Abstract
Geospatial technologies such as geographic information systems (GIS) and spatial statistics allow researchers to analyze conceptually meaningful spatial datasets on environmental health topics to target interventions and generate new hypotheses for future research. This environmental health study focuses on the relationship between racial residential segregation, housing quality and value and low birth weight in the city of Flint, Michigan. A GIS was constructed with aerial imagery as the base; and maps of racial residential segregation and tax parcels -i.e., building footprints overlaid to examine the effect of maternal exposure to housing quality and value on low birth weight in these neighborhoods. Geographically weighted regression (GWR) was used to analyze these spatial datasets. The findings from this research showed that substandard and well-maintained housing were dispersed throughout the city of Flint, with a higher density of substandard housing in areas of segregation and concentrated poverty. Pregnant mothers living in well-maintained housing in racially mixed neighborhoods received some protection compared to similar mothers living in housing with minor disrepairs. This protection was not observed for mothers living in well-maintained housing in highly segregated neighborhoods, controlling for the same risk factors. GIS and spatial statistics were essential tools in this environmental health study.
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Grady, S.C. (2011). Housing Quality and Racial Disparities in Low Birth Weight: A GIS Assessment. In: Maantay, J., McLafferty, S. (eds) Geospatial Analysis of Environmental Health. Geotechnologies and the Environment, vol 4. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-0329-2_15
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DOI: https://doi.org/10.1007/978-94-007-0329-2_15
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