Housed within the Department of Geography at the University of South Carolina (USC), the Center for GIScience and Geospatial Big Data (CeGIS) conducts interdisciplinary research and education on Geographic Information Science (GIScience) and Spatial Data Science including GIS, geospatial big data, remote sensing, spatial analysis, statistics, and modeling, geospatial artificial intelligence (GeoAI), spatial computing, geo-visualization, and cyberGIS. Faculty and students of CeGIS use, synthesize, and develop advanced spatial methods and computing technologies to address a broad range of geographic questions about hazards, public health, population, environment and climate change.
The mission of CeGIS is to 1) promote GIScience, spatial data science, and geospatial data sharing in geography and a wide range of disciplines such as health science and social science; 2) train the next-generation of GIScientists and geospatial analysts with strong problem solving skills through the integration of spatial thinking and computational thinking; and 3) advance knowledge discovery and decision making with innovative research for supporting domain applications including, but not limited to, disaster management, public health, human dynamics, and climate change. We strive to achieve the mission through fostering cross-disciplinary collaborative research and education within the center, across the campus and the nation by engaging researchers from various disciplines who share a common interest in “geospatial”, collaborating with other centers and institutes, including Hazards Vulnerability & Resilience Institute , Big Data Health Science Center, and Walker Institute, and partnering with local community organizations.
Since February 2020, the Geoinformation and Big Data Research Laboratory (GIBD) at CeGIS has been actively engaged in fighting against the COVID-19 crisis using big data, machine learning, and geospatial analysis. The studies range from extracting population flows from billions of geotagged tweets to developing web portals for large-scale human mobility data visualization and sharing. Read More ...
In August – September 2022, Dr. Susan Wang and her research team conducted field experiments to fly drones and collect field samples of marsh biomass in the North Inlet, Georgetown, South Carolina. Multiple drones were launched equipped with Lidar (Back Forty Aerial Solutions, LLC), true-color cameras and multispectral sensors. Read More ...
In response to the soaring needs of human mobility data, especially during disaster events such as the COVID-19 pandemic, and the associated big data challenges, we developed a scalable online platform for extracting, analyzing, and sharing multi-source multi-scale human worldwide mobility flows. Read More ...
This research examined the state-level statutes that govern sUAS operation and associated image collection in the United States. Figure: States with restrictions on overflights or image collection on critical infrastructure or
correctional facilities (a). States with restrictions on overflight, collecting imagery/surveillance, landing
on or flying over private property (b). Read More ...
Defined as “personal remote sensing”, small unmanned aircraft systems (sUAS) have been increasingly utilized for landscape mapping. This study tests a sUAS procedure of 3D tree surveying of a closed-canopy woodland on an earthen dam. Three DJI drones—Mavic Pro, Phantom 4 Pro, and M100/RedEdge-M assembly—were used to collect imagery in six missions in 2019–2020. Read More ...
For decades, spatial scientists have researched place connectivity, applications, and metrics. In this study, we introduced a global multi-scale Place Connectivity Index (PCI) based on spatial interactions among places revealed by geotagged tweets as a spatiotemporal-continuous and easy-to-implement measurement. Read More ...
This project aims to address computational challenges of big climate data by developing an efficient spatiotemporal indexing approach, an innovative query analytical framework, and a scalable online visual analytical system called SOVAS for interactive big climate data analysis. Read More ...
The study of migrations and mobility has historically been severely limited by the absence of reliable data or the temporal sparsity of the available data. This project seeks to develop a near real-time (one-day lag) Twitter census that gives a more temporally granular picture of local and non-local population at the county level. Read More ...
