Teaching
Research & Publications
Our Research Sponsors
Projects
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Unequal Human Responses and Health Outcomes to Extreme Heat: Evidence from the Novel Integration of Climate, Seismic and Cell Phone Location Data, TAMU College of Arts & Science, Strategic Transformative Research Program. PI.
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Collaborative Research: HNDS-I: Cyberinfrastructure for Human Dynamics and Resilience Research, National Science Foundation, Human Network and Data Science Program. Co-PI. [link]
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Climate-LEAD: Climate Effects on Localized Environmental HeAlth Disparities in Overburdened Texas Communities along Gulf Coast, National Academies of Sciences, Engineering, Medicine (NASEM), Gulf Research Program. Co-PI. [link]
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Restoring Happiness: Leveraging GeoAI and Social Engagement to Address Happiness Inequalities Post Covid and Winter Storm Uri, Texas A&M University Innovation [X]. PI.
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Geospatial Data Science: Advances and Applications, Texas A&M Institute of Data Science, Data Science Course Development Grant Program. PI.
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An Educational Learning Module of Geospatial Intelligence, Texas A&M University Presidential Transformational Teaching Grants. PI.
Research Themes
Disaster Resilience and Socio-Environmental Sustainability
Our Work
Environmental Health Inequalities
Ongoing climate change will cause more extreme events, exacerbating environmental hazards and health crises in already socially vulnerable communities. There needs an urgent effort to understand the factors driving environmental health inequalities and predict future environmental health disparities under persistent climate change to inform mitigation strategies. We study environmental health disparities in the following ways:
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develop exposure assessment framework to analyze the spatial and temporal risks to specific environmental hazards (e.g., extreme heat)
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apply geospatial data and models to understand and simulate environmental hazard exposure, and predict health disparities across geographical locations and populations under different climate change scenarios.
Community Resilience to Compound Disasters
More frequent and extreme disasters occurring simultaneously or consecutively (i.e., concurrent disasters) or causing a series of following events (i.e., cascading disasters), put the already overburdened communities, infrastructures, and human well-being under enormous pressure. Our research advances the understanding of compounding disaster impacts in three ways:
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measuring communities’ level of resilience to cumulative disasters;
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identifying the cascading effects, causal relationships, and driving factors within disasters, environmental, infrastructure, socioeconomic, and policy components;
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predicting resilience and health outcomes under varying scenarios to inform decision making and planning strategies.
Measuring and Mapping Disaster Resilience
Resilience measuring and mapping can be used to provide guidelines for allocating resources and infrastructure development, as well as for strengthening zoning regulations, environmental sensitive area protection, and building codes to reduce vulnerability and risk. However, disaster resilience is not a directly observable phenomenon and the validation of resilience index requires the use of proxies. Empirical validation of a resilience index with external reference data has posed a persistent challenge. Our work focus on:
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developing empirically validated framework to measure disaster resilience at multiple geographic scales
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analyzing multi-source geospatial data to track responses before, during, and after disaster events.
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identifying the socioeconomic, built environmental, and natural factors leading to resilience disparities.
Synthesizing Disaster Resilience Methods & Indices
Developing tools or metrics for measuring and monitoring progress of resilience is a critical component that requires extensive research. However, different fields have different emphases and the knowledge gained from the various studies are scattered and fragmented, leading to inconsistent results and interpretation of resilience in disaster research. We conducted synthesis analysis to:
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provide a literature integration framework to synthesize a large number of scholarly articles to reflect on the current state of resilience measurement.
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extract key information on resilience definition, measurement method, resilience indicators and proposed adaptation strategies.
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suggest future research needs, such as extending from static resilience measurement to dynamic system modeling, bridging the disconnection between resilience scientific research and practical actions.
Modeling Coastal Sustainability:
Climate Migration
Migration decisions related to natural disasters are complex. We study the individual, social and environmental factors leading to climate
migration in vulnerable coastal communities in the following ways:
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analyzing household telephone survey data to determine the factors with the highest influence on individual migration considerations.
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using machine learning models to model and predict migration patterns by integrating disaster, socioeconomic and environmental variables.
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predicting migration patterns under multiple hazard risks and planning strategies.
Modeling Coastal Sustainability:
Coastal Land Loss
Land loss has been an enduring and severe issue in coastal areas for decades. Previous studies on modeling and projection of land loss were mostly based on variables from the natural processes, which lead to a limited understanding of empirical modeling and inaccurate projections of future land loss patterns. We focus on:
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developing spatially explicit models of land loss, considering both natural (e.g., elevation, land fragmentation) and human factors (e.g., oil and gas well density, canal dredging)
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uncovering the complex mechanism of land loss and provided spatiotemporal projections of land loss probabilities.
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prioritizing the coastal protection and restoration efforts and set the pathway to improving the sustainability of the region.