data

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....

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.

In this post, I introduce a new Python package to generate clustergrams from clustering solutions. The library has been developed as part of the Urban Grammar research project, and it is compatible with scikit-learn and GPU-enabled libraries such as cuML or cuDF within RAPIDS.AI. When we want to do some cluster analysis to identify groups in our data, we often use algorithms like K-Means, which require the specification of a number of clusters....

Yes, the formula above is correct. Well, it depends on what we mean by correct. NDVI does not make sense Imagine the following situation. We have fetched a cloud-free mosaic of Sentinel 2 satellite data and want to measure NDVI (Normalised difference vegetation index), which uses red and near-infrared bands within this simple formula. NDVI = (NIR - Red) / (NIR + Red) The results are normalised, which in this case means that they lie between -1 and 1....