Participatory Disaster Recovery Simulation Modeling for Community Resilience Planning

Scott B. Miles

doi:10.1007/s13753-018-0202-9

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Article Navigation > International Journal of Disaster Risk Science > 2018 > 6(4): 519-529

Scott B. Miles. Participatory Disaster Recovery Simulation Modeling for Community Resilience Planning[J]. International Journal of Disaster Risk Science, 2018, 6(4): 519-529. doi: 10.1007/s13753-018-0202-9

Citation:

Scott B. Miles. Participatory Disaster Recovery Simulation Modeling for Community Resilience Planning[J]. International Journal of Disaster Risk Science, 2018, 6(4): 519-529. doi: 10.1007/s13753-018-0202-9

Citation:

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Participatory Disaster Recovery Simulation Modeling for Community Resilience Planning

doi: 10.1007/s13753-018-0202-9

Scott B. Miles

Department of Human Centered Design and Engineering, University of Washington, Seattle, WA 98195, USA

Funds:

Funding support for this article was provided by National Science Foundation Awards #1560939 and #1541025.

Available Online: 2021-04-26

Abstract

Abstract

A major challenge in enhancing the resilience of communities stems from current approaches used to identify needs and strategies that build the capacity of jurisdictions to mitigate loss and improve recovery. A new generation of resilience-based planning processes has emerged in the last several years that integrate goals of community well-being and identity into recovery-based performance measurement frameworks. Specific tools and refined guidance are needed to facilitate evidence-based development of recovery estimates. This article presents the participatory modeling process, a planning system designed to develop recovery-based resilience measurement frameworks for community resilience planning initiatives. Stakeholder engagement is infused throughout the participatory modeling process by integrating disaster recovery simulation modeling into community resilience planning. Within the process, participants get a unique opportunity to work together to deliberate on community concerns through facilitated participatory modeling. The participatory modeling platform combines the DESaster recovery simulation model and visual analytics interfaces. DESaster is an open source Python Library for creating discrete event simulations of disaster recovery. The simulation model was developed using a human-centered design approach whose goal is to be open, modular, and extensible. The process presented in this article is the first participatory modeling approach for analyzing recovery to aid creation of community resilience measurement frameworks.
- Community resilience planning,
- Disasters,
- Disaster recovery,
- Participatory modeling,
- Recovery-based performance targets,
- Simulation modeling

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References(52)

References

Arias, E., H. Eden, G. Fischer, A. Gorman, and E. Scharff. 2000. Transcending the individual human mind—creating shared understanding through collaborative design. ACM Transactions on Computer-Human Interaction 7(1): 84–113.

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.

Chang, S.E., W.D. Svekla, and M. Shinozuka. 2002. Linking infrastructure and urban economy: Simulation of water-disruption impacts in earthquakes. Environment and Planning B: Planning and Design 29(2): 281–301.

Cooke, R.M., and L.H.J. Goossens. 2004. Expert judgement elicitation for risk assessments of critical infrastructures. Journal of Risk Research 7(6): 643–656.

Çağnan, Z., and R.A. Davidson. 2007. Discrete event simulation of the post-earthquake restoration process for electric power systems. International Journal of Risk Assessment and Management 7(8): 1138–1156.

Çağnan, Z., R.A. Davidson, and S.D. Guikema. 2006. Post-earthquake restoration planning for Los Angeles Electric Power. Earthquake Spectra 22(3): 589–608.

Davis, C.A. 2013. Quantifying post-earthquake water system functionality. In Proceedings of the sixth China-Japan-US trilateral symposium on lifeline earthquake engineering, ed. C. Davis, X. Du, M. Miyajima, and L. Yan, 19–26. Reston, VA: American Society of Civil Engineers.

Drew, C.H. 2003. Transparency–considerations for PPGIS research and development. URISA Journal 15(1): 73–78.

Ehrman and Stinson. 1999. Joint fact-finding and the use of technical experts. In The consensus building handbook, ed. L. Susskind, S. McKearnan, and J. Thomas-Larmer. Thousand Oaks, CA: Sage.

Eid, M.S., and I.H. El-Adaway. 2017. Sustainable disaster recovery: Multiagent-based model for integrating environmental vulnerability into decision-making processes of the associated stakeholders. Journal of Urban Planning and Development 143(1): Article 04016022.

Evans, B.D., S. Jarvis, S.R. Schultz, and K. Nikolic. 2016. PyRhO: A multiscale optogenetics simulation platform. Frontiers in Neuroinformatics 10(1): Article 8.

FEMA (Federal Emergency Management Agency). 2017. Hazus: FEMA’s methodology for estimating potential losses from disasters. https://www.fema.gov/hazus. Accessed 29 Nov 2017.

Fernández, L., and R. Andersson. 2016. Jupyterhub at the ESS: An interactive Python computing environment for scientists and engineers. Proceedings of the seventh international particle accelerator conference, 8–13 May 2016, Busan, Korea.

Fischer, F. 2000. Citizens, experts, and the environment. Durham, NC: Duke University Press.

Frazier, A.E., C.S. Renschler, and S.B. Miles. 2013. Evaluating post-disaster ecosystem resilience using MODIS GPP data. International Journal of Applied Earth Observation and Geoinformation 21: 43–52.

Ganji, A., and S.B. Miles. 2018. Human-centered simulation modeling for critical infrastructure disaster recovery planning. Proceedings of the Global Humanitarian Technology Conference, 18–21 October 2018, San Jose, CA, USA.

Gray, S., M. Paolisso, R. Jordan, and S. Gray. 2017. Environmental modelling with stakeholders: Theory, methods, and applications. New York: Springer.

Goossens, L.H.J., R.M. Cooke, A.R. Hale, and L. Rodić-Wiersma. 2008. Fifteen years of expert judgement at TUDelft. Safety Science 46(2): 234–244.

Gonçalves, P. 2011. Balancing provision of relief and recovery with capacity building in humanitarian operations. Operations Management Research 4(1–2): 39–50.

Grinberger, A.Y., and D. Felsenstein. 2014. Bouncing back or bouncing forward? Simulating urban resilience. Urban Design and Planning 167(DP3): 115–124.

Haimar, E.A., and J.R. Santos. 2015. A stochastic recovery model of influenza pandemic effects on interdependent workforce systems. Natural Hazards 77(2): 987–1011.

Hallegatte, S., and M. Ghil. 2008. Natural disasters impacting a macroeconomic model with endogenous dynamics. Ecological Economics 68(1–2): 582–592.

Hamrick, J.B. 2016. Creating and grading IPython/Jupyter notebook assignments with NbGrader. In Proceedings of the the 47th ACM technical symposium, ed. C. Alphonce, and J. Tims, 242. New York: ACM Press.

Huling, D., and S.B. Miles. 2015. Simulating disaster recovery as discrete event processes using python. In Proceedings of the 2015 IEEE global humanitarian technology conference (GHTC), 9–12 October 2015, Seattle, WA, USA.

Hwang, S., M. Park, H.S. Lee, and S.H. Lee. 2016. Hybrid simulation framework for immediate facility restoration planning after a catastrophic disaster. Journal of Construction Engineering and Management 142(8): Article 04016026.

Kluyver, T., B. Ragan-Kelley, and F. Perez. 2016. Jupyter notebooks—a publishing format for reproducible computational workflows. In Positioning and power in academic publishing players, agents and agendas, ed. F. Loizides, and B. Schmidt, 87–90. Amsterdam, The Netherlands: IOS Press.

Korfmacher, K.S. 2001. The politics of participation in watershed modeling. Environmental Management 27(2): 161–176.

Lee, J.S., and T.J. Kim. 2007. Implementation of spatiotemporal model for infrastructure reconstruction strategy under large-scale disaster. Transportation Research Record: Journal of the Transportation Research Board 2022: 39–46.

Miles, S.B. 2000. Towards policy relevant environmental modeling: Contextual validity and pragmatic models. United States Geological Survey open-file report 00-401. Reston, VA: U.S. Department of the Interior.

Miles, S.B. 2011. Participatory model assessment of earthquake-induced landslide hazard models. Natural Hazards 56(3): 749–766.

Miles, S.B. 2014. Modeling and visualizing infrastructure-centric community disaster resilience. Proceedings of the 10th U.S. national conference on earthquake engineering: Frontiers of earthquake engineering, 21–25 July 2014, Anchorage, AK, USA.

Miles, S.B. 2015. Foundations of community disaster resilience: Well-being, identity, services, and capitals. Environmental Hazards14(2): 103–121.

Miles, S.B. 2018a. A Python library for discrete event simulation of disaster recovery (version v0.1.1-alpha). Zenodo. http://doi.org/10.5281/zenodo.1190513. Accessed 3 Dec 2018.

Miles, S.B. 2018b. Comparison of jurisdictional seismic resilience planning initiatives. PLOS Currents Disasters. https://doi.org/10.1371/currents.dis.42c24f29588cb4f887af021449949801.

Miles, S.B., and S.E. Chang. 2006. Modeling community recovery from earthquakes. Earthquake Spectra 22(2): 439–458.

Miles, S.B., and S.E. Chang. 2011. ResilUS: A community based disaster resilience model. Cartography and Geographic Information Science 38(1): 36–51.

Miles, S.B., H.V. Burton, and H. Kang. 2019. Community of practice for modeling disaster recovery. Natural Hazards Review 20(1): Article 04018023.

Morris, D.E., J.E. Oakley, and J.A. Crowe. 2014. A web-based tool for eliciting probability distributions from experts. Environmental Modelling and Software 52: 1–4.

Nejat, A., and I. Damnjanovic. 2012. Agent‐based modeling of behavioral housing recovery following disasters. Computer-Aided Civil and Infrastructure Engineering 27(10): 748–763.

Nejat, A., and S. Ghosh. 2016. LASSO model of postdisaster housing recovery: Case study of Hurricane Sandy. Natural Hazards Review 17(3): Article 04016007.

NIST (National Institute of Standards and Technology). 2016. Community resilience planning guide for buildings and infrastructure systems. NIST Special Publication 1190. Volume I. Washington, DC: U.S. Department of Commerce. https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.1190v1.pdf. Accessed 3 Dec 2018.

Nojima, N., and H. Kato. 2014. Modification and validation of an assessment model of post-earthquake lifeline serviceability based on the Great East Japan Earthquake Disaster. Journal of Disaster Research 9(2): 108–120.

Norton, B.G. 1996. Integration or reduction. In Environmental pragmatism, ed. A. Light, and E. Katz, 105–138. Abingdon, UK: Routledge.

OSSPAC (Oregon Seismic Safety Policy Advisory Commission). 2013. The Oregon Resilience Plan. Salem, OR: Oregon Seismic Safety Policy Advisory Committee.

Ouyang, M., and L. Zhao. 2014. Do topological models contribute to decision making on post-disaster electric power system restoration? Chaos: An Interdisciplinary Journal of Nonlinear Science 24(4): Article 043131.

Poland, C. 2009. The resilient city: Defining what San Francisco needs from its seismic mitigation policies. San Francisco, CA: San Francisco Planning & Urban Research Association.

Santos, J.R., K.D.S. Yu, S.A.T. Pagsuyoin, and R.R. Tan. 2014. Time-varying disaster recovery model for interdependent economic systems using hybrid input–output and event tree analysis. Economic Systems Research 26(1): 60–80.

Siebenhüner, B., and V. Barth. 2005. The role of computer modelling in participatory integrated assessments. Environmental Impact Assessment Review 25(4): 367–389.

Ustyuzhanin, A., T.D. Head, I. Babuschkin, and A. Tiunov. 2017. Everware toolkit. Supporting reproducible science and challenge-driven education, arXiv.org, 1703.01200.

WASSC. 2012. Resilient Washington State: A framework for minimizing loss and improving statewide recovery after an earthquake. Olympia, WA: State of Washington Emergency Management Council Seismic Safety Committee. http://mil.wa.gov/other-links/seismic-safety-committee-ssc. Accessed 3 Dec 2018.

Xie, W., N. Li, J.D. Wu, and X.L. Hao. 2014. Modeling the economic costs of disasters and recovery: analysis using a dynamic computable general equilibrium model. Natural Hazards and Earth System Science 14(4): 757–772.

Yasui, T., S. Shirasaka, and T. Maeno. 2014. Designing critical policy infrastructures by participatory systems analysis: The case of Fukushima’s reconstruction. International Journal of Critical Infrastructures 10(3–4): 334–336.

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