Spatiotemporal Occupancy for Building Analytics

Numerous studies on Space Syntax and Evidence-based Design explored occupancy and movements in the built environment using traditional methods for behavior mapping, such as observation and surveys. This approach, however, has majorly focused on studying such behaviors as aggregated results -totals or averagesto corroborate the idea that people's interactions are outcomes of the influence of space. The research presented in this paper focuses on capturing human occupancy with a high spatiotemporal data resolution of 1 sq.ft per second (0.1 sq.mt./s). This research adapts computer vision to obtain large occupancy datasets in a hospitalization setting for one week, providing opportunities to explore correlations among spatial configurations, architectural programs, organizational activities planned and unplanned, and time. The vision is to develop new analytics for building occupancy dynamics, with the purpose of endorsing the integration of a temporal dimension into architectural research. This study introduces the ``Isovist-minute''; a metric that captures the relationship between space and occupancy, towards a point of interest, in a dynamic sequence.