Visualizing Spatio-temporal data associated with human movement

Abstract:

Extracting patterns from large amounts of spatio-temporal data is a challenging yet important area of active study. To address this problem we have developed a visualization system that collects and intuitively displays both the gathering and moving tendencies of people in a closed environment. Two distinct graphics techniques were applied to data collected using RFID technology. The system uses time-weighted area sampling for highlighting popular areas of the information space which is combined with CIELAB color-coding for depicting directional patterns within the data. A series of usability tests were conducted to evaluate the efficiency of the proposed system as an analytic tool for the user. The experimental results demonstrate the system’s effectiveness in identifying valuable patterns within a vast collection of spatio-temporal data. This visualization technique is applicable to a wide variety of data pertaining to both space and time.

Collaborators:

• Benjamin Aeschliman
• Michael Burton
• Keith Bock
• Rod Strong
• Dr. Beomjin Kim

Related Publications:

• Benjamin Aeschliman, Beomjin Kim, Michael Burton, “A Visual Analysis of Spatio-temporal Data Associated with Human Movement,” Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp. 400-403, (2009).
• Beomjin Kim, Keith Bock, Michael Burton, Rod Strong, Benjamin Aeschliman, “VisRFID: Visualizing Customer Behavior in Geotemporal Space using RFID Technology,” Proceedings of the 20th International Conference on Software Engineering and Knowledge Engineering, pp. 422-427, (2008).