Geoinformatics advancing resilient urban planning: Risk perception, resilience assessment, and spatial simulation
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[1] Birkmann, J., Welle, T., Solecki, W., Lwasa, S., & Garschagen, M. (2016). Boost resilience of small and mid-sized cities. Nature, 537: 605−608. DOI: 10.1038/537605a. [2] Palmer, T. (2020). Resilience in the developing world benefits everyone. Nature Climate Change, 10: 794−795. DOI: 10.1038/s41558-020-0888-8. [3] Bai, X. (2024). Build networked resilience across cities. Science, 383: eado5304. DOI: 10.1126/science.ado5304. [4] Yue, Q., Lu, X., Xu, Z., Shi, Z., Tian, Y., & Wang, G. (2024). The “risk source-risk exposure-mitigation force” theoretical framework for urban safety. Engineering Mechanics, 40: 1−9. DOI: 10.6052/j.issn.1000-4750.2023.08.0602. [5] Yabe, T., Rao, P. S. C., Ukkusuri, S. V., & Cutter, S. L. (2022). Toward data-driven, dynamical complex systems approaches to disaster resilience. Proceedings of the National Academy of Sciences of the United States of America, 119: e2111997119. DOI: 10.1073/pnas.2111997119. -
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Hong W., Guo R., Yang X., et al., (2024). Geoinformatics advancing resilient urban planning: Risk perception, resilience assessment, and spatial simulation. The Innovation Geoscience 2(4): 100103. https://doi.org/10.59717/j.xinn-geo.2024.100103
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Hong W., Guo R., Yang X., et al., (2024). Geoinformatics advancing resilient urban planning: Risk perception, resilience assessment, and spatial simulation. The Innovation Geoscience 2(4): 100103. https://doi.org/10.59717/j.xinn-geo.2024.100103
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Figure 1.
Schematic diagram for Resilient Urban Planning
(A) Global distribution of hazard risks; (B) Distribution of cities preparing resilience plans; (C) Geoinformatics-supported planning for resilient cities
