Graduation Year

2023

Document Type

Dissertation

Degree

Ph.D.

Degree Name

Doctor of Philosophy (Ph.D.)

Degree Granting Department

Geography

Major Professor

Yi Qiang, Ph.D.

Committee Member

Steven Reader, Ph.D.

Committee Member

Ran Tao, Ph.D.

Committee Member

Robin Ersing, Ph.D.

Committee Member

Lei Zou, Ph.D.

Keywords

Environmental Justice, Geospatial Big Data, GIS, Mitigation, Natural Hazard, Spatial Analysis

Abstract

Community resilience reflects the ability of human communities to prepare for, respond to, recover, and learn from disastrous events. Community resilience carries different meanings in different phases of disaster management (i.e., preparedness, response, recovery, and mitigation). With the emergence of new geospatial data sources, human activities now can be captured through social media, mobile signals, and nighttime illuminations, which makes it possible to describe the conditions among various communities before, during, and after disasters. Therefore, this dissertation explored the use of different types of geospatial data sources (social media, nighttime light remote sensing, land-use data, and census survey data) during three types of disasters (winter storm, hurricane, flooding) in the following three aspects of community resilience during four phases of disaster: 1) understanding information diffusion patterns and user networks in social media during disasters (preparedness and response phases); 2) using multi-source, multi-scale geospatial data to monitor human dynamics in disasters and find meaningful indicators to measure community resilience (response and recovery phases); 3) analyzing spatiotemporal changes of disaster exposure to understand geographic disparities in hazard mitigation and long-term community resilience (mitigation phases). This study explores geospatial data sources to develop quantitative models to model community resilience in four case studies (Winter Storm Diego, Hurricane Sandy, Winter Storm Uri, and flooding). The goal of this study is to evaluate communities' capacity to effectively prepare for disasters (preparedness), minimize damage during the event (response), recover and regain functionality (recovery), and implement long-term strategies to mitigate future risks (mitigation). The developed approach is composed of four elements (exposure, damage, recovery, and mitigation) and three aspects linking the four elements (information diffusion, recovery trajectories, and population exposure). The outcomes of this study include a summary of disaster-related information diffusion patterns, an evaluation of various potential resilience indicators based on recovery trajectories, and a comprehensive assessment of long-term disaster exposure change within the contiguous United States. The outcomes will set an example of using different types of geospatial data sources to detect the socioeconomic impact on human activities during disasters. This project also sheds lights on improving the current community resilience framework by finding better indicators.

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