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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.

This paper considers the quantitative assessment of ecological settings such as neighborhoods and schools. Available administrative data typically provide useful but limited information on such settings. We demonstrate how more complete information can be reliably obtained from surveys and observational studies. Survey-based assessments are constructed by aggregating over multiple item responses of multiple informants within each setting. Item and rater inconsistency produce uncertainty about the setting being assessed, with definite implications for research design. Observation-based assessments also have a multilevel error structure. The paper describes measures constructed from interviews, direct observations, and videotapes of Chicago neighborhoods and illustrates an “ecometric” analysis—a study of bias and random error in neighborhood assessments. Using the observation data as an illustrative example, we present a three-level hierarchical statistical model that identifies sources of error in aggregating across items within face-blocks and in aggregating across face-blocks to larger geographic units such as census tracts. Convergent and divergent validity are evaluated by studying associations between the observational measures and theoretically related measures obtained from the U.S. Census, and a citywide survey of neighborhood residents.

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.

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.

Throughout the centuries Beirut has had an endless capacity for reinvention and transformation, a consequence of migration, conquest, trade, and internal conflict. The last three decades have witnessed the city center's violent self-destruction, its commercial resurrection, and most recently its national contestation, as oppositional political forces have sought to mobilize mass demonstrations and occupy strategic space. While research has been directed to the transformative processes and the principal actors involved, little attention has been given to how the next generation of Lebanese are negotiating Beirut's rehabilitation. This article seeks to address this lacuna, by exploring how postwar youth remember, imagine, and spatially encounter their city. How does Beirut's rebuilt urban landscape, with its remnants of war, sites of displacement, and transformed environs, affect and inform identity, social interaction, and perceptions of the past? Drawing on Henri Lefebvre's analysis of the social construction of space (perceived, conceived, and lived) and probing the inherent tensions within postwar youths’ encounters with history, memory, and heritage, the article presents a dynamic and complex urban imaginary of Beirut. An examination of key urban sites (Solidère's Down Town) and significant temporal moments (Independence Intifada) reveals three recurring tensions evident in Lebanese youth's engagement with their city: dislocation and liberation, spectacle and participant, pluralism and fracture. This article seeks to encourage wider discussion on the nature of postwar recovery and the construction of rehabilitated public space, amidst the backdrop of global consumerism and heritage campaigns.

In this study we examine the spatial practices and lived experiences of an understudied subgroup, observant Muslim women of Arab descent, to explore the extent to which they experience representation and inclusion in the context of Brooklyn, New York. In an attempt to provide a more in-depth understanding of space, we utilize a phenomenological approach in which gender is central. We conceptualize our analysis based on Lefebvre’s spatial triad. The narratives of the women in this study elucidate how they interpret and navigate publicly accessible urban spaces as women marked by both ethnicity and religious difference in a multicultural city such as New York. Our study finds that the physical accessibility of public spaces, the aspect that planners tend to emphasize, matters for the observant Muslim women in this study both in ways with which planners are familiar and in other ways. The main aspects of physical accessibility that facilitated their sense of inclusion and engagement in Bay Ridge public spaces are the ease of getting around, often called ‘walkability’ in planning circles, the extent of access to mass transit, and the types of destinations in the area. Streetlights and the openness of public spaces were also critical to participants’ lived experiences, as was the presence of a number of women wearing the Islamic headscarf. The latter enabled participants to become active actors in space because they marked a place as culturally, religiously, and socially appropriate for them. Participants’ lived experiences (representational space) in turn shaped and were shaped by the characteristics of physical space. For example, well-lit open spaces enabled their spatial engagement because this made them visible to the community and at the same time allowed them to see the community. For immigrant women the Arabic landscape of the neighborhood marked by the Arabic signage, the Arabic language being spoken, and women wearing the Islamic headscarf provided them an opportunity to communicate with other women who share their cultural and religious values (spatial practice), and thereby to experience a safe space of normalcy (representational space).

Ordinary kriging, a spatial interpolation technique, is commonly used in social sciences to estimate neighborhood attributes such as physical disorder. Universal kriging, developed and used in physical sciences, extends ordinary kriging by supplementing the spatial model with additional covariates. We measured physical disorder on 1,826 sampled block faces across four U.S. cities (New York, Philadelphia, Detroit, and San Jose) using Google Street View imagery. We then compared leave-one-out cross-validation accuracy between universal and ordinary kriging and used random subsamples of our observed data to explore whether universal kriging could provide equal measurement accuracy with less spatially dense samples. Universal kriging did not always improve accuracy. However, a measure of housing vacancy did improve estimation accuracy in Philadelphia and Detroit (7.9 percent and 6.8 percent lower root mean square error, respectively) and allowed for equivalent estimation accuracy with half the sampled points in Philadelphia. Universal kriging may improve neighborhood measurement.