Open Access
ARTICLE
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.
Keywords
Cite This Article
APA Style
Joan, P., Giovanni, F., Yukio, S. (2022). Classification and clustering of buildings for understanding urban dynamics<br/><br/>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<br/><br/>a framework for processing spatiotemporal data. Revue Internationale de Gomatique . 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<br/><br/>A framework for processing spatiotemporal data," Revue Internationale de Gomatique , vol. 31, no. 2, pp. 303-327. 2022. https://doi.org/10.3166/RIG.31.303-327© 2022