Human mobility data and analysis for urban resilience: A systematic review. (June 2022)

• Record Type: Journal Article
• Authors: Haraguchi, Masahiko; Nishino, Akihiko; Kodaka, Akira; Allaire, Maura; Lall, Upmanu; Kuei-Hsien, Liao; Onda, Kaya; Tsubouchi, Kota; Kohtake, Naohiko
• Is Part Of: Environment & planning. Volume 49:Number 5(2022)
• Page Start: 1507
• Page End: 1535
• Publication Date: 2022-06
• Subjects: Human mobility -- mobile phone data -- location information -- disaster resilience -- systematic review; City planning -- Periodicals; Urban ecology (Sociology) -- Periodicals; 307.11605
• DOI: 10.1177/23998083221075634
• Languages: English
• ISSNs: 2399-8083

Abstract

The impacts of disasters are increasing due to climate change and unplanned urbanization. Big and open data offer considerable potential for analyzing and predicting human mobility during disaster events, including the COVID-19 pandemic, leading to better disaster risk reduction (DRR) planning. However, the value of human mobility data and analysis (HMDA) in urban resilience research is poorly understood. This review highlights key opportunities for and challenges hindering the use of HMDA in DRR in urban planning and risk science, as well as insights from practitioners. A gap in research on HMDA for data-driven DRR planning was identified. By examining human mobility studies and their respective analytical and planning tools, this paper offers deeper insights into the challenges that must be addressed to improve the development of effective data-driven DRR planning, from data collection to implementation. In future work on HMDA, (i) the human mobility of vulnerable populations should be targeted, (ii) research should focus on disaster mitigation and prevention, (iii) analytical methods for evidence-based disaster planning should be developed, (iv) different types of data should be integrated into analyses to overcome methodological challenges, and (v) a decision-making framework should be developed for evidence-based urban planning through transdisciplinary knowledge co-production.