Authors | |
Data Type(s) | Dataset |
Natural Hazard Type(s) | Tornado, Flood, Earthquake, Hurricane |
Facilities | |
Awards | CRISP Type 2: Collaborative Research: Scalable Decision Model to Achieve Local and Regional Resilience of Interdependent Critical Infrastructure Systems and Communities | NSF-1638273 | NSF Center for Risk-Based Community Resilience Planning | NIST-70NANB15H044 | NIST Center for Risk-Based Community Resilience Planning | NIST-70NANB20H008 | NIST |
Related Work | Context | Rosenheim et al. 2019 Context | Guidotti et al. 2019 Cited By | Roohi et al. 2020 Cited By | Fereshtehnejad et al. 2021 Cited By | Sanderson et al. 2021 Cited By | Wang et al. 2021 Cited By | Amini et al. 2023 Cited By | Mazumder et al. 2023 Cited By | Nofal et al. 2023 Cited By | Enderami et al. 2024 |
Referenced Data and Software | |
Keywords | Social and Economic Population Data, Housing Unit, US Census, Synthetic Population; IN-CORE; Hazard Reduction and Recovery Center |
DOI | 10.17603/ds2-jwf6-s535 |
License | Open Data Commons Attribution |
Version | 2 |
People are the most important part of community resilience planning. However, models for community resilience planning tend to focus on buildings and infrastructure. This project provides a solution that connects people to buildings for community resilience models. The housing unit inventory method transforms aggregated population data into disaggregated housing unit data that includes occupied and vacant housing unit characteristics. Detailed household characteristics include size, race, ethnicity, income, group quarters type, vacancy type and census block. Applications use the housing unit allocation method to assign the housing unit inventory to structures within each census block through a reproducible and randomized process. The benefits of the housing unit inventory include community resilience statistics that intersect detailed population characteristics with hazard impacts on infrastructure; uncertainty propagation; and a means to identify gaps in infrastructure data such as limited building data. This project archives example data and links to the replication python code files. Python is an open source programming language and the code files provide future users with the tools to generate a 2010 housing unit inventory for any county in the United States. The method archived is reproducible, generalizable, and that the characteristics included provide a valid representation of the social and economic characteristics of a community's population in 2010. Applications of the method are reproducible in IN-CORE (Interdependent Networked Community Resilience Modeling Environment).