GIS-based planning support systems are computerized decision support systems that serve a specific purpose, assisting a person or group in completing geospatially driven planning analyses and tasks while utilizing powerful visualization tools.  Some general planning tasks that can be supported using GIS software and systems include compiling base information, evaluating courses of actions, modeling alternate futures, and monitoring results and contingencies.Researchers at CGIS have developed numerous planning support systems using powerful visualization tools.

Tidal Energy: Through funding from the U.S. Department of Energy (DOE), researchers and engineers at Georgia Tech Savannah created advanced regional ocean models to simulate tidal flows along the entire US coastline. To help visualize and disseminate the results, CGIS created an online interactive mapping site that allows users to zoom and pan over maps of color-coded information on water depth, mean current speed, and mean kinetic power density for tidal resources along the coast of the contiguous United States, Alaska, and Puerto Rico (accessible at ww.tidalstreampower.gatech.edu).

Biofuels: Working in conjunction with the Georgia Tech School of Chemical and Biomolecular Engineering, researchers at CGIS developed a Biomass Optimization Resource Information System – a visualization and analytic tool for identifying and cataloging the location, source, and amount of potential bio-fuels across the Southeastern United States.

Georgia Offshore Mapping and Planning Partnership: To help researchers, industry professionals, and policy makers better understand renewable energy potential, availability, and accessibility for a given area, CGIS has gathered and compiled a plethora of GIS datasets and maps related to renewable offshore energy in Georgia. 

Healthy Communities: Researchers at CGIS conducted a cross-sectional analysis to estimate the percentage of Georgia children who live within a safe and reasonable walking distance from school. The study showed high population density, small enrollment size, and high street connectivity were associated with higher percentages of potential walkers.