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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.

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.

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.

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.

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 todetect 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.

The recent popularity of models that capture the dynamic coevolution of both network structure and behavior has driven the need for summary indices to assess the adequacy of these models to reproduce dynamic properties of scientific or practical importance. Whereas there are several existing indices for assessing the ability of the model to reproduce network structure over time, to date there are few indices for assessing the ability of the model to reproduce individuals’ behavior patterns. Drawing on the widely used strategy of assessing model adequacy by comparing index values summarizing features of the observed data to the distribution of those index values on simulated data from the fitted model, we propose four goals that a researcher could reasonably expect of a joint structure/behavior model regarding how well it captures behavior and describe indices for assessing each of these. These reasonably simple and easily implemented indices can be used for assessing model adequacy with any dynamic network models jointly working with networks and behavior, including the stochastic actor-based models implemented within software packages such as RSien version 1.2-24. We demonstrate the use of our indices with an empirical example to show how they can be employed in practical settings, with an additional extension to modeling affiliation dynamics in two-mode networks. Key scripts are provided in the Supplemental Document (which can be found at http://smr.sagepub.com/supplemental/ ).

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.

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.

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.

Since Putnam introduced his constrict thesis in 2007, many researchers have established that ethnic diversity lowers perceptions of social cohesion, at least in the short term. The connection between ethnic diversity and social behavior, however, is less certain. In this paper we draw on social distance and social identity theories to empirically test if ethnic diversity encourages behaviors linked to social withdrawal. Using data from a longitudinal panel study of urban communities in Australia, we examine the influence of social distance on neighborhood ties, neighborly exchange, and civic engagement and assess if an individual’s social identity (ethnic or civic) strengthens or weakens these relationships. We find individuals that endorse an ethnic identity are more likely to engage in social withdrawal behaviors. Withdrawal is also more likely in neighborhoods where individuals distort the presence of minorities.

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.

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.

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.

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.

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.

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.

Author(s): Hipp, JR; Kim, JH; Forthun, B | Abstract: 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.