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 ...

Author: Marissa P. Levy

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Criminal Justice Recent Scholarship

Edited by Marilyn McShane and Frank P. Williams III

A Series from LFB Scholarly

This page intentionally left blank

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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Table of Contents

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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Opportunity, Environmental Characteristics and Crime

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.

Introduction to the Research

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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


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(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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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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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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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 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

A Multi-level Investigation of Auto Theft

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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 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.


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

A Community-level Investigation of Auto Theft

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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


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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


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


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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


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



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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



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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



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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.


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


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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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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


127

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).

128


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.


129

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)

130


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

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