Geospatial data is anything that includes a reference to geographical location, such as latitude and longitude, street address, and ZIP code. It’s important to many fields of science, including geology, geography, meteorology, climatology, biology, archeology, anthropology, oceanography, economics, and sociology. As a result, there are lots of Python libraries dedicated to working with geospatial data.

Geospatial data comprises vector and raster data (Figure 17-1). With vector data, spatial elements (think polygons, lines, and points) are represented by x and y coordinates. Examples include road centerlines, country boundaries, ...

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