Open Access

ARTICLE

Classification and clustering of buildings for understanding urban dynamics

A framework for processing spatiotemporal data

by

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), 303-328. https://doi.org/10.3166/rig31.303-328

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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Keywords

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