Reevaluating Poverty Concentration with Spatial Analysis: Detroit in the 1990s

Standard measures of poverty concentration based on census tracts may not accurately reflect neighborhood conditions because they offer a weak link to the underlying geography of a neighborhood. Changes in the spatial configuration of land use within a census tract can have the effect of increasing or decreasing the density of poverty. This study uses a dasymetric mapping technique in a raster GIS environment to intersect population data in a block group layer with land use categories from a land use layer. I produce poverty counts and rates at a much finer spatial resolution than a block group, with an explicit spatial relationship between population and surrounding neighborhood characteristics. I illustrate the technique for the City of Detroit by measuring poverty concentration change between 1990 and 2000. I find that poverty became more concentrated in space during the 1990s, counter to reports of diminishing poverty concentration that are based on the share of poor people in high-poverty tracts. [