We think spatially about research problems: We develop state-of-the-art methods for geospatial analysis; implement them through open source software tools; apply them to policy-relevant research in the social sciences; and disseminate them through education and support to a growing  worldwide  community  of  over  half  a million  spatial analysts. Locally, we are building a spatial community at the University of Chicago.

Spatial data science can be viewed as a subset of generic "data science" that focuses on the special characteristics of spatial data, i.e., the importance of "where." It treats location, distance, and spatial interaction as core aspects of the data and employs specialized methods and software to store, retrieve, explore, analyze, visualize and learn from such data. In this sense, spatial data science relates to data science as spatial statistics to statistics, spatial databases to databases, & geocomputation to computation (Anselin, 2019).