A Hybrid Decision Support System for Driving Resiliency in Texas Coastal Communities (NOAA Sea Grant)
This research seeks to augment current flood management practices in Texas coastal communities using citizen science, artificial intelligence (AI), and decision science and cyberinfrastructure. The tangible outcome of this study is a mobile app (with built-in AI and GIS capabilities) that can conduct flood risk calculation, and accurately estimate and predict the depth of floodwaters at the street-level at no extra cost. Generated data will be further incorporated in a CyberGIS-enabled spatial decision support tool for residents and first responders to improve the quality and timeliness of decision-making in the event of a flood.
The research group has organized several lab sessions and lectures for the K-12 students that come from Texas A&M Chinese School, Texas A&M Consolidated High School, and Allen Academy. As a result, K-12 students have published a poster at AAG 2020 annual meeting on the topic of "The Pathological and Economic Consequences of Houston's Air Pollution Catastrophe: A Youth's Perspective".
2020 Saari, S., Wang, R., Cairns, J., Wang, C., Li, A., Yang, E., and Brody, A., Urban Social Vulnerability Assessment under COVID-19 and Natural Disasters. University Consortium for Geographic Information Science Symposium Poster. Texas A&M GIS and Geography K-12 Education Program. Available from the web:
X Innovation Project (Texas A&M School of Innovation)
Natural disasters globally cause significant human loss and economic damage. Disaster responders often need to make quick decisions in complex situations under heavy duress. The decision goals are usually achieved through inquiry into a series of spatial parameters closely tied to specific decision objectives and their associated evaluation criteria based on diverse social, socioeconomic, and demographic conditions. In this project, we aim to design an interactive and collaborative spatial decision support system (SDSS) based on advanced cyberinfrastructure, WebGIS, and citizen science to improve situational awareness in disaster management. The proposed SDSS considers spatial and social vulnerability priorities to enhance knowledge elicitation and sharing among a diverse range of disaster responders and communities.
Social Vulnerability, Mobility, and COVID-19 Spatial Mortality Patterns (NSF Funded Converge COVID-19 Working Groups)
This Working Group aims to observe the spatial variation of the COVID-19 mortality rate with various sociodemographic and spatial variables. This science-driven project integrates advanced cyberinfrastructure, geospatial statistical models, and novel AI algorithms to analyze spatial COVID-19 mortality patterns.
A Spatial Decision Support System in Response to the COVID-19 Pandemic (TAMIDS Data Source Development Program)