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Open Access Highly Accessed Methodology

Identification of contrastive and comparable school neighborhoods for childhood obesity and physical activity research

Xingyou Zhang1*, Katherine Kaufer Christoffel23, Maryann Mason2 and Lin Liu4

Author Affiliations

1 The Robert Graham Centre for Policy Studies in Family Medicine and Primary Care, American Academy of Physicians, 1350 Conneticut Avenue, NW, Suite 201, Washington, DC 20036, USA

2 Mary Ann and J. Milburn Smith Child Health Research Program, Children's Memorial Research Center, 2300 Children's Plaza, Box157, Chicago, IL 60614, USA

3 Department of Pediatrics and Preventive Medicine, Feinberg School of Medicine, Northwestern University, 303 East Chicago Avenue, Chicago, IL 60611-3008, USA

4 Department of Geography, University of Cincinnati, Cincinnati, OH 45221-0131, USA

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International Journal of Health Geographics 2006, 5:14  doi:10.1186/1476-072X-5-14

Published: 30 March 2006

Abstract

The neighborhood social and physical environments are considered significant factors contributing to children's inactive lifestyles, poor eating habits, and high levels of childhood obesity. Understanding of neighborhood environmental profiles is needed to facilitate community-based research and the development and implementation of community prevention and intervention programs. We sought to identify contrastive and comparable districts for childhood obesity and physical activity research studies.

We have applied GIS technology to manipulate multiple data sources to generate objective and quantitative measures of school neighborhood-level characteristics for school-based studies. GIS technology integrated data from multiple sources (land use, traffic, crime, and census tract) and available social and built environment indicators theorized to be associated with childhood obesity and physical activity. We used network analysis and geoprocessing tools within a GIS environment to integrate these data and to generate objective social and physical environment measures for school districts. We applied hierarchical cluster analysis to categorize school district groups according to their neighborhood characteristics. We tested the utility of the area characterizations by using them to select comparable and contrastive schools for two specific studies.

Results

We generated school neighborhood-level social and built environment indicators for all 412 Chicago public elementary school districts. The combination of GIS and cluster analysis allowed us to identify eight school neighborhoods that were contrastive and comparable on parameters of interest (land use and safety) for a childhood obesity and physical activity study.

Conclusion

The combination of GIS and cluster analysis makes it possible to objectively characterize urban neighborhoods and to select comparable and/or contrasting neighborhoods for community-based health studies.