@Article{rig31.303-328,
AUTHOR = {Perez Joan, Fusco Giovanni, Sadahiro Yukio},
TITLE = {Classification and clustering of buildings  for understanding urban dynamics<br/><br/>A framework for processing spatiotemporal data},
JOURNAL = {Revue Internationale de Géomatique},
VOLUME = {31},
YEAR = {2022},
NUMBER = {2},
PAGES = {303--328},
URL = {http://www.techscience.com/RIG/v31n2/55785},
ISSN = {2116-7060},
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.},
DOI = {10.3166/rig31.303-328}
}