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Objectives: Criminological scholars have long been interested in how macro-level characteristics of cities, counties, or metropolitan areas are related to levels of crime. The standard analytic approach in this literature aggregates constructs of interest, including crime rates, to the macro geographic units and estimates regression models, but this strategy ignores possible sub-city-level processes that occur simultaneously. Methods: One solution uses multilevel data of crime in meso-level units within a large number of cities; however, such data is very difficult and time intensive to collect. We propose an alternative approach which utilizes insights from existing literature on meso-level processes along with meso-level socio-demographic measures in cities to impute crime data from the city to the smaller geographic units. This strategy allows researchers to estimate full multilevel models that estimate the effects of macro-level processes while controlling for sub-city-level factors. Results: We demonstrate that the strategy works as expected on a sample of 91 cities with meso-level data, and also works well when estimating the multilevel model on a sample of cities different from the imputation model, or even in a different time period. Conclusions: The results demonstrate that existing studies aggregated to macro units can yield considerably different (and therefore potentially problematic) results when failing to account for meso-level processes.

While Google Street View (GSV) has been increasingly available for large-scale examinations of urban landscapes, little is known about how to use this promising data source more cautiously and effectively. Using data for Santa Ana, California, as an example, this study provides an empirical assessment of the sensitivity of GSV-based streetscape measures and their variation patterns. The results show that the measurement outcomes can vary substantially with changes in GSV acquisition parameter settings, specifically spacing and direction. The sensitivity is found to be particularly high for some measurement targets, including humans, objects, and sidewalks. Some of these elements, such as buildings and sidewalks, also show highly correlated patterns of variation indicating their covariance in the mosaic of urban space.

A well-known challenge is measuring employment concentration across metropolitan areas and analysing the evolving spatial structure. We introduce a new approach that avoids identifying “job centres” and conceptualizes the distribution of employment based on two dimensions: (1) employment deconcentration; and (2) spatial dispersion of high employment locations. We apply this framework to study 329 US metropolitan regions based on 1 sq km. grid cells. We find diverse trajectories of metropolitan restructuring between 2000 and 2010, and substantial variation across regions in employment concentration. The new framework enables researchers to compare metropolitan regions to gain insights into the dynamic nature of metropolitan spatial structure.

We compare the relative importance of four dimensions for explaining the micro location of robberies: 1) the micro spatial scale of street segments; 2) the meso spatial scale surrounding the street segment; 3) the temporal pattern, and 4) the macro-scale of the surrounding 2.5 miles. This study uses crime, business, and land use data from New York City and aggregates it to street segments and hours of the day. Although the measures capturing the micro-scale of the street segment explained the largest amount of unique variance, the measures capturing temporal scale across hours of the day (and weekdays) explained the next largest amount of unique variance. The measures of the characteristics in the 2.5 miles macro scale explained the next largest amount of unique variance, and combined with the measures at the meso-scale explained nearly as much of the variance as the street segment measures.

Objectives: Despite theoretical interest in how dimensions of the built environment can help explain the location of crime in micro−geographic units, measuring this is difficult. Methods: This study adopts a strategy that first scrapes images from Google Street View every 20 meters in every street segment in the city of Santa Ana, CA, and then uses machine learning to detect features of the environment. We capture eleven different features across four main dimensions, and demonstrate that their relative presence across street segments considerably increases the explanatory power of models of five different Part 1 crimes. Results: The presence of more persons in the environment is associated with higher levels of crime. The auto−oriented measures—vehicles and pavement—were positively associated with crime rates. For the defensible space measures, the presence of walls has a slowing negative relationship with most crime types, whereas fences did not. And for our two greenspace measures, although terrain was positively associated with crime rates, vegetation exhibited an inverted−U relationship with two crime types. Conclusions: The results demonstrate the efficacy of this approach for measuring the built environment.

Objectives: Few studies have examined the consequences of neighborhoods for job prospects for people on parole. Specifically, networks between neighborhoods in where people commute to work and their spatial distributions may provide insight into patterns of joblessness because they represent the economic structure between neighborhoods. We argue that the network of neighborhoods provides insight into the competition people on parole face in the labor market, their spatial mismatch from jobs, as well as their structural support. Methods: We use data from people on parole released in Texas from 2006 to 2010 and create a network between all census tracts in Texas based on commuting ties from home to work. We estimate a series of multilevel models examining how network structures are related to joblessness. Results: The findings indicate that the structural position of neighborhoods has consequences for people on parole’s joblessness. Higher outdegree, reflecting neighborhoods with more outgoing ties to other neighborhoods, was consistently associated with less joblessness, while higher indegree, reflecting neighborhoods with more incoming ties into the neighborhood, was associated with more joblessness, particularly for Black and Latino people on parole. There was also some evidence of differences depending on geographic scale. Conclusions: Structural neighborhood-to-neighborhood networks are another component to understanding joblessness while people are on parole. The most consistent support was shown for the competition and structural support mechanisms, rather than spatial mismatch.

This study uses U.S. Census data on average home values in Southern California census tracts from 1960 to 2010. Using growth mixture modeling (GMM), 26 unique groups are detected capturing nonlinear change in neighborhood relative home values over this study period. There were seven broad patterns of changing home values: (1–3) decline and then rise (at high, mid, and low portions of the home value distribution); (4) rise and then decline; (5–6) a monotonic increase (either above or below the region average); and (7) a monotonic decrease. Multinomial regression models found that covariates exhibited a much stronger effect for distinguishing between the average level of home values in neighborhoods over the study period, rather than how home values changed over time.

Objectives: Although previous studies have theorized the importance of physical and social boundaries (edges) in understanding crime in place, the relationship between edges and the level of crime has been less studied empirically. The current study examines the effects of physical and social boundaries on crime in street segments. Methods: To empirically measure boundaries, we introduce an approach of looking at the differences of land use (physical boundary), socioeconomic status, or racial composition (social boundaries) on both sides of a street segment. We estimated a series of negative binomial regression models in which measures of the physical and social boundaries are included while controlling for the effects of structural characteristic and the conventional physical boundary measures of highways, parks, and rivers. Results: We observed that there are positive relationships between all three of these boundary measures and violent and property crimes. The results indicated that physical and social boundaries are important to consider in understanding the spatial patterns of crime. Moreover, the current study confirmed the moderating effects between social and physical boundaries. Conclusions: Our results indicate that although much empirical research focuses solely on physical boundaries, our measures of social and physical boundaries have important consequences for the spatial location of crime, and therefore are worthy of further research.

In the current study, we theorized that businesses in place are subject to two processes: a crime generator effect in which they heighten crime due to increased opportunities and a crime inhibition effect in which certain types of businesses can increase guardianship capability. We explicitly compare the different effects of local vs. non-local and small vs. large businesses on crime in street segments using the data in cities across the Los Angeles metropolitan region by estimating a set of negative binomial regression models for small local, large local, small non-local, and large non-local consumer facing businesses (Retail, Restaurants, Food/Drug Stores, and Services) for violent and property crime. Although we found that most of the business coefficients were positive, local businesses, and particularly small local businesses, have considerably smaller crime-enhancing effects for both violent and property crime.

Shaw and McKay advanced social disorganization theory in the 1930s, kick-starting a large body of research on communities and crime. Studies emphasize individual impacts of poverty, residential instability, and racial/ethnic heterogeneity by examining their independent effects on crime, adopting a variable-centered approach. We use a “neighborhood-centered” approach that considers how structural forces combine into unique constellations that vary across communities, with consequences for crime. Examining neighborhoods in Southern California we: (1) identify neighborhood typologies based on levels of poverty, instability, and heterogeneity; (2) explore how these typologies fit within a disorganization framework and are spatially distributed across the region; and (3) examine how these typologies are differentially associated with crime. Results reveal nine neighborhood types with varying relationships to crime.

Objectives: Although theories suggest that street network configurations (pathways) are important factors for understanding the spatial patterns of crime, relatively less attention has been paid to the association between the physical configuration of the street network and the level of crime in place. Consequently, we employed the concept of betweenness centrality in the context of the street network to empirically measure the potential foot traffic passing through a given street segment. Methods: We introduce a methodological refinement by accounting for the characteristics of origin and destination of each potential trip (where travelers are from and tend to go) using residential population in origins and destinations and the number of various types of business employees in destinations. Moreover, we posit that the effect of potential foot traffic into a given street segment will be moderated by certain social environmental characteristics such as socioeconomic status of place. By using data on a sample of 300,000 street segments in the Southern California region across 130 cities, we estimate a set of negative binomial regression models including the betweenness measures. Results: Our results show that betweenness centrality has a curvilinear relationship with violent and property crime: At lower levels, increases in betweenness results in increased crime, yet the pattern becomes crime-reducing at higher values of the betweenness measure. We also found that the pattern is moderated by the socioeconomic status of the street segment. Conclusions: The current study highlights that there is an important relationship of the physical environment in terms of the street network configuration and crime in street segments.

Objectives: Given the spatial nature of offender and target behavior, what do standard ecological studies of crime aggregating measures to different geographic units actually tell us? Methods: This study used a simple stylized simulation model of crime patterns based on offenders, an exponential distance decay function based on Euclidean distance to capture their typical mobility patterns when committing offenses, and immobile targets. Results: There were four key results. First, although a measure of targets can explain much of the variance in micro-level models, knowing where offenders live, and their typical distances traveled to offending, greatly improved the model performance. Second, accounting for the typical spatial movement of offenders before aggregating to larger units produces better results based on explanatory power. Third, the explanatory power of targets alone was much weaker when aggregating to larger units despite the fact that the simulated model of crime events was entirely based on micro processes, highlighting that variance explained is distinct from causal processes. Fourth, knowing how offenders behave in target-rich versus target-poor environment impacts the results considerably. Conclusions: The findings demonstrated the consequences of a spatially explicit model of offender and target behavior for ecological studies of crime that aggregate measures to geographic units that are either at the micro-, meso-, or macro-level.

Objectives: The current study proposes an approach that accounts for the importance of streets while at the same time accounting for the overlapping spatial nature of social and physical environments captured by the egohood approach. Our approach utilizes overlapping clusters of streets based on the street network distance, which we term street egohoods. Methods: We used the street segment as a base unit and employed two strategies in clustering the street segments: (1) based on the First Order Queen Contiguity; and (2) based on the street network distance considering physical barriers. We utilized our approaches for measuring ecological factors and estimated crime rates in the Los Angeles metropolitan area. Results: We found that whereas certain socio-demographics, land use, and business employee measures show stronger relationships with crime when measured at the smaller street based unit, a number of them actually exhibited stronger relationships when measured using our larger street egohoods. We compared the results for our three-sized street egohoods to street segments and two sizes of block egohoods proposed by Hipp and Boessen (Criminology 51(2):287–327, 2013) and found that two egohood strategies essentially are not different at the quarter mile egohood level but this similarity appears lower when looking at the half mile egohood level. Also, the street egohood models are a better fit for predicting violent and property crime compared to the block egohood models. Conclusions: A primary contribution of the current study is to develop and propose a new perspective of measuring neighborhood based on urban streets. We empirically demonstrated that whereas certain socio-demographic measures show the strongest relationship with crime when measured at the micro geographic unit of street segments, a number of them actually exhibited the strongest relationship when measured using our larger street egohoods. We hope future research can use egohoods to expand understanding of neighborhoods and crime.

To take stock of the “neighborhood effects” of drug activity, we combined theoretical insights from the drugs and crime and communities and place literatures in examining the longitudinal relationship between drug activity and crime rates at more spatially and temporally precise levels of granularity, with blocks as the spatial units and months as the temporal units. We found that drug activity on a block one month “pushes” assaultive violence into surrounding blocks the next month. Integrating perspectives form social disorganization theory with Zimring and Hawkins’ (1997) contingency causation theory, we also found that the economic resources and residential stability of the “the larger social environment”—that is, the surrounding quarter-mile egohood area—moderate drug activity’s block-level relationship to crime. These results suggest that drug activity increases assaultive violence and serious acquisitive crime rates on structurally advantaged blocks, producing a significant ecological niche redefinition for such blocks relative to others in Miami-Dade County, Florida.

This review takes stock of recent advances, as well as enduring and emerging challenges, in the area of spatial criminology. Although the notions of place and space are fundamentally intertwined, spatial criminology is distinct in its attempt to measure and theorize explicitly spatial processes and relationships. This review highlights three key themes. First, the use of increasingly smaller geographic units in recent research creates an even greater need to account for spatial behavior of persons when studying the location of crime. Second, although the explosion of spatially precise data in recent years presents exciting possibilities, we argue that theory is falling behind in guiding us in analyzing these new forms of data, and explicitly inductive approaches should be considered to complement existing deductive strategies. Third, an important direction for spatial criminology in the next decade is considering the extent to which micro- and mesolevel processes operate invariantly across different macro contexts.

Most scholars have focused on the simple presence of schools when examining their influence on neighborhood crime. This paper instead examines two school characteristics that might affect local juvenile crime. More specifically, this study uses negative binomial and logistic regression models to estimate the effects of high school dropouts and high-performing schools on juvenile violent crime in Orange County, CA. An integrated theoretical approach based on social bond and routine activity theories is utilized to guide this research. Findings from this study suggest that dropouts are associated with increases in aggravated assault and robbery incidents, but high-performing schools do not significantly affect these crime types. Finally, the models predicting juvenile crime are compared with models predicting all crime (both juveniles and adults), supporting our argument that juvenile crime is the theoretically appropriate crime measure.

Superstar cities with high-paying creative-class jobs, venture capital, and innovation are thought to be more unequal. We analyze mixing in neighbourhoods by income, education and occupation, relating this intra-urban measure with regional productivity indicators. Using non-overlapping census units and a machine-learning estimation technique that iterates over all combinations of economic, business, housing and cultural indicators, we identify ‘ingredients’ associated with economically and socially diverse neighbourhoods. Broad support is not found that neighbourhoods in superstar regions are less mixed; however, overrepresentation in creative occupations stymies mixing as does a combination of weak economic fundamentals with high shares of new housing.

This study introduces filtering theory from housing economics to criminology and measures the age of housing as a proxy for deterioration and physical disorder. Using data for Los Angeles County in 2009 to 2011, negative binomial regression models are estimated and find that street segments with older housing have higher levels of all six crime types tested. Street segments with more housing age diversity have higher levels of all crime types, whereas housing age diversity in the surrounding ½-mile area is associated with lower levels of crime. Street segments with detached single-family units generally had less crime compared with other types of housing. Street segments with large apartment complexes (five or more units) generally have more crime than those with small apartment complexes and duplexes.

Policing in the United States has experienced immense change throughout the past quarter-century. Although police agencies have shared their goals of preserving life and protecting property, their philosophies and practices for achieving these goals have differed. The present research, therefore, explores patterns in policing via a novel, typological approach. Using six waves of data (1993, 1997, 2000, 2003, 2007, and 2013) from the Law Enforcement Management and Administrative Statistics (LEMAS) data series, we first employ factor analyses to generate indices for six important policing dimensions: (1) officer diversity, (2) community policing, (3) patrol strategy diversity, (4) militancy, (5) technology, and (6) staffing rigor. Using these indices, we then employ latent class analyses to construct typologies of police agencies, and examine the distribution of such typologies across space at various points in time. Our results reveal several key findings. We detect consistent patterns in typologies across time, including classes with high militancy, high diversity, or low staffing rigor (among others). Within these sets of classes, we also detect micro-heterogeneity amongst patterns of index values: for example, subsets of classes which all score high on one dimension but score high versus low on other dimensions. Finally, we find evidence to suggest spatial convergence of typologies in one large geographic region: Southern California. By offering a multidimensional classification scheme over a 20-year period, we contribute to the policing literature by highlighting the importance and implications of studying multiple policing dimensions simultaneously.

A challenge for studies assessing routine activities theory is accounting for the spatial and temporal confluence of offenders and targets given that people move about during the daytime and nighttime. We propose exploiting social media (Twitter) data to construct estimates of the population at various locations at different times of day, and assess whether these estimates help predict the amount of crime during two-hour time periods over the course of the day. We address these questions using crime data for 97,428 blocks in the Southern California region, along with geocoded information on tweets in the region over an eight month period. The results show that this measure of the temporal ambient population helps explain the level of crime in blocks during particular time periods. The use of social media data appear promising for testing various implications of routine activities and crime pattern theories, given their explicit spatial and temporal nature.

Objectives: A growing body of research finds that immigration has a null or negative association with neighborhood crime rates. We build on this important literature by investigating the extent to which one theory, institutional completeness theory, may help explain lower crime rates in immigrant communities across the Southern California region. Specifically, we test whether two key measures of institutional completeness—the presence of immigrant/ethnic voluntary organizations in the community and the presence and diversity of immigrant/ethnic businesses in the community—account for lower crime rates in some immigrant communities. Method: Compiling a tract-level data set utilizing various data sources, we estimate negative binomial regression models predicting violent and property crime levels that include measures of institutional completeness while controlling for a range of neighborhood correlates of crime. We also account for possible endogeneity by estimating instrumental variable models. Results: The results reveal very limited support for institutional completeness theory. Conclusions: Several possible explanations for these findings are discussed.

Given the importance of the neighbourhood context for residents’ social cohesion, the current study examines the association between types of social and non-social places on three indicators of social cohesion: neighbour networks, social cohesion and neighbourhood attachment. We spatially integrate data from the census, topographic databases and a 2012 survey of 4132 residents from 148 neighbourhoods in Brisbane, Australia, and employ multilevel models to assess whether the variation in resident reports of social cohesion is attributable to land uses that function as neighbourhood social conduits. We also consider the degree to which neighbourhood fragmentation affects our indicators of social cohesion. Our findings reveal that even after controlling for the socio-demographic context of the neighbourhood and a range of individual and household control variables, residents’ reports of social cohesion are significantly associated with the types of social conduits, the diversity of land use and the degree of neighbourhood fragmentation.

Objectives: Given the evidence that crime events exhibit both a spatial and a temporal pattern, we explore whether certain social and physical environment characteristics have varying relationships with crime at different times of day. Methods: We assess this temporal question using a flexible nonlinear parametric approach on a large sample of street segments (and surrounding spatial area) in Southern California. Results: There are different temporal and spatial patterns for key measures. The presence of total employees in the surrounding area is associated with a reduced robbery risk during the daytime, but not at night. The risk of a robbery is elevated on a high retail segment on weekends during the daytime, and on high restaurant segments into the early evening on weekends. Furthermore, the presence of retail and restaurants in the surrounding area (evidence of shopping districts) was associated with elevated robbery risk in the afternoon and well into the evening. Conclusion: These different temporal patterns indicate the possibility of different mechanisms in operation.

The present research examines the long-term, bidirectional relationships between calls for service, crime, and two police patrol strategies in Santa Monica, California: foot patrol and police stops. Using nine years of monthly data (2006–2014), we estimate two sets of block-level, longitudinal models to tease apart these relationships. In our first set of models, we use police actions and calls for service in the preceding month(s) to predict crime in the subsequent month. In our second set of models, we use calls for service and crime in the preceding month(s) to predict police actions in the subsequent month. We find that while changes in calls for service and crime often precede changes in police action, changes in crime also tend to follow them. For example, police stops appear to be particularly receptive to burglary: blocks with more burglaries receive greater numbers of police stops, and blocks with more police stops have reduced odds of experiencing burglary. We also find that the length of effects of predictors varies as a function of predictor and outcome: whereas some predictors exhibit short temporal effects (e.g. one month), other predictors exhibit much longer temporal effects (e.g. twelve months). Our results thus provide important insight into the spatial and temporal relationships between police actions and police incidents. Police actions must be neatly tailored to police incidents at precise levels if long-term deterrent effects at these levels are to be achieved.

Social disorganization theory positions informal social control as central to neighborhood crime reduction. Although neighborhood ties, fear of crime, and perceived disorder influence the exercise of informal social control, there are significant sex differences for these drivers that might differentially influence men and women’s informal social control actions. Furthermore, these differences may be exaggerated under conditions that activate gendered divisions of labor. We use survey data from 4,000 residents in 148 neighborhoods and employ multilevel logistic regression to examine the relationship between sex and informal social control actions. We find that men are more likely to take action than women; however, our three-way interactions reveal family arrangements moderate the relationship between ties, fear of crime, disorder, and these actions.

In this study, we examine how different features of the built environment—density, diversity of land uses, and design—have consequences for personal networks. We also consider whether different features of the built environment have consequences for the spatial location of persons to whom one is tied by considering their distribution in local area, broader city region, and a more macro spatial scale. We test these ideas with a large sample of the Western United States for three different types of ties. Our findings suggest that the built environment is crucial for personal network structure, both in the number of social ties and where they are located.

Objectives: Examining the immigration-crime nexus across neighborhoods in the Southern California metropolitan region, this study builds on existing literature by unpacking immigration and accounting for the rich diversity that exists between immigrant groups. Methods: Using data from a variety of sources, we capture this diversity with three different approaches, operationalizing immigrant groups by similar racial/ethnic categories, areas or regions of the world that immigrants emigrate from, and where immigrants co-locate once they settle in the U.S. We also account for the heterogeneity of immigrant populations by constructing measures of immigrant heterogeneity based on each of these classifications. We compare these novel approaches with the standard approach, which combines immigrants together through a single measure of percent foreign born. Results: The results reveal that considerable insights are gained by distinguishing between diverse groups of immigrants. In particular, we find that all three strategies explained neighborhood crime levels better than the traditional approach. Conclusion: The findings underscore the necessity of disaggregating immigrant groups when exploring the immigration-crime relationship.

This article examines how municipal planning contexts can shape urban land use dynamics by investigating the parcel-level land use changes in a five-county Southern California metropolitan area between 1990 and 2005. An analysis, based on a multinomial logit model, shows that land use change patterns significantly vary by municipalities that were situated in heterogeneous planning contexts. More specifically, cities with limited ability to expand their jurisdictional boundaries are found to provide more recreational areas and urban open spaces, while restricting nonconventional land uses. However, no evidence of a shift from single-family to multifamily residential development is detected for such cities.

This paper is an empirical analysis of employment centres in the Los Angeles region from 1997 to 2014. Most extant work on employment centres focuses on identification methodology or their dynamics during a period of industrial restructuring from 1980 to 2000. We analyse employment centres using point-based, rather than census tract-based employment data and a non-parametric identification method with a single concept of proximity. We focus on changes across five key industries: knowledge-intensive business services (KIBS), retail, creative, industrial and high-tech, emphasising changes in centre composition as well as their boundaries. Results show far greater change across centres than previous longitudinal studies. Only 43% of the land area that is in an employment centre is part of one in both 1997 and 2014. Using a persistence score, centres range from stable to highly fluctuating, but emerging, persisting and dying centres are found in core and fringe areas alike. KIBS are most associated with stable centres, while high tech employment is attracted toward emerging areas and retail exists throughout. Emerging centres are more likely to have greater accessibility, while industrial employment becomes far more concentrated in centres by 2014.

Scholars have theorized how spatial boundaries (edges) can be important for understanding the location of crime, yet the empirical relationship between spatial boundaries in the environment and levels of crime is relatively less explored compared with other features of the environment. The current study extends the literature by not only studying three types of physical boundaries—interstate highways, parks, and rivers—but also one nonphysical and relatively less visible boundary—city boundaries. We analyze the relationship between crime in street segments and nearness to these four types of edges in the Southern California area. We measure nearness to these boundaries in two manners: (a) whether or not the segment is adjacent to the feature and (b) how far in physical distance the segment is to the feature.

Social disorganization theory is one of the most widely tested theories in criminology, yet few studies consider the temporal and spatial dynamics of neighborhood composition, neighborhood informal social control, and crime. To better understand these relationships, we use census data, police data, and three survey waves of data from a unique longitudinal dataset with over 4,000 respondents living across 148 neighborhoods in an Australian city undergoing rapid population growth. We employ cross-lagged reciprocal feedback models to test the central tenets of social disorganization theory and its contemporary advances for three crime types: violent crime, property crime, and drug crime. Further, we examine the reciprocal relationship between neighborhood composition, three components of informal social control (neighborhood social ties, expectations for informal social control, and the exercise of informal social control), and crime and whether socio-demographic changes in nearby neighborhoods shape these relationships over time. We find that changes in the socio-demographic composition in both focal and nearby areas influence neighborhood informal social control; however, in contrast to cross-sectional studies of social disorganization theory, our results reveal little support that neighborhood informal social control significantly decreases crime over time.

Objectives: Cross-sectional studies consistently find that neighborhoods with higher levels of collective efficacy experience fewer social problems. Particularly robust is the relationship between collective efficacy and violent crime, which holds regardless of the socio-structural conditions of neighborhoods. Yet due to the limited availability of neighborhood panel data, the temporal relationship between neighborhood structure, collective efficacy and crime is less well understood. Methods: In this paper, we provide an empirical test of the collective efficacy-crime association over time by bringing together multiple waves of survey and census data and counts of violent crime incident data collected across 148 neighborhoods in Brisbane, Australia. Utilizing three different longitudinal models that make different assumptions about the temporal nature of these relationships, we examine the reciprocal relationships between neighborhood features and collective efficacy with violent crime. We also consider the spatial embeddedness of these neighborhood characteristics and their association with collective efficacy and the concentration of violence longitudinally. Results: Notably, our findings reveal no direct relationship between collective efficacy and violent crime over time. However, we find a strong reciprocal relationship between collective efficacy and disadvantage and between disadvantage and violence, indicating an indirect relationship between collective efficacy and violence. Conclusions: The null direct effects for collective efficacy on crime in a longitudinal design suggest that this relationship may not be as straightforward as presumed in the literature. More longitudinal research is needed to understand the dynamics of disadvantage, collective efficacy, and violence in neighborhoods.

Criminologists have long noted that social networks play a role in influencing residents' fear of crime, but findings vis a vis the exact nature of that role have been mixed. More social ties may be associated with less fear of crime through their role in collective action, trust, and emotional support, but also with more fear of crime because of their role in the diffusion of information on local crime patterns. In what follows, we suggest temporal and spatial distinctions in how social ties matter for fear of crime with respect to these different mechanisms. Analysis of data from a large scale egocentric network study in Southern California provides evidence for these claims.

Due to data limitations, most studies of neighbourhood change within regions assume that change over the years of a decade is relatively constant from year-to-year. We use data on home loan information to construct annual measures of key socio-demographic measures in neighbourhoods (census tracts) in the Southern California region from 2000 to 2010 to test this assumption. We use latent trajectory modelling to describe the extent to which neighbourhood change exhibits temporal nonlinearity, rather than a constant rate of change from year to year. There were four key findings: (1) we detected nonlinear temporal change across all socio-demographic dimensions, as a quadratic function better fit the data than a linear one in the latent trajectories; (2) neighbourhoods experiencing more nonlinear temporality also experienced larger overall changes in percent Asian, percent black, and residential stability during the decade; neighbourhoods experiencing an increase in Latinos or a decrease in whites experienced more temporal nonlinearity in this change; (3) the strongest predictor of racial/ethnic temporal nonlinearity was a larger presence of the group at the beginning of the decade; however, the racial and SES composition of the surrounding area, as well as how this was changing in the prior decade, also affected the degree of temporal nonlinearity for these measures in the current decade; (4) this temporal nonlinearity has consequences for neighbourhoods: greater temporal nonlinear change in percent black or Latino was associated with larger increases in violent and property crime during the decade, and the temporal pattern of residential turnover or changing average income impacted changes in crime. The usual assumption of constant year-to-year change when interpolating neighbourhood measures over intervening years may not be appropriate.

Objectives We argue that assessing the level of crime concentration across cities has four challenges: (1) how much variability should we expect to observe; (2) whether concentration should be measured across different types of macro units of different sizes; (3) a statistical challenge for measuring crime concentration; (4) the temporal assumption employed when measuring high crime locations. Methods We use data for 42 cities in southern California with at least 40,000 population to assess the level of crime concentration in them for five different Part 1 crimes and total Part 1 crimes over 2005–2012. We demonstrate that the traditional measure of crime concentration is confounded by crimes that may simply spatially locate due to random chance. We also use two measures employing different temporal assumptions: a historically adjusted crime concentration measure, and a temporally adjusted crime concentration measure (a novel approximate solution that is simple for researchers to implement). Results There is much variability in crime concentration over cities in the top 5 % of street segments. The standard deviation across cities over years for the temporally adjusted crime concentration measure is between 10 and 20 % across crime types (with the average range typically being about 15–90 %). The historically adjusted concentration has similar variability and typically ranges from about 35 to 100 %. Conclusions The study provides evidence of variability in the level of crime concentration across cities, but also raises important questions about the temporal scale when measuring this concentration. The results open an exciting new area of research exploring why levels of crime concentration may vary over cities? Either micro-or macro-theories may help researchers in exploring this new direction.

At the end of the first paragraph under the subheading “Statistical Challenges to Measuring Crime Concentration”, the citation “(for an exception, see Levin et al. 2015)” should be changed to “(for an exception, see Levin et al. 2016).” The reference is listed with this erratum. In the second paragraph in the “Methods” section, the end of the first sentence should be changed from “… we adopted a small Monte Carlo simulation” to “… we adopted a Monte Carlo simulation procedure similar to Levin et al. (2016).”.

Scholars of New Urbanism have suggested that mixing along various dimensions in neighborhoods (e.g., income, race/ethnicity, land use) may have positive consequences for neighborhoods, particularly for economic dynamism. A challenge for empirically assessing this hypothesis is that the impact of mixing may depend on various socio-demographic characteristics of the neighborhood and takes place in a complex fashion that cannot be appropriately handled by traditional statistical analytical approaches. We utilize a rarely used, innovative estimation technique—kernel regularized least squares—that allows for nonparametric estimation of the relationship between various neighborhood characteristics in 2000 and the change in average household income in the neighborhood from 2000 to 2010. The results demonstrate that the relationships between average income growth and both income mixing and racial/ethnic mixing are contingent upon several neighborhood socio-demographic “ingredients”. For example, racial mixing is positively associated with average income over time when it occurs in neighborhoods with a high percentage of Latinos or immigrants, high population density, or high housing age mixing. Income mixing is associated with worsening average household income in neighborhoods with more poverty, unemployment, immigrants, or population density. It appears that considering the broader characteristics of the neighborhood is important for understanding economic dynamism.

This article analyzes the impact of changing housing and neighborhood characteristics on the accessibility of neighborhood businesses using Long Beach, California, as a case study. Although advocates of smart growth and New Urbanism encourage land use mixing, aggregate-level analysis can be too coarse to pick up on fine-grained aspects of urban streetscapes. This study uses assessor parcel records and a point-based business establishment data set to analyze city-wide patterns of accessibility from individual dwelling units to thirty-one types of neighborhood businesses, including grocery stores, service shops, drug stores, doctor's offices, and banks. Regression results compare parcel-level and neighborhood-level drivers of accessibility between 2006 and 2015 to gauge the aggregated effect of recent economic, demographic, and built environment changes on this aspect of urban spatial structure. Larger homes in older, multiunit buildings and higher income neighborhoods show substantial increases in accessibility to most establishment types, suggesting a trend toward both greater accessibility and larger dwelling units—despite the traditional trade-off between access and space. Although gradual increases in home and business density increased overall accessibility over this period, weaker neighborhood-level results indicate that this trend is less pronounced in high-poverty and non-white areas.

This study explores whether the broader context in which a city is located impacts the change in crime levels over the subsequent decade. This study uses a wide range of cities (those with a population of at least 10,000), over a long period of time (from 1970 to 2010). We test and find that although cities with larger population and those surrounded by a county with a larger population typically experience larger increases in crime over the subsequent decade, cities experiencing an increase in population during the current decade experience crime decreases. The study finds that cities with higher average income experience greater subsequent crime decreases, and those surrounded by counties with larger unemployment increases experience crime increases. Higher levels of income inequality and racial/ethnic heterogeneity are associated with increasing crime rates, and increasing inequality and racial/ethnic heterogeneity in the surrounding county are associated with further increases. Furthermore, this relationship has strengthened since 1970, suggesting that both scales of inequality are even more important from a public safety perspective. Finally, we tested the time invariance of these relationships, and showed that the magnitude of the relationship between city-level inequality and increasing crime has increased over the study period.

Recognition is growing that criminogenic neighborhood effects may not end at the borders of local communities, that neighborhoods are located relative to one another in ways that shape local crime rates. Inspired by this insight, this research explores the changing spatial distribution of race and income around a location and determines how such changes are associated with crime patterns and trends in neighborhoods in Los Angeles. We examine how changes from 2000 to 2010 in the income composition, racial composition, and intersection of these two constructs are linked with changes in levels of crime across local areas. We find that neighborhoods experiencing greater increases in spatial inequality in a broader area (two and a half miles around the neighborhood) experience greater increases in crime levels in the focal area over the decade, and that this pattern is strongest for neighborhoods simultaneously experiencing increasing average household income or increasing inequality. We also find that neighborhoods simultaneously experiencing increases in inequality and racial-ethnic heterogeneity experience increases in crime.

A well-known challenge to studies examining the distance of residential mobility patterns is that the estimates are often constrained to patterns only within a particular metro area or between metro areas. Thus, studies are unable to estimate the entire distance decay functional form. Using a unique data set on the distance of the most recent move for a large sample of households in twenty-three metropolitan areas in the United States over three waves, we flexibly estimate the distance decay function for the entire sample, as well as for a series of subpopulations based on key demographic information.

I propose a general theory for examining the spatial distribution of crime by specifically addressing and estimating the spatial distribution of the residences of offenders, targets, guardians, and their respective expected movement patterns across space and time. The model combines information on the locations of persons, typical spatial movement patterns, and situational characteristics of locations to create estimates of crime potential at various locations at various points in time and makes four key contributions. First, the equations make the ideas involved in the theory explicit, and they highlight points at which our current state of empirical evidence is lacking. Second, by creating measures of spatial “potentials” of offenders, targets, and guardians, this theory provides a precise grounding for operationalizing spatial effects in studies of place and crime. Third, the equations provide an explicit consideration of offenders and where they might travel and, therefore, incorporates offenders into crime-and-place research. Fourth, these equations suggest ways that researchers could use simulations to predict stable patterns, as well as changes, in the levels of crime at both micro and macro scales. Finally, I provide an empirical demonstration of the added explanatory power provided by the theory to a study of place and crime.

Using a longitudinal data set of 317 neighborhoods from 1996 to 2002 in Utrecht, The Netherlands, this study tests whether types of crime differentially impact (a) the mechanisms of social disorganization theory and (b) residents’ mobility behavior and attitudes toward the neighborhood. Neighborhoods with more cohesion have less violence 2 years later. Also, neighborhoods perceiving more violence experience lower levels of cohesion 2 years later. Higher levels of perceived violence were most important for explaining who moves out of the neighborhood, as such neighborhoods had more non-Whites and more lower income households at the next time point. Burglaries (a crime that occurs in private space) appear to increase residents’ sense of feeling responsibility for the neighborhood.

In the aftermath of one of the worst recessions in US history, high unemployment has placed millions of Americans in precarious financial positions. More than ever, Americans are opting out of traditional financial services, relying instead on “fringe lenders” such as check cashers, payday lenders, and pawnshops to manage their finances. Given their tremendous growth and the concern that consumers who are least able to pay for high-cost, high-risk financial products are most likely to use them, fringe lenders have been the subject of controversy and the focus of much research. Largely unknown, however, are the effects of fringe lenders on the communities where they are located. Given their spatial concentration in low-income neighborhoods with greater concentrations of racial and ethnic minorities—areas with typically more crime—of concern is whether fringe lenders themselves are criminogenic. We consider this by examining the impact of several types of fringe lenders on neighborhood crime rates in Los Angeles. Our findings reveal that the presence of fringe banks on a block is related to higher crime levels, even after controlling for a range of factors known to be associated with crime rates. The presence of a fringe bank also impacts crime, particularly robbery, on adjacent blocks. Whereas we find that pawnshops have little impact on crime levels, payday lenders and check cashers have a much stronger impact. Finally, we discover there are moderating effects, as the fringe lender–crime relationship is considerably reduced if the lender is located in a higher population density area.

This study examines neighborhood economic improvement, what is occurring in nearby neighborhoods, and the consequences for neighborhood crime rates. Negative binomial regression models are estimated to explain the relationship between the increase in average home values (a component of gentrification) and crime in Los Angeles between 1990 and 2000. We find that the spatial context is important, as gentrifying neighborhoods located on the “frontier” of the gentrification process have significantly more aggravated assaults than gentrifying neighborhoods surrounded by neighborhoods also undergoing improvement. Furthermore, this effect is stronger in neighborhoods that began the decade with the highest average home values. Our findings indicate that the extent to which neighborhoods are more or less embedded in a larger process of economic improvement, and where the neighborhood is at in the economic development process, has differential effects on neighborhood crime.

Although numerous theories suggest that voluntary organizations contribute to lower crime rates in neighborhoods, the evidence for this proposition is weak. Consequently, we propose a dynamic perspective for understanding the relationship between voluntary organizations and neighborhood crime that involves longitudinal analyses and the measurement of the age of organizations. By using longitudinal data on a sample of census blocks (N = 87,641) located across 10 cities, we test the relationship between age-graded measures of different types of voluntary organizations and neighborhood crime rates. We use fixed-effects negative binomial regression models that focus on change within neighborhoods of the relationship between voluntary organizations and neighborhood crime. Our results show that although each type of voluntary organization is found to exhibit crime-reducing behavior in neighborhoods, we find that many of them are consistent with what we refer to as the "delayed impact scenario"-there is a pronounced delay between the placement of a voluntary organization and a neighborhood subsequently experiencing a reduction in crime. With protective effects of organizations typically not demonstrated until several years after being in the neighborhood, these patterns suggest a need for long-term investment strategies when examining organizations.

Purpose: Prior studies have largely been unable to account for how variations in inequality across larger areas might impact crime rates in neighborhoods. We examine this broader context both in terms of the spatial area surrounding neighborhoods as well as the larger, city-level context. Although social disorganization, opportunity and relative deprivation theories are typically used to explain variations in neighborhood crime, these theories make differing predictions about crime when the broader areas that neighborhoods are embedded in are taken into account. Methods: We use data from the National Neighborhood Crime Study for 7956 neighborhoods in 79 cities. Multi-level models with spatial effects are estimated to explain the relationship between crime and city and neighborhood social and economic resources. Results: Disadvantage in the focal neighborhood and nearby neighborhoods increase neighborhood violent crime, consistent with social disorganization theory. However, relative deprivation provides a more robust explanation for understanding variation in property crime, as the difference in disadvantage between a neighborhood and nearby neighborhoods (or the broader community) explains higher levels of property crime. Conclusions: Criminologists need to account for the larger context of nearby neighborhoods, as well as the broader city, when understanding the effect of relative deprivation on neighborhood-level property crime rates.

Whereas one line of recent neighborhood research has placed an emphasis on zooming into smaller units of analysis such as street blocks, another line of research has suggested that even the meso-area of neighborhoods is too narrow and that the area surrounding the neighborhood is also important. Thus, there is a need to examine the scale at which the social ecology impacts crime. We use data from seven cities from around the year 2000 to test our research questions using multilevel negative binomial regression models (N = 73,010 blocks and 8,231 block groups). Our results suggest that although many neighborhood factors seem to operate on the microscale of blocks, others seem to have a much broader impact. In addition, we find that racially and ethnically homogenous blocks within heterogeneous block groups have the most crime. Our findings also show the strongest results for a multitude of land-use measures and that these measures sharpen some of the associations from social characteristics. Thus, we find that accounting for multiple scales simultaneously is important in ecological studies of crime.

Research has generally failed to explore whether the effect of neighborhood characteristics on home values has changed over time. We take a long-range view and study decadal changing home values in the southern California region over a 50-year period, from 1960 to 2009. We focus on the effects of racial composition and measures associated with the New Urbanism on changing home values. We find that whereas neighborhoods with more racial/ethnic minorities and racial mixing experienced relative decreases in home values in the earlier decades, this effect has effectively disappeared in the most recent decade and actually became positive for some measures. We also found that certain characteristics associated with the New Urbanism-population density, older homes, a lack of concentration of single family units-show stronger positive effects on home values in the most recent decades.