Posted in 2023

Clustergram published in the JOSS

  • 2023-09-04

The Python package Clustergram, developed in the early stage of this project, has now a software paper published in the Journal of Open Source Software (JOSS).

In 2021, we have developed a small package for visualisation and diagnostic of cluster analysis to help us understand various clustering solutions when delineating spatial signatures. You can see the original post here. The package was furhter improved over the years and was recently published in the JOSS.

Fleischmann, M., (2023). Clustergram: Visualization and diagnostics for cluster analysis. Journal of Open Source Software, 8(89), 5240,

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Participation in JRC webinar

  • 2023-04-20

On April 20th, Dani participated on a webinar organised by the Joint Research Centre and the Regional Studies association. The event was held on the occassion of the launch of a policy brief on the Future of Cities and focused on urban-rural interactions. Dani discussed the relevance of measuring the “urban” and the “rural” correctly, providing the Urban Grammar as an illustration. You can check out the slides he used in the talks page, both in HTML and PDF format.

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Participation in Taiwanese forum

  • 2023-04-12

On April 12th, Dani participated as online speaker in an event organised by the Industrial Technology Research Institute (ITRI) in Taiwan. The event, named “Masters Gathering”, and organised for the National Science and Technology Council of Taiwan Government focused on artificial intelligence in the smart city. Dani provided an overview of the Urban Grammar to the audience. Sadly, he was not able to participate live due to illness, but managed to record a short video with the presentation. You can check out the slides he used in the talks page, both in HTML and PDF format.

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AAG’23 presentation

  • 2023-04-02

On March 26th, Dani presented ongoing work at the AAG’23 conference. The talk was part of the Urban Analytics sessions co-organised between the Alan Turing Institute, Liverpool and Leeds, and focused on progress on using deep learning to predict spatial signatures from satellite imagery. As usual, you can find the slides in the talks page, both in HTML and PDF versions.

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