geosnap provides a suite of tools for exploring, modeling, and visualizing the
social context and spatial extent of neighborhoods and regions over time. It
brings together state-of-the-art techniques from geodemographics,
regionalization, spatial data science, and segregation analysis to support
social science research, public policy analysis, and urban planning. It provides
a simple interface tailored to formal analysis of spatiotemporal urban data.

Main Features:
- fast, efficient tooling for standardizing data from multiple time periods into
  a shared geographic representation appropriate for spatiotemporal analysis
- analytical methods for understanding sociospatial structure in neighborhoods,
  cities, and regions, using unsupervised ML from scikit-learn and spatial
  optimization from PySAL
- classic and spatial analytic methods for diagnosing model fit, and locating
  (spatial) statistical outliers novel techniques for understanding the
  evolution of neighborhoods over time, including identifying hotspots of local
  neighborhood change, as well as modeling and simulating neighborhood
  conditions into the future
- quick access to a large database of commonly-used neighborhood indicators from
  U.S. providers including Census, EPA, LEHD, NCES, and NLCD, streamed from the
  cloud thanks to quilt and the highly-performant geoparquet file format.