PRIME: A CyberGIS Platform for Resilience Inference Measurement and Enhancement

IF 7.1 1区 地球科学 Q1 ENVIRONMENTAL STUDIES
Debayan Mandal , Lei Zou , Rohan Singh Wilkho , Furqan Baig , Joynal Abedin , Bing Zhou , Heng Cai , Nasir Gharaibeh , Nina Lam
{"title":"PRIME: A CyberGIS Platform for Resilience Inference Measurement and Enhancement","authors":"Debayan Mandal ,&nbsp;Lei Zou ,&nbsp;Rohan Singh Wilkho ,&nbsp;Furqan Baig ,&nbsp;Joynal Abedin ,&nbsp;Bing Zhou ,&nbsp;Heng Cai ,&nbsp;Nasir Gharaibeh ,&nbsp;Nina Lam","doi":"10.1016/j.compenvurbsys.2024.102197","DOIUrl":null,"url":null,"abstract":"<div><div>In an era of increased climatic disasters, there is an urgent need to develop reliable frameworks and tools for evaluating and improving community resilience to climatic hazards at multiple geographical and temporal scales. Defining and quantifying resilience in the social domain is relatively subjective due to the intricate interplay of socioeconomic factors with disaster resilience. To broaden upon it, the choice of indicators and their subsequent ranking for the aggregation into an index is subjective in nature. This aggregation is not empirically validated and is prone to omit the nuances of localized resilience changes and causal factors affecting it, while leading to oversimplified conclusions. Meanwhile, there is a lack of scientifically and computationally rigorous, user-friendly tools that can support customized resilience assessment with consideration of local conditions. This study addresses these gaps through the power of CyberGIS with three objectives: 1) To develop an empirically validated disaster resilience model - Customizable Resilience Inference Measurement (RIM), designed for multi-scale community resilience assessment and influential socioeconomic factors identification; 2) To implement a Platform for Resilience Inference Measurement and Enhancement (PRIME) module in the CyberGISX platform backed by high-performance computing, enabling users to apply and customize RIM to compute and visualize disaster resilience; 3) To demonstrate the utility of PRIME through a representative study to understand the geographical disparities of county-level community resilience to natural hazards in the United States and identifying the driving factors of resilience in the social domain. Customizable RIM generates vulnerability, adaptability, and overall resilience scores derived from empirical parameters—hazard threat, damage, and recovery. Computationally intensive Machine Learning (ML) methods are employed to explain the intricate relationships between these scores and socioeconomic driving factors. PRIME provides a web-based notebook interface guiding users to select study areas, configure parameters, calculate and geo-visualize resilience scores, and interpret socioeconomic factors shaping resilience capacities. A representative study showcases the efficiency of the platform while explaining how the visual results obtained may be interpreted. The essence of this work lies in its comprehensive architecture that encapsulates the requisite data, analytical and geo-visualization functions, and ML models for resilience assessment. This setup provides a foundation for assessing resilience and strategizing enhancement interventions.</div></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"114 ","pages":"Article 102197"},"PeriodicalIF":7.1000,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computers Environment and Urban Systems","FirstCategoryId":"89","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0198971524001261","RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENVIRONMENTAL STUDIES","Score":null,"Total":0}
引用次数: 0

Abstract

In an era of increased climatic disasters, there is an urgent need to develop reliable frameworks and tools for evaluating and improving community resilience to climatic hazards at multiple geographical and temporal scales. Defining and quantifying resilience in the social domain is relatively subjective due to the intricate interplay of socioeconomic factors with disaster resilience. To broaden upon it, the choice of indicators and their subsequent ranking for the aggregation into an index is subjective in nature. This aggregation is not empirically validated and is prone to omit the nuances of localized resilience changes and causal factors affecting it, while leading to oversimplified conclusions. Meanwhile, there is a lack of scientifically and computationally rigorous, user-friendly tools that can support customized resilience assessment with consideration of local conditions. This study addresses these gaps through the power of CyberGIS with three objectives: 1) To develop an empirically validated disaster resilience model - Customizable Resilience Inference Measurement (RIM), designed for multi-scale community resilience assessment and influential socioeconomic factors identification; 2) To implement a Platform for Resilience Inference Measurement and Enhancement (PRIME) module in the CyberGISX platform backed by high-performance computing, enabling users to apply and customize RIM to compute and visualize disaster resilience; 3) To demonstrate the utility of PRIME through a representative study to understand the geographical disparities of county-level community resilience to natural hazards in the United States and identifying the driving factors of resilience in the social domain. Customizable RIM generates vulnerability, adaptability, and overall resilience scores derived from empirical parameters—hazard threat, damage, and recovery. Computationally intensive Machine Learning (ML) methods are employed to explain the intricate relationships between these scores and socioeconomic driving factors. PRIME provides a web-based notebook interface guiding users to select study areas, configure parameters, calculate and geo-visualize resilience scores, and interpret socioeconomic factors shaping resilience capacities. A representative study showcases the efficiency of the platform while explaining how the visual results obtained may be interpreted. The essence of this work lies in its comprehensive architecture that encapsulates the requisite data, analytical and geo-visualization functions, and ML models for resilience assessment. This setup provides a foundation for assessing resilience and strategizing enhancement interventions.
PRIME:用于复原力推理测量和增强的网络地理信息系统平台
在气候灾害日益增多的时代,迫切需要开发可靠的框架和工具,以评估和提高社区在多种地理和时间尺度上抵御气候灾害的能力。由于社会经济因素与抗灾能力之间错综复杂的相互作用,社会领域抗灾能力的定义和量化相对主观。更进一步说,指标的选择和随后的排序以汇总成指数也是主观性的。这种汇总没有经过经验验证,容易忽略当地抗灾能力变化的细微差别以及影响抗灾能力的因果因素,从而得出过于简单的结论。同时,还缺乏科学和计算严谨、用户友好的工具来支持考虑当地条件的定制化复原力评估。本研究通过 CyberGIS 的强大功能来弥补这些不足,其目标有三个:1)开发一个经过经验验证的抗灾能力模型--可定制的抗灾能力推理测量(RIM),用于多尺度社区抗灾能力评估和有影响的社会经济因素识别;2)在 CyberGISX 平台中实施抗灾能力推理测量和增强平台(PRIME)模块,以高性能计算为支撑,使用户能够应用和定制 RIM,计算和可视化抗灾能力;3) 通过一项具有代表性的研究,展示 PRIME 的实用性,以了解美国县级社区抵御自然灾害能力的地理差异,并确定社会领域抵御能力的驱动因素。可定制的 RIM 可生成脆弱性、适应性和总体复原力分数,这些分数来自经验参数--灾害威胁、损害和恢复。计算密集型机器学习(ML)方法用于解释这些分数与社会经济驱动因素之间错综复杂的关系。PRIME 提供了一个基于网络的笔记本界面,指导用户选择研究区域、配置参数、计算复原力分数并将其地理可视化,以及解释影响复原力的社会经济因素。一项具有代表性的研究展示了该平台的效率,同时解释了如何解读所获得的可视化结果。这项工作的精髓在于其全面的架构,囊括了复原力评估所需的数据、分析和地理可视化功能以及 ML 模型。这种设置为评估复原力和制定增强干预措施的战略奠定了基础。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
CiteScore
13.30
自引率
7.40%
发文量
111
审稿时长
32 days
期刊介绍: Computers, Environment and Urban Systemsis an interdisciplinary journal publishing cutting-edge and innovative computer-based research on environmental and urban systems, that privileges the geospatial perspective. The journal welcomes original high quality scholarship of a theoretical, applied or technological nature, and provides a stimulating presentation of perspectives, research developments, overviews of important new technologies and uses of major computational, information-based, and visualization innovations. Applied and theoretical contributions demonstrate the scope of computer-based analysis fostering a better understanding of environmental and urban systems, their spatial scope and their dynamics.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信