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Abstract
Multiracial neighborhood integration has become more common in U.S. metropolitan areas over the past three decades. This article takes up the question: are residents satisfied living in multiracial neighborhoods? Traditional theories of racial change predict low levels of satisfaction in these neighborhoods, while newer studies question that prediction. The article uses data representing all residents of multiracial neighborhoods in the Washington, DC, area to study neighborhood satisfaction in multiracial neighborhoods. The analysis finds evidence of shared satisfaction among residents regardless of race: large and equal shares of each racial group were satisfied. White residents were less satisfied than white residents of neighborhoods elsewhere in the metropolitan region, but were unlikely to perceive neighborhood decline. The shared satisfaction among residents of all races and the lack of racial antipathy to change among white residents suggests that multiracial neighborhoods offer sites to promote racial equity.

While scholars argue that redevelopment and gentrification result in large part from the unique preferences of middle–class residents moving to neighborhoods after decades of flight, almost all of this evidence is extrapolated from the behavior of residents already living in redeveloped neighborhoods. I argue that understanding the consequences of redevelopment, particularly urban policies advocating redevelopment, requires measuring the preferences for redeveloped neighborhoods among the broader metropolitan population. Using data from a representative sample of Chicago metropolitan area adults, I find that homeowners and renters differ in their patterns of preferences for redeveloped neighborhoods: city or suburban residence is more important for homeowners while race is a much stronger factor among renters. This reassessment of preference patterns highlights the potential for redevelopment policies to fall short of intended goals to attract investment and alleviate racial segregation.

We argue that the relative persistence of racial segregation is due, at least in part, to the process of residential search and the perceptions upon which those searches are based—a critical but often-ignored component of the residential sorting process. We examine where Chicago-area residents would "seriously consider" and "never consider" living, finding that community attraction and avoidance are highly racialized. Race most clearly shapes the residential perceptions and preferences of whites, and matters the least to blacks. Latinos would seriously consider moving to numerous neighborhoods, but controls for demographics and distance from the respondents' home make Latino preferences much like those of whites. Critically, the geography of existing segregation begets further segregation: distance from current community significantly affects perceptions of the communities into which respondents might move. While neighborhood perception may cause persistent segregation, it may also offer hope for integration with appropriate policy interventions.

Accurately measuring attributes in neighborhood environments allows researchers to study the influence of neighborhoods on individual-level outcomes. Researchers working to improve the measurement of neighborhood attributes generally advocate doing so in one of two ways: improving the theoretical relevance of measures and correctly defining the appropriate spatial scale. The data required by the first, "ecometric" neighborhood assessments on a sample of neighborhoods, are generally incompatible with the methods of the second, which tend to rely on population data. In this article, the authors describe how ecometric measures of theoretically relevant attributes observed on a sample of city blocks can be combined with a geostatistical method known as kriging to develop city block–level estimates across a city that can be configured to multiple neighborhood definitions. Using a cross-validation study with data from a 2002 systematic social observation of physical disorder on 1,663 city blocks in Chicago, the authors show that this method creates valid results. They then demonstrate, using neighborhood measures aggregated to three different spatial scales, that residents' perceptions of both fear and neighborhood disorder vary substantially across different spatial scales.

Despite consensus that neighborhoods influence children's outcomes, we know less about the mechanisms that cause neighborhood inequality and produce those outcomes. Existing research overlooks how social networks develop among people at similar points in the life course through repeated interactions in neighborhoods. Existing studies do not illuminate the ways in which these geographically based networks can influence life–altering decisions. In this article, we use qualitative interviews with White, middle–class parents in gentrifying neighborhoods in a large Northeastern city to examine how parents decided where to send their children to kindergarten. Parents reported relying heavily on information that they received from their network of other neighborhood parents whom they had befriended on the playground or at daycare in the course of their daily child–rearing routines. The daily routines of child rearing led to rich and important social networks. But tensions also emerged among parents as they made different decisions about where to send their children to kindergarten. By focusing on how life course stages affect how people use space and interact in neighborhood spaces, we can better understand how neighborhood spaces shape the decision–making process of school choice.

PURPOSE OF REVIEW: Innovations in information technology, initiatives by local governments to share administrative data, and growing inventories of data available from commercial data aggregators have immensely expanded the information available to describe neighborhood environments, supporting an approach to research we call Urban Health Informatics. This review evaluates the application of machine learning to this new wealth of data for studies of the effects of neighborhood environments on health. RECENT FINDINGS: Prominent machine learning applications in this field include automated image analysis of archived imagery such as Google Street View images, variable selection methods to identify neighborhood environment factors that predict health outcomes from large pools of exposure variables, and spatial interpolation methods to estimate neighborhood conditions across large geographic areas. SUMMARY: In each domain, we highlight successes and cautions in the application of machine learning, particularly highlighting legal issues in applying machine learning approaches to Google's geo-spatial data.

In-person audits to collect data on neighborhood characteristics offer opportunities to study the mechanisms that link neighborhood conditions to unequal outcomes for individuals and communities, but the expense and logistical difficulties associated with conducting neighborhood audits have limited their use. The images collected by Google Street View provide a promising alternative for researchers to measure neighborhood environments across cities and to examine how neighborhood conditions vary across a wider geographic scope. We describe the benefits of using "virtual" neighborhood audits and discuss the practicalities of collecting data from virtual audits. We provide an example of individual- and neighborhood-level inequality in the distribution of disorder for older adults across four cities: New York, San Jose, Philadelphia, and Detroit. Despite the promise of virtual audits, they also introduce perils that must be addressed as research progresses; we introduce and discuss those perils here.

Purpose. To determine whether body mass index (BMI) is associated with proximity to neighborhood parks, the size of the parks, their cleanliness, and the availability of recreational facilities in the parks. Design. Cross-sectional. Setting. New York City. Subjects. Adults (13,102) were recruited from 2000 to 2002 (median age 45 years, 36% male). Measures. Anthropometric and sociodemographic data from study subjects were linked to Department of Parks & Recreation data on park space, cleanliness, and facilities. Neighborhood-level sociodemographic and park proximity metrics were created for half-mile–radius circular buffers around each subject's residence. Proximity to park space was measured as the proportion of the subject's neighborhood buffer area that was total park space, large park space (a park > 6 acres) and small park space (a park #6 acres). Analysis. Hierarchical linear models were used to determine whether neighborhood park metrics were associated with BMI. Results. Higher proximity to large park space was significantly associated with lower BMI (beta = −1.69, 95% confidence interval = −2.76, –.63). Across the population distribution of proximity to large park space, compared to subjects living in neighborhoods at the 10th percentile of the distribution, the covariate-adjusted average BMI was estimated to be .35 kg/m2 lower for those living in neighborhoods at the 90th percentile. The proportion of neighborhood area that was small park space was not associated with BMI, nor was park cleanliness or the availability of recreational facilities. Conclusion. Neighborhood proximity to large park spaces is modestly associated with lower BMI in a diverse urban population.