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Classification and clustering of buildings for understanding urban dynamics

A framework for processing spatiotemporal data

Perez Joan1, Fusco Giovanni1, Sadahiro Yukio2

1. Université Côte d’Azur, CNRS, ESPACE, Nice, France
2. Department of Urban Engineering, University of Tokyo, Tokyo, Japan

Revue Internationale de Géomatique 2022, 31(2), 303-327. https://doi.org/10.3166/RIG.31.303-327©2022

Abstract

This paper presents different methods implemented with the aim of studying urban dynamics at the building level. Building types are identified within a comprehensive vector-based building inventory, spanning over at least two time points. First, basic morphometric indicators are computed for each building: area, floor-area, number of neighbors, elongation, and convexity. Based on the availability of expert knowledge, different types of classification and clustering are performed: supervised tree-like classificatory model, expert-constrained k-means and combined SOM-HCA. A grid is superimposed on the test region of Osaka (Japan) and the number of building types per cell and for each period is computed, as well as the differences between each period. Mappings are then performed, showing that building types have specific locations and dynamics. In some extreme cases, a specific building type can even gradually replace a type on a declining dynamic. Questions of data preparation, and clustering validation are also dealt with, underlining the interest of assessing the spatial distribution of clusters.

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APA Style
Joan, P., Giovanni, F., Yukio, S. (2022). Classification and clustering of buildings for understanding urban dynamics A framework for processing spatiotemporal data. Revue Internationale de Géomatique, 31(2), 303-327. https://doi.org/10.3166/RIG.31.303-327©2022
Vancouver Style
Joan P, Giovanni F, Yukio S. Classification and clustering of buildings for understanding urban dynamics A framework for processing spatiotemporal data. Revue Internationale de Géomatique. 2022;31(2):303-327 https://doi.org/10.3166/RIG.31.303-327©2022
IEEE Style
P. Joan, F. Giovanni, and S. Yukio "Classification and clustering of buildings for understanding urban dynamics A framework for processing spatiotemporal data," Revue Internationale de Géomatique, vol. 31, no. 2, pp. 303-327. 2022. https://doi.org/10.3166/RIG.31.303-327©2022



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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