Abstract
A growing body of research has explored the impact of neighborhood residence on child and adolescent health and well-being. Most previous research has used the US Census variables as the measures of neighborhood ecology, although informative census data are not designed to represent the sociological and structural features that characterize neighborhoods. Alternatively, this study explored the use of large-city administrative data and geographical information systems to develop more uniquely informative empirical dimensions of neighborhood context. Exploratory and confirmatory structural analyses of geographically referenced administrative data aggregated to the census-block group identified three latent dimensions: social stress, structural decline, and neighborhood crime. Resultant dimensions were compared through canonical regression to those derived from US Census data. The relative explanatory capacity of the city-archival and census dimensions was assessed through multilevel linear modeling to predict standardized reading and mathematics achievement of 31,742 fifth- and 28,922 eight-grade children. Results indicated that the city-archival dimensions uniquely augmented predictions, and the combination of city and census dimensions explained significantly more neighborhood effects on achievement than did either source of neighborhood information independently.
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Notes
Wilks’s lambda is used to test the significance of the first canonical correlation. If p < .05, the two sets of variables are significantly associated by canonical correlation.
Rc represents the canonical correlation, which is a form of correlation relating two sets of variables. Canonical correlations are interpreted the same as Pearson’s r; their square is the percent of variance in the canonical variate of one set of variables explained by the canonical variate for the other set along the dimension represented by the given canonical correlation.
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Acknowledgment
This research was conducted with cooperation from the Kids Integrated Data System of the City of Philadelphia, PA, USA, in conjunction with the Cartographic Modeling Laboratory, University of Pennsylvania, Philadelphia, PA, USA.
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Gross, K.S., McDermott, P.A. Use of City-Archival Data to Inform Dimensional Structure of Neighborhoods. J Urban Health 86, 161–182 (2009). https://doi.org/10.1007/s11524-008-9322-7
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DOI: https://doi.org/10.1007/s11524-008-9322-7