Building(s and) cities: Delineating urban areas with a machine learning algorithm

Daniel Arribas-Bel, Miquel-Àngel Garcia-López and Elisabet Viladecans-Marsal

Journal of Urban Economics, 2021, vol. 125, issue C

Abstract: This paper proposes a novel methodology for delineating urban areas based on a machine learning algorithm that groups buildings within portions of space of sufficient density. To do so, we use the precise geolocation of all 12 million buildings in Spain. We exploit building heights to create a new dimension for urban areas, namely, the vertical land, which provides a more accurate measure of their size. To better understand their internal structure and to illustrate an additional use for our algorithm, we also identify employment centers within the delineated urban areas. We test the robustness of our method and compare our urban areas to other delineations obtained using administrative borders and commuting-based patterns. We show that: 1) our urban areas are more similar to the commuting-based delineations than the administrative boundaries but that they are more precisely measured; 2) when analyzing the urban areas’ size distribution, Zipf’s law appears to hold for their population, surface and vertical land; and 3) the impact of transportation improvements on the size of the urban areas is not underestimated.

Keywords: Buildings; Urban areas; City size; Transportation; Machine learning (search for similar items in EconPapers)
JEL-codes: R12 R14 R2 R4 (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (5)

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Related works:
Working Paper: Building(s and) cities: Delineating urban areas with a machine learning algorithm (2020)
Working Paper: Building(s and) cities: delineating urban areas with a machine learning algorithm (2019)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:juecon:v:125:y:2021:i:c:s0094119019300944

DOI: 10.1016/j.jue.2019.103217

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