repeat victimizations (M = 1.910, S.D. = 1.692) and single victimization (M = 3.002, S.D. = 2.067). The null hypothesis was rejected; locations with repeat victimization score lower on the Lighting Index in all sample locations than those locations that were victimized once. See Table 33. Security Descriptions of the Security variables are presented in Table 29 and 30. These variables are also categorical but since they are utilized in an index are treated as continuous data. The Security index for residential areas ranges from 0-6; higher scores indicate more security. Residential areas most locations do not have security features. Security guards/dogs were present at 2.9 percent of residences, security systems were visible at 4.4 percent of residences and only 2.9 percent of residences indicated the name of the dweller on the outside. Slightly over 80 percent of residences has the address clearly visible from the street (80.9 percent) and 95.6 percent had no piled mail near the front door. Thirty-eight percent of residences had a tidy yard, 39.7 were rated somewhat tidy and 22.1 were not tidy. Remarkably, 27.9 percent of residences had an open door. See Table 29. The Security Index for commercial areas ranges from 0-5 with a higher score indicating more security. Commercial locations fared better in terms of security measures than residential locations. Roughly one-fourth of commercial locations had security signs or stickers (26.6 percent). Twenty percent had a security guard at the exit and 14.1 percent had security cameras. In few locations were gate locks (14.1 percent) and guards after hours (3.1 percent) present. About 10 percent of locations had the driver’s keys held by an attendant. See Table 30.

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Opportunity, Environmental Characteristics and Crime

Table 29. Description of Security Variables in Residential Locations Selected for Sample in Lexington, KY Variables Security Guards/Dogs Yes No Security System Visible Yes No More than one Garage Entrance Yes No Dweller’s Name on Home Yes No Address Visible from Street Yes No Piled Mail Visible Yes No Tidy Yard Yes Somewhat No Garage Door Open Yes No Security Index

Valid % (N)

Mean

SD

4.162

.990

2.9 97.1 4.4 95.6 4.4 95.6 2.9 97.1 80.9 19.1 4.4 95.6 38.2 39.7 22.1 27.9 72.1 (68)

Residential Locations - Correlations Few significant correlations outside of those associated with the Security Index were found. There was a significant correlation between visibility of security systems and piled mail. The relationship between these two variables is negative and weak to moderate (r=.303). Two variables, security system visibility and dweller’s name on home indicated a positive, weak relationship with the Security Index (r=.292 and r=.281 respectively). Significant correlations between the Security Index and piled mail (r=.327) and garage entrance (r=.436) proved positive and moderate in strength. The strength of the association between garage entrance and

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the Security Index indicated that more garage entrances are related to an increase in Security Index. Visible address (r=.567) and a tidy yard (r=.536) are the two variables most strongly related to the Security Index. Both variables indicate a strong correlation with the Security Index. See Table 31. Table 30. Description of Security Variables in Commercial Locations Selected for Sample in Lexington, KY Variables Security Signs/Stickers Yes No Security Guard at Exit Yes No Security Cameras Yes No Gate Locks After Hours Yes No Guards Present After Hours Yes No Keys Held by Attendant Yes No Security Index

Valid % (N)

Mean

SD

1.688

1.125

26.6 73.4 20.3 79.7 14.1 85.9 14.1 85.9 3.1 96.9 9.4 90.6 (64)

Residential Locations – Difference of Means Test An independent-samples t-test was conducted to determine the difference between repeat and single victimization in residential locations using the Security Index (higher scores indicate more security features). The level of significance was set at p<.05. A statistical significance was found t(66)= 2.408, p=.019 between locations with repeat victimizations (M = 3.882, S.D. = 1.142) and single victimization (M = 4.441, S.D. = .726). The null hypothesis was rejected; locations with repeat victimization score lower on the Security Index in residential locations than those locations that were victimized once. See Table 33.

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Opportunity, Environmental Characteristics and Crime

Table 31. Inter-item Pearson Correlations for Security in Residential Locations Selected for Sample in Lexington, KY Security 1 Characteristics 1. Security dog/guard 2. Security System Visible 3. Garage Entrance 4. Dweller’s Name Present 5. Visible Address 6. Piled Mail

2

3

4

5

6

7

8

9

-.037 .037 -.030 .080

.037 -.152 .108

.193

.046 -.037 .099

-.303* .143 .134

.292*

.037 .115

.303

.046 .185

.436**

.080

.037

.078 .108

.281*

.115

.156 .153

.567**

.140 .026

.327**

.089

.536** .653

7. Tidy Yard 8. Open Garage Door 9. Security Index ** Correlation is significant at the .01 level (2-tailed) * Correlation is significant at the .05 level (2-tailed

Commercial Locations - Correlations Unlike the residential correlations for the Security variables, many variables have significant correlations. Most notably, gates locked closing is significantly correlated and both positively and moderately related to presence of security signs/stickers (r=.367), security guard on location (r=.466), and security cameras on location (r=.483). There is an inverse relationship between the presence of security guards at the location and keys held by the attendant (r=-.371). At locations where there no security guards, the keys are more likely to be kept by the owner than held by an attendant. See Table 32. The Security Index was significantly correlated with all variables but keys held by attendant. Positive and moderate relationships are found between the Security Index and guards present after hours (r=.453), security guard at the location (r=.524), and security

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signs/stickers (r=.580). This relationship illustrates the salience of these variables and the Security Index. The strongest relationships, however, are demonstrated between the Security Index and security cameras (r=.677) and gates locked after closing (r=.838). These variables have strong, positive relationships with the Security Index See Table 32. Table 32. Inter-item Pearson Correlations for Security in Commercial Locations Selected for Sample in Lexington, KY Security Characteristics 1. Security signs Stickers 2. Security Guard at Location 3. Security Cameras

1

2

3

5

6

-.040 .266* .367** -.108

.072

.243

4

7 .580**

.466** .356** -.371** .524** .483** .186

-.024

.677**

4. Gates Lock .444 ** -.024 After Hours 5. Guards Present .058 After Hours 6. Keys Held By Attendant 7. Security Index ** Correlation is significant at the .01 level (2-tailed) * Correlation is significant at the .05 level (2-tailed)

.838** .453** .150

Commercial Locations – Difference of Means Test An independent-samples t-test was conducted to determine the difference between repeat and single victimization in commercial locations using the Security Index. A higher score on the Security Index indicates more security features are present at the location. The level of significance was set at p<.05. A statistical significance was not found t(62)= 1.736, p=.088 between locations with repeat victimizations (M = 1.455, S.D. = .971) and single victimization (M = 1.936, S.D. = 1.237) in commercial locations. The null hypothesis was retained. See Table 33.

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Opportunity, Environmental Characteristics and Crime

Security Index for Full Model – Difference of Means Test The final independent-samples t-test was conducted using the Security Index to determine the difference between repeat and single victimization in all sample locations. Again, a higher score on the Security Index indicates more security features at the location. The level of significance was set at p<.05. A statistical significance was found t(130)= 1.996, p=.048 between locations with repeat victimizations (M = 2.687, S.D. = 1.614) and single victimization (M = 3.246, S.D. = 1.606). The null hypothesis was rejected; locations with repeat victimization score lower on the Security Index in all sample locations than those locations that were victimized once. See Table 33. Table 33. T-test Results for Watcher and Activity Nodes Variables Comparing Repeat and Single Victimization Locations Selected for Sample in Lexington, KY Variables Watchers Residential Single Victimization Repeat Victimization Commercial Single Victimization Repeat Victimization Total Single Victimization Repeat Victimization Activity Nodes Residential Single Victimization Repeat Victimization Commercial Single Victimization Repeat Victimization Total Single Victimization Repeat Victimization Location Residential Single Victimization

t -2.559*

.382

-.672

-1.232

.287

-.235

3.804**

df 66

62

130

66

62

130

66

p

M

SD

2.544 3.309

1.047 1.393

3.409 3.694

2.983 2.974

3.093 3.358

2.246 2.300

.471 .691

.615 .844

2.387 2.242

2.124 1.904

1.385 1.455

1.800 1.651

6.182 4.853

1.479

.013

.704

.503

.222

.775

.815

.000

A Site-level Investigation of Auto Theft Variables

t

153 M

SD

.015 62 2.497** Repeat Victimization 11.677 Commercial 10.121 Single Victimization .025 130 2.266* Repeat Victimization 8.777 Total Single Victimization 7.448 Repeat Victimization .000 4.475** 66 Lighting Residential 1.823 Single Victimization .706 .012 62 2.587* Repeat Victimization Commercial 4.294 Single Victimization 3.152 .001 3.323** 130 Repeat Victimization Total 3.002 Single Victimization 1.910 Repeat Victimization .019 66 2.408* Security Residential 4.441 Single Victimization 3.882 .088 62 1.736 Repeat Victimization Commercial 1.936 Single Victimization 1.455 .048 130 1.996* Repeat Victimization Total 3.246 Single Victimization 2.687 Repeat Victimization ** Correlation is significant at the .001 level (2-tailed). * Correlation is significant at the .05 level (2-tailed).

df

p

1.288 2.139 2.781 3.326 3.409

1.266 .719 2.012 1.497 2.067 1.692

.726 1.142 1.237 .971 1.606 1.614

Review of Findings This chapter provides an analysis of W.A.L.L.S. variables and their relationship to situational features of both residential and commercial locations. This analysis indicates that lighting and locations are key features in determining target-selection factors for car thieves. Type of location and lighting are both significantly related to whether or not a car is stolen from a particular place. Repeat victimization locations had worse scores in all of the models for location and lighting than single victimization locations. Surprisingly, there is no correlation between the presence of activity nodes in a location and the presence of repeat or single

154

Opportunity, Environmental Characteristics and Crime

victimization. This is true for the Watcher variables in the commercial locations as well. Watcher variables in residential locations do indicate a relationship between “number of eyes on the street” and victimization. Higher scores on the Watcher variables indicate an absence of people watching the street. Repeat victimization locations scored higher than single victimization locations. With regard to security, the residential areas appeared to show a higher correlation between having security measures and reducing victimization. The commercial areas did not demonstrate the same relationship. When both residential and commercial areas are combined, there is a statistically significant relationship between security measures and reducing victimization.

CHAPTER 8

Discussion on the Relevance of the Environment on Auto Theft

COMMUNITY-LEVEL DISCUSSION Current literature has identified several types of locations to be criminogenic. Most notably, Brantingham and Brantingham (1993b) have addressed the concept of activity nodes as locations that create high activity. These crime generators are located in areas that intersect with the daily travel routes of offenders, as they make their way from home to work, school, shopping, or restaurants (Brantingham & Brantingham, 1993b). The pathways, or routes that offenders travel, also have considerable amounts of crime (Brantingham & Brantingham, 1993b). The community-level analysis has identified several types of locations that present auto theft opportunities, using the same rationale presented by Brantingham and Brantingham (1993a, 1993b). Beavon, Brantingham, and Brantingham (1994) suggest that streets and major roadways provide increased access and awareness of opportunities for offenders. Residential areas provide massive amounts of parking and high levels of street activity that increase opportunity for victimization. Parking facilities also provide a large number of targets, few capable guardians, and rarely utilize target hardening devices. Blocks with schools have experienced an increase in property crime, perhaps due to a concentration of unsupervised teens that are released from school at the same time. The types of locations discussed in this and the paragraph above have been used to create a base model for the opportunity structure. The relevance of several other variables, such apartments, fast food and bars, accommodations, and auto repair and parts shops, are also added to this model to determine their contribution to criminal opportunity.

155

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Opportunity, Environmental Characteristics and Crime

Were the Opportunity Structure Hypotheses Supported? A total of six models, the base model and five additional models were developed based on the hypotheses for this research project. One final model, the Alternative Model, was developed after the results of the hypotheses indicated that certain variables, parking facilities, schools, fast food restaurants, and accommodations, did not appear to significantly contribute to the opportunity structure for auto theft. Because the methodology indicates a visual inspection instead of a rigid, quantitative analysis, the hypotheses are not retained or rejected; but rather, the model is determined to either contribute or not contribute to the development of the opportunity structure. See Table 34 at the end of this section of the chapter for a Summary of Study Findings with regard to the community-level analysis. Auto Theft in Lexington, Kentucky All of the auto thefts that were committed in Lexington, Kentucky were entered into a database and geocoded onto the map of Lexington. A density was created to indicate areas that experienced the highest auto thefts between January 2000 and December 2001. According to this density, the downtown area experienced the highest auto theft victimization in the entire city. High auto theft was also reported along major travel arteries, particularly Broadway, Paris, and Bryan Station to the north east, a single hot spot on Richmond and Nicholasville to the south, and Versailles to the west. Model 1 (Base Model) The base model uses transportation networks (roadways and bus routes), parking lots, convenience stores and gas stations, and schools in order to estimate auto theft locations. This model indicates that the greatest opportunity for auto theft is spread almost entirely across the city of Lexington. The downtown area does indicate the largest hot opportunity area. However, other areas of hot opportunity seem to be disbursed throughout the city. Paris has three areas that are designated as having the highest opportunity; Nicholasville has two areas with Versailles and Richmond, indicating areas of highest opportunity in the same locations as those on the auto theft density map.

Relevance of the Environment on Auto Theft

157

At first glance this map looks nothing like the density map produced for all auto thefts. However, upon closer examination, the highest opportunity for auto theft does appear to be in the downtown area, which is consistent with the auto theft density map. The streets appear to be an important variable, as almost all of the areas of greatest opportunity are located on, or directly adjacent to, highly traveled roadways. This is also consistent with the map of auto theft densities. On the map for Model 1, several other areas appear to create opportunity for auto theft that is not mimicked by actual auto thefts. Upon examination, it appears that these areas are represented by schools and parking lots. It seems that schools and parking lots designate opportunities for auto theft that do not exist. Several sources indicate that the locations of schools have an impact on victimization. However, some of them indicate a relationship with auto theft that is influenced by other variables. Roncek and Lobosco (1983) found that the amount of use the block has is as important as how close it is located to a school; the more use a block receives, the more auto theft it reported. Perhaps this or another relationship exists in Lexington and is not being measured in this research. Roncek and Faggiani (1985) found an increase in crime in areas located within one block of public high schools. The distinction, in Lexington, between public and private school was not made in this study; nor was the distinction between elementary schools and high schools. Perhaps this contributed to the indication of auto theft opportunity where no auto theft occurred. It appears that the base model does contribute to an overall structure of auto theft opportunity. However, it is clear that certain variables included in the structure are more important than others. Upon visual inspection, street networks and both convenience stores and gas stations do contribute to the model of opportunity. Parking facilities and schools do not contribute as much to this model. In fact, these locations seem to estimate auto theft victimization in areas that are not indicated to be problematic. Model 2 (Base + Apartments) The addition of the apartment variables, in Model 2, indicates opportunity to be highest to the north east and east of the city-center. Two areas of highest auto theft opportunity are located at Paris and New Circle and on New Circle between Winchester and Richmond.

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Opportunity, Environmental Characteristics and Crime

Areas of high auto theft opportunity are also disbursed through the city with a couple dense areas in downtown and several along Nicholasville and Paris. Evidently driven by the locations of apartment complexes, several of the hot opportunity areas are located around New Circle and in the residential areas where many apartments are located. Areas of moderate and high opportunity also fall on Tates Creek, a mainly residential area. Upon comparison of this map to the density map for auto theft in Lexington, several patterns emerge. Street networks are still important, as all but one of high and highest opportunity areas are located on either major roads radiating from downtown or on New Circle. The area of high opportunity that does not traverse a major roadway is located in a neighborhood that borders both New Circle and Nicholasville. The largest area of high opportunity is located in the downtown area. This is consistent with the density of auto thefts as demonstrated in Figure 1. Similarly, the areas of opportunity at Versailles (near New Circle) and Nicholasville (near New Circle) contribute to the density of all auto thefts in Lexington, Kentucky, during the study period. Recent literature indicates that households in rental communities report more victimization than other households (Mukherjee & Carcach, 1998). This may be due to the lack of security devices provided for residents of rental communities by the apartment management. Because of the transient nature of these communities, few residents who rent will invest in purchasing and installing security devices. Mukherjee and Carcach (1998) did not mention the occurrence of auto theft, specifically, but discussed victimization rates with regard to household victimization. Despite this research, Groff (1998) and Bichler (2004) warn of the dangers of measuring repeat victimization. According to Groff (1998) areas that contain multiple units such as apartment buildings or public housing, have a greater population density which must be considered when calculating victimization rates per capita. Fortunately, in models of opportunity, this density is not only appropriate, but essential, to accurately measure the availability of targets for the offender. It appears that Model 2 contributes significantly to the opportunity structure. This model indicates a high opportunity in the downtown area, which is consistent with the auto theft reports. Model 2 also contributes by representing the hot opportunity on Versailles and Winchester, which are hot spots on the map of auto theft density. Two

Relevance of the Environment on Auto Theft

159

areas of opportunity on Richmond also add to the overall opportunity that exists in that area. As with the base model, Model 1, street networks are still important in this model as all hot spot areas cross or are very close to major roadways. Upon visual inspection, the apartment layer contributes to the overall opportunity structure. Model 3 (Base + Fast Food/Bars) Model 3 includes the base data plus the fast food and bars layer. This model indicates several areas of intense auto theft opportunity and fewer areas with low-moderate opportunity. The downtown area appears to contain the highest auto theft opportunity as two areas with highest opportunity are surrounded by a dense area of high auto theft opportunity. Four other locations appear to present high-highest opportunity, three of them at the intersections of New Circle at Broadway, Richmond, and Nicholasville. The fourth area of intense opportunity is on Tates Creek, past New Circle. This model indicates more opportunity in downtown and south, with little opportunity north and west of the city center. Upon comparing Model 3 to the density of all auto thefts, this model mimics the auto theft density of the downtown area quite well. It also contributes to the high opportunity areas on Nicholasville, Winchester, and Broadway. This model indicates areas of no-low opportunity consistent with the density of actual auto theft. The map of auto theft density indicates that Leestown, Old Frankfurt, and Harrodsburg have very little reported auto theft from 2000-2001. This is imitated in Model 3. Several areas of moderate opportunity also are similar on the two maps. Roncek and Bell (1981) found that grand theft and auto theft increased on blocks that have bars. Ford & Beveridge (2004) found that blocks with highly visible drug sales had three times as many fast food establishments as blocks with less visible drug sales. Other literature also indicates that crime, in general, is increased on blocks that contain bars (Rossmo & Fisher, 1993; Rossmo, 1994; Homel & Clarke, 1994; Block & Block, 1995). Brantingham and Brantingham (1982, 1993b) indicate that fast food restaurants are also crime generators, due to their hours of operation. Establishments that are open late at night, or all night, create greater opportunity for many types of crime.

160

Opportunity, Environmental Characteristics and Crime

It appears that Model 3 contributes significantly to the opportunity structure. However, upon examination of the model it appears that bars, not fast food locations, are responsible for this effect. This model indicates high opportunity in the downtown area (many bars are present and very few fast food locations), which is consistent with the auto theft reports. Model 3 also contributes by representing the hot opportunity on Broadway, Richmond, and Nicholasville (all due to the locations of bars), which are hot spots on the map of auto theft density. The absence of opportunity between downtown and New Circle is also apparent in this model and has not been noted elsewhere. As with all models, street networks appear to be an important factor here as many hot spots continue to cross, or are very close to, major roadways. Upon visual inspection, Model 3, the fast food and bars layer, contributes to the overall opportunity structure, mostly due to the bars. Model 4 (Base + Accommodations) Model 4 contains the base layer and the accommodations layer. Upon immediate examination it is clear that this model looks very different from the other models of auto theft opportunity. Model 4 indicates more than 10 areas with the highest opportunity for auto theft with the downtown area having low auto theft opportunity. Two areas of highest opportunity do exist in the city-center but they appear to be to the south-west and north-east of downtown. The areas of highest opportunity are located along the lines of transportation routes, but, again, appear to be spread over the entire city. Very few areas of lowmoderate opportunity are evident. Areas of high opportunity are visible surrounding the areas of highest opportunity, but very few are present on their own. Upon comparison of Model 4 to the density of all auto thefts, it is clear that this model does not offer the continuity that the density of all auto thefts conveys, or even the same pattern of opportunity that is evident in other models. In addition, several areas of opportunity indicated in Model 4 are not apparent in the density of all auto thefts. Namely, Model 4 indicates Newtown to be an area of intense auto theft opportunity; this is not indicated in the auto theft reports. Winchester is another area that does not fit the actual auto thefts. Model 4 designates virtually no auto theft opportunity along Winchester. In the map of auto theft density, the hottest hot spot stretches from downtown and along Winchester.

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Research that has been conducted regarding crime at hotels has varied. Huang, Kwag, and Streib (1998) found that auto theft was the second highest crime reported at hotels. Sherman (1989) suggests that locations with large numbers of targets are always more likely to experience crime. Hotels would be included in this description, as hotels and motels have a high number of suitable targets and movable items. Cook, Merlo, and McHugh (1993) have studied the effects of crime at hotels and have found that about half of the respondents indicated theft to be the greatest problem. Rice and Smith (2002) found that the presence of one hotel or motel on the block increased the risk of auto theft by 32 percent. While the literature indicates that hotels contribute significantly to crime, this research does not find hotels in Lexington to contribute to the opportunity model for auto theft. It appears that Model 4 does not contribute to an overall structure of auto theft opportunity. Upon visual inspection, street networks do appear to be important for criminal opportunity, but the locations of accommodations, themselves, do not appear to contribute to the opportunity structure. In fact, several locations seem to estimate auto theft victimization in areas that do not appear to have an auto theft problem. This variable was removed from the opportunity structure when the Alternate Model was created. Model 5 (Base Model + Auto Repair and Auto Parts Shops) Model 5 contains the base layer and the auto repair and auto parts shops layer. This model indicates an opportunity structure very similar to that of the density for auto theft. There are three areas that contain the highest criminal opportunity for auto theft. One area of highest auto theft opportunity is located in the downtown area, while another stretches from downtown to Winchester and New Circle. The final hot opportunity area is located between Harrodsburg and Nicholasville. The rest of the city has very low to moderate opportunity, with the exception of some hot areas on Richmond and between Richmond and Winchester. Many features appear similar when comparing Model 5 to the density for auto thefts. Primarily, the location of the downtown opportunity and the opportunity located east of downtown that extends north between Winchester and Bryan Station. The area of high opportunity on Richmond is also reflected in the density of auto theft in Lexington. The areas of low-moderate auto theft are also echoed in this

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model. One area that does not seem to mimic the auto theft density is an area of highest auto theft opportunity, between Harrodsburg and Nicholasville. During interviews with car thieves, Gant and Grabosky (2002) found that roughly 25 percent of cars where stolen for profit. Thieves also indicated that many of those stolen for profit were sold for the parts. La Vigne, Fluery, and Szakas (2000) used a distance decay model in two study areas to predict where chop shops would be located based on the stolen car. The researchers determined that in both studies the chop shop would be located less than six miles from the area where the car was found. This research indicates a direct relationship between auto repair shops and auto theft. It appears that Model 5 contributes significantly to the opportunity structure. This model indicates a high opportunity in the downtown area, which is consistent with the auto thefts reports. Model 5 also contributes by representing the hot opportunity on New Circle between Winchester and Bryan Station, which are hot spots on the map of auto theft density. The absence of opportunity between downtown and New Circle is also apparent in this model and has not been noted elsewhere. As with all models, street networks appear to be important as many hot spots continue to cross, or are very close to, major roadways. Model 5 appears to be the model with the closest resemblance to the overall opportunity structure. As recent research suggests, auto repair and parts shops offer a significant contribution to the opportunity structure for auto theft. Model 6 (Full Model) The full model combines all data from Models 1-5. This model appears to adequately approximate the density map for auto thefts in a number of areas. First, the concentration of hot opportunity in the downtown area is similar to that of auto theft in Lexington. The police department has indicated that their efforts have primarily been spent in the downtown area. The Full Model, Model 6, also indicates high opportunity for auto theft west of downtown, on Winchester and along New Circle between Winchester and Bryan Station. The density of auto theft indicates a hot spot along this area as well. Two other locations, at Broadway & New Circle and Nicholasville & New Circle, demonstrate hot spots on the density map of auto thefts, and are estimated in the Full Model. The downfall of Model 6 is that it does

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not share the intensity of the opportunity near downtown that is demonstrated in the auto theft map. Alternate Model The Alternate model has been presented in order to create a model that includes all relevant data to effectively estimate auto theft opportunity. To do this, the schools, parking lots, fast food restaurants, and accommodations layers are removed. The resulting model indicates a more concise representation of the hot opportunity areas in Lexington. The downtown area shows a high concentration of auto theft opportunity as does the area on New Circle between Winchester and Bryan Station. Other areas of opportunity are confirmed on Nicholasville, Richmond, and between Richmond & Winchester. Two hot spot locations, one on Broadway and one on Versailles, are not accounted for in this model. Despite these flaws, it appears that the Alternate Model provides the most accurate opportunity structure for auto theft. Summary of Community-level Discussion Many prediction models have been developed to calculate crime occurrence and patterns. Many more have identified locations that have crime-specific criminogenic characteristics. This research presents a model that effectively estimates the location of opportunity for auto theft in Lexington, Kentucky. A model such as this can achieve three tasks; it can assist police departments in crime prevention efforts, it can provide suggestions for cost-effective strategies to reduce crime, and it can focus patrol. By creating an opportunity structure, the police department can determine which locations are in need of crime prevention efforts. Specifically, they can survey the areas that appear to present the greatest opportunity and assess the crime prevention needs in these locations. Many of the locations require simple crime prevention procedures such as increase lighting, better signage, or increased surveillance or patrol. At approximately half of the repeat locations visited during the site-analysis, light bulbs were missing or broken. Replacing light bulbs is a very cost-effective way to increase lighting. Other locations, such as parking facilities and parking lots would benefit from signs indicating that the lot is being monitored. Locations

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experiencing a high volume of auto theft opportunity and repeat victimization would require directed patrol. This would be more expensive, but could be focused on locations that the Alternate Model indicates are at greatest risk, reducing the cost of unnecessary patrol. SITE-LEVEL DISCUSSION Many research projects have been conducted in an attempt to study and reduce the occurrence of crime. Several articles studied patterns of crime (Jacobs, 1961; Goldstein, 1979; Newman & Franck, 1980; Taylor & Gottfredson, 1986) before the term “hot spot” was used to describe these occurrences. Others found that locations with certain features were more likely to be targets than those without these features. Few studies, until recently, have integrated their findings with police technology. By using this technology to identify locations that present opportunity for auto theft but do not have auto theft, we can better understand the target-selection process of offenders. This contribution has been started, preliminarily, in the site-level analysis but must continue in future research. An in-depth examination of the relationship between opportunity and target selection will enable police to pinpoint the difference between locations that have repeat, single, and no victimization. These findings can be used to alter the environment to dramatically reduce crime. The study of any individual crime will not be complete until all circumstances that contributed to the crime are documented. Likewise, the reduction of any type of crime can not occur until the factors that caused it are discovered and removed. Simply studying crime does not change its incidence or prevalence. Research must not only be reactive in terms of studying crime that has already taken place, but must also be proactive in communicating and disseminating the results of crime analysis to the community. The results presented here would be of no value if they were shared with only the academic world and not with the community from which they were collected. Were the W.A.L.L.S. Hypotheses Supported? The site-level research included almost equal numbers of residential (68) and commercial (64) areas. The residential areas of Lexington have apartments, condominiums, and houses. Some neighborhoods support only one type of housing while others have a combination of

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styles. Many residential areas are dense with neighbors, corner stores, and parks while some have sprawling grounds and no neighbors for miles. The commercial area is experiencing revitalization; as much of the downtown area is being rebuilt. At the present time, the downtown area is mostly commercial with mixed-use in some locations. See Table 35 at the end of this chapter for a Summary of Study Findings for the W.A.L.L.S. Model. Watchers According to the three t-test results for Watchers (residential, commercial and both combined), a significant difference was found between repeat and single victimization locations in residential areas only. In residential areas, locations experiencing repeat victimization had a higher average score on the Watcher Index. A higher score on the Watcher Index indicates the absence of people watching or providing surveillance of the area. Locations that experienced repeat victimization had fewer people that could provide adequate surveillance to the location. This finding is consistent with previous research. Bennett and Wright (1984) found that surveillance and occupancy were important factors in the target selection process for burglary. When these are not present, the offender perceived the risk of burglary to be lessened (Bennett & Wright, 1984). Keister (2007) and Tseng, Duane, and Hadipriono (2004) also suggest that shrubbery and other brush can provide concealment. According to burglars who were interviewed by Brown and Bentley (1993), houses with neighbors who were perceived to react to a burglary would not be burglarized. Wilcox, Madensen, and Tillyer (2007) found target hardening strategies to work better when they were used in areas with higher levels of natural surveillance. Locations that experienced single victimization of auto theft in the two year period had significantly fewer watchers that locations experiencing more than one auto theft. Commercial locations did not follow this pattern. Pearson’s Correlations indicate a positive, moderate relationship between business and pedestrian view of the location. However, the t-test results for the commercial locations indicated a significant difference did not exist between repeat and single victimization locations. LaVigne (1994) found that in commercial areas, such as gas stations, a lack of natural surveillance allowed many occurrences of gasoline drive-offs to take place. Jacobson (1999) found that natural

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surveillance has little effect on drug dealers when they believe that citizens will not take steps to prevent or report drug use. This may explain the lack of significance in commercial areas as compared to those in residential areas. In Welsh and Farrington’s (2004) metaanalysis, they concluded that CCTV and lighting are more effective than either alone. Furthermore, surveillance, natural or otherwise, has no deterrent effect unless the offender is aware of that surveillance (Buerger, Cohn, & Petrosino, 1995). Perhaps surveillance in commercial locations in Lexington goes unnoticed by auto thieves. When both residential and commercial locations were combined, the resulting model and t-test were not significant. Recent research has indicated mixed results with regard to suggestions for increasing surveillance. Sampson and Wooldredge (1987) found that living in an area with low guardianship and surveillance increases victimization. Jacobs (1961) found that mixed land use would promote safer streets by giving people a reason to get outside, hence, providing more natural surveillance by those who had a stake in the neighborhood. However, Roncek and Bell (1981) found that “each additional bar on a residential block increases the incidence of index crimes by approximately four crimes” (p. 44) and Poister (1996) found that areas that introduced transit lines suffered a slight increase in property crime. One thing is clear, the quality of the Watcher is more important than the quantity of Watchers; those with direct ties to the community are more likely to prevent and report crimes than those without such ties. Any addition to a new area, residential, commercial, or mixed-use, must provide adequate surveillance to offset an increase in persons without ties to the community. Activity Nodes According to the three t-test results for Activity Nodes (residential, commercial, and both combined), a significant difference was not found between repeat and single victimization locations in any of the models. This is an unanticipated result. Much of the crime prevention and pattern analysis literature has indicated that activity nodes present a multitude of opportunities for offenders. McCord et al. (2007) even found the residents who live near crime-generating locations view their neighborhoods as more crime ridden and Eck et al. (2007) coined some of these crime-generating locations “risky facilities” because of the crime they create. Furthermore, many studies have pinpointed activity

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nodes, such as bars, as a great source of crime and violence. Rossmo (1995) studied bars and the effects of simultaneous closing times. He suggests that when bars dump patrons on the streets at closing time in drunk and agitated states, there is a greater chance of disturbances (Rossmo, 1995). Graham et al. (1980) suggest that some offenders use the bar as their “home base”; offenders may start at the bar, commit a crime, and return to the bar. They may also meet potential victims in their travels to and from the bar. Other scholars (Roncek & Bell, 1981) simply believe that the presence of bars or alcohol outlets on a block increase the amount of crime on that block when compared to blocks that don’t have bars (Britt et al., 2005; McCord & Ratcliffe, 2007). Another critical activity node, convenience stores, was found to contribute to robberies if they had fewer than two clerks on duty, carried large amounts of cash, and were situated in locations that provided poor natural surveillance (Hunter & Jeffery, 1992). Bellamy (1996) suggested that having two or more clerks on duty may significantly reduce repeat victimization. Chakraborti et al. (2002) found that gas stations are easy targets for crime because they are open all evening and are a predominantly cash business. In many locations, convenience stores and gas stations are located at the same address. Several other types of activity nodes have contributed to an extensive body of literature on the subject. The locations of schools contribute to crime patterns. Residential areas with schools located within one block have an increase in crime compared to locations without a school on the block (Roncek & Faggiani, 1985; Roncek & Lobosco, 1983). Hotels, although not measured in this study, have been reported as activity nodes responsible for increased theft and auto theft. Huang et al. (1998) found that hotels that are located near transportation hubs offer an increased opportunity to offenders and often report more crime. Engstad (1975) and Smith and Rice (2002) found that parking lots connected to both hotels and shopping centers had more offenses than areas without these facilities. Auto theft, especially, increased in both types of these massive parking lots. Brantingham and Brantingham (1993b, 1999) launched a wide discussion of activity nodes by mentioning the importance of not only the nodes themselves, but the paths used to get to those nodes, and the edges that are formed because of them. This research employed several of the concepts developed by Brantingham and Brantingham (1993b) with regard to the construction of the site-level measurements, and

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Brantingham and Brantingham (1999) with regard to the development of the community-level methodology regarding hot spots. Because of the extensive literature used as a basis for this site-level variable it is disconcerting that statistical significance is not achieved. There are a few reasons why this may have occurred. First, the common placement of convenience stores and gas stations together may not have provided enough variation between locations that had one and locations that had both. Second, the difference in residential areas may be much more dramatic in Lexington, Kentucky than in other cities. In Lexington, some residential areas are extremely dense with hundreds of apartments in one block while another residence may not see a neighboring house for miles. Perhaps these vast differences led to a non-significant result. Further research should be conducted to re-test these findings. Location According to the three t-test results for Location (residential, commercial, and both combined), a significant difference was found between repeat and single victimization locations in residential areas, commercial areas and when these locations were combined. In residential areas, locations experiencing a single victimization had a higher average score on the Location Index. A higher score on this index indicates a less dangerous parking site. Locations that each experienced a single auto theft victimization had more safety features for auto theft. In residential locations, these features include driveway and garage parking, a lack of access to the car from the rear of the house, heavy or consistent pedestrian traffic that has a clear view of the parking, heavy or consistent car traffic that has a clear view of the parking, and the presence of 2-way streets and no median dividing the travel routes. This finding is consistent with previous research that indicates that burglars use symbolic, as well as actual, barriers when selecting targets for residential burglary (Brown, 1985). Burglars also favored locations where the risk of neighbors who would call the police if they saw suspicious activity was low (Brown & Bentley, 1993) and locations that provided greater cover, like cul-de-sacs (Buck et al., 1993). Townsley et al. (2003) found that houses with the same layout as recently burgled homes had an increased risk of victimization due to offender familiarity. With regard to residential auto theft, cars parked

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in a garage were significantly safer than cars parked in a driveway and overwhelmingly safer than those parked on the street (Clarke & Mayhew, 1994). Kuo and Sullivan (2001) found that landscaping can alter the ability of residents to use the neighborhood and provide natural surveillance. In commercial areas, locations experiencing a single victimization also had a higher average score on the Location Index. As with residential areas, a higher score on this index indicates a less dangerous parking site. Locations that experienced a single auto theft victimization had more safety features for auto theft. In commercial locations, these features include lots that are closed at night, those privately owned, those with only one level, those with fewer than fifty parking slips, those without covered access in the back and those requiring swipe card access. These findings are also consistent with situational crime prevention research. Camp (1968) found that the physical layout of the back of the location was an important consideration in target selection. More recently, Graham (2001) indicated that locations that were closer to a highway provided better escape routes and were considered in the decision-making process. Escape routes were also studied by Webb et al. (1992) with regard to auto theft. They found that cars were stolen less often from lots that had manned exits than those that did not. Nichols (1980) also discussed landscape, albeit in a broader sense, as a factor in target selection. Perkins et al. (1992) discussed the use of territorial cues, such as plantings, to deter offenders from committing crimes on certain streets. Locations suffering repeat victimization have significantly more features that encourage auto theft. Some of these features, particularly in the residential area, are more difficult to fix. Cars parked in attached garages are safer than those that are not. Unfortunately, most residents that do not have a garage do not have room to add one. Residents who live in apartments or condominiums definitely do not have this option. Houses or apartments located near highway on-ramps can do nothing in order to change their level of risk, short of moving. In commercial locations, drivers can choose lots or garages with more surveillance over those with less. In general, they can be more aware of where they park and more easily avoid locations with increased opportunity for auto theft, than those in residential areas where parking alternatives are fewer.

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Lighting According to the three t-test results for Lighting (residential, commercial, and both combined), a significant difference was found between repeat and single victimization locations in residential areas, commercial areas, and when these locations were combined. In residential areas, locations experiencing a single victimization had a higher average score on the Lighting Index. A higher score on this index indicates an area with better lighting. Locations that each experienced repeat auto theft victimization had less lighting than those experiencing single thefts. In residential locations, lighting was measured with one reading, closest to where the car would be located. In commercial locations, a lighting measurement was taken in both the front and the back since many locations would be approached from the rear when offenders were involved in the decision-making process. In both cases, the level of lighting was correlated with the number of missing lights in the area. In commercial locations, lighting in the front and back were highly correlated. Lighting has been a significant factor in crime prevention literature with regard to many types of crime, and especially for auto theft (Keister, 2007). Bopp (1982) discussed the importance of lighting in crimes related to personal injury. He was the first to indicate that proper illumination, not just any illumination, is necessary (Bopp, 1982). Lighting is one of the easier crime prevention techniques to maintain and is fairly easy to install (Tseng et al., 2004; Willis et al., 2005). In 1985, Patterson researched the fear of the elderly with regard to public transportation. Elderly people indicated that one of their concerns related to public transportation was inadequate lighting (Patterson, 1985). Other research regarding lighting and transportation was conducted by La Vigne (1997). She found that the best lighting was recessed lighting that created no shadows and was reflective. Metro stops without such lighting would be more susceptible to crime (La Vigne, 1997). Perhaps the most accurate measure of lighting was conducted by Painter (1994) who studied the effects of lighting on street crime by measuring lux, the intensity of light given off by a source. Painter’s research also suggests that quality, not just quantity, of lighting is important (1994). Lighting has also been studied with regard to situational crime prevention for Automatic Teller Machines (Scott, 2002) and identified as one of the most important Crime Prevention Through Environmental Design (CPTED) tactics that exists

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(Smith, 1996). Ramsay (1991) found that increased lighting reduced citizen’s fear of crime but did not necessarily have a deterrent effect on offenders. In this study, lighting appears to be one of the most significant W.A.L.L.S. variables. Lighting in repeat locations was much worse than locations with single victimizations. Lighting, unlike Location, can be increased fairly inexpensively. Because of this, it is measure that can be taken in both residential and commercial areas. When lighting is added or improved, the quality of lighting should be considered as well as the sheer number of lights in any given area. Night lighting is especially important in crime prevention, and should be strategically placed at locations that are the most vulnerable. Security According to the three t-test results for Security (residential, commercial, and both combined), a significant difference was found between repeat and single victimization locations in residential areas and when both were combined, but not in commercial areas. In residential areas, locations experiencing repeat victimization had a lower average score on the Security Index. A lower score on the Security Index indicates the absence of security features that may prevent victimization. Locations that experienced repeat victimization had fewer security features that deterred offenders from the location. This finding is also consistent with previous research. Weisel (2002) found that security signs and stickers deterred some burglars. Light, Nee, and Ingram (1993) found that the location of the car and surrounding security features had an effect on the offender’s target selection practices. Commercial locations do not follow this pattern. Pearson’s Correlations indicate a positive, moderate relationship between the three most visible security measures, security signs/stickers, security guards, and security cameras. However, the t-test results for the commercial locations indicated a significant difference did not exist between repeat and single victimization locations. Beavon et al. (1994) found that offenders take cues from the environment. Perhaps in commercial locations security cues are not as prevalent as those in residential areas. In areas where the perception of territorial concern is less, offenders are more likely to commit crimes (Brown & Bentley, 1983). This may explain the significant findings for residential

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locations but not commercial locations. As discussed with regard to Watchers, Security, such as surveillance, has no deterrent effect unless the offender is aware of it (Buerger, Cohn, & Petrosino, 1995). When both residential and commercial locations were combined, the resulting model and t-test were statistically significant. However, recent research has indicated mixed results with regard to suggestions for increasing security. Some offenders are not deterred by car alarms. Fleming et al. (1994) found that only “three-quarters of the offenders said they avoid cars equipped with alarms and flee if an alarm goes off while they are attempting to steal a car” (p. 63). But, other research has found that security cameras and signs located at and around the parking areas are significantly related to a decrease in car theft (Webb et al., 1992). Summary of Site-level Discussion Current research has focused on the importance of the identification of hot spots and other locations that experience disproportionate amounts of crime. This research has identified several environmental characteristics that are present at repeat locations and missing from single victimization locations. Several of the variables presented in the W.A.L.L.S. model help to differentiate between repeat and single auto theft locations, and will better enable police to prevent crime. Increasing watchers, providing surveillance to locations, improving lighting, and increasing security have all proved to be present at locations with fewer victimizations. While none of these variables provide revolutionary information, the recognition of these variables in conjunction with site-analysis provide tangible data that the police department can use to identify crime-specific environmental characteristics. If police officers are able to collect some of these variables while taking the police report, they may be able to identify some site-level characteristics with very little effort, time, or money. By collecting these variables at the time of the crime, they would also reduce the ambiguity and time delay that is created when researchers must wait for reports and data to be entered and shared. Two very important variables that must be considered for future research are activity nodes and lighting. This project assessed activity nodes that were located in both residential and commercial areas. There were no significant findings with regard to repeat and single

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victimization locations, regardless of land use. This is an unanticipated finding since the effects of activity nodes on crime is well documented. Further research must pursue these findings and explore other measurements to verify the lack of significance found herein. Lighting was studied here and found to be significantly related to auto theft. Locations with less lighting had more auto theft. However, the method with which the lighting data were collected was not as scientific as technology will now permit. Future research should collect data on lighting using either a radiometer or photometer. Radiometers provide calibrated measurement of the amount of light power. Photometers deliver not only a calibrated measurement, but also measure the contribution of light at each wavelength to gauge efficiency. By using one of these instruments, the amount and quality of lighting can be recorded more accurately. Despite these shortcomings, this research project has provided a succinct set of environmental variables that can be measured in other police departments to identify and reduce auto theft. With very little effort, the model presented by the W.A.L.L.S. variables for auto theft can be adjusted to recognize environmental characteristics pertinent to other types of crime. This model serves as a foundation for crime analysts; they can use the W.A.L.L.S. variables to estimate which locations will experience repeat victimization after a single victimization has occurred. Once these locations have been pinpointed, situational crime prevention techniques can be used to prevent future victimization. As residential and commercial areas expand, they must look for cheap and effective ways to prevent and reduce crime. Some locations will always have greater opportunity for auto theft. It will not be possible to move entire neighborhoods away from highway entrance ramps. However, future city planners can stop building communities in these locations without providing adequate security. Smaller businesses such as convenience stores and gas stations can be located further from these highways, but will surely lose business. Despite the inability to change some forms of opportunity, there are several ways to reduce victimization without much expense. Measures such as lighting and security signs are economical and seem to have deterred criminals from committing many types of crime, including auto theft. Bright lighting is more effective than dim lighting; and can be replaced cheaply and last longer. Often, increasing the quality of lighting does not require replacing entire structure, but only swapping the bulb.

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An increase in natural surveillance does not necessarily require a large increase in the volume of Watchers, but rather clearer sightlines and fewer obstructions. By cutting bushes and trimming shrubs, a residence or business may encourage people to walk or gather on a block that they may have previously avoided. These measures, like lighting and signs, are also fairly inexpensive. The key is that any renovation in land-use must provide adequate natural or mechanical surveillance to offset a potential influx of strangers that do not have ties to the community and will not assist residents and police in meaningful crime prevention.

Table 34. Summary of Study Findings Pertaining to Community-level Variables Collected in Lexington, KY Model

Research Hypothesis

Findings

Model 1 (base)3

The base model is less likely to predict opportunities for auto theft than any of the other models.

Contributes

Model 2 (base + apartments)

The components of the base model and apartments will be a better predictor of opportunities for auto theft than the base model alone.

Contributes

Model 3 (base + fast food/bars)

The components of the base model and fast food and bars will be a better predictor of opportunities for auto theft than the base model alone.

Contributes

Model 4 (base + accommodations)

The components of the base model and accommodations will be a better predictor of opportunities for auto theft than the base model alone.

------------

Model 5 (base + auto repair/parts)

The components of the base model and auto repair/auto parts will be a better predictor of opportunities for theft than the base model alone.

Contributes

Model 6 (full)4

The full model will be a better predictor than the base model alone.

Contributes

Alternate Model

Superior model

3

The base model includes street layout, well-traveled roadways, government subsidized housing, parking lots, convenience stores, gas stations, transportation hubs, and schools. 4 The full model includes base model plus apartments, fast food, bars, accommodations, and auto repair and auto parts shops.

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Table 35. Summary of Study Findings Pertaining to W.A.L.L.S. Variables Collected in Lexington, KY Variables

Research Hypothesis

Watchers

Locations with poor surveillance/guardianship will have more auto theft than those with good surveillability.

Support-residential model

Findings

Activity Nodes

Autos parked in locations with no activity nodes in the area will have a greater likelihood of motor vehicle theft than those parked in areas with activity nodes.

Unsupported

Location

Locations that have landscape and design features that provide cover for offenders will have more auto theft than those without these features.

Support-residential, commercial, full model

Lighting

Locations with poor lighting will have more auto thefts than those with good lighting.

Support-residential, commercial, full model

Security

Locations that have less security and cues indicating security will have more auto theft than those with more security or more security cues

Support-residential; full model

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CHAPTER 9

Discussion on the Limitations to Studying the Effects of the Environment on Auto Theft

INTRODUCTION Several issues present potential threats to the reliability and validity of this study. Throughout the paper, many issues have been discussed and they are further elaborated upon in this chapter. At the forefront are the problems associated with selecting any city (especially Lexington) for the example of auto theft. Population, location, and city layout all affect the type and magnitude of the auto theft problem. After the discussion of city-selection, the subject will move to the concerns related to the validity of the community-level data; issues pertaining to statistical considerations and opportunity classifications are presented. Finally, the sample selection for the site-level analysis is discussed. Though careful consideration and deliberate steps were taken during sample selection, several locations could not be found during data collection and those that were located are subject to several types of measurement error. CITY SELECTION The city of Lexington was selected for this project for two reasons. First, the researcher was granted access to police department records in Lexington, and second, the police department was concerned about auto theft and interested in finding some ways to reduce the problem. The city was not chosen because the results of the analysis would provide extensive generalization to other locations suffering from auto theft. Lexington is a rather small city, with a population of 260,512, according to the 2000 census. Many cities across the United States that experience problems with auto theft are much larger or experience a 177

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different type of auto theft, namely, theft related to the transport of exotic vehicles via ports and waterways. Lexington does not experience this type of auto theft problem. Despite these considerations, Lexington was chosen because cities of similar size have a need for practical and inexpensive solutions to persistent, though not urgent, problems. Another danger of choosing to study auto theft in Lexington (or any other city) is the availability of data with which to conduct the analyses. The first problem relates to the length of time required to receive data. It takes almost a full year after the crime is committed and the report is written for the data themselves to be released in digital files. The means that by the time the data enter the hands of the researcher, usually a year has passed. Data sorting, organizing, and cleaning may take just as long. Since two years of data were required to eliminate some problems associated with the time-window effect, the data contained herein may seem somewhat dated. Furthermore, this has a major impact on the ability of the researcher to capture characteristics of the crime in an ever-changing environment. The site visits were conducted approximately two years after the crimes had occurred. Environmental characteristics certainly could have changed during that time period and may have a substantial impact on the interpretation of causality suggested herein. The second difficulty is that the data has been collected by someone other than the researcher and done so for a purpose other than pure research. The analysis of secondary data is mandatory in research projects concerning crimes that were previously committed. This study has tried to minimize this problem by combining both primary and secondary data collection. At the site-level, police reports were only used to locate addresses experiencing auto theft and these same reports were used to develop a density map of all auto thefts that was used in the community-level research. The collection of all data for the W.A.L.L.S. characteristics in the site-level research and all layers of the community analysis were collected as primary data, for the sole purpose of this research. By doing so, this research project has both reproduced and verified the data collected by a secondary source. The third complication with the models produced in this study is that they are only significant when applied to auto theft. The crimespecific nature of the model adds significantly to its ability to estimate locations where auto theft may be high. However, this same quality also limits the direct use of this opportunity structure to estimate locations for other types of crime. Despite this inconvenience, the

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opportunity structure can be adjusted to fit other crimes by substituting auto-theft related layers with those related to the crime in question. COMMUNITY-LEVEL ANALYSIS Several limitations are also evident at the community level. Of primary concern is the process wherein the layers are created. Several intricacies of the method will be discussed. The second issue is the specificity and qualitative issues surrounding the model. The crimespecific concern was discussed in the site-level limitations but the issue warrants further discussion here. While it is the belief that the nonstatistical nature of the model is of utmost importance, reliability and validity must be addressed. In order to conduct the community-level analysis, data for the layers were collected and compiled from various sources. Street layout, well-traveled roadways and transportation hubs were provided by Lexington-Fayette Urban County Government. An extensive internet and phonebook search located the addresses of schools, gas stations, convenience stores, parking facilities, government subsidized housing, apartments, fast food restaurants, bars, hotels, motels, auto repair, and auto parts stores. These addresses were confirmed with phone calls to the establishment to verify the address and other necessary information. Though this search was methodical, the possibility exists that some locations were not included in the dataset. This could be the result of a new business that was not yet listed or a business that was closed between the date of the crime and the data collection. Once these layers were collected and entered into the mapping software, all locations were matched using the address given in the phone book or found during the internet search. If a match could not be obtained, the business was called and proper address was substituted; if the business was closed or could not be reached, it was removed from the database. It is possible that some businesses were not included in the dataset, or were removed, during this process. When the data were converted to files in the mapping software, some layers had disproportionately fewer points than others. For example, there are fewer than ten large parking garages in Lexington, Kentucky but over 50 schools and more than 200 apartment complexes. Since the opportunity structures were based on additive models, layers with fewer points (like parking facilities) may have been underrepresented when the densities were created; and, subsequently, summed to form the opportunity structure. Though this creates no real

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concern, in and of itself, it may help to explain some of the discrepancy between the density of the auto theft reports and the opportunity structure. Related to the previous caveat is one regarding the selection of the number of classifications used for the scale in both the density of all auto thefts (Figure 1) and the densities for all models of the opportunity structure (Figures 2-8). A scale of five increments was selected for auto theft density: no auto theft, low auto theft, moderate auto theft, high auto theft, highest auto theft. The following scale was used for the models: no opportunity, low opportunity, moderate opportunity, high opportunity, and highest opportunity. This 5-point scale was used to illustrate areas of both high and highest auto theft and high and highest opportunity. A scale with these distinctions enables readers to identify areas of greatest concern (i.e. those with both high and highest scores) while distinguishing locations of critical importance (i.e. those with highest scores). Natural breaks classification was used due to its ability to sustain the heterogeneity between classifications and because it is the most common classification used by crime analysts. The last concern involving the data layers is the erroneous assumption that every spot on the opportunity structure that indicates criminal opportunity has equal opportunity for crime. When the addresses are mapped and densities are created, these densities may cover several blocks. Despite their shape, there is not the same chance of an auto theft occurring in a parking lot as one occurring inside a supermarket located 5 feet away. In fact, this scenario is virtually impossible. Nevertheless, the densities that are created herein indicate that all areas symbolized by a particular shade have the same opportunity for auto theft as other locations shaded similarly. Clearly, this is unrealistic. The remaining limitations relate to the analysis of the densities following their construction. First, the community-level analysis may be less precise than desired. Even though the unit of analysis for this part of the research is the community; the models have been presented in such a way that indicates the importance of the city as a whole. This is done purposefully because policing responsibilities are a city-wide concern. Though there are specific units that focus on residential or commercial problems, the resources are shared by the entire police force. This is important in Lexington since the police department is responsible for the entire county of Lexington-Fayette, not just the city of Lexington, itself. All discussion of locations experiencing varying levels of opportunity should always be conducted at the communitylevel first, and then reviewed at the city-level.

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Second, the research study was designed with the intention of using only the crime of auto theft as a model. This limits the generalizability of the findings to auto theft only. The models suggested here provide an adequate framework for estimation of auto theft. That being said, the models could be adjusted, rather easily, by substituting variables that are related to auto theft with variables that are related to another crime. For example, in a model concerning residential burglary, variables such as “location of the safe” or “whether or not the front door is locked” may replace “location of car” and “presence of garage”. With a thorough literature review and knowledge of the crime, an environmental model could be developed for other situational crimes. Finally, the qualitative nature of the analysis demands discussion. There are two reasons why a qualitative design was used for the community analysis. First, the intention is to use the community analysis in conjunction with the site analysis to enhance the utility of the W.A.L.L.S. variables. The second purpose is to provide police and crime analysts with a model to guide auto theft analysis. This could not be accomplished with a highly statistical or technical model that would require extensive training and valuable time. Instead, a practical model was developed that requires minimal updating and sufficiently estimates crime location and characteristics. Crime analysts can input recent crimes and locate variables that may have an effect on repeat victimization. A more advanced statistical model is not necessary to do this. SITE-LEVEL ANALYSIS In order to conduct the site-level analysis, 75 locations suffering repeat auto theft were randomly selected from a list of reported auto thefts that were recorded as suffering more than one auto theft victimization during the two year period. Because these locations were not selected at the discretion of the researcher, it was impossible to know that some of these addresses would not be able to be located during the site visits. Furthermore, despite the fact that single victimization locations were matched, by the researcher, to each repeat location, several of these locations were unable to be located. This reduced the total sample size from 150 to 132 cases. Whenever a sample is not selected completely by random assignment, there is a concern about the bias that may have entered the selection process. It would be remiss to let that pass without remark. In order to maintain the integrity and purpose of the research project it

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was necessary to match repeat and single victimization locations on as many factors as possible. Since the single locations were matched to the repeats based on proximity, zoning, building structure, and target density, one can assume that the difference in their victimization is due to other environmental characteristics that were measured by the W.A.L.L.S. variables. If this matching did not occur, a large parking facility may have been randomly matched to a small lot. If this were to occur, it would be difficult to determine if the sheer number of available targets was a factor, or if other environmental cues played a role in the offender’s decision-making process. For this reason it was salient to reduce the validity of the sample selection process in order to increase the validity of the measurements. Several potential problems occurred during the actual data collection process. In some cases, when the researcher was driving to the address, the address could not be located. This means that the address was incorrectly recorded by the police or had since been destroyed. In locations where the physical address was clearly not an area where a vehicle could park (train track, woods, inside of a cemetery) the location was removed from the dataset. In situations where it appeared that the address was recorded incorrectly but the auto theft was likely to have occurred at the location (the address on the report was 100 Main Street, Suite 111 but the location was 100 Main Street, Suite 1-110) the location was coded and remained in the dataset. The rationale here is that if all suites share the same parking lot, the suite number is not related to where the car was parked before it was stolen. At some locations the instruments had to be recorded quickly and/or the researcher had to memorize features and record the data at a later time. These situations arose in areas that were considered unsafe and/or in areas where the residents or business owners saw the researcher and were suspicious about the data collection. When this occurred the researcher would record as much information as possible and drive to a location where the rest of the instrument could be completed. Photographs were also taken at most locations in order to document the site and to provide clarity during the coding process. Even under the best circumstances, it was difficult to reduce variables down to categories at some locations. This was especially true with the “lighting” variable and the “tidy yard” variable. Categorizing lighting as poor, moderate, or good was difficult. To offset this problem, information was also collected on the number of missing lights in the area. These two variables were combined to form the Lighting Index. The “tidy yard” variable was more difficult. In

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situations where the tidiness could not easily be determined, categorization was postponed until photos could be shared with other researchers and a more accurate assessment of tidiness could be made. Often researchers are criticized when their research efforts become “part of the problem”. In one situation, it was required that the researcher explain to a resident the purpose of the photos and instrument to avoid a panicked phone call to the police department. At another location, the researcher saw a seemingly angry resident approaching the research vehicle as it pulled away. An attempt was made to be discrete in order to minimize the impact of the research on the community. When necessary, the research agenda was shared and residents were encouraged to call the police department to verify the information. The final two dilemmas with regard to the site-level analysis surround the locations themselves. The type of parking was noted for each location. However, it is impossible to know the exact location of the parked car before it was stolen. A location noted as having a driveway does not necessarily indicate that the driveway is the location where the vehicle was parked before it was stolen. In order to offset this problem, another variable was added to measure the availability of alternate parking. However, this remains a major caveat of the research study. The final limitation related to the site-analysis is the time elapsed between the commission of the theft and the site-survey. As mentioned previously, there was likely a two year span between these events. It is possible that the environment naturally changed during that time. It is also possible that the environment was changed in response to the crime. Either way, there is a possibility that the locations that were surveyed did not have the same W.A.L.L.S. characteristics prior to and/or during the time period when the car was stolen. Unfortunately, there is no way to adjust this timing in the current research study. REVIEW OF LIMITATIONS Though several limitations involving city selection, sample selection, and data collection and analysis are presented in this chapter, the research is sound and the dataset is significantly large enough to provide adequate quantitative and qualitative analyses. Despite problems with generalizability and secondary data analysis, suggestions have been offered to practitioners about the ways in which models could be defined to produce significant results using other types of crime. Secondary data has been combined with primary data collection

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in order to limit issues of reliability and problems associated with data collected for a purpose other than that of the research. Concerns regarding measurement have been verified by photographs and outside sources to ensure accuracy. Several precautions and measures have been taken to minimize these difficulties and provide a consistently reliable and valid project.

CHAPTER 10

Policy Implications: Studying the Effects of the Environment on Crime

LESSONS The W.A.L.L.S. variables have contributed significantly to the study of environmental criminology. In the past, watchers, activity nodes, location, lighting, and security have all been found to be related to victimization in some way. It seems that, with regard to auto theft, some of these variables appear to be more strongly related than others. In this study, watchers, or people who provide surveillance, are important for the reduction of crime in residential situations. In Lexington, there seems to be a communal importance of caring for neighbors. This was especially true in areas that were mainly residential instead of those with mixed-use. Citizens have a tremendous sense of community. This seems to have people walking, biking, and gathering outdoors which inadvertently contributes to increased surveillance. This increase in surveillance also enables some locations that would be considered at higher risk for auto theft, to remain crime free. In addition to a high volume of pedestrian traffic, this research shows that adequate parking structures also help to reduce crime in Lexington. Many of these locations are made even safer by increasing lighting in strategic locations and removing obstacles that cast shadows and prohibit people from using lighting to its fullest potential. Other locations, particularly one location that reported several vehicles stolen, had night lighting that was so dim, driving was impossible. A large apartment complex, pictured below, was photographed on one side during the day and on the other side at night, to demonstrate the lack of adequate lighting. 185

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In Lexington, many of the residential areas that have large, sprawling grounds seem to be well guarded. Locations on the outskirts of town have fences and guard posts, while some use call boxes that require the visitor to announce him/herself before entering. Other areas in Lexington, mainly in the downtown area, do not seem as secure. Most businesses have standard locks while a few have security systems. This seems to be consistent in most cities of similar socio-economic status and design. Below are a few photos of locations that were in the

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sample. The complex on the left had repeat victimization, while the duplex on the right had single victimization.

The most profound finding from Lexington was the absence of significance of the activity nodes. Several activity nodes in Lexington appeared to be locations that seemed almost magnetic in their attraction of crime and/or criminal activity. Some of these places, like the photo on the below (top) would seem to draw crime. This corner store has an

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ATM and a payphone, sells liquor, and is open late. The one on the bottom is a photo of part of a strip mall which also has activity nodes. Both locations had several cars stolen during the study period. But, no statistical significance was found between single and repeat victimization locations.

When analyzing the data, accommodations, fast food locations, schools, and parks did not contribute significantly to the estimation efforts of the opportunity structure. Accommodations, shown on the next page, illustrate the differences between the types of hotels being

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compared. First (top), the Catalina motel has significant parking but it is spread out all along the motel. The Days Inn (bottom photo) has a large parking lot that is adjacent to the main parking area by the hotel. Locations such as these are difficult to consider in an opportunity structure since there is absolutely no way of knowing exactly where the car was parked before it was stolen. This makes determining the environmental characteristics very difficult.

Fast food locations, like Fazoli’s and McDonald’s both show adjoining locations that may have more opportunity for auto theft than the fast food restaurants, themselves. Fazoli’s has a large parking

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facility (below, top) and McDonald’s (below, bottom) is located next to a gas station and on a major roadway. All of these factors could have contributed to the opportunity structure by providing greater opportunity for offenders by drawing them away from the fast food locations.

The locations of schools also did not contribute to the opportunity structure. All schools are disbursed throughout the city. Perhaps the lack of opportunity at schools in Lexington is a function of the unique, cohesive structure of neighborhoods there. Most schools, like the one

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pictured below, have parking lots surrounding the schools. The other side of the parking lot faces the community. The residential surveillance in Lexington may help reduce opportunity for auto theft at schools.

PRACTICAL ISSUES There are several ways that this research can benefit the community, as a whole. First is the ability for the police department to use situational crime prevention techniques, in conjunction with the findings discussed here, to help prevent crime. For Lexington, the police department should focus on increasing natural surveillance by reassessing patrol routes and helping communities set up a neighborhood watch. Also, police can focus on improving lighting, and taking minor steps to increase security, such as adding signs to indicate consequences of criminal actions. Some rental communities have signs, such as the one

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below, indicating a partnership between the police and community. More of these and other warnings should be posted, visibly, throughout the community.

City planners could also benefit from this research. As mentioned before, Lexington is involved in the process of revitalization and renovation, with several parts of the city being redesigned and rebuilt. If city planners use the models presented here and incorporate them into their plans, many locations could design out crime before it has a chance to start. The photo below is a location being rebuilt by Habitat for Humanity. Several locations around Lexington had these signs during the site visit.

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Another location that should be a concern for city planners is the location of the Lowe’s home improvement store that backs up to an inn (below). The parking lots share a property line and are poorly lit in the evening, despite the tall lamps between the two locations. These lamps offer only dim light and several were not working during the site visit. The police department noted that this part of town has a very transient nature, with several hotels that provide shelter for travelers and many rented vehicles that serve as targets for offenders. City planners should consider these characteristics when zoning and building permits are requested. Since this has not been considered for the current situation, a more permanent boundary should be erected between these two locations and lighting should be increased.

The final practical issue focuses on the physical structure and design of the city. First, the city of Lexington is based on a radial shape. The downtown area has mainly commercial locations with some residential locations, surrounded by New Circle Road. New Circle Road creates a giant circle around the city with most residential areas outside its boundary. Approximately a dozen streets radiate from the center of town. This is a feature that is unique to Lexington and is not found in many other locations.

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Second, Lexington is also a college town, wherein the University of Kentucky is located. This adds complexity to crime problems and trends. College towns may suffer from crime trends that are different from other locations. Seasonal trends have been identified by the police department. These differences include not only a pattern in the time of year that crimes occur, but also in the type of crimes that occur, with drug crimes, theft, and graffiti increasing when school is in session. Third, size and economic position are significant factors that should be considered when discussing the relevance of a model created in Lexington that will be used in other cities throughout the United States. Lexington is a relatively small town with a rather low median income. Theft, of all types, may be different in Lexington than in larger, wealthier cities. One such example of this is the location of gas stations and convenience stores. In larger cities, convenience stores are often found at addresses other than those with gas stations. Lexington has many gas stations with convenience stores at the same address which may have a significant effect on auto theft as it changes the

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movement of people and the pathways they use to get from place to place. This does not mean that findings from this research can not be used elsewhere, but readers should be cautioned about the practical applications of the models and adjustments that may need to take place if they are used in cities that are structured differently than Lexington. In addition, the W.A.L.L.S. variables and the opportunity structure can also be used to model crimes other than auto theft, as explained below. USING W.A.L.L.S. AND THE OPPORTUNITY STRUCTURE FOR OTHER CRIMES The W.A.L.L.S. variables have been used in this research to identify locations and environmental characteristics that are related to auto theft in Lexington, Kentucky. However, these models can be used to estimate the locations of other crimes in cities across the United States. A similar model has been developed for commercial burglary using the acronym S.T.E.A.L. to represent the environmental characteristics present for this crime (Bichler-Robertson & Potchak, 2002). In that model, surveillability, target-hardening, edge effect, accessibility, and liquidation potential are measured in high crime zones to determine which factors are most closely related to commercial burglary. While the variables that are recorded change based on which crime is being studied, the model for data collection and analysis remains constant. Crime analysts may use the concepts suggested in these models for other crimes by identifying variables and adjusting the data collection instrument to match the environmental factors for the crime under investigation. During this process, the city layout and size may become a factor and must be considered during data collection and analysis. In Lexington, the radial nature of the city required consideration of the street structure as it had an unusual impact on offender awareness. Cities with extensive subway networks, bus routes, or other transportation systems must include these in their opportunity structures, as well as measure the impact that the stops or hubs may have on victimization. Most importantly, the crime prevention strategy must be embedded in the “general governance” of the location in which it is implemented (Brantingham, Brantingham & Taylor, 2005). Without efforts from all community organizers and local and state government offices, long-term maintenance of any crime prevention project may be ineffective.

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THE FUTURE OF GEOGRAPHIC ANALYSES Geographic analysis is undeniably the future of crime prevention. The mission is getting the technology and all its value to work for the police department. The technology used in this study is elementary in terms of the technological possibilities that are currently available. However, getting the current technology to police departments that do not have the resources that are required to utilize it proves to be a difficult task. While many cities already use mapping and other geographical technologies in their police departments, the daily use of this software is not a reality for many small departments. Software is still expensive and there is significant cost in hiring and training full-time staff to maintain the data and produce reports. A further consideration is the need for police officers to collect more information on the police report. Such things as seasonal and weather conditions for all crimes, G.P.S. (Global Positioning System) coordinates for auto theft and other property crimes, and measurements of parking slips (length and width of slip), if relevant, are all variables that police departments should record on the original report. The need for these variables to be collected arose during this research project. In order for this to be completed, police reports would need to be updated or appendices would need to be attached to the original report. Some police departments collect data on weather conditions for driving infractions but, rarely, for other types of offenses. Weather and other seasonal changes (such as a 25 degree spike in normal temperatures) may help crime analysts to track trends in offending. In addition to environmental factors, several researchers have noted the importance of situational factors in the target selection process. Some offenders steal cars to get home faster in the evening because they missed or don’t want to wait for public transportation. If police recorded this information, it would be easier for crime analysts to identify these or other patterns. This research attempted to collect data using G.P.S. to locate the exact spot of the auto theft. G.P.S. coordinates were recorded during the first data collection trip, however, during the second and third trip they were not. As data were collected on the second and third trip, it was decided that the coordinates were unnecessary because the researcher was not provided with the exact location of the auto theft from the police report. Using the G.P.S. two years after the crime provided an inaccurate reading of where the auto theft actually occurred. It did not provide the researcher with precise information

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and, in fact, was as inexact as the street address in pinpointing the location of the vehicle. Subsequently, it is suggested that if the police department could use G.P.S. technology at the time the report it taken, standing on the exact location where the car was stolen, G.P.S. would provide more accurate data than a street address. Dimensions of the parking slips in parking lots and garages were recorded for several locations. However, in some communities it was not deemed safe enough to exit the vehicle at night and measure the spots. When safety was a concern, the researcher would either walk the spot heel-to-toe, providing an inaccurate measurement of the parking slip, or not collect the data at all. Due to this occurrence, it is suggested that this information be added to the police report. Future researchers should consider analyzing this information to determine if the size of the spot is considered during the target-selection process. There are several variables that criminologists would like police departments to collect. However, all benefits that are gained with the ease of collection are offset by the problems introduced by secondary data analysis. Academics must collect many variables themselves to ensure the reliability and validity of the measurements. Primary data collection also provides researchers with a closeness to the data that can not be experienced by looking at a spreadsheet of data prepared by someone else. Site-level data collection provides valuable street knowledge that can not be gained without such fieldwork. THE FUTURE OF CRIME PREVENTION This research contributes to the future of crime prevention in several ways. In addition to police departments and city planners, this research can support architects in their design of parking lots and garages, and help communities engage neighborhood watch in the fight against crime. Perhaps, by identifying specific locations that generate criminal opportunity, city planners will consider dispersing criminogenic locations throughout the city instead of allowing a gas station, convenience store, auto shop, and bar to be on the same block without pedestrian and car surveillance. As this research shows, it is possible to have several locations that present opportunity on the same block, as long as natural or artificial surveillance is adequate. Architects may also use these models to design parking lots and garages to maximize natural surveillance, increase security and minimize theft. Several simple crime prevention measures such as improving lighting, use of proper signage, and limiting pedestrian

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access can greatly increase safety in large parking lots and garages. By identifying features that present a greater risk to the community, architects can take these attributes and design a structure to minimize those that pose the greatest risk. The potential for auto theft will always exist when cars are present, but these structures should be designed to reduce victimization. Finally, if this research was available to the community, they could also share the responsibility of crime prevention with the police. During the data collection, it became apparent that several members of the community believed that having a light on at night was sufficient to prevent crime. They are not aware that the quality of the light is as important as the light itself. Many lights that were on motion sensors were pointed away from the area where the light should focus, while several homes with lights on the front porch barely provided enough light for the steps, let alone the walkway and driveway. Many homes were missing outdoor lighting altogether. Several neighborhoods would benefit from the sharing of information from the police to the community. This would help to better inform citizens and provide them with the tools then need to prevent crime.

Final words No crime prevention model will ever perfectly predict crime and eliminate the need for crime prevention. However, technological advancements will allow crime analysts to combine current theory with geographic analysis to estimate the locations of crime and more precisely evaluate the environment in which it occurs. The models create the necessary link between the abstract ideology of academics and the realistic needs of the police. When these models are applied in other cities, and adapted to fit other crimes, they will make crime prevention more effective.

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INDEX Groff, E., 5, 34, 37, 38, 48, 52, 158 Homel, R., 26, 47, 48, 49, 56, 64, 80, 104, 159 Human Ecology, 8 Jeffery, C.R., 4, 17, 20, 32, 39, 51, 55, 61, 65, 70, 75, 77, 80, 81, 87, 167 Kentucky, 1, 3, 4, 6, 83, 85, 87, 88, 89, 92, 94, 97, 98, 99, 107, 108, 110, 112, 119, 121, 122, 125, 126, 127, 128, 129, 130, 131, 133, 134, 135, 137, 138, 140, 143, 144, 145, 147, 148, 149, 150, 151, 156, 157, 158, 161, 162, 163, 164, 166, 168, 175, 176, 177, 178, 179, 180, 185, 186, 187, 190, 191, 192, 193, 194, 195 LaVigne, N.G., 4, 5, 14, 19, 20, 24, 28, 37, 38, 74, 75, 77, 81, 86, 88, 162, 170 Layers Accommodations, 26, 93, 114, 119, 125, 160, 188 Auto Repair/Auto Parts, 27, 93, 117, 161 Bars, 24, 64, 80, 90, 93, 112, 159, 167 Convenience Stores, 19, 65, 80, 93 Fast Food, 24, 66, 80, 93, 112, 125, 159 Gas Station, 19, 65, 80, 93, 108, 125, 133, 134, 135 Major Roadways, 14, 92 Parking Facilities, 17, 92 Residential Areas, 14, 125 Schools, 22, 67, 80, 93, 108, 121 Street, 13, 92 Transportation, 20, 93

Accommodations, 26, 93, 114, 119, 125, 160, 188 Activity Nodes, 57, 62, 80, 96, 100, 101, 104, 132, 133, 134, 135, 136, 151, 166, 176 Alcohol, 24, 64, 80, 90, 93, 112, 159, 167 Bars, 24, 64, 80, 90, 93, 112, 159, 167 Brantingham, P.L. or P.J., 1, 2, 3, 10, 11, 14, 19, 20, 21, 23, 24, 25, 32, 34, 35, 36, 39, 40, 42, 54, 62, 66, 78, 80, 85, 86, 88, 90, 97, 155, 159, 167, 195 Bus Stops, 89, 101, 133, 134, 135 Clarke, R.V., 1, 3, 4, 12, 13, 15, 17, 19, 33, 38, 39, 51, 54, 55, 56, 59, 63, 72, 76, 77, 78, 80, 81, 104, 159, 169 Cohen, L.E., 3, 12, 14, 17, 20, 30, 31, 34, 35, 36, 41, 51, 52, 53, 58, 80 Community-level Research, 84, 91 Convenience Store, 19, 65, 80, 93 Cornish, D., 3, 39, 51, 54, 55, 56, 104 Cornish, D.B., 3, 39, 51, 54, 55, 56, 104 Crime prevention, 44 Eck, J.E., 1, 3, 4, 5, 6, 13, 40, 41, 43, 53, 61, 63, 80, 166 Farrell, G., 41, 46, 48, 97 Fast Food Locations, 24, 66, 80, 93, 112, 125, 159 Felson, M., 1, 3, 12, 14, 17, 20, 31, 33, 34, 35, 36, 38, 41, 51, 52, 53, 58, 61, 80 Full Model, 5, 119, 132, 136, 141, 146, 151, 162 Gas Station, 19, 65, 80, 93, 101, 108, 119, 125, 133, 134, 135 Geographic Analysis, 2, 5

219

220 Lexington-Fayette, Kentucky, 1, 3, 4, 6, 83, 85, 87, 88, 89, 92, 94, 97, 98, 99, 107, 108, 110, 112, 119, 121, 122, 125, 126, 127, 128, 129, 130, 131, 133, 134, 135, 137, 138, 140, 143, 144, 145, 147, 148, 149, 150, 151, 156, 157, 158, 161, 162, 163, 164, 166, 168, 175, 176, 177, 178, 179, 180, 185, 186, 187, 190, 191, 192, 193, 194, 195 Lighting, 5, 14, 57, 73, 74, 81, 96, 101, 103, 104, 143, 144, 145, 146, 151, 170, 171, 173, 176, 182 Limitations, 177, 183 Locations, 5, 35, 57, 73, 81, 96, 101, 102, 104, 125, 129, 136, 137, 138, 139, 140, 141, 150, 151, 168, 169, 171, 176 Models 1, 108, 110, 114, 117, 121, 156, 157, 159, 175 2, 110, 121, 157, 158, 175 3, 112, 114, 121, 122, 159, 160, 175 4, 114, 121, 122, 160, 161, 175 5, 117, 121, 161, 162, 175 6 (Full Model), 5, 119, 122, 132, 136, 141, 146, 151, 162, 175 Opportunity Structure, 3, 29, 83, 84, 85, 92, 156 Parking Facilities, 17, 92, 100, 102, 103, 125, 126, 127, 137, 138, 140, 147, 149 Pattern, 3, 18, 28, 39, 40, 46, 49, 65, 70, 72, 84, 112, 136, 160, 165, 166, 171, 194 Pattern Theory, 1, 10, 12, 19, 39, 65, 84 Policy, 185 Layers, 15, 87 Public Housing, 15 Public Housing, 87

Index Ratcliffe, J., 4, 5, 10, 44, 49, 52, 63, 65, 69, 80, 167 Rational Choice Theory, 3, 11, 38, 51, 52, 54, 55, 56 Rengert, G., 1, 2, 3, 10, 15, 31, 49, 54, 57, 58, 73, 77, 80, 81, 168 Rossmo, D.K., 4, 24, 25, 40, 54, 64, 80, 90, 159, 167 Routine Activity Approach, 1, 3, 11, 12, 14, 34, 51, 52, 53, 56, 65, 67, 84 Schools, 22, 67, 80, 93, 108, 121 Security Devices, 76, 103 Sherman, L., 1, 25, 26, 29, 41, 46, 161 Site Survey, 99 Site-level Research, 51, 95 Situational Crime Prevention, 1, 55, 56 Spatio-temporal, 52 Target selection, 37, 39 Theory Human Ecology, 8 Pattern Theory, 1, 10, 12, 19, 39, 65, 84 Rational Choice, 3, 11, 38, 51, 52, 54, 55, 56 Routine Activity Approach, 1, 3, 11, 12, 14, 34, 51, 52, 53, 56, 65, 67, 84 Situational Crime Prevention, 1, 55, 56 Transportation Hubs, 20, 93 W.A.L.L.S. Characteristics Activity Nodes, 57, 62, 80, 96, 100, 101, 104, 132, 133, 134, 135, 136, 151, 166, 176 Lighting, 5, 14, 57, 73, 74, 81, 96, 101, 103, 104, 143, 144, 145, 146, 151, 170, 171, 173, 176, 182 Locations, 5, 35, 57, 73, 81, 96, 101, 102, 104, 125, 129, 136, 137, 138, 139, 140,

Index 141, 150, 151, 168, 169, 171, 176 Security, 76, 103 Watchers, 5, 57, 58, 62, 80, 95, 99, 100, 104, 127, 129, 130, 131, 132, 151, 165, 166, 172, 174, 176

221 Watchers, 5, 57, 58, 62, 80, 95, 99, 100, 104, 127, 129, 130, 131, 132, 151, 165, 166, 172, 174, 176 Weisburd, D., 42, 43