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This article highlights the importance of considering the proper level of aggregation when estimating neighborhood effects. Using a unique nonrural subsample from a large national survey (the American Housing Survey) at three time points that allows placing respondents in blocks and census tracts, this study tests the appropriate level of aggregation of the structural characteristics hypothesized to affect block-level perceptions of crime and disorder. I find that structural characteristics differ in their effects based on the level of aggregation employed. While the effects of racial/ethnic heterogeneity are fairly robust to the geographical level of aggregation, the stronger effects, when measured at the level of the surrounding census tract, suggest more dispersed networks are important for perceived crime and disorder. In contrast, economic resources only show a localized effect when aggregating to the block-level and differ based on the outcome; higher average income reduces disorder but increases crime, most likely by increasing the number of attractive targets. Additionally, the presence of broken households has a localized effect for social disorder but a more diffuse effect for perceived crime. These findings suggest the need for neighborhood studies of crime rates, as well as the broader neighborhood effects literature, to consider the mechanisms involved when aggregating various structural characteristics.

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.

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 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.

The local neighborhood forms an integral part of our lives. It provides the context through which social networks are nurtured and the foundation from which a sense of attachment and cohesion with fellow residents can be established. Whereas much of the previous research has examined the role of social and demographic characteristic in relation to the level of neighboring and cohesion, this paper explores whether particular environmental features in the neighborhood affect social porosity. We define social porosity as the degree to which social ties flow over the surface of a neighborhood. The focus of our paper is to examine the extent to which a neighborhood's environmental features impede the level of social porosity present among residents. To do this, we integrate data from the census, topographic databases and a 2010 survey of 4,351 residents from 146 neighborhoods in Australia. The study introduces the concepts of wedges and social holes. The presence of two sources of wedges is measured: rivers and highways. The presence of two sources of social holes is measured: parks and industrial areas. Borrowing from the geography literature, several measures are constructed to capture how these features collectively carve up the physical environment of neighborhoods. We then consider how this influences residents' neighboring behavior, their level of attachment to the neighborhood and their sense of neighborhood cohesion. We find that the distance of a neighborhood to one form of social hole-industrial areas-has a particularly strong negative effect on all three dependent variables. The presence of the other form of social hole-parks-has a weaker negative effect. Neighborhood wedges also impact social interaction. Both the length of a river and the number of highway fragments in a neighborhood has a consistent negative effect on neighboring, attachment and cohesion.

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. 鉴于邻里环境对居民社会凝聚力的重要性，本项研究考察了社交和非社交场所类型之间的关联，我们所依据的是社会凝聚力的三个指标：邻里网络、社会凝聚力和邻里依恋。我们在空间上整合来自人口普查，地形数据库和2012年对来自澳大利亚布里斯班148个社区的4132名居民的调查数据，并采用多层次模型来评估：居民所报告的社会凝聚力差异是否可归因于街区将土地用作邻里社交管道的情况。我们还考虑了社区分裂对我们社会凝聚力指标的影响程度。我们的研究结果表明，即使在控制了社区的社会人口背景和一系列个人和家庭控制变量之后，居民报告的社会凝聚力与社交管道的类型、土地利用的多样性和社区分裂程度显著相关。

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.

To explore the co-evolution of friendship tie choice and alcohol use behavior among 1,284 adolescents from 12 small schools and 976 adolescents from one big school sampled in the National Longitudinal Study of Adolescent to Adult Health (AddHealth), we apply a Stochastic Actor-Based (SAB) approach implemented in the R-based Simulation Investigation for Empirical Network Analysis (RSiena) package. Our results indicate the salience of both peer selection and peer influence effects for friendship tie choice and adolescent drinking behavior. Concurrently, the main effect models indicate that parental monitoring and the parental home drinking environment affected adolescent alcohol use in the small school sample, and that parental home drinking environment affected adolescent drinking in the large school sample. In the small school sample, we detect an interaction between the parental home drinking environment and choosing friends that drink as they multiplicatively affect friendship tie choice. Our findings suggest that future research should investigate the synergistic effects of both peer and parental influences for adolescent friendship tie choices and drinking behavior. And given the tendency of adolescents to form ties with their friends' friends, and the evidence of local hierarchy in these networks, popular youth who do not drink may be uniquely positioned and uniquely salient as the highest rank of the hierarchy to cause anti-drinking peer influences to diffuse down the social hierarchy to less popular youth. As such, future interventions should harness prosocial peer influences simultaneously with strategies to increase parental support and monitoring among parents to promote affiliation with prosocial peers.

Prior research frequently observes a positive cross-sectional relationship between various neighborhood structural characteristics and crime rates, and attributes the causal explanation entirely to these structural characteristics. We question this assumption theoretically, proposing a household-level model showing that neighborhood crime might also change these structural characteristics. We test these hypotheses using data on census tracts in 13 cities over a ten-year period, and our cross-lagged models generally find that, if anything, crime is the stronger causal force in these possible relationships. Neighborhoods with more crime tend to experience increasing levels of residential instability, more concentrated disadvantage, a diminishing retail environment, and more African Americans ten years later. Although we find that neighborhoods with more concentrated disadvantage experience increases in violent and property crime, there is no evidence that residential instability or the presence of African Americans increases crime rates ten years later.

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.