Geospatial visualization made easy with geoplot02/07/2017
geoplot is a tool which fills a gap which I have perceived, in my own work, as
a serious shortcoming in the geospatial Python ecosystem: the need for a high-level geospatial plotting library.
Geospatial data is any form of data which has a location to it. Most of the data generated today has such a geospatial context, context which is, in turn, oftentimes important to understanding the data itself. Thanks to its ease-of-use and deep ecosystem, the Python programming language has emerged as a leading choice in the performance of data analytics, geospatial data analytics included.
geoplot aims to be seaborn for geospatial data. Hence it comes with the following
High-level plotting API:
geoplotis cartographic plotting for the 90% of use cases. All of the standard-bearer maps that you’ve probably seen in your geography textbook are easily accessible, as are many more novel options.
Native projection support: The most fundamental peculiarity of geospatial plotting is
projection: how do you unroll a sphere onto a flat surface (a map) in an accurate way? The answer depends on what
you’re trying to depict.
geoplotprovides these options.
Compatibility with matplotlib: While
matplotlibis not a good fit for working with geospatial data directly, it’s a format that’s well-incorporated by other geospatial tools. For compatibility,
geoplotprovides an option for emiting pure
Built with modern geospatial Python in mind: Geospatial data is a fast-moving target in the
Python ecosystem. Innovations in recent years have made working with such data much easier than it once was, which
geoplotleverages with an easy-to-use and widely compatible API.
Here are some examples of things that
geoplot can do:
Head over to the the GitHub repository to learn more.