Geography

I have been working with street networks for a long time. My first analysis will date probably back to 2017 or so. Most of those focused on the same aspect - understanding the morphology. Yet, practically none of the networks I was able to obtain reflected morphology directly. Rather, they captured transportation networks, with all the detailed intersections, every tiny roundabout, slipway, double carriageway, and so on. Which is pretty annoying when you are interested in a representation of space, not of traffic lines. It bothered me so much that in 2020, I started exploring ways to simplify such transportation networks to morphological ones. And a couple of days ago, we have released a Python package called neatnet That does exactly that. ...

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. The most notable is the support of spherical geometries. Where geopandas warns that a computation of area or a distance in geographic (longitude, latitude) coordinates is incorrect (more on this behavior below), {sf} returns a correct value. The same support is on the geopandas roadmap but it will not be done by tomorrow.2 Same could be said about the extent of support of spatial statistics but that is the same as with any statistics. R was built for it. ...

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. ...

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.DataFrame. Each of them can be then processed in parallel and combined when necessary. On top of that, the whole pipeline can be scaled to a cluster of machines and can deal with out-of-core computation, i.e. with datasets that do not fit the memory. ...

We have a new paper published in the Geographical Analysis on the opportunities current developments in geographic data science within the Python ecosystem offer to urban morphology. To sum up - there’s a lot to play with and if you’re interested in the quantification of urban form, there’s no better choice for you at the moment. Urban morphology (study of urban form) is historically a qualitative discipline that only recently expands into more data science-ish waters. We believe that there’s a lot of potential in this direction and illustrate it on the case study looking into the evolution of urban patterns, i.e. how different aspects of urban form has changed over time. ...

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. ...

This is a short story of one open-source algorithm and its journey from QGIS to mapclassify, to be used within GeoPandas. I am writing it to illustrate the flow within the open-source community because even though this happens all the time, we normally don’t talk about it. And we should. The story Sometimes last year, I asked myself a question. How hard would it be to port topological colouring tool from QGIS to be used with GeoPandas? Considering that this particular QGIS tool is written in Python, it seemed to be an easy task. ...

Sometimes our lines and polygons are way too complicated for the purpose. Let’s say that we have a beautiful shape of Europe, and we want to make an interactive online map using that shape. Soon we’ll figure out that the polygon has too many points, it takes ages to load, it consumes a lot of memory and, in the end, we don’t even see the full detail. To make things easier, we decide to simplify my polygon. ...