Identifying Data Guideline Needs for Community and Regional Resilience Modeling
March 19, 2021
Resilience is the ability to prepare for anticipated hazards, adapt to changing conditions, and withstand and recover rapidly from disruptions. An everincreasing volume of resilience-focused research is being pursued worldwide, ranging from single commercial facilities seeking to maintain continuity of operations to community, regional, and national scales. One commonality regardless of analysis scale is that all modeling endeavours require data, often from a wide range of sources, in a wide range of formats, and across a wide range of disciplines. A four-page extended abstract of each computational platform and their data structure can be found here.
Resilience computational platform overviews: models, scales, hazards, damage, recovery, and data presentations from the following:
- Center for Risk-Based Community Resilience Planning
- National Institute of Standards and Technology
- Federal Emergency Management Agency/HAZUS
- Simcenter: Computational Modeling and Simulation Center
- DesignSafe Cyber Infrastructure
- The University of Michigan, Civil and Env. Engineering
Community- and regional-scale simulations include geographically distributed buildings, infrastructure networks, social institutions, demographics, and economics. The data types and models used in these simulations are therefore quite diverse in structure, format and type, and rooted in disparate disciplines, including engineering, social science, medicine, emergency management and economics. Massive amounts of data are needed to fully characterize a community, its residents and its asset inventory.
Here damage prediction includes prediction of damage sustained by physical infrastructure and the resulting loss of functionality as well as damage sustained by social institutions. Damage prediction is complicated by interdependencies between systems. Damage data are diverse in structure, format and type.
Recovery following a hazard event is not only a function of damage and loss of functionality, but depends on socio-economic variables for households and businesses such as race/ethnicity, household income, educational level, and local governanace structure. While such data are often available through the U.S. Census (other sources depending on country), they are aggregated to provide privacy to individuals and thus data are available at scales that differ substantially from those characterizing damage to infrastructure components. Data-driven models are often used for modeling impacts to social institutions and demographic changes whereas physics-based models are often used for infrastructure systems.
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Mitigation can improve the performance of existing infrastructure and reduce the level of damage for hazard events. Effective mitigation is essential for community and regional resilience and depends on (1) characterization of existing infrastructure and supported social and economic functions and (2) goals for performance and recovery for hazard events. However, not all structures should be mitigated, as mitigation options may not be cost effective for many structures.
The ability to measure the change in the resilience outcome for a community- or regional-scale model through changes in policy represents a unique challenge to modeling and data. It is important to keep in mind that not all policies can be effectively modeled by numerically changing components or states within a model. The implementation of a mitigation strategy is often driven by policy. Mitigation may be a policy being put into action and, if not effective, can then result in a policy change.
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