Scalable Segregation Analytics

This project develops new methods for the treatment of uncertainty in the American Community Survey data, a new framework for statistical inference in the space-time analysis of segregation, and scalable algorithms for regionalization and spatially explicit aggregation of areal units.

The project will develop a new scalable spatio-temporal data management layer to support the application of these three sets of analytics to large datasets. Major outputs include the PySAL segregation package, and its associated measures of statistical inference and decomposition