This paper examines the connection of immigration and diversity to homicide by advancing a recently developed approach to modeling spatial dynamics-geographically weighted regression. In contrast to traditional global averaging, we argue on substantive grounds that neighborhood characteristics vary in their effects across neighborhood space, a process of "spatial heterogeneity." Much like treatment-effect heterogeneity and distinct from spatial spillover, our analysis finds considerable evidence that neighborhood characteristics in Chicago vary significantly in predicting homicide, in some cases showing countervailing effects depending on spatial location. In general, however, immigrant concentration is either unrelated or inversely related to homicide, whereas language diversity is consistently linked to lower homicide. The results shed new light on the immigration-homicide nexus and suggest the pitfalls of global averaging models that hide the reality of a highly diversified and spatially stratified metropolis.In a context of record increases in the ethnic and foreign-born population in the US in recent years, heated debates over immigration policy have rekindled old fears about the threat of the "criminal alien." Due to post-1965 changes in the composition of immigration waves, especially the decade of the 1990s, a wider range of immigrant and ethnic groups is coloring neighborhoods across the country. Yet the implications of immigration and diversity for neighborhood change and crime are not well understood. In this paper, we focus on homicide as a leading indicator of violence and seek to advance our knowledge of the role that immigration plays. We do so by making two analytical moves.First, we argue that to properly understand the effects of immigration on violence requires a framework that distinguishes segregation, or spatial concentration, from diversity. In this paper we focus equal attention on the separate roles of immigrant concentration, on the one hand, and diversity, on the other, in predicting homicide rates across urban neighborhoods. This analytical distinction is potentially important for understanding the complex social processes leading to homicide. The immigration literature to date has insufficiently tackled this issue.Second, we argue that the effect of immigration and diversity on neighborhood homicide rates is fruitfully conceptualized as a phenomenon that is contingent on local structural and spatial contexts. Traditional "global" methods of analysis reveal the role of neighborhood characteristics on homicide averaged across space. While we often use these methods ourselves and while they have spawned a rich literature across many decades by a large group of scholars, what remains largely unexplored is the extent of spatial heterogeneity in macrolevel processes. Baller et al. (2001) yield misleading results unless regional interactions are explicitly take into account." The same logic applies to models that assess locally specific covariations within a city (Cahill and Mulli...

Crime is one of the most important social problems in the country, affecting public safety, children development, and adult socioeconomic status. Understanding what factors cause higher crime is critical for policy makers in their efforts to reduce crime and increase citizens' life quality. We tackle a fundamental problem in our paper: crime rate inference at the neighborhood level. Traditional approaches have used demographics and geographical influences to estimate crime rates in a region. With the fast development of positioning technology and prevalence of mobile devices, a large amount of modern urban data have been collected and such big data can provide new perspectives for understanding crime. In this paper, we used large-scale Point-Of-Interest data and taxi flow data in the city of Chicago, IL in the USA. We observed significantly improved performance in crime rate inference compared to using traditional features. Such an improvement is consistent over multiple years. We also show that these new features are significant in the feature importance analysis.

No abstract

This article treats social capital as a multidimensional phenomenon along which neighborhoods are differentially organized. The authors assess this notion by linking two original surveys carried out in Chicago based on community residents (N = 8,782) and positional leaders (N = 2,822) representing six organizational dimensions. These data are used to examine both the dimensionality and structural predictors of neighborhood social organization. Results show that the social capital of Chicago communities encapsulates four distinct dimensions at the residential level and two at the leadership level. Moreover, dimensions of leadership-based social capital are for the most part inversely related to resident-based social capital and differentially predicted by concentrated disadvantage, residential stability, and racial/ethnic diversity. Based on multidimensional scaling and clustering of the communities, the authors derive a conceptual typology highlighted by four distinct groups-Cosmopolitan Efficacy, Urban Villages, Institutional Alienation, and Conduct Norms. The authors discuss implications and suggest new directions for exploration of community differentiation.

A long history of research has indicated that neighborhood poverty increases youth's risk taking and delinquency. This literature predominantly has treated neighborhoods as independent of their surroundings despite rapidly growing ecological evidence on the geographic clustering of crime that suggests otherwise. This study proposes that to understand neighborhood effects, investigating youth's wider surroundings holds theoretical and empirical value. By revisiting longitudinal data on more than 1500 low‐income youth who participated in the Moving to Opportunity (MTO) randomized intervention, this article explores the importance of extended neighborhoods (neighborhoods and surroundings) and different concentrated disadvantage configurations in shaping gender differences in risk taking and delinquency. The results from two‐stage, least‐squares analyses suggest that the extended neighborhoods matter and they matter differently by gender. Among girls, extended neighborhoods without concentrated disadvantage were associated with lower risk‐taking prevalence than extended neighborhoods with concentrated disadvantage. In contrast, among boys, localized concentration of disadvantage was associated with the highest prevalence of risk taking and delinquency. Interactions between the immediate and surrounding neighborhoods were similarly associated with differential opportunity and social disorganization mediators. Among the more critical potential mediators of the link between localized disadvantage and boys’ risk taking were delinquent network ties, strain, and perceived absence of legitimate opportunities for success.