Integrating GIS Community Resilience Assessment: Multidisaster Perspective
Publication: ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 10, Issue 2
Abstract
Community resilience is the fundamental capacity of a community to cope with a crisis or disruption and mitigate the adverse effects of a disaster. Identifying and quantifying community resilience before a disaster occurs is increasingly becoming a prerequisite for managers to make informed decisions and take action. Assessing community resilience for multiple disasters is more complex and dynamic than for a single disaster. This paper proposes a multidisaster community resilience assessment method that comprises three indexes: the topology and seismic performance–based physical resilience index (TSP-PRI), the medical care–based health resilience index (MC-HRI), and the capital and population–based socioeconomic resilience index (CP-SRI). These indexes are computed using accurate and objective data on building information, road information, and government statistical yearbooks. GIS offer rich geospatial analysis functions for network infrastructure systems that involve geographic references. A plug-in has been developed in ArcGIS Pro to link these data to geospatial modeling, which can automatically calculate the TSP-PRI, MC-HRI, and CP-SRI. This decision-making tool can be used to systematically and visually examine the disaster characteristics and topological attributes of communities before and after the occurrence of disasters. Furthermore, the -means clustering algorithm was applied to classify the types and characteristics of these three indexes to prioritize investments for different communities. A case study of community waterlogging and earthquakes in Nanjing, China, is presented to show the feasibility and effectiveness of the proposed approach.
Practical Applications
Assessing community resilience prior to a disaster is crucial for informed decision-making and effective action by managers. This study presents a thorough assessment method that includes three indexes for assessing the resilience of various communities in multidisaster scenarios. To facilitate the assessment process, a plug-in has been developed in ArcGIS Pro, enabling automated computation of these indexes using reliable and objective data. A case study was conducted in 11 districts of Nanjing, China, examining the flooding and earthquake disasters. The districts were then grouped into clusters with similar resilience characteristics, utilizing the -means clustering algorithm. This facilitated the prioritization of investments in different communities. The proposed method offers a comprehensive and quantitative framework that helps managers to measure and compare community resilience across districts and disasters. Furthermore, the method has the potential to be generalized and applied to other communities or countries, providing a valuable framework for resilience assessment in diverse settings.
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Data Availability Statement
All data that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This research was supported by the Ministry of Education of Humanities and Social Science project of China (Grant No. 23YJA630069) and the National Natural Science Foundation of China (Grant Nos. T221101034 and 72134002).
References
Abunyewah, M., M. O. Erdiaw-Kwasie, S. A. Okyere, G. Thayaparan, M. Byrne, J. Lassa, K. K. Zander, M. N. Fatemi, and K. Maund. 2023. “Influence of personal and collective social capital on flood preparedness and community resilience: Evidence from Old Fadama, Ghana.” Int. J. Disaster Risk Reduct. 94 (Aug): 103790. https://doi.org/10.1016/j.ijdrr.2023.103790.
Aghababaei, M., and M. Koliou. 2022. “An agent-based modeling approach for community resilience assessment accounting for system interdependencies: Application on education system.” Eng. Struct. 255 (Mar): 113889. https://doi.org/10.1016/j.engstruct.2022.113889.
Aghababaei, M., and M. Koliou. 2023. “Community resilience assessment via agent-based modeling approach.” Comput.-Aided Civ. Infrastruct. Eng. 38 (7): 920–939. https://doi.org/10.1111/mice.12916.
Barrett, C. B., and M. A. Constas. 2014. “Toward a theory of resilience for international development applications.” Proc. Natl. Acad. Sci. USA 111 (40): 14625–14630. https://doi.org/10.1073/pnas.1320880111.
Bruneau, M., S. E. Chang, R. T. Eguchi, G. C. Lee, T. D. O’Rourke, A. M. Reinhorn, M. Shinozuka, K. Tierney, W. A. Wallace, and D. von Winterfeldt. 2003. “A framework to quantitatively assess and enhance the seismic resilience of communities.” Earthquake Spectra 19 (4): 733–752. https://doi.org/10.1193/1.1623497.
Bruneau, M., and A. Reinhorn. 2007. “Exploring the concept of seismic resilience for acute care facilities.” Earthquake Spectra 23 (1): 41–62. https://doi.org/10.1193/1.2431396.
Cai, H., N. S. N. Lam, Y. Qiang, L. Zou, R. M. Correll, and V. Mihunov. 2018. “A synthesis of disaster resilience measurement methods and indices.” Int. J. Disaster Risk Reduct. 31 (Oct): 844–855. https://doi.org/10.1016/j.ijdrr.2018.07.015.
Cains, M. G., and D. Henshel. 2019. “Community as an equal partner for region-based climate change vulnerability, risk, and resilience assessments.” Curr. Opin. Environ. Sustainability 39 (Sep): 24–30. https://doi.org/10.1016/j.cosust.2019.06.005.
Ceferino, L., A. Kiremidjian, and G. Deierlein. 2018. “Probabilistic model for regional multiseverity casualty estimation due to building damage following an earthquake.” ASCE-ASME J. Risk Uncertainty Eng. Syst. Part A: Civ. Eng. 4 (3): 04018023. https://doi.org/10.1061/AJRUA6.0000972.
Choi, E., and J. Song. 2022. “Clustering-based disaster resilience assessment of South Korea communities building portfolios using open GIS and census data.” Int. J. Disaster Risk Reduct. 71 (Mar): 102817. https://doi.org/10.1016/j.ijdrr.2022.102817.
Cohen, O., D. Leykin, M. Lahad, A. Goldberg, and L. Aharonson-Daniel. 2013. “The conjoint community resiliency assessment measure as a baseline for profiling and predicting community resilience for emergencies.” Technol. Forecasting Social Change 80 (9): 1732–1741. https://doi.org/10.1016/j.techfore.2012.12.009.
Cutter, S. L., B. J. Boruff, and W. L. Shirley. 2003. “Social vulnerability to environmental hazards.” Soc. Sci. Q. 84 (2): 242–261. https://doi.org/10.1111/1540-6237.8402002.
De France, K., G. R. Hancock, D. M. Stack, L. A. Serbin, and T. Hollenstein. 2022. “The mental health implications of COVID-19 for adolescents: Follow-up of a four-wave longitudinal study during the pandemic.” Am. Psychol. 77 (1): 85–99. https://doi.org/10.1037/amp0000838.
Dueñas-Osorio, L., J. I. Craig, and B. J. Goodno. 2007. “Seismic response of critical interdependent networks.” Earthquake Eng. Struct. Dyn. 36 (2): 285–306. https://doi.org/https://doi.org/10.1002/eqe.626.
Foster, K. A. 2012. “In search of regional resilience.” In Vol. 4 of Urban and regional policy and its effects: Building resilient regions, 24–59. Washington, DC: Brookings Institution Press.
Gaode Map. 2021. “Amap development platform.” Accessed August 1, 2023. https://lbs.amap.com.
Geospatial Data Cloud. 2023. “DEM digital elevation data.” Accessed August 1, 2023. https://www.gscloud.cn.
Gerges, F., R. H. Assaad, H. Nassif, E. Bou-Zeid, and M. C. Boufadel. 2023. “A perspective on quantifying resilience: Combining community and infrastructure capitals.” Sci. Total Environ. 859 (Feb): 160187. https://doi.org/10.1016/j.scitotenv.2022.160187.
Gerges, F., H. Nassif, X. Geng, H. A. Michael, and M. C. Boufadel. 2022a. “GIS-based approach for evaluating a community intrinsic resilience index.” Nat. Hazard. 111 (2): 1271–1299. https://doi.org/10.1007/s11069-021-05094-w.
Gerges, F., H. Nassif, T. Herrington, and M. C. Boufadel. 2022b. “A GIS-based approach for estimating community transportation exposure and capacity in the context of disaster resilience.” Sustainable Horiz. 3 (Nov): 100030. https://doi.org/10.1016/j.horiz.2022.100030.
Grabowska, S., S. Saniuk, and B. Gajdzik. 2022. “Industry 5.0: Improving humanization and sustainability of Industry 4.0.” Scientometrics 127 (6): 3117–3144. https://doi.org/10.1007/s11192-022-04370-1.
Hassan, E. M., and H. Mahmoud. 2019. “Full functionality and recovery assessment framework for a hospital subjected to a scenario earthquake event.” Eng. Struct. 188 (Jun): 165–177. https://doi.org/10.1016/j.engstruct.2019.03.008.
Hassan, E. M., and H. Mahmoud. 2020. “An integrated socio-technical approach for post-earthquake recovery of interdependent healthcare system.” Reliab. Eng. Syst. Saf. 201 (Mar): 106953. https://doi.org/10.1016/j.ress.2020.106953.
Hughes, W., Q. Lu, Z. Ding, and W. Zhang. 2023. “Modeling tree damages and infrastructure disruptions under strong winds for community resilience assessment.” ASCE-ASME J. Risk Uncertainty Eng. Syst. Part A: Civ. Eng. 9 (1): 04022057. https://doi.org/10.1061/AJRUA6.RUENG-956.
Javadpoor, M., A. Sharifi, and M. Roosta. 2021. “An adaptation of the baseline resilience indicators for communities (BRIC) for assessing resilience of Iranian provinces.” Int. J. Disaster Risk Reduct. 66 (Dec): 102609. https://doi.org/10.1016/j.ijdrr.2021.102609.
Johnsson, I., and T. Balstrøm. 2021. “A GIS-based screening method to identify climate change-related threats on road networks: A case study from Sweden.” Clim. Risk Manage. 33 (Jan): 100344. https://doi.org/10.1016/j.crm.2021.100344.
Khazai, B., J. Anhorn, and C. G. Burton. 2018. “Resilience performance scorecard: Measuring urban disaster resilience at multiple levels of geography with case study application to Lalitpur, Nepal.” Int. J. Disaster Risk Reduct. 31 (Oct): 604–616. https://doi.org/10.1016/j.ijdrr.2018.06.012.
Koliou, M., J. W. van de Lindt, T. P. McAllister, B. R. Ellingwood, M. Dillard, and H. Cutler. 2020. “State of the research in community resilience: Progress and challenges.” Sustainable Resilient Infrastruct. 5 (3): 131–151. https://doi.org/10.1080/23789689.2017.1418547.
Li, S.-Q., Y.-S. Chen, H.-B. Liu, and C. Del Gaudio. 2023. “Empirical seismic vulnerability assessment model of typical urban buildings.” Bull. Earthquake Eng. 21 (4): 2217–2257. https://doi.org/10.1007/s10518-022-01585-8.
Nanjing Municipal Bureau Statistics. 2021. “Nanjing statistical yearbook: 2021.” Nanjing Bureau of Statistics website. Accessed August 1, 2023. https://tjj.nanjing.gov.cn/material/njnj_2021/index.htm.
Nofal, O. M., K. Amini, J. E. Padgett, J. W. van de Lindt, N. Rosenheim, Y. M. Darestani, A. Enderami, E. J. Sutley, S. Hamideh, and L. Duenas-Osorio. 2023. “Multi-hazard socio-physical resilience assessment of hurricane-induced hazards on coastal communities.” Resilient Cities Struct. 2 (2): 67–81. https://doi.org/10.1016/j.rcns.2023.07.003.
OSM (OpenStreetMap). 2021. “OpenStreetMap.” Accessed August 1, 2023. https://www.openstreetmap.org.
Ostadtaghizadeh, A., A. Ardalan, D. Paton, H. Jabbari, and H. R. Khankeh. 2015. “Community disaster resilience: A systematic review on assessment models and tools.” PLoS Curr. 7. https://doi.org/10.1371/currents.dis.f224ef8efbdfcf1d508dd0de4d8210ed.
Ouyang, M., L. Hong, Z.-J. Mao, M.-H. Yu, and F. Qi. 2009. “A methodological approach to analyze vulnerability of interdependent infrastructures.” Simul. Modell. Pract. Theory 17 (5): 817–828. https://doi.org/10.1016/j.simpat.2009.02.001.
Parsons, M., S. Glavac, P. Hastings, G. Marshall, J. McGregor, J. McNeill, P. Morley, I. Reeve, and R. Stayner. 2016. “Top-down assessment of disaster resilience: A conceptual framework using coping and adaptive capacities.” Int. J. Disaster Risk Reduct. 19 (Oct): 1–11. https://doi.org/10.1016/j.ijdrr.2016.07.005.
Qiang, Y., Q. Huang, and J. Xu. 2020. “Observing community resilience from space: Using nighttime lights to model economic disturbance and recovery pattern in natural disaster.” Sustainable Cities Soc. 57 (Dec): 102115. https://doi.org/10.1016/j.scs.2020.102115.
Ramkissoon, H. 2022. “Prosociality in times of separation and loss.” Curr. Opin. Psychol. 45 (Jun): 101290. https://doi.org/10.1016/j.copsyc.2021.11.008.
Scherzer, S., P. Lujala, and J. K. Rød. 2019. “A community resilience index for Norway: An adaptation of the baseline resilience indicators for communities (BRIC).” Int. J. Disaster Risk Reduct. 36 (Jun): 101107. https://doi.org/10.1016/j.ijdrr.2019.101107.
Singh-Peterson, L., P. Salmon, N. Goode, and J. Gallina. 2014. “Translation and evaluation of the baseline resilience indicators for communities on the Sunshine Coast, Queensland Australia.” Int. J. Disaster Risk Reduct. 10 (Jan): 116–126. https://doi.org/10.1016/j.ijdrr.2014.07.004.
Tang, Y., and S. Huang. 2019. “Assessing seismic vulnerability of urban road networks by a Bayesian network approach.” Transp. Res. Part D Transp. Environ. 77 (Aug): 390–402. https://doi.org/10.1016/j.trd.2019.02.003.
Thrysoe, C., T. Balstrom, M. Borup, R. Lowe, B. Jamali, and K. Arnbjerg-Nielsen. 2021. “FloodStroem: A fast dynamic GIS-based urban flood and damage model.” J. Hydrol. 600 (Sep): 126521. https://doi.org/10.1016/j.jhydrol.2021.126521.
Xiao, Y., X. Zhao, Y. Wu, Z. Chen, H. Gong, L. Zhu, and Y. Liu. 2022. “Seismic resilience assessment of urban interdependent lifeline networks.” Reliab. Eng. Syst. Saf. 218 (Feb): 108164. https://doi.org/10.1016/j.ress.2021.108164.
Yang, Y., S. T. Ng, S. Zhou, F. J. Xu, D. Li, and H. Li. 2020. “A federated pre-event community resilience approach for assessing physical and social sub-systems: An extreme rainfall case in Hong Kong.” Sustainable Cities Soc. 52 (Apr): 101859. https://doi.org/10.1016/j.scs.2019.101859.
Yao, K., and S. Chen. 2023. “Percolation-based resilience modeling and active intervention of disrupted urban traffic network during a snowstorm.” J. Transp. Eng. Part A Syst. 149 (5): 04023027. https://doi.org/10.1061/JTEPBS.TEENG-7364.
Yoon, D. K., J. E. Kang, and S. D. Brody. 2016. A measurement of community disaster resilience in Korea. 3rd ed. 436–460. London: Taylor & Francis.
Zhang, F. 2022. “The community resilience measurement throughout the COVID-19 pandemic and beyond -an empirical study based on data from Shanghai, Wuhan and Chengdu.” Int. J. Disaster Risk Reduct. 67 (Jan): 102664. https://doi.org/10.1016/j.ijdrr.2021.102664.
Zhang, N., S. Yang, and P. Jia. 2022. “Cultivating resilience during the COVID-19 pandemic: A socioecological perspective.” Annu. Rev. Psychol. 73 (1): 575–598. https://doi.org/10.1146/annurev-psych-030221-031857.
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Published In
ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 10 • Issue 2 • June 2024
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© 2024 American Society of Civil Engineers.
History
Received: Aug 25, 2023
Accepted: Nov 4, 2023
Published online: Feb 6, 2024
Published in print: Jun 1, 2024
Discussion open until: Jul 6, 2024
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