Participatory Disaster Recovery Simulation Modeling for Community Resilience Planning
Scott B. Miles
International Journal of Disaster Risk Science ›› 2018, Vol. 9 ›› Issue (4) : 519 -529.
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
| [1] |
|
| [2] |
|
| [3] |
|
| [4] |
|
| [5] |
|
| [6] |
|
| [7] |
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. |
| [8] |
|
| [9] |
Ehrman and Stinson |
| [10] |
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. |
| [11] |
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. |
| [12] |
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. |
| [13] |
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. |
| [14] |
|
| [15] |
|
| [16] |
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. |
| [17] |
|
| [18] |
|
| [19] |
|
| [20] |
|
| [21] |
|
| [22] |
|
| [23] |
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. |
| [24] |
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. |
| [25] |
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. |
| [26] |
|
| [27] |
|
| [28] |
|
| [29] |
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. |
| [30] |
|
| [31] |
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. |
| [32] |
|
| [33] |
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. |
| [34] |
Miles, S.B. 2018b. Comparison of jurisdictional seismic resilience planning initiatives. PLOS Currents Disasters. https://doi.org/10.1371/currents.dis.42c24f29588cb4f887af021449949801. |
| [35] |
|
| [36] |
|
| [37] |
Miles, S.B., H.V. Burton, and H. Kang. 2019. Community of practice for modeling disaster recovery. Natural Hazards Review 20(1): Article 04018023. |
| [38] |
|
| [39] |
|
| [40] |
Nejat, A., and S. Ghosh. 2016. LASSO model of postdisaster housing recovery: Case study of Hurricane Sandy. Natural Hazards Review 17(3): Article 04016007. |
| [41] |
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. |
| [42] |
|
| [43] |
|
| [44] |
OSSPAC (Oregon Seismic Safety Policy Advisory Commission) The Oregon Resilience Plan, 2013, Salem, OR: Oregon Seismic Safety Policy Advisory Committee |
| [45] |
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. |
| [46] |
|
| [47] |
|
| [48] |
|
| [49] |
Ustyuzhanin, A., T.D. Head, I. Babuschkin, and A. Tiunov. 2017. Everware toolkit. Supporting reproducible science and challenge-driven education, arXiv.org, 1703.01200. |
| [50] |
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. |
| [51] |
|
| [52] |
|
/
| 〈 |
|
〉 |