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I am sitting on a train back to Prague after two days of discussing tooling for spatial data science available in the Python, R and Julia ecosystems, with occasional excursions to the worlds of Rust, JavaScript or ESRI. I am coming back from the Spatial Data Science across Languages (SDSL) workshop and I’d like to share a few thoughts1 while they’re fresh. Different maturity of ecosystems As a Python developer, I must admit that what the R-Spatial community managed to create is impressive and is in some aspects further that where we are....

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....

If you ever wanted to polygonize (i.e. create polygons formed from the linework of a set of geometries, as per shapely’s definition) something like a street network with shapely, you may have noticed missing geometries in the result, like in the case of Vienna below. I had the same issue, and this is a note to myself with a solution. The result of shapely.polygonize with gaps that should not be there....

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....

This is the map we have created for the Urban Grammar AI project. It is created using open source software stack and hosted on GitHub, for free. This post will walk you through the whole process of generation of the map, step by step, so you can create your own. It is a bit longer than usual, so a quick outline for better orientation: By the end of this tutorial, you will be able to take your vector data and turn them into a fully-fledged vector map hosted on GitHub with no cost involved, for everyone to enjoy....

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....

The workshop organised during the GeoPython 2022 together with Joris van den Bossche 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. Workshop resources are available on Github. Annotation 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, …)....

Using Python for data science is usually a great experience, but if you’ve ever worked with pandas or GeoPandas, you may have noticed that they use only a single core of your processor. Especially on larger machines, that is a bit of a sad situation. Developers came up with many solutions to scale pandas, but the one that seems to take the lead is Dask. Dask (specifically dask.dataframe as Dask can do much more) creates a partitioned data frame, where each partition is a single pandas....

Paul Ramsey saw a spatial join done using a GPU and tried to do the same with PostGIS, checking how fast that is compared to the GPU-based RAPIDS.AI solution. Since Paul used parallelisation in PostGIS, I got curious how fast Dask-GeoPandas is on the same task. So, I gave it a go. import download import geopandas import dask_geopandas import dask.dataframe from dask.distributed import Client, LocalCluster Let’s download the data using Paul’s query, to ensure we work with the same CSV....

The final paper based on my PhD thesis is (finally!) out in the Environment and Planning B: Urban Analytics and City Science. We looked into ways of identifying patterns of urban form and came up with the Methodological foundation of a numerical taxonomy of urban form. You can read it on the journal website (open access). We use urban morphometrics (i.e. data-driven methods) to derive a classification of urban form in Prague and Amsterdam, and you can check the results in online interactive maps - http://martinfleis....