National and local policies to improve diet in low-income US populations include increasing physical access to grocery stores and supermarkets in underserved neighborhoods. In a pilot study that ev...

The census tract—based residential segregation literature rests on problematic assumptions about geographic scale and proximity. We pursue a new tract-free approach that combines explicitly spatial concepts and methods to examine racial segregation across egocentric local environments of varying size. Using 2000 Census data for the 100 largest U.S. metropolitan areas, we compute a spatially modified version of the information theory index H to describe patterns of Black—White, Hispanic-White, Asian-White, and multigroup segregation at different scales. We identify the metropolitan structural characteristics that best distinguish micro-segregation from macro-segregation for each group combination, and we decompose their effects into portions due to racial variation occurring over short and long distances. A comparison of our results with those from tract-based analyses confirms the value of the new approach.

This article addresses an aspect of racial residential segregation that has been largely ignored in prior work: the issue of geographic scale. In some metropolitan areas, racial groups are segregated over large regions, with predominately white regions, predominately black regions, and so on, whereas in other areas, the separation of racial groups occurs over much shorter distances. Here we develop an approach—featuring the segregation profile and the corresponding macro/micro segregation ratio—that offers a scale-sensitive alternative to standard methodological practice for describing segregation. Using this approach, we measure and describe the geographic scale of racial segregation in the 40 largest U.S. metropolitan areas in 2000. We find considerable heterogeneity in the geographic scale of segregation patterns across both metropolitan areas and racial groups, a heterogeneity that is not evident using conventional “aspatial” segregation measures. Moreover, because the geographic scale of segregation is only modestly correlated with the level of segregation in our sample, we argue that geographic scale represents a distinct dimension of residential segregation. We conclude with a brief discussion of the implications of our findings for investigating the patterns, causes, and consequences of residential segregation at different geographic scales.

Exposure science has developed rapidly and there is an increasing call for greater precision in the measurement of individual exposures across space and time. Social science interest in an individual’s environmental exposure, broadly conceived, has arguably been quite limited conceptually and methodologically. Indeed, in social science, we appear to lag behind our exposure science colleagues in our theories, data, and methods. In this article, we discuss a framework based on the concept of spatial polygamy to demonstrate the need to collect new forms of data on human spatial behavior and contextual exposures across time and space. Adopting new data and methods will be essential if one wants to better understand social inequality in terms of exposure to health risks and access to health resources. We discuss the opportunities and challenges focusing on the potential seemingly offered by focusing on human mobility and, specifically, the utilization of activity space concepts and data. A goal of the article is to spatialize social and health science concepts and research practice vis-à-vis the complexity of exposure. The article concludes with some recommendations for future research, focusing on theoretical and conceptual development promoting research on new types of places and human movement, the dynamic nature of contexts, and training.

The application of geographically weighted regression (GWR) - a local spatial statistical technique used to test for spatial nonstationarity - has grown rapidly in the social, health, and demographic sciences. GWR is a useful exploratory analytical tool th