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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
    1. Marie-Pierre de Bellefon & Pierre-Philippe Combes & Gilles Duranton & Laurent Gobillon & Clément Gorin, 2019. "Delineating urban areas using building density," PSE-Ecole d'économie de Paris (Postprint) halshs-02492519, HAL.
    2. Garcia-López, Miquel-Àngel & Hémet, Camille & Viladecans-Marsal, Elisabet, 2017. "Next train to the polycentric city: The effect of railroads on subcenter formation," Regional Science and Urban Economics, Elsevier, vol. 67(C), pages 50-63.
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    Cited by:

    1. González-Val, Rafael, 2020. "The Spanish spatial city size distribution," MPRA Paper 101195, University Library of Munich, Germany.

    More about this item

    Keywords

    Buildings; urban areas; city size; transportation; machine learning;

    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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