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Big Data for Community Resilience Assessment: A Critical Review of Selected Global Tools

In: Proceedings of the 24th International Symposium on Advancement of Construction Management and Real Estate

Author

Listed:
  • Mohammed Abdul-Rahman

    (The Hong Kong Polytechnic University)

  • Edwin H. W. Chan

    (The Hong Kong Polytechnic University)

  • X. Li

    (Sun Yat-Sen University)

  • Man Sing Wong

    (The Hong Kong Polytechnic University)

  • Pengpeng Xu

    (Chongqing University)

Abstract

As the global call to build sustainable cities becomes louder, the community resilience concept is also fast becoming popular in the global scientific and policy discourse. To put this concept in perspective, a lot of methodologies have been developed in the last two decades. This paper critically reviews 12 selected global community resilience assessment tools using content analysis. The results show that none of the selected tools use big data for their assessments, they mainly rely on literature review, stakeholders’ input, expert opinions and field testing. The results also show that the selected tools are mostly formulated using top-down approaches and only half of them provide action plans after their resilience assessment. Most of the tools also do not account for cross-scale relationships and temporal dynamism. The study concludes that new community resilience assessment tools need to employ iterative processes, encourage participation, and incorporate the use of big data, machine learning and artificial intelligence to take care of spatiotemporal dynamism.

Suggested Citation

  • Mohammed Abdul-Rahman & Edwin H. W. Chan & X. Li & Man Sing Wong & Pengpeng Xu, 2021. "Big Data for Community Resilience Assessment: A Critical Review of Selected Global Tools," Springer Books, in: Gui Ye & Hongping Yuan & Jian Zuo (ed.), Proceedings of the 24th International Symposium on Advancement of Construction Management and Real Estate, pages 1345-1361, Springer.
    • Handle: RePEc:spr:sprchp:978-981-15-8892-1_94
    DOI: 10.1007/978-981-15-8892-1_94
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