Please use this identifier to cite or link to this item:
Title: Building(s and) cities: delineating urban areas with a machine learning algorithm
Author: Arribas-Bel, Daniel
García López, Miquel-Àngel
Viladecans Marsal, Elisabet
Keywords: Economia urbana
Política urbana
Desenvolupament urbà
Geografia econòmica
Urban economics
Economic geography
Urban policy
Urban development
Issue Date: 2019
Publisher: Institut d’Economia de Barcelona
Series/Report no: [WP E-IEB19/10]
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.
Note: Reproducció del document publicat a:
It is part of: IEB Working Paper 2019/10
Appears in Collections:IEB (Institut d’Economia de Barcelona) – Working Papers

Files in This Item:
File Description SizeFormat 
IEB19-10_Arribas+Garcia+Viladecans.pdf18 MBAdobe PDFView/Open

This item is licensed under a Creative Commons License Creative Commons