Indoor spaces, where we spend the majority of our lives, greatly impact our work, social interactions, and well-being. In recognition of the central role that buildings play in our lives, architects and designers have increasingly focused on creating spaces that intentionally promote interaction and collaboration between building occupants. One challenge arising from this trend is evaluating the efficacy of new designs. This study used object tracking data for the Fall 2023 semester from a collection of lidar sensors installed in a portion of a mixed-use academic building on a university campus to algorithmically detect occupancy and serendipitous collisions between people - patterns of simultaneous movement and pause that indicate that two or more individuals have stopped and had a meaningful interaction. The algorithm detected over 14,000 collisions throughout the semester with high spatial and temporal precision. Occupancy and collisions were highly related over several scales of temporal and spatial analysis. Furthermore, several interesting patterns emerged, including (a) collisions peaked early in the semester, then declined before leveling off, (b) occupancy peaked in mid-afternoon, while collisions peaked in the late afternoon and early evening, (c) collisions peaked later in the week than did occupancy, and (d) specific hotspots were apparent at important nodes such as the bottom of stairs and near elevators. The patterns found in this study can provide insight as to how interactions can be measured using remote sensing data, and can aid designers in attempting to increase collaboration in shared indoor environments. / Master of Science / We spend lots of our times in buildings, and they are very important for our well-being. Designers have recently been focusing on promoting collaboration and interaction between people within building spaces. Despite their importance, these interactions within buildings have been challenging to categorize and analyze. This study used object-tracking data for the Fall 2023 semester from a collection of lidar sensors, which were intermittently placed in the ground-floor public spaces of a new hybrid residential-academic university building on Virginia Tech's Blacksburg campus. A computer program was written to parse through this data, and detect unplanned collisions between people; patterns of movement and pause that indicate that two or more people have stopped and had a meaningful interaction (for example, running into a friend while walking down the hallway). This study was able to detect collisions relatively well using a computer algorithm. The patterns and distributions of these collisions were then analyzed in time and space. The number of collisions and the number of people present in the space were highly related on all scales of time and space. In terms of time itself, collisions happened the most at the beginning of the semester, where they then dropped off. Collisions happened more frequently both later in the day (in afternoon, evening, and night hours) and later in the week (on Thursday, Friday, and Saturday). In terms of space, these collisions happened most frequently in the areas around the elevator, at the base of the stairs, and in the building's main lobby area. They happened less in hallways and near some seating areas. The patterns revealed from this study can help us better understand how to detect interactions between people within buildings, and can help designers increase the amount of these interactions.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/119270 |
| Date | 04 June 2024 |
| Creators | Flack, Addison Harris |
| Contributors | Geography, Pingel, Thomas, Baird, Timothy D., Abaid, Nicole Teresa |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | ETD, application/pdf |
| Rights | Creative Commons Attribution-NonCommercial 4.0 International, http://creativecommons.org/licenses/by-nc/4.0/ |
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