Spatial Data Science (also referred to as Geospatial Data Science or Location Data Science) is an emerging, high-value segment at the intersection of Data Science and Geographical Information System (GIS). Capabilities in this new category are in high demand in a wide range of verticals including Insurance/Financial Services, Real Estate, Municipal & Government, Management Consulting, Retail, Utilities, Telecommunications, Private Equity/Hedge Funds, and many more. Spatial Data Science treats location, proximity, adjacency, and broader spatial interactions and relationships as core variables within the dataset. Moreover, Spatial Data Science employs specialized methods and software to store, retrieve, explore, analyze, visualize, make predictions, and learn from such data.
GIS is the traditional gateway to carrying out a location-based analysis, ranging from simple reports, to intersecting data, to more complex spatial models. GIS is more recently applying to a wider range of users, all with very different use cases. A recent Carto Whitepaper, entitled “The State of Spatial Data Science in Enterprise 2020”, argues that the GIS community is moving beyond its own traditional silos and that Spatial Data Science is growing rapidly as a result. For example, geo location data is more and more starting to be used in generalist data science models across industries. Data Scientists typically don’t know or think about this as inclusion of “GIS or location data”. Instead, they think of location as another dimension of their data and consider potentially enriching their data with demographics.