Spatial data science is a field that combines the principles of data science with the analysis of spatial data and geographic information. It involves the use of statistical, mathematical, and computational techniques to understand and analyze data that is linked to a specific location on the earth’s surface.
One of the key goals of spatial data science is to enable decision makers to understand and analyze patterns, trends, and relationships in spatial data in order to inform better decision making. This can be used in a wide range of applications, including disaster management, urban planning, environmental analysis, and marketing.
A geographic information system (GIS) is the core fundamental tool for supporting high quality spatial data science. GIS is a technology that allows users to capture, store, manipulate, analyze, and present spatial data. It can be used to create maps, perform spatial analysis, and visualize data in a spatial context.