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

打印
作者
Daniel Arribas-Bel;M.-À. Garcia-López;Elisabet Viladecans-Marsal
来源
来源 JOURNAL OF URBAN ECONOMICS,Vol.125,P.
语言
英文
关键字
Buildings;Urban areas;City size;Transportation;Machine learning;R12;R14;R2;R4
作者单位
Geographic Data Science Lab, Department of Geography and Planning, University of Liverpool, Roxby Building, 74 Bedford St S, Liverpool, L69 7ZT, United Kingdom;Department of Applied Economics, Universitat Autònoma de Barcelona, Edifici B, Facultat d’Economia i Empresa, Cerdanyola del Vallès 08193, Spain;Department of Economics, Universitat de Barcelona, John M. Keynes 1–11, Barcelona 08034, Spain;The Barcelona Institute of Economics, Universitat de Barcelona, 08034 Barcelona, Spain;The Centre for Economic Policy Research (CEPR);Geographic Data Science Lab, Department of Geography and Planning, University of Liverpool, Roxby Building, 74 Bedford St S, Liverpool, L69 7ZT, United Kingdom;Department of Applied Economics, Universitat Autònoma de Barcelona, Edifici B, Facultat d’Economia i Empresa, Cerdanyola del Vallès 08193, Spain;Department of Economics, Universitat de Barcelona, John M. Keynes 1–11, Barcelona 08034, Spain;The Barcelona Institute of Economics, Universitat de Barcelona, 08034 Barcelona, Spain;The Centre for Economic Policy Research (CEPR)
摘要
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.