Our lab research aims to bridge the gap between Computer and Information Science (CIScience) and Geographic Information Science (GIScience) by generating new computational algorithms and methods to solve computing challenges and big spatial data analysis with applications in earth science and geography, such as natural hazards, human mobility and digital agriculture. In particular, our research focuses on the following three interlocked areas:

Spatial Computing and Optimization

This research theme enhances computing, data mining, artificial intelligence, and computer vision technologies by incorporating geographic data, principles, and theories. GIScience faces two specific information technology challenges: computing intensity of large-scale simulations and the 5Vs of data (Volume, Velocity, Variety, Veracity, and Value). In particular, we have been developing a Spatial Computing theory and method that involves cutting-edge computational techniques, examines spatiotemporal patterns of computing infrastructure, data, and applications, and most importantly leverages such patterns to design corresponding computational methods and algorithms.

Spatial Big Data Analytics

Our research synthesizes novel technologies and data to solve geographic problems by developing new spatial data analytics and fusion methods. Big data streams from both physical  (e.g., Remote Sensing) and social (e.g., social media) sensing networks have brought exciting opportunities for GIScience. Yet, novel approaches and techniques for adequately utilizing and synthesizing such data are still being developed. Our research addresses some of the most pressing challenges faced when leveraging new data streams and fusing them with other spatial data sources through state-of-the-art machine learning, computer vision, and natural language processing technologies

Spatial Science for Societal Good

This work applies Spatial Computing, Spatial Data Science, and Big Data to a wide range of environmental and social problems, such as flood extent mapping, flood depth estimation, hazard prediction, situational awareness information extraction, damage impact assessment, evacuation behavior analytics and predication, land cover/use mapping, social media user background inference (e.g., home/work location, socioeconomic status, demographic information), human daily trajectory modeling and mining, frequent trajectory pattern mining, next visit location prediction, urban informatics, social inequality/segregation measurement and analysis, climate change modeling, and dust storm simulation.