TY - JOUR
T1 - Integration of detailed household and housing unit characteristic data with critical infrastructure for post-hazard resilience modeling
AU - Rosenheim, Nathanael
AU - Guidotti, Roberto
AU - Gardoni, Paolo
AU - Peacock, Walter Gillis
N1 - Funding Information:
This work was funded by the Center for Risk-Based Community Resilience Planning, a NIST-funded Center of Excellence [Grant Number 70NANB15H044]. The authors thank colleagues from the Center for Risk-Based Community Resilience Planning who provided insight and valuable expertise that greatly assisted the research in particular Profs. Dan Cox and Andre Barbosa for information on the seismic model of Seaside, Oregon. The authors also acknowledge Prof. James Rosenheim at Texas A&M University for helpful discussions and suggestions. References to any specific commercial products do not imply the authors’ endorsement.
Funding Information:
This work was supported by the National Institute of Standards and Technology [70NANB15H044]. This work was funded by the Center for Risk-Based Community Resilience Planning, a NIST-funded Center of Excellence [Grant Number 70NANB15H044]. The authors thank colleagues from the Center for Risk-Based Community Resilience Planning who provided insight and valuable expertise that greatly assisted the research in particular Profs. Dan Cox and Andre Barbosa for information on the seismic model of Seaside, Oregon. The authors also acknowledge Prof. James Rosenheim at Texas A&M University for helpful discussions and suggestions. References to any specific commercial products do not imply the authors? endorsement.
Publisher Copyright:
© 2019 Informa UK Limited, trading as Taylor & Francis Group.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - Social systems
KW - community resilience
KW - physical infrastructure
KW - social vulnerability
KW - synthetic population
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U2 - 10.1080/23789689.2019.1681821
DO - 10.1080/23789689.2019.1681821
M3 - Article
AN - SCOPUS:85082007691
VL - 6
SP - 385
EP - 401
JO - Sustainable and Resilient Infrastructure
JF - Sustainable and Resilient Infrastructure
SN - 2378-9689
IS - 6
ER -