Yesterday we kickstarted the ESA’s Living Planet Symposium with the a workshop on vector data cubes using Xvec package built on top of Xarray, Shapely and GeoPandas. If you did not manage to be there but are still interested, the workshop material is available below. The workshop covered the concept of VDC using both coordinate geometry and variable geometry with applications (and a combination of the two) using real-world data and use cases. ...
During GISRUK 2025, organised in April 2025 at the University of Bristol, I had an opportunity to deliver a workshop on urban morphometrics, titled Urban Morphology with Python: City Structure as Predictor and Target. Workshop materials The workshop has not been recorded but the material is openly available on the link above.
The recorrding of a talk delivered during the GeoPython 2024 together with Joris Van den Bossche. GeoPandas is one of the core packages in the Python ecosystem to work with geospatial vector data and has recently released its first major version - 1.0. This talk will give an overview of recent developments in GeoPandas and the broader ecosystem and provide an outlook on where it is heading. 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/pyogrio, 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. Recently, the development that started more than ten years ago resulted in version 1.0. ...
The workshop organised during the GeoPython 2024 together with Joris Van den Bossche. After 10 years since the first release, GeoPandas reached version 1.0. This workshop will showcase how to get the most out of the recent enhancements and develop a code ready for 2024 and beyond. GeoPandas is one of the core components of the GeoPython ecosystem, providing the critical infrastructure to work with vector spatial data, usually reserved for the context of GIS, in Python. Although central to many applications and supporting many users, GeoPandas was technically not considered stable (even though it was practically for a long time). With GeoPandas 1.0, this is no longer the case, and the project has reached maturity and stability we can build upon in years to come. ...
We have released GeoPandas 1.0! Yes, I am excited and a bit relieved as it took a bit longer than expected. Anyway, it’s out and we’re waiting to hear what we broke 🙃. It is a major milestone for GeoPandas, not only in a semantic sense, but it literally closes a long development cycle. If you have been following the ecosystem for a while, you might know the story, but it is worth refreshing your memories. So, very briefly: ...
After 10 years since the first release, GeoPandas reached version 1.0. This workshop will showcase how to get the most out of the recent enhancements and develop a code ready for 2024 and beyond. Workshop resources are available on Github. Annotation GeoPandas is one of the core components of the GeoPython ecosystem, providing the critical infrastructure to work with vector spatial data, usually reserved for the context of GIS, in Python. Although central to many applications and supporting many users, GeoPandas was technically not considered stable (even though it was practically for a long time). With GeoPandas 1.0, this is no longer the case, and the project has reached maturity and stability we can build upon in years to come. ...
The GeoPandas team is racing towards the 1.0 release, nearly 10 years after 0.1 made it to PyPI. As with any major release, it brings some changes. This post highlights those I feel are the most important and invites you to test the 1.0.0-alpha1 and later rc versions before we let the stable version fly to the world. Please test! As of today, GeoPandas 1.0 is out as a pre-release 1.0.0-alpha1. If you read it during April or May 2024, please install the pre-release and test your code against it. If anything looks odd, please let us know! ...
With the release of Shapely 2.0, the GeoPandas-based code that have been optimised years ago may no longer provide the best performance. The workshop organised during the GeoPython 2023 together with Joris Van den Bossche showed how to change that and write efficient and convenient GeoPandas code that uses the benefits of the latest developments in the Python geospatial ecosystem. Workshop resources are available on Github. Annotation The Python geospatial ecosystem is constantly evolving, rushing towards better usability, new features, fewer bugs and increasing performance. As a result, the code that might have been optimised a few years ago may not provide the ideal experience today. Such a shift has recently happened in the GeoPandas world. After years of development, Shapely 2.0, a major refactoring of the famous Python wrapper of GEOS, came out in December 2022, switching the way how we think about vector geometry handling. From former scalar geometries requiring for-loops to go through, we can now interact with arrays of them in a performant vectorised manner. These developments directly affect how GeoPandas shall be used, which are the good practices and which are no longer up to speed. ...
A talk from the OpenGeoHub Summer School 2022. Workshop materials Recording The ecosystem of packages for spatial data handling and analysis in Python is extensive and covers both vector and raster analytics from small to large distributed data. This talk covers only a small part, focusing on vector data processing with GeoPandas at its core. First, it covers what GeoPandas is and how it relates to other packages and combines them into a user-friendly API. Then it looks at what GeoPandas enables with a light introduction to PySAL Python Spatial Analysis Library for spatial statistics and modelling. The final part touches on the scalability issue and introduces how to handle parallel computation and big data using GeoPandas and Dask. All of that is illustrated by hands-on tasks on real-world data. ...
The workshop organised together with James D. Gaboardi during the Spatial Data Science Symposium 2022 is now available online. See the recording below and access the workshop material on Github from which you can even run the code online, in your browser. Annotation Martin & James will walk you through the fundamentals of analysis of the structure of cities. You will learn what can be measured, how to do that in Python, and how to use those numbers to capture the patterns that make up cities. Using a real-life example, you will learn every step of the method, from data retrieval to detection of individual patterns.