CRISP Type 2/Collaborative Research: Resilience Analytics: A Data-Driven Approach for Enhanced Interdependent Network Resilience

CRISP 类型 2/协作研究：弹性分析：增强相互依赖的网络弹性的数据驱动方法

• 批准号: 1541155
• 负责人: Andrea Tapia
• 金额: $ 82.72万
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-10-15 至 2022-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1541155&HistoricalAwards=false
• 关键词: CRISP; Type; Collaborative; Research; Resilience

Recent natural disasters have challenged our traditional approaches of planning for and managing disruptive events. Today, social media provides an opportunity to make use of community-driven data to help us understand the resilience, or lack thereof, of community networks (e.g., friends, neighborhoods) physical infrastructure networks (e.g., transportation, electric power) and networks of service providers (e.g., emergency responders, restoration crews). This Critical Resilient Interdependent Infrastructure Systems and Processes (CRISP) collaborative research integrates multiple disciplinary perspectives in engineering, computer science, and social science to address how community-driven data can help (i) understand the behavior of these interdependent networks before, during, and after disruptions, and (ii) more effectively reduce their vulnerability to and enhance their recovery after a disruption. The results will significantly improve our understanding and management of infrastructure recovery from natural disasters. Two research components comprise this effort in resilience analytics. The first component creates a network model of the interdependence of infrastructure networks, the community networks that they serve, and the service networks engaged to respond after a disruption. This component will explore the functional relationships between community resilience and infrastructure network performance. Model results will enable decision makers to understand the balance of resilience across the several networks and regions. The second component integrates the interdependent network model with community-sourced data to develop a framework of data analytics to better understand and plan for resilience. This component builds on research in the field of socio-technical systems relating to the analysis of social media data monitored after a disruption. The methods will assess the value of information provided by crowd-sourced data with expertise of community social scientists. This project draws upon multiple methods across several disciplines. The multidisciplinary methods explored in this project are essential for a breakthrough in resilience analytics. This project aims at taking a significant step forward in our understanding of how real-time data from social media and other sources can describe, predict, and prescribe practices to manage interdependent networks in crises.

最近的自然灾害挑战了我们计划和管理破坏性事件的传统方法。今天，社交媒体提供了一个机会，可以利用社区驱动的数据来帮助我们了解社区网络(例如，朋友、社区)、物理基础设施网络(例如，交通、电力)和服务提供商网络(例如，应急人员、修复人员)的弹性或缺乏。这种关键的弹性相互依赖的基础设施系统和流程(CRISP)协作研究整合了工程、计算机科学和社会科学的多个学科视角，以解决社区驱动的数据如何帮助(I)了解这些相互依赖的网络在中断前、中断期间和中断后的行为，以及(Ii)更有效地降低它们在中断之前、期间和之后的脆弱性，并增强它们在中断后的恢复能力。这一结果将大大提高我们对自然灾害后基础设施恢复的理解和管理。复原力分析方面的这项工作包括两个研究组成部分。第一个组件创建了基础设施网络、它们所服务的社区网络以及在中断后参与响应的服务网络之间相互依赖的网络模型。本部分将探讨社区复原力和基础设施网络性能之间的功能关系。模型结果将使决策者能够了解多个网络和地区之间的弹性平衡。第二个组成部分将相互依存的网络模型与来自社区的数据相结合，以开发一个数据分析框架，以便更好地了解和规划复原力。这一构成部分以社会技术系统领域的研究为基础，这些研究涉及对中断后监测的社交媒体数据进行分析。这些方法将评估由社区社会科学家的专业知识提供的众包数据提供的信息的价值。这个项目利用了几个学科的多种方法。这个项目中探索的多学科方法对于弹性分析的突破至关重要。该项目旨在使我们在理解社交媒体和其他来源的实时数据如何描述、预测和规定在危机中管理相互依赖的网络的实践方面向前迈进一大步。

项目成果

Analytically comparing disaster resilience across multiple dimensions

跨多个维度分析比较抗灾能力

• DOI: 10.1016/j.seps.2018.12.005
• 发表时间: 2018
• 期刊: Socio-Economic Planning Sciences
• 影响因子: 6.1
• 作者: Zobel, Christopher W.;Baghersad, Milad
• 通讯作者: Baghersad, Milad

Establishing a frame of reference for measuring disaster resilience

• DOI: 10.1016/j.dss.2020.113406
• 发表时间: 2021
• 期刊: Decis. Support Syst.
• 作者: C. Zobel;C. MacKenzie;M. Baghersad;Yuhong Li

The roles of prior experience and the location on the severity of supply chain disruptions

• DOI: 10.1080/00207543.2021.1948136
• 发表时间: 2021-06
• 期刊: International Journal of Production Research
• 影响因子: 9.2
• 作者: M. Baghersad;C. Zobel;P. Lowry;Sutirtha Chatterjee