Understanding Population Segregation from Landsat ETM+ Imagery: A Geographically Weighted Regression Approach

Abstract

This study attempts to understand population segregation issues in Milwaukee County, Wisconsin utilizing remote sensing and regression technologies. Population segregation was measured with a local segregation index D_i based on the theory of the index of dissimilarity. Remote sensing information was extracted from a Landsat ETM+ image through spectral mixture analysis, unsupervised classification, and texture analysis. Global ordinary least squares (OLS) regression and geographically weighted regression (GWR) analyses were applied to explore the relationships between population segregation and remote sensing variables. Results indicate that remote sensing information has the potential to increase our understanding of socio-cultural issues such as population segregation.