Detecting Group Configurations in Shared Spaces: A Heuristic for Understanding Space Use Behavior

The rapid development and deployment of “smart space” technology in commercial and educational buildings has brought efficiencies and comfort to users. Yet, the “smart” is largely limited to knowledge gained from occupancy and activity data. These limited data explain what is happening in a space but nothing about how effectively the space is being used. In this work, we explore how to understand effective space use and how it may impact future smart spaces and their design. As part of this investigation, we design generalizable heuristics derived from observable human behaviors and space usage to detect group configurations in public spaces. The resultant system models similar qualities of a space that end-users will judge and measure themselves before use. The effectiveness of our system is validated against a crowdsourced study. Our system serves as a foundation for applications building upon improving space use efficacy.