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Building morphometric analysis as a tool for urban renewal: Identifying post-Second World War mass public housing development potential

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  • Idan Porat
  • Dalit Shach-Pinsly

Abstract

The focus of this study is on the ability of morphometrics and building pattern recognition to improve top-down urban renewal processes by identifying post-Second World War mass housing suitable for urban renewal. We used two conceptual frameworks: the first from the field of urban design and architecture and the second from the field of multi-parametric analysis and geographic information system. A survey of a sample of typical post-Second World War mass housing units based on historical blueprints was developed to identify geometric indicators. The geometric indicators were transformed into a geographic information system parametric model for the identification of post-Second World War mass housing units and sites in current urban plans on a city scale. The model was implemented in the city of Haifa, Israel, as a case study. The analysis results indicate 1288 buildings in 283 urban sites suitable for urban renewal, 10% of the present city’s housing stock.

Suggested Citation

  • Idan Porat & Dalit Shach-Pinsly, 2021. "Building morphometric analysis as a tool for urban renewal: Identifying post-Second World War mass public housing development potential," Environment and Planning B, , vol. 48(2), pages 248-264, February.
    • Handle: RePEc:sae:envirb:v:48:y:2021:i:2:p:248-264
    DOI: 10.1177/2399808319861977
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    References listed on IDEAS

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    1. Scanlon, Kathleen & Fernández Arrigoitia, Melissa & Whitehead, Christine M E, 2015. "Social housing in Europe," LSE Research Online Documents on Economics 62938, London School of Economics and Political Science, LSE Library.
    2. Philippe Apparicio & Anne-Marie Séguin & Daniel Naud, 2008. "The Quality of the Urban Environment Around Public Housing Buildings in Montréal: An Objective Approach Based on GIS and Multivariate Statistical Analysis," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 86(3), pages 355-380, May.
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    Cited by:

    1. Stéphane C. K. Tékouabou & Jérôme Chenal & Rida Azmi & Hamza Toulni & El Bachir Diop & Anastasija Nikiforova, 2022. "Identifying and Classifying Urban Data Sources for Machine Learning-Based Sustainable Urban Planning and Decision Support Systems Development," Data, MDPI, vol. 7(12), pages 1-19, November.
    2. Hadas Shadar & Dalit Shach-Pinsly, 2022. "From Public Housing to Private Housing: Neglect of Urban Qualities during the Urban Regeneration Process," Land, MDPI, vol. 11(6), pages 1-17, June.

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