Spatial Data Science Methods for Improving Models

Spatial data science uses many of the same techniques and algorithms as traditional data science, but the spatial component can add a large amount of additional information by combining with other sources at the same location (e.g., census, geolocated tweets), using realtime routing services, or even by using the spatial structure of the distribution of the data. In this talk, I will present lessons learned on extracting more information from spatial data than is typically used in data science projects. I will do this by highlighting two tools we recently used for client projects (spatially-constrained clustering, probabilistic principal component analysis), and present about the structure of spatial data in general that can be readily added to models.