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… Continue reading Talk at ISUF 2021: Classifying urban form at a national scale
Author: Martin Fleischmann
The Urban Atlas: Methodological Foundation of a Morphometric Taxonomy of Urban Form
The Urban Atlas: Methodological Foundation of a Morphometric Taxonomy of Urban Form is the title of my PhD Thesis defended in January 2021 at the University of Strathclyde. Thanks to Ombretta and Sergio for guiding me along the way! Abstract No two cities in the world are alike. Each urban environment is characterised by a… Continue reading The Urban Atlas: Methodological Foundation of a Morphometric Taxonomy of Urban Form
Clustergam: visualisation of cluster analysis
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… Continue reading Clustergam: visualisation of cluster analysis
Spatial Analytics + Data Talk
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… Continue reading Spatial Analytics + Data Talk
3 – 10 = 65529. What?
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. The… Continue reading 3 – 10 = 65529. What?
The journey of an algorithm from QGIS to GeoPandas
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,… Continue reading The journey of an algorithm from QGIS to GeoPandas
Morphological tessellation
Imagine you are trying to analyse a city, and you want primarily to understand its structure. You look at buildings, their dimensions and patterns they form, you look at a street network, and then you want to understand detailed patterns of density. This last point requires a specification of an aerial unit, and morphological tessellation is… Continue reading Morphological tessellation
Line simplification algorithms
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… Continue reading Line simplification algorithms
Confused terminology in urban morphology
This is a short introduction of our recently published paper Measuring urban form: Overcoming terminological inconsistencies for a quantitative and comprehensive morphologic analysis of cities, which is essentially one of the background chapters of my PhD (hopefully finished later this year). When I started my work, which is focusing on measuring of urban form (or urban… Continue reading Confused terminology in urban morphology