IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v11y2019i23p6622-d290206.html
   My bibliography  Save this article

Urban Shape and Built Density Metrics through the Analysis of European Urban Fabrics Using Artificial Intelligence

Author

Listed:
  • Francisco Javier Abarca-Alvarez

    (Department of Urban and Spatial Planning, University of Granada, 18071 Granada, Spain
    Higher Technical School of Architecture, University of Granada, 18071 Granada, Spain)

  • Francisco Sergio Campos-Sánchez

    (Department of Urban and Spatial Planning, University of Granada, 18071 Granada, Spain
    Higher Technical School of Architecture, University of Granada, 18071 Granada, Spain)

  • Fernando Osuna-Pérez

    (Department of Urban and Spatial Planning, University of Granada, 18071 Granada, Spain)

Abstract

In recent decades, the concept of urban density has been considered key to the creation of sustainable urban fabrics. However, when it comes to measuring the built density, a difficulty has been observed in defining valid measurement indicators universally. With the intention of identifying the variables that allow the best characterization of the shape of urban fabrics and of obtaining the metrics of their density, a multi-variable analysis methodology from the field of artificial intelligence is proposed. The main objective of this paper was to evaluate the capacity and interest of such a methodology from standard indicators of the built density, measured at various urban scales, (i) to cluster differentiated urban profiles in a robust way by assessing the results statistically, and (ii) to obtain the metrics that characterize them with an identity. As a case study, this methodology was applied to the state of the art European urban fabrics (N = 117) by simultaneously integrating 13 regular parameters to qualify urban shape and density. It was verified that the profiles obtained were more robust than those based on a limited number of indicators, evidencing that the proposed methodology offers operational opportunities in urban management by allowing the comparison of a fabric with the identified profiles.

Suggested Citation

  • Francisco Javier Abarca-Alvarez & Francisco Sergio Campos-Sánchez & Fernando Osuna-Pérez, 2019. "Urban Shape and Built Density Metrics through the Analysis of European Urban Fabrics Using Artificial Intelligence," Sustainability, MDPI, vol. 11(23), pages 1-23, November.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:23:p:6622-:d:290206
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/11/23/6622/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/11/23/6622/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Philip Steadman, 2014. "Density and Built Form: Integrating ‘Spacemate’ with the Work of Martin and March," Environment and Planning B, , vol. 41(2), pages 341-358, April.
    2. Kenworthy, Jeffrey R. & Laube, Felix B., 1999. "Patterns of automobile dependence in cities: an international overview of key physical and economic dimensions with some implications for urban policy," Transportation Research Part A: Policy and Practice, Elsevier, vol. 33(7-8), pages 691-723.
    3. Jun Yan & Jean-Claude Thill, 2009. "Visual Data Mining in Spatial Interaction Analysis with Self-Organizing Maps," Environment and Planning B, , vol. 36(3), pages 466-486, June.
    4. Thomas Hatzichristos, 2004. "Delineation of Demographic Regions with GIS and Computational Intelligence," Environment and Planning B, , vol. 31(1), pages 39-49, February.
    5. Ronald L. Wasserstein & Nicole A. Lazar, 2016. "The ASA's Statement on p -Values: Context, Process, and Purpose," The American Statistician, Taylor & Francis Journals, vol. 70(2), pages 129-133, May.
    6. Tom Kauko, 2005. "Using the Self-Organising Map to Identify Regularities across Country-Specific Housing-Market Contexts," Environment and Planning B, , vol. 32(1), pages 89-110, February.
    7. Ahfeldt, Gabriel M. & Pietrostefani, Elisabetta, 2017. "The compact city in empirical research: A quantitative literature review," LSE Research Online Documents on Economics 83638, London School of Economics and Political Science, LSE Library.
    8. Gabriel M. Ahfeldt & Elisabetta Pietrostefani, 2017. "The Compact City in Empirical Research: A Quantitative Literature Review," SERC Discussion Papers 0215, Centre for Economic Performance, LSE.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Dorota Kamrowska-Załuska, 2021. "Impact of AI-Based Tools and Urban Big Data Analytics on the Design and Planning of Cities," Land, MDPI, vol. 10(11), pages 1-19, November.
    2. Chen Zuo & Chengcheng Liang & Jing Chen & Rui Xi & Junfei Zhang, 2023. "Machine Learning-Based Urban Renovation Design for Improving Wind Environment: A Case Study in Xi’an, China," Land, MDPI, vol. 12(4), pages 1-18, March.
    3. 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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Lu Liu & Yu Tian & Haiquan Chen, 2023. "The Costs of Agglomeration: Misallocation of Credit in Chinese Cities," Land, MDPI, vol. 12(3), pages 1-19, February.
    2. Ahlfeldt, Gabriel M. & Pietrostefani, Elisabetta, 2019. "The economic effects of density: A synthesis," Journal of Urban Economics, Elsevier, vol. 111(C), pages 93-107.
    3. Blaudin de Thé, Camille & Carantino, Benjamin & Lafourcade, Miren, 2021. "The carbon ‘carprint’ of urbanization: New evidence from French cities," Regional Science and Urban Economics, Elsevier, vol. 89(C).
    4. Garcia-López, Miquel-Àngel, 2019. "All roads lead to Rome … and to sprawl? Evidence from European cities," Regional Science and Urban Economics, Elsevier, vol. 79(C).
    5. Miklós Radics & Panayotis Christidis & Borja Alonso & Luigi dell’Olio, 2024. "The X-Minute City: Analysing Accessibility to Essential Daily Destinations by Active Mobility in Seville," Land, MDPI, vol. 13(10), pages 1-36, October.
    6. Tianhui Fan & Andrew Chapman, 2022. "Policy Driven Compact Cities: Toward Clarifying the Effect of Compact Cities on Carbon Emissions," Sustainability, MDPI, vol. 14(19), pages 1-19, October.
    7. Peter Bibby & John Henneberry & Jean-Marie Halleux, 2021. "Incremental residential densification and urban spatial justice: The case of England between 2001 and 2011," Urban Studies, Urban Studies Journal Limited, vol. 58(10), pages 2117-2138, August.
    8. Paolo Veneri & Andre Comandon & Miquel‐Àngel Garcia‐López & Michiel N. Daams, 2021. "What do divided cities have in common? An international comparison of income segregation," Journal of Regional Science, Wiley Blackwell, vol. 61(1), pages 162-188, January.
    9. Gallé, Johannes, 2023. "City Shape and Air Pollution," Ruhr Economic Papers 1012, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    10. Carlo Cambini & Lorien Sabatino & Sarah Zaccagni, 2021. "The Faster the Better? The Effect of Ultra-Fast Broadband on Students’ Performance," CEBI working paper series 21-14, University of Copenhagen. Department of Economics. The Center for Economic Behavior and Inequality (CEBI).
    11. Chen, Shiyi & Jiang, Lingduo & Liu, Wanlin & Song, Hong, 2022. "Fireworks regulation, air pollution, and public health: Evidence from China," Regional Science and Urban Economics, Elsevier, vol. 92(C).
    12. Jyotirmoy Sarkar, 2018. "Will P†Value Triumph over Abuses and Attacks?," Biostatistics and Biometrics Open Access Journal, Juniper Publishers Inc., vol. 7(4), pages 66-71, July.
    13. Gössling, Stefan, 2016. "Urban transport justice," Journal of Transport Geography, Elsevier, vol. 54(C), pages 1-9.
    14. Chatelain, Jean-Bernard & Ralf, Kirsten, 2018. "Publish and Perish: Creative Destruction and Macroeconomic Theory," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 46(2), pages 65-101.
    15. Segurado, Pedro & Gutiérrez-Cánovas, Cayetano & Ferreira, Teresa & Branco, Paulo, 2022. "Stressor gradient coverage affects interaction identification," Ecological Modelling, Elsevier, vol. 472(C).
    16. Uwe Hassler & Marc‐Oliver Pohle, 2022. "Unlucky Number 13? Manipulating Evidence Subject to Snooping," International Statistical Review, International Statistical Institute, vol. 90(2), pages 397-410, August.
    17. Kim, Jae H., 2017. "Stock returns and investors' mood: Good day sunshine or spurious correlation?," International Review of Financial Analysis, Elsevier, vol. 52(C), pages 94-103.
    18. Souche, Stéphanie, 2009. "Un exemple d’estimation de la demande de transport urbain," Revue d'économie régionale et urbaine, Editions NecPlus, vol. 2009(04), pages 759-779, December.
    19. Gergely Ganics & Atsushi Inoue & Barbara Rossi, 2021. "Confidence Intervals for Bias and Size Distortion in IV and Local Projections-IV Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(1), pages 307-324, January.
    20. Hirschauer, Norbert & Grüner, Sven & Mußhoff, Oliver & Becker, Claudia & Jantsch, Antje, 2020. "Can p-values be meaningfully interpreted without random sampling?," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 14, pages 71-91.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:11:y:2019:i:23:p:6622-:d:290206. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.