Geovisualization of Temporal Change in Urban Racial Geography: A Step towards Explaining Persistent Segregation

To better understand the persistence of residential racial segregation in U.S. cities, it is essential to develop testable, spatially explicit models dynamics. However, original census data are not formatted a way that facilitates testing such models. In this study, we propose novel geoprocessing pipeline transforms into image-like geovisualization urban segregation. The process consists converting grid format, transforming subpopulation shares each cell CMYK color, and multiyear clustering values followed by classification smoothing images. When applied from multiple censuses, our methodology generates time series snapshots depict spatio-temporal dynamics change. As demonstration, have utilized generate images for period spanning 1990 2020 seven major cities. Detailed results shown Chicago Atlanta. By visually examining these series, identified patterns challenge conventional Schelling model Instead, findings point towards incorporates interplay preferential growth diffusion as significant factors shape complex composition areas.