Talks

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, …). One of the limitations of those core tools is a sub-optimal performance and limited scaling possibilities. ...

During the Spatial Data Science Conference 2021, I had a chance to deliver a workshop illustrating the application of PySAL and momepy in understanding the structure of cities. The recording is now available for everyone. The materials are available on my GitHub and you can even run the whole notebook in your browser using the MyBinder service.

I had a chance to present our ongoing work on the classification of the (built) environment in Great Britain during the International Seminar on Urban Form 2021, which was held virtually in Glasgow. I was presenting the classification of urban form, one component of Spatial Signatures we’re developing as part of the Urban Grammar AI project together with Dani Arribas-Bel. The video of the presentation is attached below, as well as the abstract. ...

On March 30, 2021, I had a chance to deliver a talk as part of the Spatial Analytics + Data Seminar Series organised by the University of Newcastle (Rachel Franklin), the University of Bristol (Levi Wolf) and the Alan Turing Institute. The recording of the event is now available on YouTube. Spatial Signatures: Dynamic classification of the built environment This talk introduces the notion of “spatial signatures”, a characterisation of space based on form and function. We know little about how cities are organised over space influences social, economic and environmental outcomes, in part because it is hard to measure. It presents the first stage of the Urban Grammar AI research project, which develops a conceptual framework to characterise urban structure through the notions of spatial signatures and urban grammar and will deploy it to generate open data products and insight about the evolution of cities. ...