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Latino immigrant presence in urban neighborhoods has been linked with reduced neighborhood cohesion in social disorganization-based ethnic heterogeneity hypotheses and enhanced cohesion in immigration revitalization approaches. Using the 2000–2002 Los Angeles Family and Neighborhood Survey and the 1994–1995 Project on Human Development in Chicago Neighborhoods Community Survey, we explore the association between Latino immigrant concentration and both levels of, and agreement about, neighborhood collective efficacy. Findings from multilevel models with heteroskedastic variance indicate that Latino immigrant concentration exhibits a nonlinear association with collective efficacy. At low levels, increases in Latino immigrant concentration diminish collective efficacy, consistent with a heterogeneity hypothesis. The negative association between Latino immigrant concentration and collective efficacy declines in magnitude as immigrant concentration increases and, particularly in LA, becomes positive beyond a threshold, consistent with an immigration revitalization effect. We also find an inverse nonlinear pattern of association with the variance of collective efficacy. At low levels, increasing Latino immigrant concentration increases the variance of collective efficacy (reflecting more disagreement), but beyond a threshold, this association becomes negative (reflecting increasing agreement). This pattern is observed in both LA and Chicago. The prevalence of social interaction and reciprocated exchange within neighborhoods explains a modest proportion of the Latino immigrant concentration effect on mean levels of collective efficacy in Chicago, but does little to explain effects on the mean in LA or effects on the variance in either LA or Chicago. These findings offer insight into the complex role Latino immigrant presence plays in shaping neighborhood social climate.

This article assesses the sources and consequences of public disorder. Based on the videotaping and systematic rating of more than 23,000 street segments in Chicago, highly reliable scales of social and physical disorder for 196 neighborhoods are constructed. Census data, police records, and an independent survey of more than 3,500 residents are then integrated to test a theory of collective efficacy and structural constraints. Defined as cohesion among residents combined with shared expectations for the social control of public space, collective efficacy explains lower rates of crime and observed disorder after controlling neighborhood structural characteristics. Collective efficacy is also linked to lower rates of violent crime after accounting for disorder and the reciprocal effects of violence. Contrary to the “broken windows” theory, however, the relationship between public disorder and crime is spurious except perhaps for robbery.

A lack of collective efficacy in neighborhood is associated with social and physical disorder and related anti-social actions. It is less clear, however, whether collective efficacy in neighborhood also enhances prosocial, other-regarding behavior. We studied this association by employing the Lost Letter Technique in a large-scale field experiment. Our data stem from 1,240 letters dropped in a representative sample of 110 Dutch neighborhoods, combined with neighborhood data based on a survey of residents (SSND2, n=996) and information provided by Statistics Netherlands. We distinguish between two conditions (1) location of the lost letter, that is, behind a car's windshield wiper on the sidewalk; and (2) type of addressee, that is, a Dutch name or a Turkish/Moroccan name. When we decompose collective efficacy into social cohesion and shared expectations of social control, we find that shared control expectations clearly matter for the rate of posted letters. Social cohesion has no effect. Furthermore, a high percentage of non-Western residents, high residential mobility, and a relatively low local income level are negatively related to the rate of posted letters.

The concept of street efficacy, defined as the perceived ability to avoid violent confrontations and to be safe in one's neighborhood, is proposed as a mechanism connecting aspects of adolescents'“imposed” environments to the choices they make in creating their own “selected” environments that minimize the potential for violent confrontations. Empirical models using data from the Project on Human Development in Chicago Neighborhoods suggest that street efficacy is substantially influenced by various aspects of the social context surrounding adolescents. Adolescents who live in neighborhoods with concentrated disadvantage and low collective efficacy, respectively, are found to have less confidence in their ability to avoid violence after controlling for an extensive set of individual- and family-level factors. Exposure to violence also reduces street efficacy, although it does not explain the association between collective efficacy and individual street efficacy. Adolescents' confidence in their ability to avoid violence is shown to be an important predictor of the types of environments they select for themselves. In particular, adolescents with high levels of street efficacy are less likely to resort to violence themselves or to associate with delinquent peers.

A number of recent studies have used surveys of neighborhood informants and direct observation of city streets to assess aspects of community life such as collective efficacy, the density of kin networks, and social disorder. Raudenbush and Sampson (1999a) have coined the term “ecometrics” to denote the study of the reliability and validity of such assessments. Random errors of measurement will attenuate the associations between these assessments and key outcomes. To address this problem, some studies have used empirical Bayes methods to reduce such biases, while assuming that neighborhood random effects are statistically independent. In this paper we show that the precision and validity of ecometric measures can be considerably improved by exploiting the spatial dependence of neighborhood social processes within the framework of empirical Bayes shrinkage. We compare three estimators of a neighborhood social process: the ordinary least squares estimator (OLS), an empirical Bayes estimator based on the independence assumption (EBE), and an empirical Bayes estimator that exploits spatial dependence (EBS). Under our model assumptions, EBS performs better than EBE and OLS in terms of expected mean squared error loss. The benefits of EBS relative to EBE and OLS depend on the magnitude of spatial dependence, the degree of neighborhood heterogeneity, as well as neighborhood's sample size. A cross-validation study using the original 1995 data from the Project on Human Development in Chicago Neighborhoods and a replication of that survey in 2002 show that the empirical benefits of EBS approximate those expected under our model assumptions; EBS is more internally consistent and temporally stable and demonstrates higher concurrent and predictive validity. A fully Bayes approach has the same properties as does the empirical Bayes approach, but it is preferable when the number of neighborhoods is small.

Drawing on the social disorganization tradition and the social ecological perspective of Jane Jacobs, the authors hypothesize that neighborhoods composed of residents who intersect in space more frequently as a result of routine activities will exhibit higher levels of collective efficacy, intergenerational closure, and social network interaction and exchange. They develop this approach employing the concept of ecological networks—two-mode networks that indirectly link residents through spatial overlap in routine activities. Using data from the Los Angeles Family and Neighborhood Survey, they find evidence that econetwork extensity (the average proportion of households in the neighborhood to which a given household is tied through any location) and intensity (the degree to which household dyads are characterized by ties through multiple locations) are positively related to changes in social organization between 2000–2001 and 2006–2008. These findings demonstrate the relevance of econetwork characteristics—heretofore neglected in research on urban neighborhoods—for consequential dimensions of neighborhood social organization.

This study examined what factors best predict residents’ concerns about neighborhood safety. One-hundred and twenty-two participants were selected from a large, Midwestern metropolitan area. All participants lived in high crime areas. Participants completed a 22-item questionnaire that assessed their perceptions of neighborhood safety and vigilance. These items were clustered as: 1) Community care and vigilance, 2) neighborhood safety concerns, 3) physical incivilities, and 4) social incivilities. Police crime data were also used in the analyses. Our findings suggest that aspects of the broken window theory, collective efficacy, and place attachments/territoriality play a role in affecting residents’ concerns about neighborhood safety.

This paper assesses and synthesizes the cumulative results of a new “neighborhood-effects” literature that examines social processes related to problem behaviors and health-related outcomes. Our review identified over 40 relevant studies published in peer-reviewed journals from the mid-1990s to 2001, the take-off point for an increasing level of interest in neighborhood effects. Moving beyond traditional characteristics such as concentrated poverty, we evaluate the salience of social-interactional and institutional mechanisms hypothesized to account for neighborhood-level variations in a variety of phenomena (e.g., delinquency, violence, depression, high-risk behavior), especially among adolescents. We highlight neighborhood ties, social control, mutual trust, institutional resources, disorder, and routine activity patterns. We also discuss a set of thorny methodological problems that plague the study of neighborhood effects, with special attention to selection bias. We conclude with promising strategies and directions for future research, including experimental designs, taking spatial and temporal dynamics seriously, systematic observational approaches, and benchmark data on neighborhood social processes.

The collection of large-scale administrative records in electronic form by many cities provides a new opportunity for the measurement and longitudinal tracking of neighborhood characteristics, but one that will require novel methodologies that convert such data into research-relevant measures. The authors illustrate these challenges by developing measures of “broken windows” from Boston’s constituent relationship management (CRM) system (aka 311 hotline). A 16-month archive of the CRM database contains more than 300,000 address-based requests for city services, many of which reference physical incivilities (e.g., graffiti removal). The authors carry out three ecometric analyses, each building on the previous one. Analysis 1 examines the content of the measure, identifying 28 items that constitute two independent constructs, private neglect and public denigration. Analysis 2 assesses the validity of the measure by using investigator-initiated neighborhood audits to examine the “civic response rate” across neighborhoods. Indicators of civic response were then extracted from the CRM database so that measurement adjustments could be automated. These adjustments were calibrated against measures of litter from the objective audits. Analysis 3 examines the reliability of the composite measure of physical disorder at different spatiotemporal windows, finding that census tracts can be measured at two-month intervals and census block groups at six-month intervals. The final measures are highly detailed, can be tracked longitudinally, and are virtually costless. This framework thus provides an example of how new forms of large-scale administrative data can yield ecometric measurement for urban science while illustrating the methodological challenges that must be addressed.