Martin Fleischmann, Joris Van den Bossche, Kelsey Jordahl, Matthew John Richards, James McBride, Jacob Wasserman, Brendan Ward, and Levi John Wolf have published the canonical paper1 on GeoPandas2: “GeoPandas: Fundamental data structures for vector spatial data in Python”. The paper is open access at ScienceDirect. From the introduction:
Spatial analysis, within the field of urban science or beyond, is ultimately enabled by tools. Hence, the tools that we use must be accessible, freely re-distributed, and be simple to build upon. Python has become one of the key languages for (spatial) data science and therefore must have reliable tools that achieve these objectives. In this article, we outline some of the fundamental tools for spatial data science in Python. Core among them is GeoPandas, an open-source library that provides data structures and methods to handle spatial data using vector geometries, and is designed with a user-friendly API to simplify spatial data handling.
The paper was published roughly 2 years after GeoPandas 1.0 (which it [was supposed to accompany][supposed]). The paper covers GeoPandas’s history, its core components, dependencies, and ecosystem, and points out limitations as well as future directions for development.
Footnotes
A canonical paper is a foundational academic publication that is intended to serve as the standard reference for a topic, in this case GeoPandas.↩︎
GeoPandasis an extension to the popular data science libraryPandasthat enables support for geospatial data. [supposed]: https://fosstodon.org/@geopandas/117112554571679594↩︎