住宅洪水损害对社区恢复的非地域性和溢出效应：从高分辨率洪水索赔和流动性数据中获得的启示

IF 10.5 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Sustainable Cities and Society Pub Date : 2024-10-29 DOI:10.1016/j.scs.2024.105947

Junwei Ma , Russell Blessing , Samuel Brody , Ali Mostafavi

{"title":"住宅洪水损害对社区恢复的非地域性和溢出效应：从高分辨率洪水索赔和流动性数据中获得的启示","authors":"Junwei Ma , Russell Blessing , Samuel Brody , Ali Mostafavi","doi":"10.1016/j.scs.2024.105947","DOIUrl":null,"url":null,"abstract":"<div><div>Examining the relationship between vulnerability of the built environment and community recovery is crucial for understanding disaster resilience. Yet, this relationship is rather neglected in the existing literature due to limitations in the availability of empirical datasets needed for such analysis. In this study, we combined fine-resolution flood damage claim data (composed of both insured and uninsured losses) and human mobility data (composed of millions of movement trajectories) during the 2017 Hurricane Harvey in Harris County, Texas, to specify the extent to which vulnerability of the built environment (i.e., residential flood damage) affects community recovery (based on the speed of human mobility recovery) locally and regionally. We examined such relationship using spatial lag, spatial reach, and spatial decay models to measure the extent of spillover effects of residential flood damage on community recovery. The results indicate that: first, the severity of residential flood damage significantly affects the speed of community recovery. A greater extent of residential flood damage suppresses community recovery not only locally but also in the surrounding areas; second, the spillover effects of residential flood damage on community recovery decay with distance from the highly damaged areas with a spatial reach of up to 31.2 miles (49.92 Km); third, areas display heterogeneous spatial decay coefficients, which are associated with urban form and structure features such as the density of facilities and roads. These findings provide a novel data-driven characterization of the spatial spillover effects of residential flood damage on community recovery and move us closer to a better understanding of complex spatial diffusion processes that shape community resilience to hazards. This study also provides valuable insights for emergency managers and public officials seeking to mitigate the non-local effects of flood damage.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"117 ","pages":"Article 105947"},"PeriodicalIF":10.5000,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Non-locality and spillover effects of residential flood damage on community recovery: Insights from high-resolution flood claim and mobility data\",\"authors\":\"Junwei Ma , Russell Blessing , Samuel Brody , Ali Mostafavi\",\"doi\":\"10.1016/j.scs.2024.105947\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Examining the relationship between vulnerability of the built environment and community recovery is crucial for understanding disaster resilience. Yet, this relationship is rather neglected in the existing literature due to limitations in the availability of empirical datasets needed for such analysis. In this study, we combined fine-resolution flood damage claim data (composed of both insured and uninsured losses) and human mobility data (composed of millions of movement trajectories) during the 2017 Hurricane Harvey in Harris County, Texas, to specify the extent to which vulnerability of the built environment (i.e., residential flood damage) affects community recovery (based on the speed of human mobility recovery) locally and regionally. We examined such relationship using spatial lag, spatial reach, and spatial decay models to measure the extent of spillover effects of residential flood damage on community recovery. The results indicate that: first, the severity of residential flood damage significantly affects the speed of community recovery. A greater extent of residential flood damage suppresses community recovery not only locally but also in the surrounding areas; second, the spillover effects of residential flood damage on community recovery decay with distance from the highly damaged areas with a spatial reach of up to 31.2 miles (49.92 Km); third, areas display heterogeneous spatial decay coefficients, which are associated with urban form and structure features such as the density of facilities and roads. These findings provide a novel data-driven characterization of the spatial spillover effects of residential flood damage on community recovery and move us closer to a better understanding of complex spatial diffusion processes that shape community resilience to hazards. This study also provides valuable insights for emergency managers and public officials seeking to mitigate the non-local effects of flood damage.</div></div>\",\"PeriodicalId\":48659,\"journal\":{\"name\":\"Sustainable Cities and Society\",\"volume\":\"117 \",\"pages\":\"Article 105947\"},\"PeriodicalIF\":10.5000,\"publicationDate\":\"2024-10-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Sustainable Cities and Society\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2210670724007716\",\"RegionNum\":1,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"CONSTRUCTION & BUILDING TECHNOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Sustainable Cities and Society","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2210670724007716","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CONSTRUCTION & BUILDING TECHNOLOGY","Score":null,"Total":0}

引用次数: 0

摘要

研究建筑环境的脆弱性与社区恢复之间的关系对于了解抗灾能力至关重要。然而，由于分析所需的经验数据集的可用性有限，这种关系在现有文献中被忽视。在本研究中，我们结合了德克萨斯州哈里斯县在 2017 年飓风哈维期间的精细分辨率洪灾损失索赔数据（包括投保和未投保损失）和人员流动数据（包括数百万人的流动轨迹），以明确建筑环境（即住宅洪灾损失）的脆弱性在多大程度上影响了当地和区域的社区恢复（基于人员流动的恢复速度）。我们使用空间滞后模型、空间到达模型和空间衰减模型对这种关系进行了研究，以衡量住宅洪水损害对社区恢复的溢出效应程度。结果表明：首先，居民点洪灾损失的严重程度会显著影响社区恢复的速度。居民点洪灾受损程度越严重，不仅会抑制当地的社区恢复，还会抑制周边地区的社区恢复；其次，居民点洪灾受损对社区恢复的溢出效应会随着距离受损严重地区的距离而衰减，空间范围可达 31.2 英里（49.92 千米）；第三，各地区显示出不同的空间衰减系数，这与设施和道路密度等城市形态和结构特征有关。这些发现为住宅洪水损害对社区恢复的空间溢出效应提供了新颖的数据驱动特征，使我们更接近于更好地理解形成社区抗灾能力的复杂空间扩散过程。这项研究还为寻求减轻洪水灾害非本地影响的应急管理人员和政府官员提供了宝贵的见解。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Non-locality and spillover effects of residential flood damage on community recovery: Insights from high-resolution flood claim and mobility data

Examining the relationship between vulnerability of the built environment and community recovery is crucial for understanding disaster resilience. Yet, this relationship is rather neglected in the existing literature due to limitations in the availability of empirical datasets needed for such analysis. In this study, we combined fine-resolution flood damage claim data (composed of both insured and uninsured losses) and human mobility data (composed of millions of movement trajectories) during the 2017 Hurricane Harvey in Harris County, Texas, to specify the extent to which vulnerability of the built environment (i.e., residential flood damage) affects community recovery (based on the speed of human mobility recovery) locally and regionally. We examined such relationship using spatial lag, spatial reach, and spatial decay models to measure the extent of spillover effects of residential flood damage on community recovery. The results indicate that: first, the severity of residential flood damage significantly affects the speed of community recovery. A greater extent of residential flood damage suppresses community recovery not only locally but also in the surrounding areas; second, the spillover effects of residential flood damage on community recovery decay with distance from the highly damaged areas with a spatial reach of up to 31.2 miles (49.92 Km); third, areas display heterogeneous spatial decay coefficients, which are associated with urban form and structure features such as the density of facilities and roads. These findings provide a novel data-driven characterization of the spatial spillover effects of residential flood damage on community recovery and move us closer to a better understanding of complex spatial diffusion processes that shape community resilience to hazards. This study also provides valuable insights for emergency managers and public officials seeking to mitigate the non-local effects of flood damage.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Sustainable Cities and Society Social Sciences-Geography, Planning and Development

CiteScore

22.00

自引率

13.70%

发文量

810

审稿时长

27 days

期刊介绍： Sustainable Cities and Society (SCS) is an international journal that focuses on fundamental and applied research to promote environmentally sustainable and socially resilient cities. The journal welcomes cross-cutting, multi-disciplinary research in various areas, including: 1. Smart cities and resilient environments; 2. Alternative/clean energy sources, energy distribution, distributed energy generation, and energy demand reduction/management; 3. Monitoring and improving air quality in built environment and cities (e.g., healthy built environment and air quality management); 4. Energy efficient, low/zero carbon, and green buildings/communities; 5. Climate change mitigation and adaptation in urban environments; 6. Green infrastructure and BMPs; 7. Environmental Footprint accounting and management; 8. Urban agriculture and forestry; 9. ICT, smart grid and intelligent infrastructure; 10. Urban design/planning, regulations, legislation, certification, economics, and policy; 11. Social aspects, impacts and resiliency of cities; 12. Behavior monitoring, analysis and change within urban communities; 13. Health monitoring and improvement; 14. Nexus issues related to sustainable cities and societies; 15. Smart city governance; 16. Decision Support Systems for trade-off and uncertainty analysis for improved management of cities and society; 17. Big data, machine learning, and artificial intelligence applications and case studies; 18. Critical infrastructure protection, including security, privacy, forensics, and reliability issues of cyber-physical systems. 19. Water footprint reduction and urban water distribution, harvesting, treatment, reuse and management; 20. Waste reduction and recycling; 21. Wastewater collection, treatment and recycling; 22. Smart, clean and healthy transportation systems and infrastructure;