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

点击翻译按钮获取中文摘要

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. We 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.