Identifying building typologies and their spatial patterns in the metropolitan areas of Marseille and Osaka
Joan Perez (),
Giovanni Fusco (),
Alessandro Araldi () and
Takashi Fuse ()
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Joan Perez: The University of Tokyo
Giovanni Fusco: Université Côte-Azur, CNRS, ESPACE
Alessandro Araldi: Université Côte-Azur, CNRS, ESPACE
Takashi Fuse: The University of Tokyo
Asia-Pacific Journal of Regional Science, 2020, vol. 4, issue 1, 193-217
Abstract Buildings are an essential component of urban form. Urban morphologists know that buildings can be classified by type, but types are specific to given cultural areas. In a transnational context, detailed expert knowledge is not always available, hence the need for identifying typologies of buildings inductively from large urban databases exists. This paper presents the application of a Bayesian Network clustering protocol to buildings and the study of the spatial aggregates of the obtained family types for two metropolitan areas located in countries with marked cultural and societal differences: Osaka-Kobe in Japan and Marseille-Provence in France. Six indicators of building characteristics are calculated and used to perform the clustering: Footprint Surface, Elongation, Convexity, Number of Adjoining Neighbors, Height and Specialization. Cluster results are first extracted, detailed and analyzed and then, building type prevalence is studied at the metropolitan scale using local indicators of network-constrained clusters (ILINCS). The building families obtained through clustering show that these two coastal metropolitan areas are made up of apparently similar “ingredients” (very similar typologies are found at the relatively coarse level of detail of our study), but with different weights and spatial organization. This approach is appropriate for the automated processing of large building datasets and the results are a good entry point to study the link between building families, urban development periods and urban functions.
Keywords: Building; Clustering; Typology; Geoprocessing; Japan; France; Osaka; Marseille (search for similar items in EconPapers)
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