Before diving into some of the most used GIS data types, a little background is required about what type of information geographical data represents. Earlier in this book, the distinction between raster and vector data was mentioned. All GIS data is comprised of one or the other, but a combination of both vectors and rasters is also possible. When deciding on which data type to use, consider the scale and type of geographical information represented by the data, which in turn determines what Python data libraries to use. As is illustrated in the following examples, the choice for a certain Python library can also depend on personal preference, and there may be various ways to do the same task.

In the geospatial world, raster data comes in the form of aerial imagery or satellite data, where each pixel has an associated value that corresponds to a different color or shade. Raster data is used for large continuous areas, such as differentiating between different temperature zones across various parts of the world. Other popular applications are elevation, vegetation, and precipitation mapping.

Rasters can also be used as input for creating vector maps, where, for example, objects such as roads and buildings can be distinguished (an example being the standard map view when navigating to Google Maps). Vector data itself consists of points, lines, and polygons to distinguish features in a geographical space, such as administrative boundaries. These are built up from individual points that have spatial relationships with each other that are described in an associated data model. Vectors maintain the same sharpness the more you zoom-in, while raster data will look more coarse-grained.

Now that you know what geographical data represents, let's discuss the most used geospatial data formats for vector and raster data.