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Delineating urban areas using building density

Author

Listed:
  • Marie-Pierre de Bellefon

    (INSEE Paris, PSE - Paris School of Economics - UP1 - Université Paris 1 Panthéon-Sorbonne - ENS-PSL - École normale supérieure - Paris - PSL - Université Paris Sciences et Lettres - EHESS - École des hautes études en sciences sociales - ENPC - École des Ponts ParisTech - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement)

  • Pierre-Philippe Combes

    (GATE Lyon Saint-Étienne - Groupe d'Analyse et de Théorie Economique Lyon - Saint-Etienne - ENS de Lyon - École normale supérieure de Lyon - UL2 - Université Lumière - Lyon 2 - UCBL - Université Claude Bernard Lyon 1 - Université de Lyon - UJM - Université Jean Monnet - Saint-Étienne - CNRS - Centre National de la Recherche Scientifique)

  • Gilles Duranton

    (University of Pennsylvania)

  • Laurent Gobillon

    (PSE - Paris School of Economics - UP1 - Université Paris 1 Panthéon-Sorbonne - ENS-PSL - École normale supérieure - Paris - PSL - Université Paris Sciences et Lettres - EHESS - École des hautes études en sciences sociales - ENPC - École des Ponts ParisTech - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement)

  • Clément Gorin

    (GATE Lyon Saint-Étienne - Groupe d'Analyse et de Théorie Economique Lyon - Saint-Etienne - ENS de Lyon - École normale supérieure de Lyon - UL2 - Université Lumière - Lyon 2 - UCBL - Université Claude Bernard Lyon 1 - Université de Lyon - UJM - Université Jean Monnet - Saint-Étienne - CNRS - Centre National de la Recherche Scientifique)

Abstract
We develop a new dartboard methodology to delineate urban areas using detailed information about building location, which we implement using a map of all buildings in France. For each pixel, our approach compares actual building density after smoothing to counterfactual smoothed building density computed after randomly redistributing buildings. We define as urban any area with statistically significant excess building density. Within urban areas, extensions to our approach allow us to distinguish 'core' urban pixels and detect centres and subcentres. Finally, we develop novel one-and two-sided tests that provide a statistical basis to compare maps with different delineations, which we use to assess the robustness of our approach and to document large differences between our preferred delineation and the corresponding official one.

Suggested Citation

  • Marie-Pierre de Bellefon & Pierre-Philippe Combes & Gilles Duranton & Laurent Gobillon & Clément Gorin, 2019. "Delineating urban areas using building density," Working Papers halshs-02368040, HAL.
    • Handle: RePEc:hal:wpaper:halshs-02368040
    Note: View the original document on HAL open archive server: https://shs.hal.science/halshs-02368040
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    References listed on IDEAS

    as
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    Cited by:

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    5. Arribas-Bel, Daniel & Garcia-López, M.-À. & Viladecans-Marsal, Elisabet, 2021. "Building(s and) cities: Delineating urban areas with a machine learning algorithm," Journal of Urban Economics, Elsevier, vol. 125(C).
    6. Asia Bernardi & Michele Licata & Francesco Seitone & Giandomenico Fubelli, 2024. "MAGUS (Model for the Analysis of Geomorphological Urban Systems): From Conception to Validation on the Historic City Center of Turin (Italy)," Land, MDPI, vol. 13(11), pages 1-20, November.
    7. Pennerstorfer, Dieter & Yontcheva, Biliana, 2021. "Local market definition in competition analysis: An application to entry models," Economics Letters, Elsevier, vol. 198(C).
    8. Pierre-Philippe Combes & Clément Gorin & Shohei Nakamura & Mark Roberts & Benjamin Stewart, 2023. "An Anatomy of Urbanization in Sub-Saharan Africa," SciencePo Working papers hal-04345529, HAL.
    9. 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.
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    More about this item

    Keywords

    urban area definition; dartboard approach; Jaccard indices;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • 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

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