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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
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    References listed on IDEAS

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    3. 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.

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