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

Author

Listed:
  • Daniel Arribas-Bel

    (University of Liverpool)

  • Miquel-Àngel Garcia-López

    (Universitat Autònoma de Barcelona & IEB)

  • Elisabet Viladecans-Marsal

    (Universitat de Barcelona & IEB)

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.

Suggested Citation

  • Daniel Arribas-Bel & Miquel-Àngel Garcia-López & Elisabet Viladecans-Marsal, 2019. "Building(s and) cities: delineating urban areas with a machine learning algorithm," Working Papers 2019/10, Institut d'Economia de Barcelona (IEB).
    • Handle: RePEc:ieb:wpaper:doc2019-10
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    References listed on IDEAS

    as
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    Full references (including those not matched with items on IDEAS)

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    Cited by:

    1. Gaëtan Montero & Geoffrey Caruso & Mohamed Hilal & Isabelle Thomas, 2023. "A partition-free spatial clustering that preserves topology: application to built-up density," Journal of Geographical Systems, Springer, vol. 25(1), pages 5-35, January.
    2. Bosker, Maarten & Park, Jane & Roberts, Mark, 2021. "Definition matters. Metropolitan areas and agglomeration economies in a large-developing country," Journal of Urban Economics, Elsevier, vol. 125(C).
    3. Rafael González-Val, 2021. "The Spanish spatial city size distribution," Environment and Planning B, , vol. 48(6), pages 1609-1631, July.
    4. de Bellefon, Marie-Pierre & Combes, Pierre-Philippe & Duranton, Gilles & Gobillon, Laurent & Gorin, Clément, 2021. "Delineating urban areas using building density," Journal of Urban Economics, Elsevier, vol. 125(C).
    5. de Bellefon, Marie-Pierre & Combes, Pierre-Philippe & Duranton, Gilles & Gobillon, Laurent & Gorin, Clément, 2021. "Delineating urban areas using building density," Journal of Urban Economics, Elsevier, vol. 125(C).
    6. Ahlfeldt, Gabriel M. & Barr, Jason, 2022. "Viewing urban spatial history from tall buildings," Regional Science and Urban Economics, Elsevier, vol. 94(C).
    7. Morgan Ubeda, 2020. "Local Amenities, Commuting Costs and Income Disparities Within Cities," Working Papers halshs-03082448, HAL.
    8. Huicheng Feng & Xiaoxiang Tang & Cheng Zou, 2024. "Optimizing the Layout of Service Facilities for Older People Based on POI Data and Machine Learning: Guangzhou City as an Example," Land, MDPI, vol. 13(5), pages 1-15, May.
    9. Stephan Heblich & David Krisztian Nagy & Alex Trew & Yanos Zylberberg, 2023. "The Death and Life of Great British Cities," Working Papers 2023_09, Business School - Economics, University of Glasgow.
    10. Imryoung Jeong & Hyunjoo Yang, 2021. "Using maps to predict economic activity," Papers 2112.13850, arXiv.org, revised Apr 2022.
    11. Pengfei Ban & Wei Zhan & Qifeng Yuan & Xiaojian Li, 2021. "Delineating the Urban Areas of a Cross-Boundary City with Open-Access Data: Guangzhou–Foshan, South China," Sustainability, MDPI, vol. 13(5), pages 1-17, March.
    12. Vanhatalo, Jaana & Partanen, Jenni, 2022. "Exploring the spectrum of urban area key figures using data from Finland and proposing guidelines for delineation of urban areas," Land Use Policy, Elsevier, vol. 112(C).
    13. Hiroyuki Usui, 2023. "Cost-Efficient Urban Areas Minimising the Connection Costs of Buildings by Roads: Simultaneous Optimisation of Criteria for Building Interval and Built Cluster Size," Networks and Spatial Economics, Springer, vol. 23(1), pages 65-96, March.
    14. Puente-Ajovín, Miguel & Ramos, Arturo & Sanz-Gracia, Fernando & Arribas-Bel, Daniel, 2020. "How sensitive is city size distribution to the definition of city? The case of Spain," Economics Letters, Elsevier, vol. 197(C).
    15. Dubé, Jean & Dieng, Ousmane & Lévesque, Mathieu & Racine, Antoine & Roberge, Olivier & Trapé, Thibault, 2023. "How public policies and other events can shape spatial distribution of local activities over time? An investigation based on spatial micro-data," Land Use Policy, Elsevier, vol. 125(C).
    16. O’Driscoll, Conor & Crowley, Frank & Doran, Justin & McCarthy, Nóirín, 2023. "Land-use mixing in Irish cities: Implications for sustainable development," Land Use Policy, Elsevier, vol. 128(C).

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    More about this item

    Keywords

    Buildings; urban areas; city size; transportation; machine learning;
    All these keywords.

    JEL classification:

    • R12 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Size and Spatial Distributions of Regional Economic Activity; Interregional Trade (economic geography)
    • R14 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Land Use Patterns
    • R2 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Household Analysis
    • R4 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics

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