Implication of building inventory accuracy on physical and socio-economic resilience metrics for informed decision-making in natural hazards

Abstract

The data and information available at the community-scale are directly linked to the ability to make a resilience-informed decision in natural hazards. This paper develops a systematic approach to quantify the implication of building inventory accuracy on resilience metrics for informed decision-making across engineering, economic and sociological dimensions at the community level. The method of approach consists of: (1) data and information availability, (2) community model development, (3) spatial hazard analysis, (4) physical damage and functionality analysis, and (5) socio-economic impact analysis. This process begins by generating a series of increasingly diminished data quality cases, i.e., increasing the apparent lack of knowledge about the building’s structural attributes within a community and developing computational models for each case. Then, damage and functionality analysis are performed to obtain building-level damage estimates, which are then fed into a computable general equilibrium model as well as a population dislocation model to compute a series of physical, economic, and socio-demographics resilience metrics. The estimated metrics are used to quantify the effects of diminishing data availability on physical and socio-economic metrics within the community. The proposed methodology is demonstrated using the illustrative example of the Memphis Metropolitan Statistical Area (MMSA) in Tennessee, USA.

Keywords:

• Building inventory
• community resilience
• decision making
• functionality
• natural hazards
• physical damage
• social and economic impact

Notes

1 The eight PUMA (Public Use Microdata Area) regions are: A—Memphis City (Central Riverside), B—Memphis City (Central Midtown), C—Memphis (North) & Bartlett (Southwest) Cities, D—Shelby County (North) - Bartlett (North & East) & Millington Cities, E—Memphis (East) Lakeland Cities & Arlington Town (South), F—Shelby County (Southeast) – Collierville Town & Germantown City, G—Memphis City (Southeast), H—Memphis City (Southwest)

Additional information

Funding

The authors would like to thank three anonymous reviewers for their valuable comments that improved quality of this paper. Funding for this study was provided as part of a cooperative agreement between the US National Institute of Standards and Technology and Colorado State University (NIST Financial Assistance Award Numbers: 70NANB15H044 and 70NANB20H008). The views expressed are those of the authors and may not represent the official position of the National Institute of Standards and Technology or the US Department of Commerce.