VTechWorks staff will be away for the Thanksgiving holiday beginning at noon on Wednesday, November 27, through Friday, November 29. We will resume normal operations on Monday, December 2. Thank you for your patience.
 

Tracking Disaster Dynamics for Urban Resilience: Human-Mobility and Semantic Perspectives

Simple item page
dc.contributor.authorWang, Yanen
dc.contributor.committeechairTaylor, John E.en
dc.contributor.committeechairGarvin, Michael J.en
dc.contributor.committeememberWang, Qien
dc.contributor.committeememberMoore, Ignacio T.en
dc.contributor.committeememberShealy, Earl W.en
dc.contributor.departmentCivil and Environmental Engineeringen
dc.date.accessioned2019-12-04T07:01:27Zen
dc.date.available2019-12-04T07:01:27Zen
dc.date.issued2018-06-11en
dc.description.abstractFostering urban resilience and creating agility to disaster response is an urgent task faced by cities worldwide in the context of climate change and increasing frequencies of natural disasters. Understanding and tracking the dynamic process of resilience to disasters is the first step to operationalize the concept of urban resilience. In this dissertation, I present four related but evolutionary perspectives to investigate the impact of natural disasters on interactive human-environment systems as well as the dynamic process of resilience, including human mobility, spatial networks, and coupled mobility and sentiment perspectives. In the first, human-mobility perspective, I examine the nuanced impact of a severe winter storm on human mobility patterns and the relationship between perturbed mobility during the storm and recurrent mobility under normal circumstances. In the second, where I adopt a spatial network perspective, I investigate the dynamic process of resilience over time by analyzing networked human-spatial systems using an ecology-inspired approach. The third perspective involves sentiment as an additional factor to human mobility to understand urban dynamics during an earthquake. In this perspective, I explore the relation between disaster magnitude and a population's collective sentiment, as well as temporal correlations between sentiment and mobility. Each of the three empirical studies employs a quantitative, empirical research methodology and uses voluntarily reported geo-referenced data collected through a Twitter Streaming API. After multiple investigations on diverse types of natural disaster (e.g. severe winter storm, flooding, hurricane, and earthquake), I develop a Detecting Urban Emergencies Technique (DUET), as the fourth part of my dissertation, for identifying and tracking general types of emergencies in a short period without prior definitions of emergent topics. Research findings from the three empirical studies and the proposed DUET detection technique introduce a new lens and approach for understanding population dynamics and achieving urban resilience. This dissertation contributes to a more complete understanding of urban resilience to disasters with crowdsourced data, and enables more effective urban informatics in the face of extreme events.en
dc.description.abstractgeneralCities worldwide are facing the challenges of climate change and increasing frequencies of natural disasters. The first step of enhancing disaster responses in urban areas is to operationalize the concept of urban resilience by understanding the impact of disasters on urban systems at both spatial and temporal scales. In this dissertation, urban systems are characterized by individuals’ movements, networks of spatial units, and population’s sentiment, which also form three different but evolutionary perspectives to investigate the impact over time. In the first, human-movement perspective, I examine the nuanced impact of a severe winter storm on individuals’ movement patterns and the relationship between individuals’ most frequented locations (e.g. home or working places) under normal circumstances and their visited locations during the winter storm. In the second, where I adopt a spatial network perspective, I investigate the temporal process of resilience by analyzing networked human-spatial systems pre-, during, and post-disaster using an ecology-inspired approach. The third perspective involves sentiment, which is a measurement of people’s emotion and attitude, as an additional factor to human movement to understand the impact of an earthquake on the urban system. In this perspective, I explore the relation between an earthquake’s magnitude and a population’s collective sentiment, as well as how sentiment and movement changed over time. These three empirical studies use a quantitative research methodology with geotagged tweets, which are collected from a Twitter Streaming API. After many investigations on different types of natural disaster, I develop a Detecting Urban Emergencies Technique (DUET) for identifying and tracking general types of emergencies in a short period. The empirical findings and the proposed DUET detection technique introduce a bottom-up perspective for understanding disasters’ impact and enhancing urban resilience. This dissertation contributes to a more complete understanding of disaster resilience in urban areas with crowdsourced data, and enables more open and effective disaster communication.en
dc.description.degreePHDen
dc.format.mediumETDen
dc.identifier.othervt_gsexam:15462en
dc.identifier.urihttp://hdl.handle.net/10919/95913en
dc.publisherVirginia Techen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectBig Dataen
dc.subjectDisaster Dynamicsen
dc.subjectEmergency Detectionen
dc.subjectHuman Mobilityen
dc.subjectSentimenten
dc.subjectTwitteren
dc.subjectUrban Resilienceen
dc.titleTracking Disaster Dynamics for Urban Resilience: Human-Mobility and Semantic Perspectivesen
dc.typeDissertationen
thesis.degree.disciplineCivil Engineeringen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.leveldoctoralen
thesis.degree.namePHDen

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Wang_Y_D_2018.pdf
Size:
4.01 MB
Format:
Adobe Portable Document Format
Download

Collections

Doctoral Dissertations