Spatial analysis is a type of GIS analysis that uses math and geometry to understand patterns that happen over space and time, including patterns of human behavior and natural phenomena.
When performing Spatial analysis or spatial data science, the right tools can open a world of free and collaborative analytics capabilities without costly software licenses.
We are going to give you a quick tour of some of the open source Python libraries available for geospatial analysis. All of these libraries can be easily integrated with JupyterLab and scale to large datasets.
Let’s get to it:
In GIS, the term “vector” describes discrete geometries (points, lines, polygons) with related attribute data (e.g. name, county identifier, population). We can use different geometries to represent the same phenomena depending on our scale and level of measurement. For instance, we can represent the White House as either a point, line, or polygon depending on whether we want to look at a building point-of-interest, building outline, or building footprint.
GeoPandas is all about making it easy to work with geospatial data in Python. It expands on the built-in pandas data types within a new data structure called the GeoDataFrame. GeoPandas wraps the foundational Python packages Shapely and Fiona, both great packages created by Sean Gillies.
Moving down in the stack from GeoPandas, Shapely wraps GEOS and defines the actual geometry objects (points, lines, polygons) and the spatial relationships between them (e.g. adjacency, within, contains). You can use shapely directly without GeoPandas, but in a dataframe-centric world, Shapely is less of a direct tool and more a dependency for higher-level packages.
Fiona can read and write many kinds of geospatial vector data and easily integrates with other Python GIS libraries. It relies on OGR / GEOS for reading shapefiles, geopackages, geojson, topojson, KML, GML from both the local filesystem and cloud services like Amazon S3 by wrapping Python’s boto3 library.
Spatialpandas supports Pandas and Dask extensions for vector-based spatial and geometric operations. It is a good tool for working with vectorized geometric algorithms using Numba or Python. The library was first used for polygon rasterization with Datashader and since has become its own standalone project.
Rasters are regularly gridded datasets like GeoTIFFs, JPGs, and PNGs. Regular grids are useful in representing continuous phenomena that are not cleanly represented by points, lines, and polygons. For instance, in analyzing weekly rainfall for Seattle, we would first start with weather station rainfall measurements (points), and interpolate values to create a raster (continuous-surface) to represent rainfall over the entire city.
GDAL is the Geospatial Data Abstraction Library which contains input, output, and analysis functions for over 200 geospatial data formats. It supports APIs for all popular programming languages and includes a CLI (command line interface) for quick raster processing tasks (resampling, type conversion, etc.).
