4
Crime and Neighborhood Change

Jeffrey Fagan


There is broad agreement in both popular culture and social science that rates of crime and delinquency vary across neighborhoods. Yet researchers and citizens disagree on whether these differences are attributable to characteristics and relationships among of persons who live in neighborhoods, or if there are factors about the neighborhoods themselves that influence crime rates independently of the persons who live there. The question becomes further complicated as neighborhoods change over time, since both the composition of the neighborhoods and the broader features of those neighborhoods are changing simultaneously. The challenge in this chapter is to assess research on the influence of neighborhood change on changes in crime rates and to determine the unique knowledge that neighborhood studies contribute to the understanding and control of crime.

Accordingly, this chapter reviews research on factors that influence changes in crime rates between and within neighborhoods in cities over time. First is a brief review of local area studies of neighborhood and crime, focusing on neighborhood “effects”: the structures and processes in neighborhoods that are thought to affect trajectories of crime over time. Next the chapter identifies challenges in theory, measurement, and analysis that affect estimates of why and how neighborhood crime rates change, including size and definition of spatial units, mutual and reciprocal relations between units, the endogeneity of criminal justice enforcement and neighborhood ecology, the influences of macro-changes (i.e., the political economy of cities) on local crime rates, constraints of observational and administrative data, theoretical specifications of neighborhood and measurement and analytic strategies. Illustrations from recent research



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4 Crime and Neighborhood Change Jeffrey Fagan There is broad agreement in both popular culture and social science that rates of crime and delinquency vary across neighborhoods. Yet researchers and citizens disagree on whether these differences are attributable to char- acteristics and relationships among of persons who live in neighborhoods, or if there are factors about the neighborhoods themselves that influence crime rates independently of the persons who live there. The question becomes further complicated as neighborhoods change over time, since both the composition of the neighborhoods and the broader features of those neighborhoods are changing simultaneously. The challenge in this chapter is to assess research on the influence of neighborhood change on changes in crime rates and to determine the unique knowledge that neigh- borhood studies contribute to the understanding and control of crime. Accordingly, this chapter reviews research on factors that influence changes in crime rates between and within neighborhoods in cities over time. First is a brief review of local area studies of neighborhood and crime, focusing on neighborhood “effects”: the structures and processes in neighborhoods that are thought to affect trajectories of crime over time. Next the chapter identifies challenges in theory, measurement, and analysis that affect estimates of why and how neighborhood crime rates change, including size and definition of spatial units, mutual and reciprocal rela- tions between units, the endogeneity of criminal justice enforcement and neighborhood ecology, the influences of macro-changes (i.e., the politi- cal economy of cities) on local crime rates, constraints of observational and administrative data, theoretical specifications of neighborhood and measurement and analytic strategies. Illustrations from recent research 

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 UNDERSTANDING CRIME TRENDS on crime trends in New York City highlight the challenges of estimating neighborhood influences on crime trends. INTRODUCTION For several decades, research on neighborhood and community variation in crime and delinquency focused on identifying cross-sectional between- area differences in rates of violent or property crime. Often constrained by data limitations, these studies have adopted a static view of community or neighborhood, assuming that differences in crime rates between neighbor- hoods were stable over time, and that these differences reflected differences in the characteristics of communities that were stable over time (see, for example, Bursik, 1984). Shaw and McKay (1943), for example, showed that crime rates were predictably higher in socially disorganized communities, independent of the residents of those areas. More recently, Land, McCall, and Cohen (1990) suggested that the social and economic correlates of crime were stable over time and across different spatial aggregations. More recent studies have adopted a dynamic, developmental perspec- tive to the study of social and economic behaviors in communities and neighborhoods. Recent interest in neighborhood effects has produced new research on small-area variations in child development and child maltreat- ment, teenage sexual behavior and childbearing, school dropout, home ownership, and several indicators of health, suicide, disorder, drug use, and adolescent delinquency (see, e.g., Brooks-Gunn et al., 1993; Coulton, Korbin, Su, and Chow, 1995; Crane, 1991; Gould, 1990; Gould et al., 1990; Harding, 2003; Wilkinson and Fagan, 1996). These studies make strong claims that growing up in neighborhoods characterized by concentrated socioeconomic disadvantage has enduring consequences on child and adolescent development. These disadvantages are thought to affect adults as well, attenuating their access to decent hous- ing, job networks that provide access to stable family-sustaining wages, and quality education to prepare them for changing labor markets (Jargowsky, 1997; Massey and Denton, 1993; Wilson, 1987).1 But fewer studies have recognized that neighborhoods are dynamic 1 Not everyone agrees, however, citing weak evidence that there are neighborhood effects independent of the consequences of growing up in poor families on individuals that are net of the aggregate effects on poor people concentrated in poor places (Jencks and Mayer, 1990; see, generally, Raudenbush and Sampson, 1999). Indeed, just how important neighborhoods are can be gauged by the relative contributions of neighborhood effects and individual factors in multilevel studies of covarying change over time (Raudenbush and Sampson, 1999). Recent work by Harding (2003) suggests that after adjusting for the selection biases that produce the concentration effects of poor people in specific neighborhoods, there are important negative effects of growing up in a low-poverty neighborhood on school dropout and teen pregnancy.

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 CRIME AND NEIGHBORHOOD CHANGE entities that change over time (like people), and that these transformations are likely to produce complex and changing outcomes in several indicators of social and economic life, including crime (see Sampson, Morenoff, and Gannon-Rowley, 2002, for a review). This perspective reflects a large body of research that recognizes that rates of social and health behaviors vary in communities over time and that the characteristics of communities influence those rates. That is, communities have natural developmental histories that parallel changes in social behaviors of persons over the life course. And while it naturally follows that neighborhood effects would also influence crime, there has been less attention in recent studies to the question of how changing neighborhood contexts influence crime (see, for exceptions, Bellair, 2000; Fagan and Davies, 2004). The few studies thus far on crime and neighborhood change point to complex interactions and (nonrecursive) feedback processes between crime and the social dynamics and compositional characteristics of neighbor- hoods (Bellair, 2000). Other studies (e.g., Fagan et al., 2007; Morenoff, Sampson, and Raudenbush, 2001) suggest that processes of diffusion and contagion explain changes over time in homicides and violence as neighbor- hoods change (see, also, Ludwig and Kling, 2007). Taylor and Covington (1988) examined crime rates in Philadelphia neighborhoods to show how neighborhood change, including gentrification, increased both relative deprivation in stable but poor areas and created new crime opportunities that raised the risks of crime in the improving adjacent ones. Schwartz (1999) linked changes in housing prices to declines in violent crime across New York City police precincts, net of changes in social indicators, and Tita, Petras, and Greenbaum (2006) tied violent crime to weaker housing prices. And some researchers discount the importance of changes in social ecology, whether citywide or in specific neighborhoods, in explaining recent changes in crime (Zimring, 2006). But these studies are relatively rare data points that offer limited answers to the larger question of the relationship between neighborhood change and crime. The science of studying crime and neighborhood change is still developing, both conceptually and methodologically. Since the early Chicago School work (described below), few studies have applied a developmental perspective to chart the natural history of neighborhood change and crime in different areas of modern cities. While neighborhood change is not a necessary condition to produce changes in crime, the broad fact of differ- ences within and between neighborhoods in crime rates over time challenges theories that are built on cross-sectional, time-limited differences in violence rates from one area to the next. This chapter reviews research on neighborhood change and crime and identifies challenges in theory, measurement, and methods. The study of neighborhoods over time has created a rich body of sociological theory

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 UNDERSTANDING CRIME TRENDS to conceptualize space and its effects on people, both individually and as collectives or aggregates. But studies of neighborhood change have been rare and usually limited to a few neighborhoods in single cities. Most rely on one of two types of research enterprises: qualitative methods that focus on social organization and exchanges between persons and groups, or observational data on social, economic, or health indicators. Few have been prospective, and most have limited their theoretical questions to social structure. Analytic methods that model within-person change can be applied to neighborhoods, but the translation is not simple, and it may be the case that methods have not yet developed to address the complicated questions of endogeneity of crime and area change, the spatial dependence of neighborhoods and the shared and diffused processes of change across natural or administrative borders, or the simultaneity of crime changes and neighborhood changes. After a review of studies on crime and neighbor- hood change, the chapter discusses five challenges that confront research in this area. These challenges are illustrated with data from a panel study of violent crime in New York City neighborhoods for the period 1985-2000. The chapter concludes by outlining an agenda for building an infrastructure of data that will sustain research on neighborhood differences in crime. CRIME AND NEIGHBORHOOD CHANGE Interest in neighborhood change as a predictor of changing crime rates can be traced to the Chicago School traditions of studying “natural social areas” whose identities are the products of complex social and economic factors, sometimes endogeneous (Park, Burgess, and McKenzie, 1925) and sometimes imposed from the outside by political economic dynamics (Logan and Molotch, 1988; Suttles, 1970). Despite this interest, there have been surprisingly few longitudinal studies of neighborhood change and changes in crime rates. The good news is that these few studies converge in several areas to inform theory and research. Physical and social deterioration is a persistent theme of neighborhood change in these studies. Taub, Taylor, and Dunham (1984) used survey and archival data and physical observations to weave a story about crime and neighborhood change in eight Chicago neighborhoods. They report on a reciprocal dynamic in which crime experiences—both direct and vicarious victimization—degrade residents’ investments in social control and upkeep. These visual cues of deterioration, together with subjective evaluations about the likelihood of crime and other adverse events, in turn cued citi- zens that the neighborhood had approached a racial “tipping point” that would trigger a sharp spike in crime, motivating some residents to move away. Schuerman and Kobrin (1986) also implicated physical deterioration in the shift of a neighborhood from low to high crime. They used a series

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 CRIME AND NEIGHBORHOOD CHANGE of cross-sectional analyses to identify three distinct stages of neighborhood change—emerging, transitional, and enduring—that characterized the natu- ral history of neighborhood evolution from a stable low-crime area into a high-crime area.2 Harrell and Gouvis (1994) also used a residual change analysis over two decennial censuses to predict increases in crime associated with changes in neighborhood ecology. Their predictions weakened in areas where residential mobility increased, a response to deterioration similar to the narratives voiced by respondents in the Taylor et al. (1984) survey. A second thread in these studies is the reciprocal influence of adjacent neighborhoods to increase crime rates. Taylor and Covington (1988) used residual change scores in census variables (1970 and 1980) to assess two indicators of violence (aggravated assault, murder, and nonnegligent man- slaughter) in 277 Baltimore neighborhoods. Their study used two time points, not the 10 between the decennial censuses. The two most salient neighborhood changes during the decade were the emergence of a large number of gentrifying neighborhoods and the descent of several older, minority neighborhoods into an “underclass” status. They focused on the process of gentrification, located neighborhood change in both relative deprivation and social disorganization theories, and identified components of violence attributable to each process. As neighborhoods became more homogeneously poorer and socially isolated, they experienced increas- ing violence. In the gentrifying neighborhoods, violence increased as their status and stability increased relative to the increasingly poor adjacent neighborhoods. Morenoff and Sampson (1997) also examined this dynamic, focusing on violent crime over three decades in Chicago’s 862 census tracts as a function of population loss and the concentration of socioeconomic disadvantage. Using residual changes in the decennial census to measure neighborhood ecology, they identified a dynamic process in which homicide animated population loss, and the replacement process induced higher rates of spa- tially concentrated homicide and patterns of diffusion to other neighbor- hoods experiencing similar changes. They identified race-specific effects in homicide, spatial proximity to homicide, and socioeconomic disadvantage associated with African American population gains and white population loss. Heitgerd and Bursik (1987) also examined neighborhood change from 1960-1970 and analyzed juvenile court referrals to show that even stable, 2 Changes signaling neighborhood deterioration and rising crime rates include a shift from single to multiple-family dwellings, as well as increases in residential mobility, unrelated individuals and broken families, the ratio of children to adults, minority group populations, women in the labor force, and nonwhite and Spanish-surname population with advanced education, structural domains long associated with social area theories of crime.

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 UNDERSTANDING CRIME TRENDS well-organized communities could have high rates of delinquency when the adjacent neighborhoods experienced rapid racial change. Finally, several studies have analyzed neighborhood change to iden- tify turning points in the natural history of neighborhood development to pinpoint when crime rates change and grow. Bursik and Webb (1982) updated Shaw and McKay’s (1943) original data on juvenile court refer- rals Chicago’s 74 local community areas to show that ecological shifts in neighborhoods were associated with deflections in a neighborhood’s crime rates. Analyzing these data once again, Bursik (1984) identified correlates of neighborhood crime rates in each decade from 1940 to 1970. The sharp change in correlates in 1950 suggested an ecological shift that was linked to a turning point in neighborhoods’ crime rates. Bursik and Grasmick (1992, 1993) used hierarchical linear models to estimate crime rate change from 1930 to 1970, again identifying an ecological shift in 1950 that preceded increases in crime. More recent work has charted variation in trajectories of crime— specifically, homicide—in neighborhoods over time (e.g., Fagan and Davies, 2007; Griffiths and Chavez, 2004; Kubrin and Weitzer, 2003; Weisburd et al., 2004). The empirical solutions identify numerous patterns of rise and fall in homicide rates over time in neighborhoods in cities, using initial starting points of social structural characteristics of neighborhoods at the outset of the panel as predictors. But these studies don’t link changes in homicide to changes in neighborhoods and are silent on the contemporane- ous changes in neighborhood and crime. Although each of these studies offers important clues about neighbor- hood change and crime, they also are limited in some important ways. First, most have used census tracts to bound and characterize neighborhoods. The older Chicago studies are an exception, but the 74 areas are large, heterogeneous aggregates of several smaller neighborhoods, a strategy that might mask important influences in smaller corners of these larger areas. For smaller areal units, there is no consensus whether census block groups or tracts or other boundaries—such as street segments in Weisburd’s Seattle analysis—are either socially meaningful or theoretically appropriate to study either community structure or social processes (see Bursik, 1988). There are alternatives to using either administratively drawn boundaries or micro-units. For example, Fagan and Davies (2004), as well as Fagan, West, and Holland (2003), use boundaries drawn in New York that integrated residents’ perceptions of the natural boundaries of their neighborhoods, proscribed by their attribution of shared belonging among residents, with census and other administrative boundaries that provide data conveniences for consistent measurement and comparability across studies (see Jackson and Manbeck, 1998). Research with these alternate social-spatial configu- rations may yield more accurate units to specify social processes, but these

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 CRIME AND NEIGHBORHOOD CHANGE may run into other types of data problems and limit comparability between studies. Defining the appropriate space is a conceptual as well as empirical challenge, as illustrated later on. Second, because census data are collected decennially, researchers inter- ested in neighborhood change have limited their study periods to these fixed 10-year intervals. Other studies use much shorter time windows, limiting their analyses to shorter periods in which the window for estimating change may be artifactually short. Yet crime trends usually don’t cooperate with the attributes and characteristics of the decennial censuses. Crime trends can be quite volatile within a decade or even span decades, and inferences about changes in crime rates at a decade apart can be quite misleading (see, for example, Fagan and Davies, 2004, and Fagan, Davies, and Holland, 2007, on the roller coaster of crime rates in New York from the early 1980s through 2000). The nonlinear patterns of these changes demand not only more frequent and disaggregated measurement of local conditions, but also more complex functional forms for analysis, including quadratic terms for time parameters to allow for curvilinear changes in crime rates as well as the predictors of crime. Third, studies of neighborhood change in crime rates vary in the speci- ficity of the crime form and the theoretical linkages that would predict changes in specific types of crime. Some studies specify linkages to violence based on carefully specified theories, and others measure changes in more global measures of crime without disaggregating crime into dimensions that might be differentially predicted by alternate theories. For example, Wilson and Kelling’s (1982) theory of “broken windows” suggested that signs of disorder launched a contagious process that signaled to would-be criminals that there was no guardianship in an area, in turn leading to higher crime rates. Their general theory had no correspondence to any specific crime type, and subsequent empirical tests showed quite limited predictive power for any specific form of crime (Harcourt, 2001; Sampson and Raudenbush, 1999). In contrast, Taylor and Covington (1988) hypoth- esized and confirmed that the juxtaposition of contrasting trajectories of change may accelerate violence by creating targets of robbery opportunity in newly gentrified areas adjacent to chronically poor ones, but not neces- sarily other crimes. These studies provide robust evidence of variation in the rates of change over time in crime between spatial units in cities, variation that cannot be explained simply by aggregating the social attributes and characteristics of individuals in these areas. They also contain lessons for theory and policy. Making ecological claims about factors that have variable effects risks theoretical error and possibly policy missteps. For example, cities experiencing steep crime declines may in fact have localized crime trends that either oppose the aggregate trend across areas, or that may mask more

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 UNDERSTANDING CRIME TRENDS complex if not conflicting results in local areas, results that may challenge the broader citywide claim when viewed as a function of policy instruments (e.g., policing) or theoretically salient factors (e.g., immigration, the siting and form of public housing). Also, the benefits and burdens of declining crime in cities may not be shared by all citizens of a city. If the rise and fall in crime trends over time between neighborhoods varies by gender, age, or race, there may be local conditions that expose these population groups to—or inoculate them from—harm. Accordingly, these potential disparities raise the stakes in advancing the science of studying crime and neighbor- hood change, especially when crime rates are rising and falling at different rates and in different directions in neighborhoods in a city, and when other cities are experiencing similar volatility at the same time. A parallel question is the extent of covariation between neighborhood change and crime trends. There is good evidence linking neighborhood differences in social structure and other ecological factors to differences in crime rates and, more recently, to the growth and contraction of crime (Fagan and Davies, 2004). But there is less evidence about whether struc- tural or other types of changes in neighborhoods are causally linked to changes in neighborhood crime rates. And little is known about whether the pace of changes in neighborhoods itself can influence crime rates. So conceptualizing and measuring neighborhood change on these putative predictors of neighborhood crime trends also raise research challenges. FIVE CHALLENGES On both ends of this question, our understanding of patterns and trends in neighborhoods and crime trends is influenced by our choices of spatial units, crime specifications, theoretical perspectives, and analytic methods, as well as the limitations of measurement. These decisions influ- ence both the substantive claims of research and their compatibility with other studies. There also are larger conceptual questions about how one thinks about space within cities and the interdependencies of these spatial units. Different spatial units matter in different way, depending on the ques- tion. In this section, these challenges are identified and illustrated. What Spatial Resolution? One simple empirical fact emerging from neighborhood studies is that the extent of observed heterogeneity in patterns over time in cities depends on the size of the spatial area studied. The size of the area and its spatial resolution depend on the question at hand, and the selection of a spatial unit thus becomes a theoretical question. But the variation of units in neigh- borhood studies begs the question of how area size affects the estimation

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 CRIME AND NEIGHBORHOOD CHANGE of neighborhood effects. Bursik and Grasmick (1993) argue that findings are robust across units of different sizes, whereas Coulton, Korbin, Su, and Chow (1995) say that unit size makes a difference. Whether unit size mat- ters because of aggregation biases or because of the theoretical question at hand is difficult to disentangle. Weisburd et al. (2004, p. 291) analyzed changes in crime rates over 14 years in street segments, which are two or more faces on both sides of a street between two intersections. Using group-based trajectory modeling with a poisson distribution (Nagin, 2005; Nagin and Land, 1993), they identified 18 distinct trajectories of crime, using aggregate counts of crime incidents. No tests were reported to distinguish the 18 groups on dimen- sions of neighborhood social structure or social organization. Weisburd et al. (2004) also reported temporal heterogeneity among the street segments: Eight trajectories were stable (accounting for 84 percent of the total street segments), three were increasing, and seven were decreasing over time. Although several factors may explain the high degree of heterogeneity in the Seattle study, two stand out. First, the fine resolution of the spatial unit and the use of general (multidimensional) crime categories yielded numerous and complex micro-trends over time. There were 29,849 street segments in Seattle, and over 2 million crime incidents over the 14-year period that were linked to specific geographic coordinates and “placed” on a block face. Nearly one in five was eliminated because they occurred at street intersections and could not be assigned to a street segment. With this many data points and observations, complex and diverse patterns are not surprising, especially over a lengthy period of observation. Whether these distinct patterns reflect real—theoretically meaningful—differences or noisy data is hard to sort out. Second, five crime categories were used to characterize incidents. The most frequent were Uniform Crime Reports index crimes (11.4 percent), and nontrivial traffic violations the least com- mon (4.7 percent). If different neighborhood configurations and social ecologies are associated with different crime categories, the Seattle study captured four dimensions at once: time, ecological risk, temporal change, and crime type. Fine resolution in trends might be expected when the four dimensions are collapsed. Weisburd et al. were interested in street segments because of their concern for identifying the “hot spots” of crime and the prevention poten- tial for focusing limited legal resources on places where crime risks are highest. Other studies also are concerned with the effects of policing on crime trends but use larger spatial aggregations, such as police precincts in New York (Fagan, West, and Holland, 2003; Corman and Mocan, 2000; Rosenfeld, Fornango, and Rengifo, 2007) or smaller police units such as beats and districts in Chicago (Papachristos, Meares, and Fagan, 2007). These are administratively defined areas that reflect the units where police

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0 UNDERSTANDING CRIME TRENDS resources are allocated and managed and are conveniences for compiling data and examining variation in how police deploy resources. They also have the advantage of remaining stable over time.3 While precincts may have had social meaning at one time, they now are socially and economi- cally heterogeneous areas whose value for testing theories of social control is contested (see, e.g., Wooldredge, 2002). The limitations on administra- tive borders may be most important in studies that attempt inferences in administrative areas where distinct population subgroups regularly interact with legal actors. Studies using police district aggregations often control for the dif- ferences in their social makeup by including both covariates for relevant population characteristics and fixed effects for the districts or precincts. For example, Papachristos et al. (2007) examined the effects of a gun vio- lence suppression program using police beats in Chicago. Chicago police departments are organized into 28 police districts, and each district is then subdivided into beats. The beats were more homogeneous and socially meaningful than the larger districts, and Papachristos et al. were able to focus on specific areas where police efforts and crime both were concen- trated. They examined crime trends over 84 police beats in 4 of Chicago’s 28 police districts, showing strong downward trends for all beats but steeper slopes for the experimental group. They used propensity scores to identify treatment effects in a quasi-experimental design, controlling also for trends in other areas of the city. The use of beats struck a compromise between the artificiality of police boundaries and the scale of area that would be most likely to capture networked offenders in small social spaces. Papachristos controlled for the mutual influences of these spaces by includ- ing a measure of spatial dependence (Moran’s I). Fagan et al. (2003) and others (e.g., Corman and Mocan, 2000; Messner et al., 2007; Rosenfeld et al., 2007) examined the effects of policing policies—order maintenance policing, drug enforcement—on crime rates in New York’s 75 police precincts. These precincts are large, with an average population of over 110,000 persons in 2000, and variable in size (standard deviation = 50,194). Some precincts are more racially and economically diverse than others and often include several smaller, more homogeneous neighborhoods. Other precincts include commercial areas that were virtually empty at night (Wall Street) or with different daytime and nighttime popu- lations.4 Police resources are allocated in precincts based on crime trends and patterns, and within precincts, specific beats are resourced in real time. 3 However, New York City added a precinct in 1993, at the outset of the crime decline that lasted a decade. 4 For example, the 22nd precinct is Central Park, where there is no population and little crime overall.

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 CRIME AND NEIGHBORHOOD CHANGE These differences matter. When Fagan et al. (2003) further disaggregated precincts into neighborhoods to reestimate local area affects of policing on crime, they reported different predictors of crime patterns in neighborhoods over time compared with the predictors at the precinct level. Spatially smaller micro-trends, such as the ones detected by Weisburd et al. (2004) in Seattle, or the neighborhood models identified by Fagan et al. (2003) and Fagan and Davies (2004) in New York, may be masked in larger spatial aggregations, such as precincts or police districts. Covariates that control for compositional differences between precincts usually are computed from aggregations of census tract data. These aggregations of multiple neighborhoods in police districts raise risks of identification problems—if crime trends are a function of local social area or neighbor- hood effects (crime markets, population concentrations), these smaller area effects may be masked when heterogeneous, multineighborhood police districts or precincts are the unit of analysis. The most common spatial unit used in analyzing crime trends (and many other neighborhood effects) is the tract (Hipp, 2007a,b; see also Sampson et al., 2002, for more detail). Tracts are smaller in both area and population and have the advantage of greater social homogeneity. But they also raise problems of spatial dependence since neighborhoods may span several tracts (this is discussed and illustrated in the next section). Tracts also change over time, multiplying as populations grow in a tract. Tracts in commercial areas have low populations, requiring the use of “journey” files that estimate the daytime and nighttime populations of tracts based on a complex algorithm using commuting times. Other aggregations, such as planning districts in Chicago and neighbor- hoods in New York, solve these problems in terms of articulating “natural” boundaries that encompass areas with social meaning to their residents. For example, New York has defined neighborhood boundaries based on the work of Kenneth Jackson and John Manbeck (1998). Using histori- cal data, tract boundaries, and interviews with local residents, they drew 330 neighborhood areas, each encompassing about 7 census tracts and between 25,000 and 45,000 people. Figures 4-1a and 4-1b show the rela- tionship between neighborhoods and precincts and also precincts and cen- sus tracts. These differences in area size and specification matter in the identifica- tion of crime trends. Figure 4-2 shows the results of semiparametric mixture (trajectory) models to identify trends in homicides over time in New York from 1985-2000. The top figure shows that we can identify four trajectory groups for neighborhoods, while three are identified for tracts in the bottom graph. The highest risks are concentrated in about one in nine neighborhoods, but one in five tracts. For neighborhoods, there is a second trajectory with more modest increases and declines. Each analysis shows stability in 45 per-

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 UNDERSTANDING CRIME TRENDS immigration. Other domains of urban policy, including family and child support or child care, public assistance experiments, and mental health interventions, can also be mined to see if there are unintended or collateral effects on crime. The design of these initiatives often falls short of the standards of social experimentation, yet there is much to be learned from a series of quasi-experiments that can be run on neighborhoods with dif- ferent paths of change that have experienced one or more of these social interventions. Building a Data Infrastructure for Understanding Neighborhoods and Crime Research on neighborhoods and crime often begins anew with each project. Researchers reach into archives of existing data and approach agen- cies for updates and supplements to bring the measures up to date. Rarely is updating routinized in agencies unless there are institutional norms or legislative mandates to do so. (Crime may be an exception, based on both reporting mandates and needs for good data to support investigations.) Compiling reliable measures of the complex dimensions of neighborhoods over a period of time necessary to identify changes is a difficult challenge (see, for example, Tatian, 2003). Data are maintained separately by agency, rarely aggregated to similar spatial units, and (in the extreme) in languages that are better suited to administrative needs than for research. These dif- ficulties are compounded by the diverse theoretical interests that are identi- fied in this chapter. An infrastructure for neighborhood data in cities is needed to support research on neighborhoods and crime, and such an infrastructure should be maintained in archives that are accessible to users with minimal admin- istrative burdens. The Neighborhood Change Database is one such effort. Privacy concerns are limited in these proposals, since crime data often are aggregated administratively, as are data on attributes and characteristics of neighborhood ecology. Risks to human subjects are mitigated in neighbor- hood research that focuses on changes in rates of crime or social structural and other ecological parameters in areas over time. For example, neighbor- hood studies are likely to rely on observational data that often is deidenti- fied to reduce risks from accidental disclosure. But privacy concerns may arise in the study of the social organization of neighborhoods and networks in them. Here, we can emphasize the importance of the regulatory functions in universities and research institutes that are charged with the protection of human research subjects from social risk and psychological harm. Beyond these regulatory strategies, the social norms and ethical stan- dards of researchers and their professional associations also can buttress respect for privacy and confidentiality. For example, the identities of dis-

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 CRIME AND NEIGHBORHOOD CHANGE tressed neighborhoods should be guarded whenever possible, to prevent stigmatization in the form of redlining or other deinvestments. Yet this raises a tension when spatial analyses of neighborhoods are employed, and the results are often displayed using maps. One could argue plausibly that there is value in both national archives and local or regional ones. My preference is for the local. Archives of cross-city data are challenged to construct files with similar elements so as to avoid measurement errors arising from inconstancy in the underlying meaning of variables that may be based on different metrics across vari- able local contexts. Local data archives should feature data drawn from in the city or region and contributed by institutions and agencies under local working agreements and data-sharing arrangements. For example, within the Neighborhood Change Database project are more than 15 local supplemental archives. Locally designed archives have the advantage of building on national templates for both observational and survey data and then enriching these through measures that capture the texture of each city’s neighborhoods. These additional elements could include direct observa- tions of neighborhood interaction data that are coupled with surveys and local administrative datasets on compositional characteristics of neighbor- hoods as well as social outcomes across a range of behavioral dimensions (Raudenbush and Sampson, 1999). A useful example of a dataset design that addresses both individ- ual and neighborhood change is the Program in Human Development in Chicago Neighborhoods, in which the sampling design explicitly anticipates analyses of both individuals and neighborhood effects as well as their multi- level or hierarchical effects (Raudenbush and Sampson, 1999; Sampson, Raudenbush, and Earls, 1997). In Los Angeles, the Los Angeles Family and Neighborhoods Study is a similar effort that has produced a rich dataset paralleling the structure and interests of the Chicago study (Sastry et al., 2006; Pebly and Sastry, 2006), although with emphasis on developmental outcomes and less focus on antisocial behavior. In the Los Angeles study, neighborhood appears to have independent effects on child development net of individual and family characteristics, and the explained variance of neighborhood factors well outweighs the other effects (Pebley and Sastry, 2006). There are a number of administrative datasets, ongoing surveys, and other data massing and integration projects that can be incorporated into these local archives or that can serve as templates for the design of a local archive. For example, the New York HVS, the Youth Risk Behavior Survey, the National Longitudinal Survey of Youth 97 (NLSY97), and others in progress all have local components that could be expanded and designed into local ongoing efforts. Surveys should also delve into the social interactions of neighborhoods to better understand the moving parts of

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 UNDERSTANDING CRIME TRENDS neighborhood social control. In the health care system, vital statistics data in most cities and states provide addressable data on fatalities that can supplement police records. Most cities maintain zoning and housing indi- cators (sale prices, rent indices, etc.) to allow for measurement of the built environment in neighborhoods. School, health care, and public assistance records all can provide important information on composition that can supplement surveyed and observed data. The final consideration is the pace of change and the schedule and spacing of data points. What are reasonable assumptions about neighbor- hood change and crime change that would determine the right frequency of observations? Some changes are slow, as in changes in the built envi- ronment, and others may be relatively quick, as in the case of the sudden population shift in Washington Heights reported by Fagan (1992). This pace itself can churn neighborhoods in a way to quickly change both patterns of social interaction and other neighborhood barometers such as crime rates. This would suggest more frequent observations, certainly more frequent than the decennial census and closer in timing to the Census Bureau’s American Community Survey. A second consideration is the lag time that is theorized between change in a causal factor and the observed change in a social outcome. These lag times will vary by outcome domain: school test scores may improve more slowly than will changes in the crime rate. The design of such archives and the infrastructure that is created will require both resources and political will to set institutional incentives for agencies to contribute. Crime data in particular may be a political question; there are risks in transparency that inelasticity in crime rates will be seen as political failure. What incentives are there for police to create stronger and more accessible crime data with local addressability, incentives that can offset the political risks that some departments may fear? There are two ways to address these requirements. Open records laws often provide the institutional aegis for the release of information on crime and neighbor- hoods to sustain research.8 One way to address this is by shifting social and professional norms toward more open and transparent data systems to monitor changes in local crime rates that mirror changes in each city’s neighborhoods. 8 See, for example, Florida’s Open Records Law, FL Statutes §119 (http://www.leg.state. fl.us/statutes/index.cfm?app_mode=display_statute&url=ch0119/ch0119.htm), describing the requirements and procedures for publicly available information while setting forth privacy restrictions that safeguard sensitive information about individuals.

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