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

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 d...

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Authors: Arribas-Bel, Daniel, García López, Miquel-Àngel, Viladecans Marsal, Elisabet
Format: article
Status:Published version
Publication Date:2021
Country:España
Institution:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repository:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:2445/195068
Online Access:https://hdl.handle.net/2445/195068
Access Level:Open access
Keyword:Economia urbana
Política urbana
Desenvolupament urbà
Geografia econòmica
Urban economics
Economic geography
Urban policy
Urban development
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spelling Building(s and) cities: Delineating urban areas with a machine learning algorithmArribas-Bel, DanielGarcía López, Miquel-ÀngelViladecans Marsal, ElisabetEconomia urbanaPolítica urbanaDesenvolupament urbàGeografia econòmicaUrban economicsEconomic geographyUrban policyUrban developmentThis 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.Elsevier2023202320212023info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersion20 p.application/pdfhttps://hdl.handle.net/2445/195068Articles publicats en revistes (Economia)reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)InglésReproducció del document publicat a: https://doi.org/10.1016/j.jue.2019.103217Journal of Urban Economics, 2021, vol. 125, núm. 103217, p. 1-20https://doi.org/10.1016/j.jue.2019.103217cc-by (c) Arribas Bel et al., 2021https://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:recercat.cat:2445/1950682026-05-29T05:05:01Z
dc.title.none.fl_str_mv Building(s and) cities: Delineating urban areas with a machine learning algorithm
title Building(s and) cities: Delineating urban areas with a machine learning algorithm
spellingShingle Building(s and) cities: Delineating urban areas with a machine learning algorithm
Arribas-Bel, Daniel
Economia urbana
Política urbana
Desenvolupament urbà
Geografia econòmica
Urban economics
Economic geography
Urban policy
Urban development
title_short Building(s and) cities: Delineating urban areas with a machine learning algorithm
title_full Building(s and) cities: Delineating urban areas with a machine learning algorithm
title_fullStr Building(s and) cities: Delineating urban areas with a machine learning algorithm
title_full_unstemmed Building(s and) cities: Delineating urban areas with a machine learning algorithm
title_sort Building(s and) cities: Delineating urban areas with a machine learning algorithm
dc.creator.none.fl_str_mv Arribas-Bel, Daniel
García López, Miquel-Àngel
Viladecans Marsal, Elisabet
author Arribas-Bel, Daniel
author_facet Arribas-Bel, Daniel
García López, Miquel-Àngel
Viladecans Marsal, Elisabet
author_role author
author2 García López, Miquel-Àngel
Viladecans Marsal, Elisabet
author2_role author
author
dc.subject.none.fl_str_mv Economia urbana
Política urbana
Desenvolupament urbà
Geografia econòmica
Urban economics
Economic geography
Urban policy
Urban development
topic Economia urbana
Política urbana
Desenvolupament urbà
Geografia econòmica
Urban economics
Economic geography
Urban policy
Urban development
description 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.
publishDate 2021
dc.date.none.fl_str_mv 2021
2023
2023
2023
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/2445/195068
url https://hdl.handle.net/2445/195068
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Reproducció del document publicat a: https://doi.org/10.1016/j.jue.2019.103217
Journal of Urban Economics, 2021, vol. 125, núm. 103217, p. 1-20
https://doi.org/10.1016/j.jue.2019.103217
dc.rights.none.fl_str_mv cc-by (c) Arribas Bel et al., 2021
https://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv cc-by (c) Arribas Bel et al., 2021
https://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 20 p.
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv Articles publicats en revistes (Economia)
reponame:Recercat. Dipósit de la Recerca de Catalunya
instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
instname_str Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
reponame_str Recercat. Dipósit de la Recerca de Catalunya
collection Recercat. Dipósit de la Recerca de Catalunya
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.812429

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