This research aims to demonstrate the use of massive social media data to interactively analyze spatiotemporal events across spatial and temporal scales, by establishing a data-driven framework using cyberGIS to resolve aforementioned challenges. Specifically, FluMapper—an application on the CyberGIS Gateway—is employed as a case study to demonstrate the data-driven framework and seamless integration of massive location-based social media data and spatial analytical services within the online problem solving environment of the Gateway.

FluMapper presents integrated results from two complementary spatial analyses: (i) an interactive exploration of spatial distribution of flu risk and (ii) dynamic mapping of movement patterns, across multiple spatial, and temporal scales. The seamless integration of these two analyses through the framework illustrates the potential of cyberGIS to resolve the compute and data challenges of analyzing near real-time social media data in an efficient and scalable manner and to support interactive visualization.

Sponsored by: National Science Foundation (NSF)

People: Anand Padmanabhan, Shaowen Wang