Human Spatial Behavior, Sensor Informatics, and Disaggregate Data

Abstract

With the increasing availability of tracking technology, researchers have new tools for examining patterns of human spatial behavior. However, due to limitations of GPS, traditional tracking tools cannot be applied reliably indoors. Monitoring indoor movement can significantly improve building management, emergency operations, and security control; it can also reveal relationships among spatial behavior and decision making, the complexity of such spaces, and the existence of different strategies or approaches to acquiring and using knowledge about the built environment (indoors and out). By employing methods from computer science and GIS we show that pedestrian indoor movement trajectories can be successfully tracked and analyzed with existing sensor and WiFi-based positioning systems over long periods of time and at fine grained temporal scales. We present a month-long experiment with 37 participants tracked through an institutional setting and demonstrate how post-processing of the collected sensor dataset of over 36 million records can be employed to better understand indoor human behavior.