Neighborhood Structures and Crime: A Spatial Analysis (Criminal Justice: Recent Scholarship)

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Author: George Kikuchi

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Criminal Justice Recent Scholarship

Edited by Marilyn McShane and Frank P. Williams III

A Series from LFB Scholarly

Neighborhood Structures and Crime A Spatial Analysis

George Kikuchi

LFB Scholarly Publishing LLC El Paso 2010

Copyright © 2010 by LFB Scholarly Publishing LLC All rights reserved. Library of Congress Cataloging-in-Publication Data Kikuchi, George, 1980Neighborhood structures and crime : a spatial analysis / George Kikuchi. p. cm. -- (Criminal justice: recent scholarship) Includes bibliographical references and index. ISBN 978-1-59332-396-7 (hardcover : alk. paper) 1. Criminal statistics. 2. Neighborhoods. 3. Crime analysis. 4. Spatial analysis (Statistics) I. Title. HV6208.K55 2010 364.2'2--dc22 2010001568

ISBN 978-1-59332-396-7 Printed on acid-free 250-year-life paper. Manufactured in the United States of America.

Table of Contents

Tables ..................................................................................................vii Figures .................................................................................................. ix Acknowledgement ..............................................................................xiii Chapter 1. Introduction .......................................................................... 1 Introduction.................................................................................... 1 Statement of Problem..................................................................... 2 Theoretical Orientation .................................................................. 5 Summary...................................................................................... 12 Chapter 2. Longitudinal Analysis of Crime Rates at the Neighborhood Level .......................................................................... 15 Introduction.................................................................................. 15 Explaining Crime Waves ............................................................. 17 Analysis of Change ...................................................................... 19 Research Questions...................................................................... 22 Data.............................................................................................. 24 Exploratory Spatial Data Analysis ............................................... 26 Method I: Growth Curve Models................................................. 27 Results: Growth Curve Model ..................................................... 31 Method II: Spatial Panel Model ................................................... 49 Results: Spatial Panel Model ....................................................... 59 Summary...................................................................................... 64 Chapter 3. An Analysis of Spatially Varying Associations between Neighborhood Characteristics and Crime ............................................ 69 Introduction................................................................................. 69 Research Question ...................................................................... 72 Spatial Dependency and Spatial Heterogeneity .......................... 72 Theoretical Expectations regarding Spatial Variability .............. 77 Methods ...................................................................................... 79 Data............................................................................................. 84 Results for Spatial Regression Models ....................................... 87 Results for Geographically Weighted Regression Models.......... 94 v

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Neighborhood Structures and Crime: A Spatial Analysis

Summary................................................................................... 107 Chapter 4. A Spatial Analysis of Criminal Offenders’ Target Selection ............................................................................................ 113 Introduction............................................................................... 113 Neighborhood Characteristics and Crime ................................. 115 Journeys to Crime ..................................................................... 117 Target Selection as Rational Choice ......................................... 119 A Summary of Literature Review ............................................. 120 Hypotheses................................................................................ 121 Data........................................................................................... 125 Method ...................................................................................... 129 Results....................................................................................... 131 Summary................................................................................... 138 Chapter 5. Conclusion........................................................................ 145 What is Special about Space in Criminology? .......................... 145 Theoretical Implications of the Longitudinal Analysis of Crime ........................................................................................ 148 Theoretical Implications of the Analysis of Spatial Dependency and Spatial Heterogeneity .................................... 150 Theoretical Implications for Criminal Offenders’ Target Selection.................................................................................... 155 Policy Implications ................................................................... 157 Limitations of the Current Research and Directions for Future Research.................................................................................... 161 Appendices A. Maps of Seattle Data ............................................................ 165 B. Maps of Philadelphia Data ................................................... 175 C. Maps of Glendale Data ........................................................ 179 References ......................................................................................... 181 Index ................................................................................................. 197

Tables

2.1 Moran’ I Spatial Autocorrelation Coefficients of Crime Rates and OLS Residuals...................................................................... 27 2.2 Baseline Growth Curve Models of Homicide, Robbery, Burglary, and Auto Theft Rates per 1,000 People ....................................... 32 2.3 Comparisons of Observed Means and Predicted Levels of Crime Based on the Baseline Growth Curve Models ............................. 34 2.4 Variance-Covariance Matrix of Random Effects of Growth Curve Models ......................................................................................... 36 2.5 Growth Curve Models of Crimes between 1960 and 2005 with Neighborhood Characteristics as Time Invariant Predictors........ 40 2.6 Changes in Neighborhood Characteristics as Time Variant Predictors. .................................................................................... 46 2.7 Spatial Panel Models of Changes in Crime Rates Predicted by Changes in Neighborhood Characteristics ................................... 60 3.1 Descriptive Statistics for Neighborhood Characteristics................ 87 3.2 OLS Regression of Crime Rates on Neighborhood Characteristics.............................................................................. 88 3.3 Spatial Regression of Crime Rates on Neighborhood Characteristics.............................................................................. 92 3.4 Spatially Varying Coefficient Estimates from Geographically Weighted Regression of Violent Crimes...................................... 96 3.5 Spatially Varying Coefficient Estimates from Geographically Weighted Regression of Property Crimes.................................... 98 4.1 Descriptive Statistics for Neighborhood Characteristics.............. 127 4.2 Data Structures of a Conditional Logit Model (Hypothetical Data) .......................................................................................... 128 4.3 Conditional Logit Models of Offenders’ Target Selection Predicted by Neighborhood Characteristics............................... 131 4.4 Conditional Logit Models of Offenders’ Target Selection using Racial Characteristics of Neighborhoods and Offenders ........... 134 vii

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4.5 Conditional Logit Models of Offender Target Selection by Youth and Adult Offenders........................................................ 137 4.6 Conditional Logit Models of Target Selection by Single and Multi-Offenders ......................................................................... 139

Figures

2.1 Five Trajectories of Neighborhood Crime Rates (Hypothetical Data) ............................................................................................ 29 2.2 An Averaged Trajectory of Burglary Rates across Five Neighborhoods............................................................................. 29 2.3 Predicted Trajectories of Homicide for Average Neighborhoods and Increasingly Socially Disorganized Neighborhoods ............ 45 3.1 Global Statistics ............................................................................. 83 3.2 Local Statistic ................................................................................ 83 3.3 Spatially Varying Regression Coefficients of the Percentage Male Youth on Aggravated Assault.......................................... 101 3.4 Spatially Varying Regression Coefficients of Racial Heterogeneity on Aggravated Assault ....................................... 101 3.5 Spatially Varying Regression Coefficients of Residential Language Ability on Aggravated Assault ................................. 101 3.6 Spatially Varying Regression Coefficients of Population Density on Aggravated Assault.............................................................. 102 3.7 Spatially Varying R-square of Geographically Weighted Regression of Aggravated Assault............................................ 102 3.8 Spatially Varying Regression Coefficients of Racial Heterogeneity on Robbery ......................................................... 102 3.9 Spatially Varying Regression Coefficients of the Percentage Public Transportation on Robbery ............................................ 103 3.10 Spatially Varying Regression Coefficients of Language Ability on Robbery ................................................................... 103 3.11 Spatially Varying R-square of Geographically Weighted Regression of Robbery............................................................... 103 3.12 Spatially Varying Regression Coefficients of Racial Heterogeneity on Residential Burglary...................................... 104 3.13 Spatially Varying Regression Coefficients of Language Ability on Residential Burglary ............................................................ 104 ix

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3.14 Spatially Varying Regression Coefficients of Socio-economic Disadvantage on Residential Burglary....................................... 104 3.15 Spatially Varying Regression Coefficients of Residential Mobility on Residential Burglary............................ 105 3.16 Spatially Varying Regression R-square of Geographically Weighted Regression of Residential Burglary . 105 3.17 Spatially Varying Regression Coefficients of Language Ability on Auto Theft................................................................. 105 3.18 Spatially Varying Regression Coefficients of Socio-economic Disadvantage on Auto Theft ...................................................... 106 3.19 Spatially Varying R-squares of Geographically Weighted Regression of Auto Theft........................................................... 106 4.1 Spatial Distribution of Burglary Rates per 1,000 Households in Glendale, Arizona ...................................................................... 127 4.2 A Criminal Offender’s Choice and Alternatives.......................... 128 A.1 Homicide Rate per 1,000 People................................................. 165 A.2 Robbery Rate per 1,000 People................................................... 166 A.3 Burglary Rate per 1,000 Households .......................................... 166 A.4 Auto Theft Rate per 1,000 People............................................... 167 A.5 The Proportion of the Foreign Born Population.......................... 167 A.6 The Proportion of Children Living with Both Parents ................ 168 A.7 The Proportion of Public Transportation Users .......................... 168 A.8 The Proportion of Employed Females ........................................ 169 A.9 The Proportion of Male Youth (Ages 15-24).............................. 169 A.10 Residential Mobility.................................................................. 170 A.11 Racial Heterogeneity................................................................. 170 A.12 Socio-economic Disadvantage .................................................. 171 A.13 The Parameter Estimates of Homicide Trajectory .................... 171 A.14 The Parameter Estimates of Robbery Trajectory ...................... 172 A.15 The Parameter Estimates of Burglary Trajectory...................... 172 A.16 The Parameter Estimates of Auto Theft Trajectory .................. 173 B.1 The Spatial Distribution of Crime in Philadelphia ...................... 175 B.2 The Spatial Distribution of Demographic Variables in Philadelphia (1)......................................................................... 176 B.3 The Spatial Distribution of Demographic Variables in Philadelphia (2)......................................................................... 177 C.1 The Spatial Distribution of Socio-Demographic Characteristics in Glendale, AZ (1) ................................................................... 179

Figures

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C.2 The Spatial Distribution of Socio-Demographic Characteristics in Glendale, AZ (2) ................................................................... 180

Acknowledgements

First of all, I would like to thank Dr. Scott Feld, Dr. Raymond Florax, Dr. Jack Spencer, and Dr. Scott Desmond for helping me as I prepared this book. Thanks also go to Purdue University for providing me with financial support. I would also like to thank the Seattle Police Department, the Philadelphia Police Department, the Inter-university Consortium for Political and Social Research (ICPSR), and Mr. Bryan Hill for providing me with data and Dr. Wim Bernasco and Dr. Paul Elhorst for providing me with statistical program codes for conditional logit modeling and spatial panel modeling. Many thanks also go to Mr. Leo Balk for making this book possible. Finally, but not the least, my deepest gratitude to my parents who have given me moral support for pursuing an academic career.

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CHAPTER 1

Introduction

INTRODUCTION Spatial and temporal analyses of crime have been classic topics in criminology. For example, several of the first empirical studies in criminology by European scholars, such as Guerry, Quetelet, and Durkheim, involved analyses of the spatial distribution of crime in France (Beirne 1993). Furthermore, in the United States, Shaw and McKay (1942) conducted a seminal study of juvenile delinquency in Chicago that illustrated the spatial concentration of juvenile delinquency in the inner city over time. Spatial concentrations of crime in cities were also vividly illustrated by a study in Minneapolis (Sherman et al. 1989). Sherman and his colleagues found that 3% of the city’s addresses generated more than 50% of the calls to police and five of the top ten crime hot spots included bars and taverns. An analysis of crime based upon neighborhood characteristics is important for criminological inquiries for several reasons. First, crimes are not randomly distributed over space. In fact, crimes are so highly concentrated over space that the addresses of past crimes were six times more predictive for future crime than known offenders’ identities (Sherman 1995). Second, certain neighborhood conditions, such as income inequality and job unavailability, give rise to criminal motivation. Regardless of demographic characteristics, individuals living in disadvantaged neighborhoods are more likely to develop criminal inclinations than those living in affluent neighborhoods. Third, certain neighborhood and place characteristics are crime conducive, offering suitable opportunities with low levels of guardianship. For example, neighborhoods with high residential turnover provide anonymity for potential offenders who live outside the neighborhood. Existing studies also indicate the risk of store clerks being murdered 1

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varies considerably by types of business establishments because of the routine activities of both motivated criminals and legitimate citizens that occur there (Davis 1987). Any individual behavior is a product of interaction between the person and the setting (Felson and Clarke 1998). Crime, in particular, requires offenders and victims and social contexts that unite them (Miethe and McDowall 1993; Miethe and Meier 1990; Miethe and Meier 1994; Rountree et al. 1994; Wilcox et al. 2003). Crime occurs in micro-environments that have unique configurations of social and physical characteristics that provide varying levels of crime opportunity and guardianship. Even highly motivated offenders may give up committing a crime if the perceived chances of getting caught are high. A crime-conducive environment translates individuals’ criminal inclinations into action. Therefore, criminological inquiries need to explicitly incorporate spatial and temporal contexts of crime. STATEMENT OF PROBLEM While space and neighborhood characteristics are important in criminological research, empirical studies have been hindered because space is difficult to statistically analyze. Reviews of existing research on neighborhoods and crime have noted several limitations (Anselin et al. 2000; Kubrin and Weitzer 2003a). These limitations can be summarized as a failure to incorporate spatial and temporal dynamics of crime at the neighborhood level. First, although there are many studies that have examined the relationship between neighborhood characteristics and crime using cross-sectional data (e.g., Messner and Tardiff 1985; Miethe and Meier 1990; Miethe et al. 1987; Sampson and Groves 1989), the temporal aspects of crime at the neighborhood level have received limited attention (Bursik and Grasmick 1992; Kubrin and Herting 2003). Existing studies have relied mainly on cross-sectional data and failed to examine changes in neighborhood characteristics and crime rates over time. Official crime statistics (e.g., Uniform Crime Reports) indicate the United States experienced a rapid increase in violent crime between 1960 and the late 1980s, followed by a decrease. Many cities followed similar patterns of crime rate increases and decreases. What is less known, however, is whether neighborhoods

Introduction

3

within cities followed a similar rapid increase and decrease in crime over time. Despite city-level statistics showing crime rate increases, many neighborhoods might have remained safe. Several authors argue that neighborhood changes in crime over time, or the criminal careers of communities, have not been fully investigated (Maltz 1995; Schuerman and Kobrin 1986; Sherman 1995). Although there are studies that have conducted longitudinal analyses of crime at the macro level (e.g., states, counties, and cities), it is important to recognize that neighborhood characteristics are likely to vary considerably within cities. The process of social disorganization and criminal opportunities generating crimes is likely to operate at the neighborhood level. Second, despite the widely recognized importance of space in criminology, it has been difficult to fully incorporate spatial effects of crime and neighborhood characteristics in regression models. As crimes are likely to be spatially concentrated, researchers need to explicitly incorporate this spatial effect in their regression models (i.e., spatial dependency). As the level of crime in surrounding neighborhoods is often a strong predictor of the level of crime in a neighborhood, the omission of a spatially lagged crime variable is a model misspecification. Additionally, as a result of the spatial concentration of crime, the residuals (i.e., the difference between the ) predicted and actual values: Yi − Yi ) of ordinary least squares (OLS) regression are likely to be correlated, which violates an assumption of OLS regression. In plain English, the non-independence of observations in spatial data can be explained as observations close to each other are more similar than those far apart. Violating the assumption of independence results in the inefficient estimation of regression coefficients and biased estimation of standard errors (which means flawed statistical significance tests). Thus, without an explicit consideration of spatial effects, statistical inferences are no-longer trustworthy. In addition to spatial dependency, there are several reasons to suspect that structural relationships between neighborhood characteristics and crime vary across space (i.e., spatial heterogeneity) (Anselin et al. 2000; Fotheringham 1997; Fotheringham et al. 2002a; Wilson 2005). Although a theory may predict a simple linear relationship between a neighborhood characteristic and crime, there may exist unobserved contextual effects that are specific to certain parts of the study area. For example, social disorganization theory

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hypothesizes that economic deprivation gives rise to criminal motivation and there is a positive association between the percentage of households in poverty and the level of crime. If a disadvantage neighborhood is surrounded by affluent neighborhoods, the effect of economic deprivation on criminal motivation may be stronger, as residents’ relative deprivation will be stronger than in other areas. As OLS regression is topologically invariant (meaning the spatial arrangement of data does not matter in OLS) and cannot address these spatial processes, existing studies have not fully investigated the spatial dynamics of crime at the neighborhood level. For both spatial dependency and spatial heterogeneity, one needs regression models specifically designed to incorporate these spatial processes. As such spatial regression models, and especially statistical packages to conduct estimation, have been developed only relatively recently, many criminological studies do not fully examine the spatial dynamics of neighborhood characteristics and crime. Third, the spatial analysis of crime has often relied on crime incident location data and failed to account for where offenders are coming from. Some neighborhoods may be vulnerable to outsiders, while other neighborhoods experience high crime rates due to offenders living within the area. Different types of crime prevention strategies may be necessary depending on where offenders are coming from. Crimes committed by insiders may be more suitably prevented through a developmental model of crime prevention (e.g., youth counseling, community centers for youth activities, and improved schools). However, crimes committed by outsiders may be more suitably prevented by changing the opportunity structure of neighborhoods (e.g., strengthening the ability to identify potential outsiders). These three limitations of existing research can be summarized as a failure to account for spatial and temporal dynamics of crime and neighborhood characteristics. This research proposes to complete a series of studies to better understand how neighborhoods influence criminal opportunity and behavior by taking advantage of recent methodological developments in psychology and spatial econometrics. In particular, based on social disorganization theory and routine activities theory, this book examines: 1) longitudinal changes in neighborhood characteristics and crime; 2) spatially heterogeneous associations between neighborhood characteristics and crime; and 3)

Introduction

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criminal offenders’ target selection. These studies are attempts to further understand how neighborhood spatial and temporal dynamics influence crime rates. THEORETICAL ORIENTATION Using social disorganization theory and routine activities theory as a theoretical framework, this study develops research questions about the temporal and spatial distribution of neighborhood characteristics conducive to crime. Social Disorganization Theory Shaw and McKay (1942) conducted a study of the spatial distribution of juvenile delinquency in Chicago in the 1920s, which led them to propose social disorganization theory. Based on their spatial analysis, Shaw and McKay recognized that high crime areas were spatially concentrated in the inner city. Also, crime rates were high in the inner city and gradually decreased with distance from the city center. After mapping the “delinquency areas” of Chicago for many years, Shaw and McKay also discovered the spatial configuration of high crime areas remained the same over time. That is, juvenile delinquency was heavily concentrated in inner city neighborhoods over time, despite an almost total change in the racial and ethnic composition of residents. In addition to their findings on the distribution of juvenile delinquency, Shaw and McKay also found that neighborhoods in the inner city were characterized by various social problems. First, these inner city neighborhoods were economically disadvantaged, as indicated by a high proportion of people on public assistance, low median rent, low rate of housing ownership, and high unemployment. Housing conditions were deteriorated and the jobs that were available to residents were typically industrial jobs, while clerical and professional jobs were typically available to suburban residents. Second, these high-crime neighborhoods were residentially unstable and characterized by a high residential turnover rate. Third, various health problems were noted in the inner city, such as infant mortality, tuberculosis, and mental disorder. Fourth, the high-crime

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neighborhoods were characterized by racial heterogeneity. According to Shaw and McKay, the mixture of various ethnic groups hindered the realization of common values and goals among neighborhood residents. Based on these empirical findings, Shaw and McKay argued the spatial distribution of juvenile delinquency was a function of ecological characteristics, not the personal characteristics of individuals living in neighborhoods. That is, their theoretical model suggested that structural characteristics, such as poverty and residential mobility, led to the social disorganization of neighborhoods, which in turn resulted in an increase in crime. While Shaw and McKay’s empirical study and theoretical framework were influential in developing subsequent studies of crime, several limitations of their work have been noted (Bursik 1988; Kornhauser 1978; Kubrin and Weitzer 2003a; Sampson and Groves 1989). Most notably, Shaw and McKay’s theoretical model did not articulate the causal mechanism that linked neighborhood social disorganization to high crime rates. In fact, based upon their theoretical model, two versions of social disorganization theory can be conceived (Kornhauser 1978). First, a strain variant of social disorganization theory links structural characteristics and crime through the frustrated wants of residents living in disadvantaged neighborhoods. Frustrated wants (strain) are the result of discrepancies between aspirations and expectations. While money and social status are assumed to be universal aspirations, legitimate means to achieve them are not available to everyone. For residents living in economically disadvantaged neighborhoods, illicit activities often provide easier means to meet their aspirations. That is, the strain variant of social disorganization theory hypothesizes that the criminal motivation and frustrated wants resulting from the structural characteristics of neighborhoods is the intervening variable leading to an increase in crime. Second, a control variant of social disorganization theory links structural characteristics and crime through diminished informal social control. Informal social control in socially disorganized neighborhoods is weakened in several ways. Due to high residential mobility and racial heterogeneity, residents of disorganized neighborhoods cannot establish or maintain consensus about norms and values. A sense of belonging, attachment to community, social solidarity, and social

Introduction

7

cohesion are weakened. Residents who intend to leave their neighborhoods as soon as they can afford to do so have little vested interests in addressing neighborhood problems. Without common goals among neighborhood residents, effective social control cannot be implemented and criminal opportunities flourish. Trash on streets, abandoned buildings, graffiti, and drunken people become signs that neighborhood residents care little about social problems in their neighborhoods. Such physical and social incivility also lower the moral costs for potential offenders who target these neighborhoods. Furthermore, Shaw and McKay (1942) observed that individuals living in disorganized neighborhoods have fewer stakes in conformity, which lowers controls or restraints against committing crimes. As conflicting value systems existed (e.g., conventional norms vs. delinquent subcultures), juveniles living in disorganized neighborhoods often did not develop attachments to conformity. Having been exposed to delinquent subcultures, these juveniles become low in internal control and develop beliefs that are inconsistent with conventional values and norms. Moreover, as traditional social institutions, such as family and school, deteriorate, individuals with limited education, no jobs, and no attachment to significant others have fewer moral costs and stakes in conformity to prevent them from getting involved in illegal activities. In essence, the control model of social disorganization theory argues that social control, created internally and externally, influence the costs of crime. High levels of social control in organized neighborhoods make it difficult for residents to commit crimes, while diminished informal social control lowers stakes in conformity and weakens restraints from committing crimes. Comparing the two variants of social disorganization theory, Kornhauser (1978) argues that the control version is more consistent with empirical research than the strain version. Further extending the control variant of social disorganization theory, several studies used survey data to identify the role of social ties and social networks among neighborhood residents in establishing informal social control (Bursik and Grasmick 1993b; Elliott et al. 1996; Lowenkamp et al. 2003; Sampson 1991; Sampson and Groves 1989; Veysey and Messner 1999). For example, Sampson and Groves (1989) showed that the effects of structural characteristics on the level of crime were mediated by the extent of participation in community activities,

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the breadth of local friendship networks, and the presence of unsupervised youths. Their argument is that the structural characteristics of a neighborhood, such as poverty, residential mobility, and racial composition, decrease social ties among neighbors, participation in community organizations, and informal monitoring of neighborhood youth, which in turn results in an increase in the level of crime. Additionally, Bursik and Grasmick (1993b) argue that social control in neighborhoods is anchored at various social and physical institutions (e.g., family, school, religious institutions, political groups). In particular, they argue the level of social organization and social control vary across neighborhoods, depending on the extent of internal social cohesion among community members, as well as the extent of community leaders’ ties to external resources (e.g., policy makers). Bursik and Grasmick’s model of social disorganization theory is often called a systemic model that emphasizes the importance of relational networks among community residents. Their systemic model suggests that structural characteristics, such as socio-economic status, heterogeneity, and residential instability, affect the formation of relational networks, which in turn affects varying types of social control (e.g., private control, parochial control, public control, and socialization). In contrast to Bursik and Grasmick’s theoretical model, Sampson (2004) argues that social ties do not necessarily lead to effective social control. Instead, he argues that collective efficacy, defined as mutual trust among residents and a willingness to intervene when problems arise, is the key variable that links social ties and the level of social control (Morenoff et al. 2001; Sampson 2004; Sampson et al. 1997). Using a multilevel framework, Sampson and his colleagues show that collective efficacy can be reliably measured at the neighborhood level. Together with concentrated disadvantage, immigrant concentration, residential instability, and collective efficacy, their empirical analysis explained a substantial amount (70%) of the variability in violence. More importantly, although the level of violence is high in disadvantaged neighborhoods, collective efficacy mediated a substantial amount of the effects of socio-economic disadvantage and residential instability on the level of violence in neighborhoods.

Introduction

9

In sum, social disorganization theory provides a theoretical framework to understand relationships between neighborhood characteristics and crime. The strain variant of social disorganization theory argues that increased criminal motivation produced by neighborhood disadvantage is an intervening variable that connects neighborhood characteristics and crime rates. Thus, the distribution of crime over space is a result of variation in the distribution of motivated offenders. The control variant of social disorganization theory, on the other hand, links the structural characteristics of neighborhoods to the level of crime through informal social control. Social ties among community members, vested interests in community problems, and shared value systems help to strengthen informal social control. The control variant of social disorganization theory explains the spatial distribution of crime by varying levels of social control that neighborhoods exert. Routine Activities Theory In addition to social disorganization theory, routine activities theory has been used to explain the distribution of crime over space, especially at the micro level. Routine activities theory views criminal incidents as the intersection of offenders and victims under specific circumstances. In particular, routine activities theory argues that a crime occurs when a motivated offender and a suitable target converge in time and space in the absence of capable guardians (Cohen and Felson 1979). Interestingly, the theory argues that the amount of crime can change even without an increase/decrease in the number of offenders. The number of crimes can increase as the availability of suitable targets increases. Decreasing the level of guardianship changes criminal opportunity, which leads to an increase in criminal behaviors. Even holding the quantity of all three elements constant, the crime rate can still increase as the frequency of convergence of the three elements increases in time and space. It is important to note that routine activities theory is not a theory that explains the emergence of criminal motivation. Instead, the theory treats criminal motivation as given and focuses on the contexts and situations in which crime is most likely to occur. In order to explain criminal events, routine activities theory assumes that human behaviors

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are products of rational choice. Thus, criminal behavior can be predicted based on the costs and benefits involved in the commission of a crime. The costs and benefits involved in crime vary depending on situations and targets. For example, while targets in close proximity to motivated offenders’ houses are convenient (benefits), the likelihood of being detected (costs) becomes high. Targets with high monetary values (benefits) are also likely to have strong guardianship (costs). In essence, routine activities theory considers opportunity a necessary condition, if not sufficient, for a crime to occur. Thus, the routine activities theory contends that crimes are non-randomly distributed over space and time because opportunity is not uniformly distributed over space and time. Under the routine activities framework, places and social contexts can facilitate (or inhibit) crime in several ways. First, particular physical features of a place can affect the level of social control and criminal opportunities (Clarke 2002; McNulty and Holloway 2000; Sampson 2002; Weisel 2002). For example, high rise buildings reduce natural surveillance because residents are living vertically and are removed from monitoring activities at the street level. Furthermore, residents may not know each other because of high residential turnover, which provides anonymity for potential offenders. Apartment complexes also have the same structure, doors, and locks across units, which increases the number of potential targets for crime with the same methods. Second, regardless of physical features, the crime level at places is also affected by routine activities that occur there (Block and Block 1995; Davis 1987; Duffala 1976; Eck 1995; Roncek and Maier 1991; Sherman et al. 1989). For example, bars and taverns may attract people with low self control. Alcohol consumption at these places may further promote potential violent encounters. Abandoned buildings and rundown housing may provide an attractive market for illicit drug dealers without fear of complaints from dwelling owners. In addition to micro-level variation in crime at the place level, routine activities theory also provides a theoretical framework for understanding crime at the macro level. In fact, Cohen and Felson (1979) first applied routine activities theory to explain crime rates at the aggregate level. Their point of departure was an apparent contradiction between improving social conditions and increasing crime rates in the

Introduction

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1960s. While the socio-economic conditions of American citizens, especially African Americans, improved substantially (e.g., increased level of education, increased median household income, drop in unemployment, decrease in the number of persons living below the poverty line), crime rates sharply increased. As conventional criminological theories at the time would have predicted a decrease in crime under these circumstances, such crime trends provided a challenge. Cohen and Felson (1979) argued that the increase in crime rates was attributable to changes in the routine activity patterns of American citizens. An increase in non-household activities caused by the increased labor force participation of married women left many houses unguarded. An increase in non-household activities shifted the domain of routine activities from private to public. The wider availability of small, high-value products, such as TVs and radios, meant an increase in suitable targets for crime. Using the Uniform Crime Reports data measured between 1947 and 1974, Cohen and Felson (1979) found that increased non-household activities, defined as the proportion of married women with jobs and non-husband-wife households, were significantly associated with increased crime rates, after controlling for unemployment rates and the proportion of male youths in the population. In sum, routine activities theory argues that the routine activities of both legitimate citizens and motivated offenders lead to variation in criminal opportunities. Places and neighborhoods may become crime hot spots because large numbers of people are attracted for reasons not related to criminal motivations (e.g., shopping malls and transportation hubs). Crime hot spots may also emerge as motivated offenders are attracted to particular places and neighborhoods due to suitable opportunities for crime. Furthermore, an increase in the level of crime can occur if the level of guardianship changes (e.g., removing attendants at parking lots). By framing crime incidents as the temporal and spatial intersection of motivated offenders and unguarded targets, routine activities theory provides a theoretical framework to better understand temporal and spatial specific opportunity structures for crime.

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SUMMARY Space is a substantively important topic in criminology. Many theoretical and empirical criminologists have incorporated space in their studies. In fact, one of the first quantitative studies of crime in America was a spatial analysis of juvenile delinquency in Chicago by Shaw and McKay (1942; 1969). In addition to social disorganization theory, other theoretical perspectives, such as routine activities theory and environmental criminology, also recognize the role of space in shaping criminal behavior (Brantingham and Brantingham 1981b; Cohen and Felson 1979). Aside from theoretical works, space has obvious and practical importance in criminology. For example, most police departments conduct patrol activities based on beats. Law enforcement agencies often conduct crackdowns on particular offenses in select areas. In fact, the locations of past crimes can be more predictive of future crime than the identities of known offenders (Sherman 1995). It has also been found that drug activity in one neighborhood contributes to the level of other crimes in surrounding neighborhoods (Felson 2006; McCord and Ratcliffe 2007; Renegert et al. 2005; Rengert et al. 2000). Despite the importance of space to criminology, space is methodologically difficult to analyze. Two types of spatial effects, spatial dependency and spatial heterogeneity, provide challenges to conventional statistical methods. While many statistical models, including ordinary least squares (OLS) regression models, assume independently distributed observations, spatial units are hardly independent observations (i.e., spatial dependency). The characteristics of a neighborhood are likely to be similar to those of surrounding neighborhoods. Furthermore, what happens in one neighborhood is likely to affect surrounding neighborhoods. For example, focused crime prevention activities have been shown to lower the level of crime not only in a target neighborhood, but also surrounding neighborhoods, an effect known as the diffusion of benefits (Bowers and Johnson 2003; Clarke and Eck 2005; Weisburd et al. 2006). Researchers in spatial econometrics also argue that traditional regression models often fail to capture something special about space. That is, relationships between neighborhood characteristics and crimes can vary in strength and direction over space (i.e., spatial

Introduction

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heterogeneity) (Fotheringham et al. 2002a; Fotheringham et al. 2002b; LeSage 2004). For example, as human behaviors in downtown areas can be quite different from other areas, social processes of neighborhood characteristics affecting the level of crime can be different in downtown areas. As space is a continuous surface, however, a simple dichotomy of downtown vs. periphery may neglect important spatial processes. In addition to methodological problems, data availability has also hindered research on neighborhoods and crime. For example, many neighborhood level studies of crime have been cross-sectional, ignoring temporal dynamics of change. Although Shaw and McKay’s analysis provided insights on the longitudinal aspects of neighborhoods and crime, their conclusions emphasized the temporal stability of neighborhood characteristics and the level of crime. Although such findings led Shaw and McKay to argue for the importance of contextual effects, rather than compositional effects, more recently researchers have argued that changes in neighborhood characteristics are important for explaining changes in crime over time (Bursik and Grasmick 1992; Chamlin 1989; Griffiths and Chavez 2004; Kubrin and Herting 2003; Sampson et al. 1997; Sherman 1995; Weisburd et al. 2004). As longitudinal data of crime are often available for large scale macrolevel units of analysis, such as county and state, however, empirical studies on longitudinal changes in crime at the neighborhood level have been limited. Furthermore, aggregate analyses of neighborhood characteristics and crime sometimes neglect that crime incidents are ultimately individual behaviors. For example, many neighborhood level studies establish a statistical association between the socio-economic disadvantage of neighborhoods and the level of crime. Such analysis, however, does not explain if offenders are committing crimes in their own neighborhoods, due to an increase in frustrated wants, or if they are coming from outside neighborhoods, due to reduced guardianship. An analysis of offenders’ behavior can benefit from information about known criminals. Data that include the addresses of known offenders have not been as widely available as crime rate data. This book addresses these limitations of previous research in three analytical chapters, followed by a summary chapter. Based on theoretical discussions provided in Chapter 1, each subsequent chapter provides a detailed discussion of a particular problem, as well as

14


statistical analyses. As previous studies have neglected longitudinal changes in crime, Chapter 2 presents a longitudinal analysis of crime at the neighborhood level using data from 1960 to 2005. In order to examine spatially heterogeneous associations between neighborhood characteristics and crime, Chapter 3 explicitly examines two types of spatial effects, spatial dependency and spatial heterogeneity. As crimes are ultimately individual behaviors, aggregate studies of crime often miss the role of offenders in generating crimes. Chapter 4 overcomes this problem by combining data on neighborhood characteristics and criminal offenders’ travel patterns. Based upon empirical analyses from Chapters 2 to 4, Chapter 5 synthesizes the results and discusses both the theoretical importance of the findings and policy implications.

CHAPTER 2

Longitudinal Analysis of Crime Rates at the Neighborhood Level

INTRODUCTION Historically, the crime rate in the United States experienced a rapid increase between 1960 and the early 1990’s, followed by a gradual decrease. According to the Uniform Crime Reports, produced by the Federal Bureau of Investigation, violent crime in the United States dropped sharply from 758.2 crimes per 100,000 people in 1991 to 473.5 crimes per 100,000 people in 2006 (Federal Bureau of Investigation 2006). Given that violent crime had been increasing in a relatively linear fashion since 1960, the decline in violent crime after 1991 is particularly striking. In contrast to the dramatic increase and decrease in violent crime, property crime increased and decreased more regularly over time. Although the level of property crime also increased rapidly after 1961, it stayed around 5,000 crimes per 100,000 people, with random fluctuations, between 1975 and 1991. Property crime gradually decreased after 1991 and reached a low point (3,334.5 crimes per 100,000 people) in 2006. The dramatic decline in the level of crime after 1990 not only caught the attention of criminologists, but policy makers and the media as well (Conklin 2003; Sacco 2005; Zimring 2007). A variety of factors contribute to the creation of “crime waves” (Sacco 2005), or longitudinal changes in the level of crime. Some criminologists argue that changes in the demographic composition of the population lead to changes in crime (Phillip and Laub 1998; Steffensmeier and Harer 1999), while others argue that demographic composition has a limited affect on crime (Levitt 1999). While the media and scholarly research often attributes changes in the crime rate to economic conditions (Allen 1996; Bursik and Grasmick 1993a), the mechanisms by which 15

16


economic conditions affect the level of crime are more complicated than some might think (Conklin 2003; Freeman 1995; Young 1993). Furthermore, changes in social institutions, such as family, political, and economic institutions, may affect the enforcement of conformity, which subsequently affects the level of crime (Lafree 1998a; Lafree 1998b). Additionally, the level of crime is associated with the availability of guns and drugs (Blumstein 1995). The rapid increase in crime in the 1970s has also been attributed to changes in the routine activities of individuals (Cohen and Felson 1979). Increased exposure to motivated offenders is likely to increase violent crimes, while a reduction in household guardianship may contribute to an increase in residential burglary. Finally, technological innovations also affect the level of crime. For example, the increased production of portable, high value items, such as car stereos, laptop computers, and portable music players, creates attractive targets for thieves (Cohen and Felson 1979; Roman and Chalfin 2007; Sacco 2005). What is less understood, however, is whether neighborhoods within a city have followed the same trends in crime rate changes, as most neighborhood level studies have utilized a cross-sectional design (Bursik 1988; Kubrin and Weitzer 2003a; Sherman 1995). Some neighborhoods may have experienced a faster increase in crime rates than others. Dramatic increases and decreases in city-wide crime statistics may be caused by crime rate changes in a few neighborhoods. In fact, despite the increases and decreases of city-wide crime rates, some neighborhoods may have remained safe neighborhoods throughout the entire period. Sherman (1995) and Maltz (1995) argued that a longitudinal analysis of neighborhood changes and crime rates could borrow several concepts identified in developmental criminology. That is, just as individuals go through several stages in escalating their anti-social behavior, criminal places (and neighborhoods) may also go through such stages as onset, growth, escalation in crime seriousness, persistence, decline, and termination. Furthermore, as Moffitt (1993) provides a taxonomy to categorize adolescents in terms of their involvement in criminal activities (i.e., adolescence-limited offenders and life course persistent offenders), there may be different types of neighborhoods that have unique pathways of changes in the level of crime over time.


17

As most neighborhood studies have used cross-sectional data, it is possible that neighborhood characteristics based on social disorganization theory and routine activities theory do not explain and predict crime rate trends over time. Some structural characteristics may explain the initial level of crime rates, but may fail to predict changes (either increase or decrease) over time. That is, a life course perspective on neighborhoods has been lacking in previous research. In order to overcome the limitation in previous neighborhood research that have been based on cross-sectional data, this chapter sets up a series of research question to analyze crime trends at the neighborhood level over time. First, how can neighborhood crime rate trajectories between 1960 and 2005 be described? Is there evidence of a systematic trend in crime rate changes over time? How do individual neighborhoods deviate from the overall crime rate trajectory? Are crime rate trajectories best captured by linear or non-linear trends? Second, is there any association between the initial level of crime and rate of change over time? For example, do high crime neighborhoods get worse by further increasing their levels of crime over time? Do safe neighborhoods with relatively low levels of crime experience slower increases in the levels of crime than other neighborhoods? Third, what are the neighborhood characteristics that are associated with the initial level of crime and rates of change over time? Drawing on social disorganization theory and routine activities theory, various neighborhood characteristics, such as socio-economic disadvantage, residential instability, and demographic composition, are included in statistical models in order to explain individual variability in crime rate trajectory forms. EXPLAINING CRIME WAVES Research that examines national crime trends indicates that various factors affect the level of crime over time (Conklin 2003; Sacco 2005; Zimring 2007). First, the demographic composition of the population is likely to affect the level of crime (Blumstein 1995; Blumstein et al. 1989; Steffensmeier and Harer 1999). In particular, an increased proportion of male youths increases crimes because young males are the most crime prone segment of the population (Gottfredson and Hirschi 1990; Moffitt 1993; Piquero et al. 2007). Second, socio-

18


economic conditions are associated with the level of crime (Allen 1996; Bursik and Grasmick 1993a; Kubrin and Herting 2003). A decrease in the proportion of households in poverty is likely to contribute to better parenting practices and improved school performance, which in turn results in lower rates of crime and delinquency (Agnew 2005). A decrease in unemployment also reduces the level of crime, as jobs provide increased stakes in conformity, direct supervision, and means to obtain money through legal channels. Third, various aspects of the criminal justice system affect the level of crime. Among others, improved policing strategies, more police, increased incarceration, and tougher gun and drug control laws may reduce the level of crime (Blumstein 1995; Bowling 1999; Braga 2001; Kelling and Coles 1996). Fourth, changes in the pattern of routine activities among citizens affect opportunities for crime, which in turn affect the level of crime (Cohen and Felson 1979). For example, an increased participation of married women in the workforce leaves houses unguarded during the day. Technological innovations also create criminal opportunity by producing portable items with high monetary values. For example, the development of laptop computers and portable music players increases the number of attractive targets due to light weight and high monetary value (Roman and Chalfin 2007; Sacco 2005). It is unlikely that any of the above factors affect the level of crime independently. Rather, several factors are probably interrelated in their effect on crime trends. For example, a stronger economy, represented by higher median incomes and fewer people in poverty, is associated with parenting practices and the school performance of juveniles. Increased employment in legitimate businesses is also likely to affect drug markets and juveniles’ involvement in gangs. Furthermore, these factors are not likely to affect trends for different crime types equally. For example, changes in drug markets may affect the level of burglary, robbery, and homicide, but not rape. Increases in the number of employed women may also represent increases in non-household activities (decreases in guardianship at houses) and in exposure to motivated offenders. Such changes in opportunities for crime are not likely to be associated with crimes occurring in domestic situations, such as domestic violence. Finally, these factors are likely to affect the level of crime in neighborhoods differently. For example, an increased availability of guns and drugs in a city may increase the level of


19

violence in economically disadvantaged neighborhoods, while they may not affect the level of crime in affluent suburbs. ANALYSIS OF CHANGE Social disorganization theory argues that crime rates increase when neighborhoods lack effective informal social control and/or neighborhoods increase frustrated wants of neighborhood residents (Bursik 1988; Bursik and Grasmick 1993b; Kornhauser 1978; Kubrin and Weitzer 2003a; Sampson and Groves 1989). Structural characteristics, such as residential mobility, racial heterogeneity, and socio-economic disadvantage, often hinder the development of social ties among residents and identification of common goals, which in turn decreases the informal social control that is needed to regulate crime and disorder in neighborhoods. Economic deprivation is also likely to fuel criminal motivation, which increases the number of motivated offenders. Routine activities theory, on the other hand, explains the source of crime incidents through a crime triangle of the motivated offender, attractive targets, and capable guardians (Cohen and Felson 1979; Felson 2006). In particular, the theory argues that a crime occurs when a motivated offender and attractive target intersect in time and space in the absence of a capable guardian. While empirical research has largely supported the predictions of social disorganization theory and routine activities theory (e.g., Miethe and Meier 1994; Sampson and Groves 1989), many of these studies have used cross-sectional data (Bursik 1988; Byrne and Sampson 1986; Kubrin and Weitzer 2003a). Thus, it has not been fully examined if the results based on crosssectional data apply equally to longitudinal relationships between neighborhood characteristics and the level of crime. Nonetheless, there are several studies that have examined longitudinal data on crime at the macro level (Bursik and Webb 1982; Chamlin 1989; Heitgerd and Bursik 1987; Morenoff and Sampson 1997; Taylor and Covington 1988). Using residual change scores and crime data from 1940 to 1970, Bursik and Webb (1982) found that some variables were associated with changes in specific time periods, while the same variables did not predict changes in other time periods. Bursik and Webb’s study illustrates that different neighborhood characteristics causally affect crime rate changes in a specific historical

20


time period. Chamlin (1989) also examined models of social disorganization theory and crime by including both temporally static and dynamic predictors and found that changes in ecological characteristics affected changes in the level of crime. In particular, using residual change scores, Chamlin found that changes in residential mobility and poverty affected changes in robbery, while changes in population size and the Gini index of economic concentration affected changes in homicide. Based on the findings from his temporally dynamic models, Chamlin argued that unexpected changes in the ecological structure of cities would disrupt mechanisms of informal social control, which in turn would result in an increase in crime. Until recently, longitudinal analysis of crime at the neighborhood level has been limited to using difference scores and/or residual change scores to examine changes between two time points. The use of residual change scores is attractive in ecological studies of crime for at least two reasons (Bursik and Grasmick 1992; Kubrin and Herting 2003). First, the predicted values calculated in the process of producing residual change scores automatically adjust for changes that other neighborhoods are experiencing. Thus, residual change scores remove the effects of ongoing changes that are common to other areas in a specific time period. Second, residual change scores are statistically independent of the initial level of a variable. A residual change score of zero represents the expected level of a variable at time t, given the level of a variable at time t-1. Thus, residual change scores represent unexpected changes that are not accounted for by the prior state of a variable (e.g., crime rates). Despite such attractiveness, residual change scores (or an analysis of two time points to examine changes, in general) suffer from several limitations. First, residual change scores only examine betweenneighborhood effects and fail to account for within-neighborhood variations over time (Bursik and Grasmick 1992; Kubrin and Herting 2003; Rogosa 1995). That is, with data measured at two time points, the variability of neighborhood trajectories cannot be examined and the parameters of the growth function are assumed to be the same across individual neighborhoods. Such an assumption, however, is not realistic, as it is likely that neighborhoods differ in their trajectories of crime (e.g., functional forms and rates of change). Second, as it is rather cumbersome to create residual change scores, typical applications of the method have examined only changes between two


21

time points. Pair-wise comparisons, such as Time 1 against Time 2 and Time 2 against Time 3, however, assume that changes in earlier time points are independent of changes in later time points. That is, changes between Time 1 and Time 2 do not affect changes between Time 2 and Time 3. Such an analytical approach only provides a snapshot of the overall crime trends that neighborhoods experience. Third, an analysis of two time points does not allow researchers to assess the functional form of changes over time, as a straight line is the most complex functional form that can be fitted through two points (Rogosa 1995). As illustrated by crime rates between 1960 and 2005 in the United States, cities experienced a rapid increase in crime followed by a decrease (or deceleration) of crime rates (Federal Bureau of Investigation 2006). Such non-linear trends cannot be captured with an analysis of two time points. Although an analysis of two time points provides more information than an analysis of one time point (i.e., cross-sectional analysis), the dynamic nature of changes in the level of crime and neighborhood trajectories cannot be fully examined by such a method. In order to overcome these limitations, a few recent studies have advocated for using growth curve modeling in order to analyze longitudinal crime data (Bursik and Grasmick 1992; Griffiths and Chavez 2004; Harada 2007; Kubrin and Herting 2003; Weisburd et al. 2004). For example, Kubrin and Weitzer (2003b) found that, after disaggregating homicides into three subtypes (altercations, felonies, and domestic killings), each homicide type had a different trajectory pattern over time. Furthermore, including neighborhood characteristics in their growth curve models as predictors indicated the trajectory for each homicide type was predicted by different neighborhood characteristics. Moreover, using a group-based, semi-parametric method for analyzing trajectories (Nagin 1999; Nagin 2005), an analysis of homicide rates in Chicago between 1980 and 1995 found that only 6% of the neighborhoods were classified as high-crime neighborhoods (Griffiths and Chavez 2004). While these neighborhoods experienced dramatic increases and decreases, they also maintained higher crime rates than other neighborhoods throughout the period. All other neighborhoods experienced slight changes, if any, in crime rates and remained relatively safe. The development of these studies that utilize growth curve models reflects a growing interest among criminologists in search of the

22


sources of change and stability in the level of crime at the neighborhood level. Despite a theoretical interest in the longitudinal nature of crime and neighborhood characteristics, empirical analyses have been largely hindered due to the absence of appropriate statistical models. Furthermore, many longitudinal studies of crime are based on city-level and county-level data, while social disorganization and routine activities are more likely to operate at the neighborhood level. Thus, as a more appropriate test of social disorganization theory and routine activities theory in the longitudinal context, this study examines the relationships between neighborhood characteristics and the level of crime between 1960 and 2005 at the neighborhood level. RESEARCH QUESTIONS Based upon social disorganization theory and routine activities theory, this research examines the sources of stability and change in the level of crime across neighborhoods. In particular, the following research questions guide an analysis of neighborhood characteristics and the level of crime. Research Question 1: Is there evidence of a systematic trend and individual variability in trajectories of crime over time? In order to answer this question, unconditional growth curve models with only crime variables are examined. That is, changes in crime rates are examined by analyzing linear and non-linear rates of change, without considering neighborhood characteristics. Research Question 2: Is there any association between the initial level of crime and rates of change over time? Do neighborhoods with higher initial levels of crime experience a faster increase in crime than neighborhoods with lower initial levels of crime? Do neighborhoods with a faster increase in crime experience slower non-linear change (deceleration) in subsequent time periods? In order to answer this research question, covariances among the random effects of unconditional growth curve models will be analyzed.


23

Research Question 3: What neighborhood characteristics predict the initial level and rate of change in crime rates? In order to answer this question, conditional growth curve models will be specified where neighborhood characteristics are introduced as timevariant predictors. That is, neighborhood characteristics are allowed to change over time and such time-variant characteristics are used to predict the trajectory of crime across neighborhoods. Hypotheses The following hypotheses are tested to analyze changes in neighborhood characteristics and the level of crime. In general, changes in neighborhood characteristics are hypothesized to be related to changes in crime rates. H1: Lower socio-economic disadvantage in a neighborhood is associated with lower levels of crime (decreased criminal motivation and increased stakes in conformity). H2: Lower residential mobility in a neighborhood is associated with lower levels of crime (increased informal social control and vested interests in community). H3: Lower racial/ethnic heterogeneity in a neighborhood is associated with lower levels of crime (the presence of value/norm consensus and increased informal social control). H4: A higher proportion of children living with both parents in a neighborhood is associated with lower levels of crime (increased direct control) H5: A higher proportion of immigrants in a neighborhood is associated with higher levels of crime (lack of value/norm consensus and decreased informal social control) Additionally, several hypotheses based on routine activities theory are: H6: A lower proportion of women with jobs in a neighborhood is associated with lower levels of crime (decreased guardianship and increased exposure to motivated offenders). H7: A lower proportion of workers taking public transportation in a neighborhood is associated with lower levels of crime (decreased exposure to motivated offenders).

24


H8: A lower proportion of male youths in a neighborhood is associated with lower levels of crime (the decreased number of motivated offenders) DATA Crime and census data measured at the census tract level (N=113) in Seattle in 1960, ‘70, ‘80, ‘90, 2000, and 2005 were used. The crime data in 1960, ‘70, and ‘80 were originally collected by Miethe and Meier (1994) and obtained through the Inter-university Consortium for Political and Social Research (study number 9741). The crime data in 1990, 2000, and 2005 were directly obtained by contacting the Seattle Police Department. Crime variables followed the definitions in the Uniform Crime Reports, collected by the Federal Bureau of Investigation, and included homicide, robbery, burglary, and auto theft. The crime variables were calculated as rates per 1,000 people per census tract. Neighborhood characteristics that are theoretically important for explaining the level of crime were selected based on social disorganization theory and routine activities theory. Three key theoretical concepts of social disorganization theory are socioeconomic disadvantage, residential instability, and racial heterogeneity. First, as indicators of socio-economic disadvantage, four variables included in this study were the percentage unemployed, percentage of households below the official poverty line, percentage of population over 25 years of age who do not have a high school or equivalency diploma, and percentage of households whose occupancy exceeds 1.0 person per room. These four variables were combined through principal component analysis to create a factor representing the socioeconomic disadvantage of neighborhoods. The factor loadings for these variables across time exceeded at least 0.6 for each time period and most of the factor loadings were above 0.8. The extracted factor explained 60% to 78% of the variance in the observed data for each year. Chronbach’s alpha for these variables was 0.7 to 0.85 for each year. Second, a factor representing neighborhood instability was extracted from five variables using principal component analysis. The original variables for this factor included percentage of the population


25

ages 5 and over who changed their address in the past five years, percentage vacant houses, percentage renter occupied housing units, percentage multiple-housing units (i.e., more than one housing unit per building, like an apartment building), and the percentage divorced. Factor loadings of these variables for each time period were above 0.70 and, on average, 70% of the variance of the observed data was explained by this factor for each time period. Third, using the demographic composition of neighborhoods, a measure of racial heterogeneity was created. In particular, percentage White, percentage African American, and percentage other race were extracted from the census data. While recent census data provide detailed racial categories, the 1960 and 1970 census only provided these three racial categories. An indicator of racial heterogeneity was calculated using these three racial variables. In particular, the formula used for calculating the racial heterogeneity index is 3

1−

∑p

2 i

,

i =1

where pi is the proportion of each racial category (Sampson and Groves 1989). A higher value for the index represents a greater degree of racial heterogeneity in neighborhoods. The maximum value of the index is 0.667 when a neighborhood is 33% White, 33% African American, and 33% other race, while the minimum value is zero when one racial group dominates a census tract. In addition to these three factors and variables representing key concepts of social disorganization, other demographic characteristics were also included. First, following Shaw and McKay’s study (1942; 1969), the percentage of the population that is foreign born was included in the analysis. Second, the percentage of children living with both parents was included in order to assess direct control of youths by parents. Finally, a series of variables were identified from routine activities theory that indicate the opportunity structure of neighborhoods. As a measure of exposure to motivated offenders, the percentage of workers who use public transportation was included. Furthermore, the percentage of employed females was included as a measure of exposure to motivated offenders and level of household guardianship. Third, the percentage of males who are between 15 and 24 years old was used to

26


control for the proportion of criminally prone individuals (i.e. motivated offenders). EXPLORATORY SPATIAL DATA ANAYSIS Prior to the analysis of longitudinal changes in neighborhood characteristics and the level of crime, the data were explored in order to uncover spatial effects underlying the data. Table 2.1 shows the Moran’s I spatial autocorrelation coefficients for crime at each time point. In order to analyze spatial autocorrelation, the queen specification was used to create a spatial weights matrix. Thus, spatial units were defined as neighbors when they either shared boundaries or vertices (i.e., the specification is called the queen specification based on the movement of the queen piece in the game of chess). A positive value of Moran’s I indicates that similar values of a variable are spatially clustered, while a negative value of Moran’s I indicates that dissimilar values of a variable are spatially clustered. For example, in 1960, the Moran’s I was positive and significant (0.352), indicating that neighborhoods with high levels of homicide were spatially clustered and that neighborhoods with low levels of homicide were spatially clustered. Overall, the results indicated that all crime measures were spatially clustered, which is typical for neighborhood crime rate data. What is more important for the subsequent analysis of associations between neighborhood characteristics and the level of crime is whether the spatial autocorrelation remains after taking into account neighborhood characteristics. While many regression models, including ordinary least squares (OLS), assume independently distributed error terms, statistical analysis on spatial data often results in spatially correlated residuals. Violating the assumption of independently distributed residuals in OLS regression affects coefficient estimates, standard error estimates, and statistical tests depending on underlying spatial processes (i.e., spatially autocorrelated dependent variable or spatially autocorrelated errors). Thus, Table 2.1 also presents the results of Moran’s I spatial autocorrelation coefficient of OLS residuals after neighborhood characteristics were included in the OLS regression models. The results indicated that the residuals of the OLS regression of crime on neighborhood characteristics were significant for only six of the twenty regression models (five years *


27

four crime types). Furthermore, of the six significant Moran’s I, four were weak in strength (approximately 0.1 and below). That is, although all crime measures were spatially clustered, the results indicated the spatial concentration was explained to a large extent by neighborhood characteristics. Thus, it was concluded that statistical problems caused by spatial data were not considerable for the Seattle data that this research examined and that aspatial statistical models were likely to be valid (although a supplemental analysis was also conducted using spatial panel models). Table 2.1 Moran’ I Spatial Autocorrelation Coefficients of Crime Rates and OLS Residuals Homicide Robbery Homicide

Residuals

Robbery

1960

0.352

0.016

0.339

0.027

1970

0.529

0.218**

0.460

0.271**

1980

0.522

0.122**

0.434

0.054

1990

0.301

-0.055

0.406

-0.042

2000

0.081

-0.110

0.443

-0.068

Burglary

Residuals

Auto Theft

Burglary

Residuals

Auto Theft

Residuals

1960

0.554

0.035

0.599

1970

0.462

0.020

0.461

0.098*

1980

0.424

0.034

0.498

0.061

1990

0.463

0.028

0.498

0.114**

2000

0.331

0.038

0.431

0.067*

-0.016

METHOD I: GROWTH CURVE MODELS This study focuses on the sources of stability and change in the level of crime at the neighborhood level over time. Are there any systematic

28


patterns in crime rate changes? Is there individual variability in pathways of neighborhoods in their criminal careers? What structural characteristics predict the initial level of crime and rates of change over time? A growth curve model is suitable for answering these research questions because the method analyzes a trajectory at the group level along with individual variability in growth patterns (Bollen and Curran 2006; Bursik and Grasmick 1992; Kubrin and Herting 2003; RabeHesketh and Skrondal 2002; Singer and Willett 2003). The basic idea behind the growth curve model is to estimate regression lines (or curves) for each individual observation (Figure 2.1). It is quite possible that such regression lines vary considerably in their functional forms across individuals. Some may show an increase, while others show a static pattern over time. The varying regression lines at the individual level are then smoothed to produce summary measures (e.g., mean and variance) that characterize the average trend for individuals as a whole (Figure 2.2). It is this unobserved curve that is believed to underlie and to have given rise to the observed data. While various regression lines based on observed data reflect individual level patterns, the unobserved curve represents the group level trend. Formally, a growth curve model can be considered as a multilevel model where there are two levels (Bollen and Curran 2006; Bursik 2001; Rabe-Hesketh and Skrondal 2002; Singer and Willett 2003). The first level equation that assesses within neighborhood changes is

yit = α i + β i (Time ) it + ε it

where yit is a crime rate for neighborhood i at time t, and α i and β i are an intercept and slope that characterizes the trajectory pattern for each neighborhood. The subscript i of α and β indicates possible variation across individual neighborhoods in trajectory patterns. In order to capture this variation across individual neighborhoods, the second level equations that express the intercept and slope are formed as α i = µα + ξ and β i = µ β + ξ . αi

βi

The ξ indicates the extent of deviation from the mean intercept and slope for each neighborhood in the trajectory patterns. Substituting α i

30

Trajectories of Burglary Rates for Five Neighborhoods

Block Group

20

3532 002 3536 005

10

3550 002 3564 001

0

3564 004

2000

2001

2002

2003

2004

2005

Figure 2.1 Five Trajectories of Neighborhood Crime Rates (Hypothetical Data)

0

10

20

30

The Averaged Trajectory and Means of Burglary Rates

2000

2001

2002

2003

2004

2005

Figure 2.2 An Averaged Trajectory of Burglary Rates across Five Neighborhoods

30


and β i in the level-1 equation gives the combined model:

yit = {µα + (Time ) it µ β } + [ξ + ξ (Time ) it + ε it ] . αi

βi

The terms in the first bracket reflect a fixed component, while the terms in the second bracket reflect a random component. That is, while the fixed component captures the overall trajectory pattern across neighborhoods, the random component reflects individual variability in trajectory patterns. Using the same notations, a quadratic trajectory can be expressed as: Level 1: yit = α i + β 1i (Time ) it + β 2 i (Time ) it + ε it 2

α i = µα + ξ

αi

Level 2: β1i = µ β 1 + ξ

β 2i = µβ 2 + ξ

β 1i

β 2i

Using a growth curve for the analysis of change is advantageous for several reasons (Bollen and Curran 2006; Bursik and Grasmick 1992; Duncan et al. 1999; Kubrin and Herting 2003; Singer and Willett 2003). First, a growth curve model provides summary measures (e.g., mean and variance) to characterize an underlying trajectory that has given rise to a large set of observations. For example, the initial level of crime rates and the shape and rates of change over time can be analyzed with a growth curve. Second, various functional forms of change over time can be analyzed. For example, changes can be linear (increase or decrease) or quadratic (acceleration or deceleration). Third, both time-invariant and time-variant covariates can be incorporated in analytic models to explain variability in the initial level of crime and rates of change over time at the individual level. For example, economically disadvantaged neighborhoods may have higher levels of crime at the start, as well as a faster increase in crime over time. Finally, covariation between the initial level and rates of change can also be examined. For example, those neighborhoods that start with a high crime rate may experience a faster increase in crime rates over time. In short, a growth curve model allows researchers to simultaneously assess the overall trends (the group level trajectory), along with individual variability in such trends. This is especially attractive for a temporally dynamic model of social disorganization


31

theory, as the growth curve model allows for variation in neighborhood trajectories of crime rate changes, as well as variations in structural characteristics over time. Furthermore, the growth curve model allows researchers to fully assess continuous changes over time, as opposed to temporal autoregressive structures in which snap shots of each time point are examined. RESULTS: GROWTH CURVE MODELS As a modeling strategy, a series of growth curve models were examined. First, two sets of models were compared where the first was a linear trajectory model, while the second model was a non-linear trajectory model with a quadratic term. The purpose of the analysis was to identify trajectory forms for each crime type and to analyze the goodness of fit of the trajectory in explaining varying levels of crime over time. An appropriate model specification could be evaluated in two ways: 1) by comparing Bayesian Information Criterion (BIC) across two models; and 2) by comparing predicted values with observed values. Table 2.2 presents the estimates of growth curve parameters for trajectories of each crime time, as well as fit statistics to assess model fit. Before interpreting growth curve parameters, model fit statistics can be looked at to compare linear and non-linear models. Unlike Rsquared in ordinary least square (OLS) regression, which shows the amount of explained variance, BIC in and of itself is not interpretable. BIC, instead, can be used to make comparisons across models. In general, the model with the lower BIC value is the preferred model. Furthermore, Raftery (1996) suggests that absolute differences in BIC of 0-2 are weak evidence, 2-6 are positive evidence, 6-10 are strong evidence, and 10 and above are very strong evidence. Using these rules, quadratic models were found to be better models for all crime types. BIC values for quadratic models were smaller than for linear models and absolute differences in BIC indicated strong evidence for such conclusions (i.e. absolute differences in BIC exceeded 10). Given crime rates increased sharply in the 1960’s and 1970’s and gradually decreased subsequently, quadratic trajectories of crime trends in these time periods were in accord with initial expectations.

BIC

Time squared

Time

Intercept

-756.9

-834.7

3915.5

3343.3

(0.142)

(0.652)

(0.003)

(0.091)

3.853

(0.201)

1.089

5185.9

(0.264)

-0.907

4559.9

(0.223)

-3.4842

(0.936)

15.139

(0.701)

10.831

Quadratic

Burglary

(1.890)

20.35

Quadratic Linear

-0.789

(0.011)

(0.003)

0.284

(0.479)

3.184

Linear

Robbery

-0.013

0.048

(0.010)

(0.013) -0.009

0.069

Quadratic

0.102

Linear

Homicide

3964.4

(0.169)

2.775

(0.444)

4.405

Linear

3836.2

(0.084)

0.203

(0.381)

1.815

(0.330)

5.015

Quadratic

Auto Theft

Table 2.2 Baseline Growth Curve Models of Homicide, Robbery, Burglary, and Auto Theft Rates per 1,000 People


33

Estimates of growth curve parameters can be interpreted to further examine the nature of change over time for each crime type. The results of quadratic growth curve for homicide indicated that an average homicide trajectory was characterized by: 2

Homicidet = 0.069 + 0.048Timet − 0.013Timet

The first term, intercept, indicated the predicted level of homicide for time = 0. That is, the model indicated the predicted level of homicide in 1960 was 0.069 per 1,000 population. As the linear term was positive and the non-linear term was negative, this indicated the homicide trajectory had an initial increase followed by a decrease over time. By substituting time = 1 for 1970, time = 2 for 1980, time = 3 for 1990, time = 4 for 2000, and time = 4.5 for 2005, predicted levels of homicide based on the modeled trajectory could be computed. In particular, the predicted levels of homicide were 0.069 (1960), 0.104 (1970), 0.113 (1980), 0.096 (1990), 0.053 (2000), and 0.022 (2005). As observed means for homicide in these time periods were 0.058 (1960), 0.122 (1970), 0.104 (1980), 0.089 (1990), 0.051 (2000), and 0.037 (2005), it was concluded that the modeled trajectory closely represented the observed changes in homicide. Trajectory forms of other crime types can also be represented using the same notation. 2

Robberyt = 1.089 + 3.853 * Timet − 0.789 * Timet

2

Burglaryt = 10.831 + 15.139 * Timet − 3.484 * Timet 2

AutoTheftt = 5.015 + 1.815 * Timet + 0.203 * Timet

Similar to homicide, trajectories for robbery and burglary were characterized by an initial linear increase and subsequent non-linear decrease over time. As comparisons between predicted values and observed means in Table 2.3 indicate, the modeled trajectories closely followed observed trends for both of these crimes between 1960 and 2005. Unlike homicide, robbery, and burglary, the trajectory form for auto theft was characterized by an initial linear increase followed by a non-linear increase (i.e., acceleration). Comparing the observed means to the predicted level of auto theft indicated that the model closely followed observed trends in auto theft during these time periods. Thus, the answer to research question one was that all crime trajectories between 1960 and 2005 were best captured by non-linear

0.058 0.123 0.104 0.089 0.051 0.038

1960

1970

1980

1990

2000

2005

Observed

0.022

0.053

0.096

0.113

0.104

0.069

Predicted

Homicide

2.743

2.854

6.142

4.679

4.340

0.832

Observed

2.450

3.877

5.547

5.639

4.153

1.089

Predicted

Robbery

12.442

10.499

22.955

27.200

26.141

8.249

Observed

8.406

15.643

24.892

27.173

22.486

10.831

Predicted

Burglary

17.013

14.368

12.125

7.051

8.437

3.834

Observed

17.293

15.523

12.287

9.457

7.033

5.015

Predicted

Auto Theft

Table 2.3 Comparisons of Observed Means and Predicted Levels of Crime Based on the Baseline Growth Curve Models


35

trajectories. For homicide, robbery, and burglary, the trajectories were best represented by an initial linear increase, followed by non-linear decrease (i.e., deceleration). For auto theft, the trajectory was best represented by initial linear increase, followed by non-linear increase (i.e., acceleration). Such descriptions of trajectories are group-level trends, however. It is reasonable to assume that crime rate trajectories for each neighborhood will vary. In order to examine how individual neighborhoods vary in their trajectory forms, random components of the growth curve were analyzed. Table 2.4 presents the variancecovariance matrix of growth curve parameters (intercept, linear term, and non-linear term) that showed variability in initial level and rate of change at the individual neighborhood level. The diagonal elements of the matrix represent the individual variability (variance) in the estimates of the intercept, linear, and quadratic terms. As all of these variance estimates were statistically significant, the results indicated that the initial level of crime, as well as rate of linear and non-linear change, varied considerably for each neighborhood. For example, although the estimated mean level of homicide in 1960 was 0.069, the levels of homicide varied significantly for each neighborhood (variance = 0.015, p

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