Integration of detailed household and housing unit characteristic data with critical infrastructure for post-hazard resilience modeling

Nathanael Rosenheim, Roberto Guidotti, Paolo Gardoni, Walter Gillis Peacock

Research output: Contribution to journalArticlepeer-review

Abstract

This paper presents a methodology that generates and links high-resolution spatial data on households and housing units with heterogeneous characteristics (i.e., size, tenure status, occupied, and vacant) to residential buildings which in turn are linked to critical infrastructure. The methodology utilizes areal demographic data from the US Census, which are probabilistically linked to an inventory of housing units located in residential buildings. By allocating high-resolution household socio-demographic data to housing units in single and multi-family residential structures themselves linked to critical infrastructure systems, coupled engineering-social science modeling is possible. This paper presents a workflow for linking social science and engineering data to enable integrated models for community resilience. The methodology is applied to Seaside, Oregon, a coastal community with a year-round population of over 6,000 persons. The application highlights the benefits of integrating social science and engineering data. Benefits include facilitating coupled modeling, accounting for uncertainty, visualization, and spatial exploration of modeled results.

Original languageEnglish (US)
JournalSustainable and Resilient Infrastructure
DOIs
StateAccepted/In press - 2019

Keywords

  • Social systems
  • community resilience
  • physical infrastructure
  • social vulnerability
  • synthetic population

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Building and Construction
  • Geography, Planning and Development
  • Safety, Risk, Reliability and Quality

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