Tracing the Curves of Bouncing Back: Data-Driven Methods for Assessing Community and Infrastructure Resilience in Natural Disasters

Authors: Yi Qiang*, University of Hawaii - Manoa
Topics: Spatial Analysis & Modeling, Geographic Information Science and Systems, Hazards and Vulnerability
Keywords: community resilience, infrastructure resilience, big data, natural disaster, recovery
Session Type: Paper
Day: 4/6/2020
Start / End Time: 8:00 AM / 9:15 AM
Room: Colorado, Sheraton, IM Pei Tower, Majestic Level
Presentation File: No File Uploaded


Abstract: Considerable work has been done to understand resilience of human communities and develop useful metrics to measure it. Despite the extensive work on the conceptual frameworks, definitions, and qualitative discussions, quantitative assessment of resilience are still challenging due to the lack of empirical observations about human and infrastructure in real disaster events. In the era of big data, various types of sensors generate geospatial data at an unprecedented rate, which provide ample opportunities to sense the disturbance and recovery process of human activities in natural disasters. This presentation introduces a series of methods for assessing community and infrastructure resilience using various types of data including census data, remote sensing imageries, social media data and crowdsourced data. Based on these methods, a conceptual framework is proposed to quantify resilience based on recovery trajectories of human communities and infrastructures during disasters. This research aims to fill the critical gap of empirical data and assessment methods for disaster resilience. Compared to the traditional approaches (e.g. the index approach), the empirical assessments helped to understand the complexity of resilience and provide actionable information for disaster management, urban planning and development of sustainable community and infrastructure systems.

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