Search

Despite the substantial number of studies on the relationships between crime patterns and built environments, the impacts of street-level built environments on crime patterns have not been definitively determined due to the limitations of obtaining detailed streetscape data and conventional analysis models. To fill these gaps, this study focuses on the non-linear relationships and threshold effects between built environments and local crime patterns at the level of a street segment in the City of Santa Ana, California. Using Google Street View (GSV) and semantic segmentation techniques, we quantify the features of the built environment in GSV images. Then, we examine the non-linear relationships and threshold effects between built environment factors and crime by applying interpretable machine learning (IML) methods. While the machine learning models, especially Deep Neural Network (DNN), outperformed negative binomial regression in predicting future crime events, particularly advantageous was that they allowed us to obtain a deeper understanding of the complex relationship between crime patterns and environmental factors. The results of interpreting the DNN model through IML indicate that most streetscape elements showed non-linear relationships and threshold effects with crime patterns that cannot be easily captured by conventional regression model specifications. The non-linearities and threshold effects revealed in this study can shed light on the factors associated with crime patterns and contribute to policy development for public safety from crime.

Geospatial population data are typically organized into nested hierarchies of areal units, in which each unit is a union of units at the next lower level. There is increasing interest in analyses at fine geographic detail, but these lowest rungs of the areal unit hierarchy are often incompletely tabulated because of cost, privacy, or other considerations. Here, the authors introduce a novel algorithm to impute crosstabs of up to three dimensions (e.g., race, ethnicity, and gender) from marginal data combined with data at higher levels of aggregation. This method exactly preserves the observed fine-grained marginals, while approximating higher-order correlations observed in more complete higher level data. The authors show how this approach can be used with U.S. census data via a case study involving differences in exposure to crime across demographic groups, showing that the imputation process introduces very little error into downstream analysis, while depicting social process at the more fine-grained level.

The presence of employment centers provides the potential for reducing commuting distance. However, employment centers have distinct attributes, which may lead to varied impacts on commuting outcomes. We examine how proximity to employment centers can influence commuting distance with consideration of the heterogeneity of employment centers and workers. Specifically, we consider various attributes of employment centers related to location, persistency, job density, industry diversity, and size and analyze their impacts on the commuting patterns of low- and high-income workers using panel (2002-2019) data. Our analysis of the Los Angeles region shows that increasing proximity to the nearest employment center decreases commuting distance even after controlling for the job attributes located in the neighborhood of workers. The results further suggest that employment centers are not equal in terms of their impact on commute distance and that their impact is different for commuters from different income groups. Our findings contribute to the literature by deciphering the location and attributes of employment centers that may exert a greater impact on commuting patterns.

Abstract: Research finds that immigration and crime are not related across neighbourhoods, contrary to social disorganization theory and consistent with the immigration revitalization thesis. This research, however, is largely silent as to any possible nonlinear effects. Yet social theory offers sound reasons for why the immigration–crime association may be nonlinear; explanations, including immigrant/ethnic enclave theory and immigrant victimization theory, underscore potential concentration effects—albeit in different ways. Using a novel dataset with information on crime in over 15,000 neighbourhoods across a diverse range of US cities, we examine whether or not the immigration–crime association is nonlinear. We find that for both violent and property crime, a nonlinear relationship best captures the relationship. In additional analyses, we determine the theoretical perspective with which the findings are most consistent.

Drawing on recent scholarship on mass incarceration and prisoner re-entry, this study examines the reciprocal relationship between returning parolees and neighbourhood crime rates in five large cities in Texas. Besides the more common approach of counting the number of people on parole in communities (parolee concentration), we propose a novel approach for measuring people on parole by capturing their exposure in the community as parolee embeddedness (i.e., the cumulative number of days that people on parole resided in the neighbourhood). Results show that parolee concentration has a significant positive effect on both violent and property crime, but parolee embeddedness is significantly associated with reductions in violent and property crime. Our findings detect different effects depending on the measurement of people on parole and their community context, illustrating the need to better understand the dynamics of parolee re-entry in the era of mass incarceration.

While there is a long history of racial change in the United States, and how this plays out within neighbourhoods, a key recurring question is whether some neighbourhoods are able to achieve and maintain racial diversity, or whether they simply transition to dominance by a new racial group. We test and find evidence of 1631 neighbourhoods across the United States from 1980 to 2020 that exhibit persistent racial diversity (PRD), and assess where this PRD occurs. Our analysis shows that PRD neighbourhoods (PRDNs) are likely to be present in counties with more economic opportunities–that is, counties with higher socioeconomic status (SES). PRDNs themselves, however, tend to be relatively lower SES neighbourhoods within relatively higher SES counties, suggesting that affordable locations surrounded by more economic opportunities may have served as an environment in which diversity can persist over a long period of time in the United States.

BackgroundThis study investigated whether socio-spatial factors surrounding U.S. skilled nursing facilities related to Covid-19 case counts among residents, staff, and facility personnel and deaths among residents.MethodsWith data on 12,403 U.S. skilled nursing facilities and Census data we estimated multilevel models to assess relationships between facility and surrounding area characteristics from June 2020 to September 2022 for cumulative resident and facility personnel case counts and resident deaths.ResultsFacilities with more Black or Latino residents experienced more cases (IRR = 1.005; 1.004) and deaths (IRR = 1.008) among residents during the first six months of the pandemic, but were no different thereafter. Facilities with more racial/ethnic heterogeneity and percent Black or Latino in the surrounding buffer experienced more Covid-19 cases and deaths in the first six months, but no such differences were observed in the subsequent 24 months. Facilities surrounded by higher percent Latino consistently experienced more cases among staff and facility personnel over the study period (IRR = 1.006; 1.001).ConclusionsFindings indicated socio-spatial health disparities in cases among residents, staff, and facility personnel in the first six months of the pandemic, with some disparities fading thereafter. This pattern likely suggests the importance of the adoption and adherence to pandemic related safety measures in skilled nursing facilities nationwide.

Objectives: Longitudinal studies of the relationship between neighborhood change and changes in crime typically focus exclusively on the net level of change in key socio-demographic characteristics. Methods: We instead propose a demographic accounting strategy that captures the composition of neighborhood change: our measures capture which types of people are more likely to leave, stay, or enter the neighborhood. We use data for 3,325 tracts in the Southern California region over nearly two decades of 2000–2010 and 2010–2017 and construct flow measures based on race/ethnicity; the length of residence of owners and renters; the age structure. Results: These flow measures improve the predictive power of the models—implying important theoretical insights. Neighborhoods with higher percentages of middle-aged residents who recently entered the neighborhood exhibit larger increases in violent and property crime. The relative stability of those in the highest crime-prone ages (aged 15–29) is associated with the largest increases in violent and property crime. The greater loss of Black and Asian residents decreased crime while moderate outflows of Latinos increased crime. The mobility of long- and short-term renters was related to crime changes. Conclusions: This new technique will likely encourage further theoretical innovation for the neighborhoods and crime literature.

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.

We explore neighborhoods in Southern California from 1980 to 2010 that exhibit persistent racial diversity (PRD) and the consequences of this PRD. Initial exploratory analyses show that the racial composition of the area surrounding the neighborhood in 1980 is associated with which neighborhoods become PRDs. Our primary analyses compare how PRD neighborhoods change over time (1980–2010) based on several socio-demographic measures to a matched group of non-PRD neighborhoods that had similar characteristics in 1980. The key finding is that PRD neighborhoods improved more on per capita income and percent in poverty compared to their matched tracts from 1980 to 2010. We also found that there was not a single route to persistent diversity, but rather a myriad of pathways through which racial/ethnic diversity can persist over a long time period at the neighborhood level.

Although prior studies have examined the association between the presence of various types of business facilities and crime in place, less attention has been paid to how the effects of businesses can be temporally different based on their age. We focus on four consumer-facing business types: 1) retail, 2) service, 3) restaurant, and 4) food and drug stores. For each type, we construct block level measures of the number of businesses, the average business age, and the standard deviation of business age. We estimate fixed-effects negative binomial regression models to test the effects of these measures on crime in blocks, controlling for a range of factors known to be associated with crime rates. The average age of businesses was robustly associated with lower crime rates and such pattern was most pronounced in blocks with a greater business presence.

The current study examines the relationship between the level of social activity and crime in place. We theoretically conceptualized the social activity as a combination of two essential elements of vitality and diversity. Our results suggest that level of social activity has a crime enhancing effect on both violent and property crime. We also found that there are positive interaction effects between the measures of vitality and diversity. This study contributes to the field by introducing a theoretically driven concept of social activity and empirically showing how the two dimensions of social activity—vitality and diversity—have independent effects that multiplicatively impact the level of crime at a location.

Prior research measures immigration by only accounting for where immigrants live. We argue that this approach misses the activity spaces of immigrants, which also impact crime but are not always located in their residential communities. The present study uses an alternative definition of immigration—immigrant-ethnic activity space (IEAS)—that accounts for both the residential location and routine activities of immigrants. Additionally, given the crime-reducing effects associated with immigration, including for high-risk populations, we consider whether IEAS protects against reoffending for ex-inmates. Using Cox hazards models, we examine the relationship between IEAS and recidivism across the communities of five ethnic groups. Results show that the IEAS of all groups are inversely associated with recidivism. However, ex-prisoner concentration amplifies the risk for recidivism in the IEAS of some groups.

While street view imagery has accumulated over the years, its use to date has been largely limited to cross-sectional studies. This study explores ways to utilize historical Google Street View (GSV) images for the investigation of neighborhood change. Using data for Santa Ana, California, an experiment is conducted to assess to what extent deep learning-based semantic segmentation, processing historical images much more efficiently than visual inspection, enables one to capture changes in the built environment. More specifically, semantic segmentation results are compared for (1) 248 sites with construction or demolition of buildings and (2) two sets of the same number of randomly selected control cases without such activity. It is found that the deep learning-based semantic segmentation can detect nearly 75% of the construction or demolition sites examined, while screening out over 60% of the control cases. The results suggest that it is particularly effective in detecting changes in the built environment with historical GSV images in areas with more buildings, less pavement, and larger-scale construction (or demolition) projects. False-positive outcomes, however, can emerge due to the imperfection of the deep learning model and the misalignment of GSV image points over years, showing some methodological challenges to be addressed in future research.

There is conflicting evidence in the literature regarding the relationship between residents’ social networks and their perceptions of neighborhood collective efficacy. This study proposes addressing this challenge with several theoretically motivated refinements using a large spatially stratified sample of residents in the Western United States. First, we consider various distinct types of social relationships, and find that our novel measure of neighborhood safety ties is much more strongly related to perceived collective efficacy than is a measure of socializing relationships. Second, we explicitly account for the spatial distribution of ties, and find that it is not just local neighborhood ties that increase a sense of cohesion or informal social control, but that more spatially distant ties also matter. Third, we make a distinction between urban and rural areas, finding that in rural areas, social ties from an even broader area are associated with stronger feelings of collective efficacy.

Much of the work concerning economic dynamism has focused on its aggregate-level implications, while there have been rising concerns about business displacement at the community or neighborhood level. In this article, we analyze this important (restructuring) process using detailed establishment-level business information and explore how it manifests itself across space within the Los Angeles—Long Beach—Santa Ana, CA Urbanized Area. We also investigate the association between business churning and neighborhood-level housing vacancy rates to understand the implications of dramatic changes in the business landscape. We find that housing vacancies are more likely to increase in urban neighborhoods with a higher establishment death rate, while the creation of new businesses can mitigate the association to some extent. We also detect substantial variation across decades not only in the spatial distribution of business churning but also in its association with housing vacancy rates, suggesting the evolving nature of business dynamics and their implications.

There is interest in understanding which characteristics of metropolitan areas impact the length of time or distance residents spend commuting. We utilize two measures recently introduced to the urban literature capturing distinct dimensions of employment decentralization –the level of employment deconcentration and employment spatial dispersion in metropolitan areas – to assess how they are related to commuting patterns across metropolitan areas. These two measures of urban/metropolitan spatial structure avoid challenges in identifying “job centers” and allow for a more systematic investigation of how employment decentralization affects commuting patterns. Furthermore, we detect key differences for the implications of these measures for commuting across 329 US metropolitan regions based on their population size. We find that greater employment deconcentration in very small MSAs is associated with longer commute times and distances, whereas greater employment deconcentration in large or very large MSAs is associated with shorter commutes. And whereas spatial dispersion is not related to commute times in very small MSAs, greater spatial dispersion is associated with longer commutes in very large MSAs. This study also shows that the spatial pattern of employment in regions, captured by these new measures, is associated with the proportion of very short and very long duration commutes.

Objectives: Current strategies for locating where offenders live either focus exclusively on individual suspects or generalize to entire neighborhoods. However, better estimates of where offenders are located may improve models of the ecological distribution of crime, and forecasts of the locations of future crime incidents. Methods: We propose a novel reverse spatial patterning (RSP) strategy that estimates where offenders may live based on the spatial locations of crime events. We rely on a distance decay function – based on the consistent finding that offenders do not travel far to commit crime – and Hipp's (2016) general theory of spatial crime patterns, to work backwards from the locations of actual crime events to make predictions about where offenders may live in subsequent years. We then use these estimates in models predicting crime locations. We create two versions of the RSP: one which assumes everyone is equally likely to offend, and another that creates an estimate assuming disproportionate offending across persons. Results: We test the effectiveness of our proposed strategy for these two measures using offense and arrest data from St. Petersburg, FL, and assess how well they predict the location of offenders (proxied by arrestees) and future crime events. We find consistent evidence that our RSP strategy provides better predictions of the locations of where offenders are located and also future crime incidents across a variety of crime types compared to existing strategies. Conclusion: The RSP approach is useful for creating estimates of where offenders live, which allow for better predictions of the locations of future crime incidents. These better forecasts will allow for more efficient allocation of police resources and targeted crime suppression efforts.

We examine the impact of immigrant-serving organizations on neighborhood crime in the Los Angeles Metropolitan area, while accounting for other community correlates of crime as well as potential endogeneity. We estimate longitudinal negative binomial regression models that test for the main, mediating, and moderating effects of immigrant-serving organizations. We found that immigrant-serving organizations generally have crime-reducing effects for all types of crime. We also find that high immigrant concentration is associated with lower levels of crime in general, and this effect is moderated by the number of organizations, which underlines the importance of accounting for these organizations when studying the nexus of immigrant concentration and neighborhood 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: 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.

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.

Whereas existing ecology of crime research frequently uses a cross-sectional design, an open question is whether theories underlying such studies will operate similarly in longitudinal research. Using latent trajectory models and longitudinal data in half-mile egohoods from the Southern California region over a 10-year period (2000–2010), we explore this question and assess whether the changes in key measures of social disorganization theory are related to changes in violent or property crime through three possible relationships: 1) a monotonic relationship, 2) an asymmetric relationship, and 3) a perturbation relationship in which any change increases crime. We find evidence that measures can exhibit any of these three possible relationships, highlighting the importance of not assuming monotonic relationships. Most frequently observed are asymmetric relationships, which we posit are simultaneously capturing more than one theoretical process of neighborhoods and crime. Specific findings include asymmetric relationships between change in concentrated disadvantage, racial/ethnic minority composition, or population and violent crime, as well as relationships between change in Asian composition or population and property crime. We consider how this strategy opens a needed area of future research assessing how measures for other theories operate as environments change.

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.

A social context can be viewed as an entity or unit around which a group of individuals organize their activities and interactions. Social contexts take such diverse forms as families, dwelling places, neighborhoods, classrooms, schools, workplaces, voluntary organizations, and sociocultural events or milieus. Understanding social contexts is essential for the study of individual behaviors, social networks, and the relationships between the two. Contexts shape individual behaviors by providing an avenue for non-dyadic conformity and socialization processes. The co-participation within a context affects personal relationships by acting as a focus for tie formation. Where participation in particular contexts confers status, this effect may also lead to differences in popularity within interpersonal networks. Social contexts may further play a moderating role in within-network influence and selection processes, providing circumstances that either amplify or suppress these effects. In this paper we investigate the joint role of co-participation via social contexts and dyadic interaction in shaping and being shaped by individual behaviors with the context of a U.S. high school. Implications for future study of social contexts are suggested.

The uneven spread of COVID-19 has resulted in disparate experiences for marginalized populations in urban centers. Using computational models, we examine the effects of local cohesion on COVID-19 spread in social contact networks for the city of San Francisco, finding that more early COVID-19 infections occur in areas with strong local cohesion. This spatially correlated process tends to affect Black and Hispanic communities more than their non-Hispanic White counterparts. Local social cohesion thus acts as a potential source of hidden risk for COVID-19 infection.

The present study examines holistic neighborhood change in Los Angeles County across three decades between 1980 and 2010. Using Census tract data, we conduct a latent class analysis to identify classes of neighborhood change for each decade according to housing dynamics, age structure, racial-ethnic composition and churning, and socioeconomic characteristics, and describe latent classes indicative of gentrification. Further, we assess the degree to which tracts experience sustained or repeated gentrification over the 30 year period. In line with more recent conceptualizations of gentrification as a broad urban process, we find that gentrification occurs in a wide range of neighborhoods, and manifests itself differently according to shifts in population characteristics, with many tracts experiencing more than one successive period of gentrification over the 30 year period.

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.

Criminological theories have posited that the built environment impacts where crime occurs; however, measuring the built environment is difficult. Furthermore, it is uncertain whether the built environment differentially impacts crime in high-disadvantage neighborhoods. This study extracts features of the built environment from Google Street View images with a machine learning semantic segmentation strategy to create measures of fences, walls, buildings, and greenspace for over 66,000 street segments in Los Angeles. Results indicate that the presence of more buildings on a segment was associated with higher crime rates and had a particularly strong positive relationship with robbery and motor vehicle theft in low-disadvantage neighborhoods. Notably, fences and walls exhibited different relationships with crime. Walls, which do not allow visibility, were strongly negatively related to crime, particularly for robbery and burglary in high-disadvantage neighborhoods. Fences, which allow visibility, were associated with fewer robberies and larcenies, but more burglaries and aggravated assaults. Fences only exhibited a negative relationship with violent crime when they were located in low-disadvantage neighborhoods. The results highlight the importance of accounting for the built environment and the surrounding level of disadvantage when exploring the micro-location of crime.

Adolescent drinking remains a prominent public health and socioeconomic issue in the USA with costly consequences. While numerous drinking intervention programs have been developed, there is little guidance whether certain strategies of participant recruitment are more effective than others. The current study aims at addressing this gap in the literature using a computer simulation approach, a more cost-effective method than employing actual interventions. We first estimate stochastic actor-oriented models for two schools from the National Longitudinal Study of Adolescent to Adult Health (Add Health). We then employ different strategies for selecting adolescents for the intervention (either based on their drinking levels or their positions in the school network) and simulate the estimated model forward in time to assess the aggregated level of drinking in the school at a later time point. The results suggest that selecting moderate or heavy drinkers for the intervention produces better results compared to selecting casual or light drinkers. The intervention results are improved further if network position information is taken into account, as selecting drinking adolescents with higher in-degree or higher eigenvector centrality values for intervention yields the best results. Results from this study help elucidate participant selection criteria and targeted network intervention strategies for drinking intervention programs in the USA.

Using social network data from the American Social Fabric Project (ASFP), this study examines how the distance to social alters may lead to different perceptions of neighborhood and city attachment among urban versus rural residents, and considers which types of relations play influential roles in shaping attachment. Overall, a key finding is that having more local neighborhood ties is positively associated with attachment at both the neighborhood level and city level, holding for any social relationship in our sample and for urban and rural environments. Notably, long distance ties are not irrelevant for attachment; rather, we see that long distance ties are associated with greater neighborhood and city attachment. Among different social relations measured, neighborhood safety ties consistently show the strongest positive relationship with neighborhood and city attachment. Surprisingly, we find that the spatial distribution of social ties appears more consequential for attachment in the rural sample than it does in the urban sample. Further, geographically dispersed ties also matter for urban versus rural settings: physically close and midrange core discussion ties are associated with weaker attachment for urban residents, whereas they do not affect rural residents’ perceptions of attachment.

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.

Purpose: The current study simultaneously examines the effects of three different characteristics of the built environment based on the theoretical conceptualizations of density, diversity, and design (3D). Methods: By using data of 211,155 street segments in the Southern California metropolitan region across 130 cities, we estimated a set of negative binomial regression models including the 3D measures of the built environment, while accounting for the effects of social structural characteristics of place. Furthermore, the current study examines the potential moderating effects of each 3D feature on crime. Results: We found that higher levels of business density are consistently associated with higher levels of crime. The diversity measure is associated with moderately higher levels of crime, whereas the design measure consistently exhibited a negative relationship with crime. Furthermore, we found that the diversity and design measures moderated the business density relationship with crime. Conclusion: The results of the current study suggest that it is necessary to examine the different types of physical environment simultaneously to understand the effects of physical environment and the spatial patterns of crime.

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.

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.

Despite decades of research on adolescent friendships, little is known about adolescents who are more likely to form ties outside of school. We examine multiple social and ecological contexts including parents, the school, social networks, and the neighborhood to understand the origins and health significance of out of school ties using survey data from the National Longitudinal Study of Adolescent to Adult Health (N = 81,674). Findings indicate that out of school (more than in-school) friendships drive adolescent deviance and alcohol use, and youth with such friends tend to be involved in school activities and are central among their peer group. This suggests that intervention efforts aimed at reducing deviance and underage drinking may benefit from engaging youth with spanning social ties.

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.

Standard epidemiological models for COVID-19 employ variants of compartment (SIR or susceptible-infectious-recovered) models at local scales, implicitly assuming spatially uniform local mixing. Here, we examine the effect of employing more geographically detailed diffusion models based on known spatial features of interpersonal networks, most particularly the presence of a long-tailed but monotone decline in the probability of interaction with distance, on disease diffusion. Based on simulations of unrestricted COVID-19 diffusion in 19 US cities, we conclude that heterogeneity in population distribution can have large impacts on local pandemic timing and severity, even when aggregate behavior at larger scales mirrors a classic SIR-like pattern. Impacts observed include severe local outbreaks with long lag time relative to the aggregate infection curve, and the presence of numerous areas whose disease trajectories correlate poorly with those of neighboring areas. A simple catchment model for hospital demand illustrates potential implications for health care utilization, with substantial disparities in the timing and extremity of impacts even without distancing interventions. Likewise, analysis of social exposure to others who are morbid or deceased shows considerable variation in how the epidemic can appear to individuals on the ground, potentially affecting risk assessment and compliance with mitigation measures. These results demonstrate the potential for spatial network structure to generate highly nonuniform diffusion behavior even at the scale of cities, and suggest the importance of incorporating such structure when designing models to inform health care planning, predict community outcomes, or identify potential disparities.

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.