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Geographic areas of different sizes and shapes of polygons that represent counts or rate data are often encountered in social, economic, health, and other information. Often political or census boundaries are used to define these areas because the information is available only for those geographies. Therefore, these types of boundaries are frequently used to define neighborhoods in spatial analyses using geographic information systems and related approaches such as multilevel models. When point data can be geocoded, it is possible to examine the impact of polygon shape on spatial statistical properties, such as clustering. We utilized point data (alcohol outlets) to examine the issue of polygon shape and size on visualization and statistical properties. The point data were allocated to regular lattices (hexagons and squares) and census areas for zip-code tabulation areas and tracts. The number of units in the lattices was set to be similar to the number of tract and zip-code areas. A spatial clustering statistic and visualization were used to assess the impact of polygon shape for zip- and tract-sized units. Results showed substantial similarities and notable differences across shape and size. The specific circumstances of a spatial analysis that aggregates points to polygons will determine the size and shape of the areal units to be used. The irregular polygons of census units may reflect underlying characteristics that could be missed by large regular lattices. Future research to examine the potential for using a combination of irregular polygons and regular lattices would be useful.

<p class="Default">Addressing minority health and health disparities has been a missing piece of the puzzle in Big Data science. This article focuses on three priority opportunities that Big Data science may offer to the reduction of health and health care disparities. One opportunity is to incorporate standardized information on demographic and social determinants in electronic health records in order to target ways to improve quality of care for the most disadvantaged popula­tions over time. A second opportunity is to enhance public health surveillance by linking geographical variables and social determinants of health for geographically defined populations to clinical data and health outcomes. Third and most impor­tantly, Big Data science may lead to a better understanding of the etiology of health disparities and understanding of minority health in order to guide intervention devel­opment. However, the promise of Big Data needs to be considered in light of significant challenges that threaten to widen health dis­parities. Care must be taken to incorporate diverse populations to realize the potential benefits. Specific recommendations include investing in data collection on small sample populations, building a diverse workforce pipeline for data science, actively seeking to reduce digital divides, developing novel ways to assure digital data privacy for small populations, and promoting widespread data sharing to benefit under-resourced minority-serving institutions and minority researchers. With deliberate efforts, Big Data presents a dramatic opportunity for re­ducing health disparities but without active engagement, it risks further widening them.</p><p class="Default"><em>Ethn.Dis;</em>2017;27(2):95-106; doi:10.18865/ed.27.2.95.</p>

This edited volume brings together leading researchers from the United States, the United Kingdom and Europe to look at the processes leading to segregation and its implications. With a methodological focus, the book explores new methods and data sources that can offer fresh perspectives on segregation in different contexts. It considers how the spatial patterning of segregation might be best understood and measured, outlines some of the mechanisms that drive it, and discusses its possible social outcomes. Ultimately, it demonstrates that measurements and concepts of segregation must keep pace with a changing world. This volume will be essential reading for academics and practitioners in human geography, sociology, planning and public policy

This edited volume brings together leading researchers from the United States, the United Kingdom and Europe to look at the processes leading to segregation and its implications. With a methodological focus, the book explores new methods and data sources that can offer fresh perspectives on segregation in different contexts. It considers how the spatial patterning of segregation might be best understood and measured, outlines some of the mechanisms that drive it, and discusses its possible social outcomes. Ultimately, it demonstrates that measurements and concepts of segregation must keep pace with a changing world. This volume will be essential reading for academics and practitioners in human geography, sociology, planning and public policy