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Visualization of Occupant Behavior in an Open Academic Space Through Image Analysis

  • Mathew SchwartzEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1028)

Abstract

Between agent simulation and circulation diagrams within design pedagogy, the prediction of occupant movement in space is integral to the informed design process. At the same time, trends in higher education have led to more open-ended spaces that are then studied for the unexpected ways in which students collaborate. These studies, by the unpredictable nature, must be done post-occupancy. In this paper, occupant behavior is visualized from an image dataset over a 9 day period in an open student environment. The methods for extracting behavior through this large dataset are presented. The results are then reflected on in regard to the role of circulation diagrams for interior design and spatial planning.

Keywords

Occupant behavior Circulation Computation Post-occupancy evaluation Interior design 

Notes

Acknowledgements

I thank Richard O’Brien from the Digital Learning and Technology Support department in NJIT for assisting in the setup of the networked hard drive for storing recorded data. Additionally, the support of the Facilities department and Murray Center for Women in Technology was instrumental in allowing and assisting with the camera installation. Finally, Florencia Pozo, who worked on early versions of the literature review.

The study was reviewed by the NJIT IRB: Number F373-18.

References

  1. 1.
  2. 2.
    Barritt, M., et al.: Observations from an open, connected, and evolving learning environment the improvisational, risk-taking, and risky culture of openness, evolution, and connection most define design Lab 1 and its ability to support effective, authentic learning and engage (2013). www.scup.org/phe. https://search.proquest.com/openview/7669d0f141710ad071745c2580a25ff3/1?cbl=47536&pq-origsite=gscholar
  3. 3.
    Berry, J., Park, K.: A passive system for quantifying indoor space utilization (2017). http://papers.cumincad.org/cgi-bin/works/Show?acadia17_138
  4. 4.
    Dziedzic, J., Yan, D., Novakovic, V.: Occupant migration monitoring in residential buildings with the use of a depth registration camera. Procedia Eng. 205, 1193–1200 (2017).  https://doi.org/10.1016/J.PROENG.2017.10.352. https://www.sciencedirect.com/science/article/pii/S1877705817350270CrossRefGoogle Scholar
  5. 5.
    Gil, J., Tobari, E., Lemlij, M., Rose, A., Penn, A.R.: The differentiating behaviour of shoppers: clustering of individual movement traces in a supermarket. Royal Institute of Technology (KTH) (2009)Google Scholar
  6. 6.
    Groner, N.E.: A decision model for recommending which building occupants should move where during fire emergencies. Fire Saf. J. 80, 20–29 (2016).  https://doi.org/10.1016/J.FIRESAF.2015.11.002. https://www.sciencedirect.com/science/article/pii/S0379711215300412CrossRefGoogle Scholar
  7. 7.
    Hing, A.: Understanding the plan: a studio experience. J. Inter. Des. 31(3), 10–20 (2006).  https://doi.org/10.1111/j.1939-1668.2006.tb00528.x. http://doi.wiley.com/10.1111/j.1939-1668.2006.tb00528.xCrossRefGoogle Scholar
  8. 8.
    Kontovourkis, O.: Design of circulation diagrams in macro-scale level based on human movement behavior modeling. Autom. Constr. 22, 12–23 (2012).  https://doi.org/10.1016/J.AUTCON.2011.10.002. https://www.sciencedirect.com/science/article/pii/S0926580511001944CrossRefGoogle Scholar
  9. 9.
    Kuhn, H.W.: The Hungarian method for the assignment problem. Naval Res. Logist. Q. 2(1–2), 83–97 (1955).  https://doi.org/10.1002/nav.3800020109. http://doi.wiley.com/10.1002/nav.3800020109MathSciNetCrossRefzbMATHGoogle Scholar
  10. 10.
    Langevin, J., Wen, J., Gurian, P.L.: Simulating the human-building interaction: development and validation of an agent-based model of office occupant behaviors. Build. Environ. 88, 27–45 (2015).  https://doi.org/10.1016/J.BUILDENV.2014.11.037. https://www.sciencedirect.com/science/article/pii/S0360132314004090CrossRefGoogle Scholar
  11. 11.
    Mehrabian, A., Diamond, S.G.: Seating arrangement and conversation. Sociometry 34(2), 281 (1971).  https://doi.org/10.2307/2786417. https://www.jstor.org/stable/2786417?origin=crossrefCrossRefGoogle Scholar
  12. 12.
    Munkres, J.: Algorithms for the assignment and transportation problems. J. Soc. Ind. Appl. Math. 5(1), 32–38 (1957).  https://doi.org/10.1137/0105003. http://epubs.siam.org/doi/10.1137/0105003MathSciNetCrossRefzbMATHGoogle Scholar
  13. 13.
    Narahara, T.: The Space Re-Actor : walking a synthetic man through architectural space (2007). https://dspace.mit.edu/handle/1721.1/39255
  14. 14.
    Seer, S., Brändle, N., Ratti, C.: Kinects and human kinetics: a new approach for studying pedestrian behavior. Transp. Res. Part C: Emerg. Technol. 48, 212–228 (2014).  https://doi.org/10.1016/J.TRC.2014.08.012. https://www.sciencedirect.com/science/article/pii/S0968090X14002289CrossRefGoogle Scholar
  15. 15.
    Spankie, R.: Drawing Out the Interior. AVA/Academia (2009). https://westminsterresearch.westminster.ac.uk/item/90zqv/drawing-out-the-interior
  16. 16.
    Suzuki, S., Be, K.: Topological structural analysis of digitized binary images by border following. Comput. Vis. Graph. Image Process. 30(1), 32–46 (1985).  https://doi.org/10.1016/0734-189X(85)90016-7. https://www.sciencedirect.com/science/article/pii/0734189X85900167CrossRefzbMATHGoogle Scholar
  17. 17.
    Zivkovic, Z., van der Heijden, F.: Efficient adaptive density estimation per image pixel for the task of background subtraction. Pattern Recogn. Lett. 27(7), 773–780 (2006).  https://doi.org/10.1016/J.PATREC.2005.11.005. https://www.sciencedirect.com/science/article/pii/S0167865505003521CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.New Jersey Institute of TechnologyNewarkUSA

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