{"title":"灾后住房恢复估算:2017 年塔布斯大火和 2018 年坎普大火的数据和经验教训","authors":"Jeonghyun Lee , Rodrigo Costa , Jack W. Baker","doi":"10.1016/j.ijdrr.2024.104912","DOIUrl":null,"url":null,"abstract":"<div><div>Post-wildfire housing recovery is a complex process for which systematically collected data remains scarce. Consequently, our ability to anticipate obstacles and plan for housing recovery from future events is limited. This study leverages housing permit datasets collected in Santa Rosa and Unincorporated Sonoma County, impacted by the 2017 Tubbs Fire, and Paradise, impacted by the 2018 Camp Fire. Permit and tax assessor data are combined to gain insights into the recovery processes for these communities. Although the percentage of rebuilt destroyed homes varies significantly between regions, the peak construction demand occurs around 1.5 years after each wildfire, with a substantial decline in the reconstruction rate after 2.5 years. Moreover, the pace of transition from permit application to reconstruction completion is similar across all three regions. Using this finding, we propose a methodology to forecast the number of parcels rebuilt per unit of time based on observations from prior events. A proof-of-concept application of the proposed methodology indicates that it estimates long-term housing recovery patterns based on permit application data collected within one year of the event. These findings indicate that a longitudinal housing recovery data database would help forecast housing recovery from future disasters by providing a source for early empirical validation of predictive models.</div></div>","PeriodicalId":13915,"journal":{"name":"International journal of disaster risk reduction","volume":null,"pages":null},"PeriodicalIF":4.2000,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Post-disaster housing recovery estimation: Data and lessons learned from the 2017 Tubbs and 2018 Camp Fires\",\"authors\":\"Jeonghyun Lee , Rodrigo Costa , Jack W. Baker\",\"doi\":\"10.1016/j.ijdrr.2024.104912\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Post-wildfire housing recovery is a complex process for which systematically collected data remains scarce. Consequently, our ability to anticipate obstacles and plan for housing recovery from future events is limited. This study leverages housing permit datasets collected in Santa Rosa and Unincorporated Sonoma County, impacted by the 2017 Tubbs Fire, and Paradise, impacted by the 2018 Camp Fire. Permit and tax assessor data are combined to gain insights into the recovery processes for these communities. Although the percentage of rebuilt destroyed homes varies significantly between regions, the peak construction demand occurs around 1.5 years after each wildfire, with a substantial decline in the reconstruction rate after 2.5 years. Moreover, the pace of transition from permit application to reconstruction completion is similar across all three regions. Using this finding, we propose a methodology to forecast the number of parcels rebuilt per unit of time based on observations from prior events. A proof-of-concept application of the proposed methodology indicates that it estimates long-term housing recovery patterns based on permit application data collected within one year of the event. These findings indicate that a longitudinal housing recovery data database would help forecast housing recovery from future disasters by providing a source for early empirical validation of predictive models.</div></div>\",\"PeriodicalId\":13915,\"journal\":{\"name\":\"International journal of disaster risk reduction\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.2000,\"publicationDate\":\"2024-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International journal of disaster risk reduction\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2212420924006745\",\"RegionNum\":1,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"GEOSCIENCES, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International journal of disaster risk reduction","FirstCategoryId":"89","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2212420924006745","RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"GEOSCIENCES, MULTIDISCIPLINARY","Score":null,"Total":0}
Post-disaster housing recovery estimation: Data and lessons learned from the 2017 Tubbs and 2018 Camp Fires
Post-wildfire housing recovery is a complex process for which systematically collected data remains scarce. Consequently, our ability to anticipate obstacles and plan for housing recovery from future events is limited. This study leverages housing permit datasets collected in Santa Rosa and Unincorporated Sonoma County, impacted by the 2017 Tubbs Fire, and Paradise, impacted by the 2018 Camp Fire. Permit and tax assessor data are combined to gain insights into the recovery processes for these communities. Although the percentage of rebuilt destroyed homes varies significantly between regions, the peak construction demand occurs around 1.5 years after each wildfire, with a substantial decline in the reconstruction rate after 2.5 years. Moreover, the pace of transition from permit application to reconstruction completion is similar across all three regions. Using this finding, we propose a methodology to forecast the number of parcels rebuilt per unit of time based on observations from prior events. A proof-of-concept application of the proposed methodology indicates that it estimates long-term housing recovery patterns based on permit application data collected within one year of the event. These findings indicate that a longitudinal housing recovery data database would help forecast housing recovery from future disasters by providing a source for early empirical validation of predictive models.
期刊介绍:
The International Journal of Disaster Risk Reduction (IJDRR) is the journal for researchers, policymakers and practitioners across diverse disciplines: earth sciences and their implications; environmental sciences; engineering; urban studies; geography; and the social sciences. IJDRR publishes fundamental and applied research, critical reviews, policy papers and case studies with a particular focus on multi-disciplinary research that aims to reduce the impact of natural, technological, social and intentional disasters. IJDRR stimulates exchange of ideas and knowledge transfer on disaster research, mitigation, adaptation, prevention and risk reduction at all geographical scales: local, national and international.
Key topics:-
-multifaceted disaster and cascading disasters
-the development of disaster risk reduction strategies and techniques
-discussion and development of effective warning and educational systems for risk management at all levels
-disasters associated with climate change
-vulnerability analysis and vulnerability trends
-emerging risks
-resilience against disasters.
The journal particularly encourages papers that approach risk from a multi-disciplinary perspective.