Martin Fleischmann

This workshop introduces the Dask-GeoPandas library and walks you through its key components, allowing you to take a GeoPandas workflow and run it in parallel, out-of-core and even distributed on a remote cluster.

The geospatial Python ecosystem provides a nice set of tools for working with vector data, including Shapely for geometry operations and GeoPandas to work with tabular data (and many other packages for IO, visualization, domain specific processing, …). One of the limitations of those core tools is a sub-optimal performance and limited scaling possibilities. The PyData ecosystem is increasingly embracing Dask as a tool of choice when the scale of the task goes beyond capabilities of Pandas or Numpy. Over the last years, effort has been put in improving the performance through vectorized interfaces to GEOS, the underlying C library of Shapely. In turn, that enables releasing the GIL and makes the Dask - GeoPandas combination more interesting. Since GeoPandas is an extension to the pandas DataFrame, the same way how Dask scales pandas can be applied on GeoPandas as well. Initial effort to build a bridge between Dask and GeoPandas is currently taking the shape of the dask-geopandas library. This workshop provides an introduction to dask-geopandas and walks you through the key aspects of the library. We will touch the specificity of vector geospatial data when it comes to parallelisation, cover use cases where dask-geopandas provides major benefits as well as those, where it currently struggles. You will learn how to turn your GeoPandas workflow to Dask-GeoPandas one and what are the rules of thumb when doing so. Prerequisites: * basic familiarity with GeoPandas Level: * intermediate

Other people: Joris Van den Bossche

While academic research heavily depends on open-source software, the relationship is often one-way. We believe that designing research in close relation to open-source development is beneficial for all parties and present one way of doing that, by turning a research project into a component of the open-source ecosystem.

Academic research often depends on open-source software. Still, researchers do not contribute back that often due to the lack of institutional incentives, time demands, or an imposter syndrome (“my code is too messy”). However, open-source software development doesn’t have to be detached from academic work. The first step is a decision to make the code open. Then the question is, how? From an academic standpoint, packing up the functionality into a new package instead of contributing to existing libraries could lead to additional publications that matter in career progress. However, from an open-source standpoint, such an approach widens the ecosystem’s fragmentation and threatens its sustainability. In this talk, we outline why we chose the path benefiting open source over the academic benefits, how we did it and our vision of academic work closely linked to open-source development. We illustrate this approach in our work on the Urban Grammar AI project, combining aspects of radical openness of the process, making research code available as it is written, and enhancing existing libraries when we need new functionality. It led to significant contributions to the GeoPandas and PySAL ecosystem, a release of one independent package with functionality that didn’t fit elsewhere, and further developments of a canonical Docker container for geographic data science.

GeoPandas is one of the core packages in the Python ecosystem to work with geospatial vector data. This talk will give an overview of recent developments in GeoPandas and the broader ecosystem.

GeoPandas is one of the core packages in the Python ecosystem to work with geospatial vector data. By combining the power of several open source geo tools (GEOS/Shapely, GDAL/fiona, PROJ/pyproj) and extending the pandas data analysis library to work with geographic objects, it is designed to make working with geospatial data in Python easier. This talk will give an overview of recent developments in the GeoPandas community, both in the project itself as in the broader ecosystem of packages on which GeoPandas depends or that extend GeoPandas. We will highlight some changes and new features in recent GeoPandas versions, such as the new interactive explore() visualisation method, improvements in joining based on proximity, better IO options for PostGIS and Apache Parquet and Feather files, and others. But some of the important improvements coming to GeoPandas are happening in other packages. The Shapely 2.0 release is nearing completion, and will provide fast vectorized versions of all its geospatial functionalities. This will help to substantially improve the performance of GeoPandas. In the area of reading and writing traditional GIS files using GDAL, the pyogrio package is being developed to provide a speed-up on that front. Another new project is dask-geopandas, which is merging the geospatial capabilities of GeoPandas with the scalability of Dask. This way, we can achieve parallel and distributed geospatial operations.

Other people: Joris Van den Bossche