Abstract
The demand for building occupancy estimation continues to grow in a wide array of application domains, such as population distribution modeling, green building technologies, public safety, and natural hazards loss analytics. While much has been gained in using survey diaries, sensor technologies, and dasymetric modeling, the volume and velocity of social media data provide a unique opportunity to measure and model occupancy patterns with unprecedented temporal and spatial resolution. If successful, patterns or occupancy curves could describe the fluctuations in population across a 24 h period for a single building or a class of building types. Although social media hold great promise in responding to this need, a number of challenges exist regarding representativeness and fitness for purpose that, left unconsidered, could lead to erroneous conclusions about true building occupancy. As a mode of discussion, this chapter presents an explicit social media model that assists in delineating and articulating the specific challenges and limitations of using social media. It concludes by proposing a research agenda for further work and engagement in this domain.
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Notes
- 1.
The term “visitor” means, more broadly, the occupant, and includes visitors, employees, and so forth.
- 2.
Assuming the first post \( e_{i}^{1} \) occurred while at the facility, then this is the latest arrival time possible for the ith visitor. Such an assumption could be confirmed for georeferenced data.
- 3.
A uniform distribution means that visitors remain at the museum for some period of time between 1 and 3 h.
References
Axhausen K, Zimmermann A, Schönfelder S, Rindsfüser G, Haupt T (2000) Observing the rhythms of daily life: a six-week travel diary. Transportation 29(2):95–124
Bhaduri B, Bright E, Coleman P, Urban M (2007) LandScan USA: a high-resolution geospatial and temporal modeling approach for population distribution and dynamics. GeoJournal 69(1):103–117
Gonzalez MC, Hidalgo CA, Barabasi A-L (2008) Understanding individual human mobility patterns. Nature 453(7196):779–782
Hong T, Lin H-W (2013) Occupant behavior: impact on energy use of private offices. Berkeley National Laboratory and the Green Energy and Environment Laboratories, Industrial Technology Research Institute, Taiwan, ROC
Jaiswal K, Wald D, Earle P, Porter K, Herne M (2011) Earthquake casualty models within the USGS Prompt Assessment of Global Earthquakes for Response (PAGER) system. In: Spence R, So E, Scawthorn C (eds) Human casualties in earthquakes. Springer, New York, pp 83–94
Klepeis N, Nelson W, Ott W, Robinson J, Tsang A, Switzer P, Behar J, Hern S, Engelmann W (2001) The National Human Activity Pattern Survey (NHAPS): a resource for assessing exposure to environmental pollutants. J Expo Anal Environ Epidemiol 11:231–252
Langford M (2013) An evaluation of small area population estimation techniques using open access ancillary data. Geogr Anal 45:324–344. doi:10.1111/gean.1201
Leyk S, Nagle NN, Buttenfield BP (2013) Maximum entropy dasymetric modeling for demographic small area estimation. Geogr Anal 45:285–306. doi:10.1111/gean.12011
Martani C, Lee D, Robinson P, Britter R, Ratti C (2012) ENERNET: studying the dynamic relationship between building occupancy and energy consumption. Energy Build 47:584–591
Melfi R, Rosenblum B, Nordman B, Christensen K (2011) Measuring building occupancy using existing network infrastructure. In: Proceedings of the 2011 international green computing conference and workshops, IEEE Computer Society, Orlando, FL, July 25–28, 2011
Meyn S, Surana A, Lin Y, Oggianu S, Narayanan S, Frewen T (2009) A sensor-utility-network method for estimation of occupancy distribution in buildings. In: Joint 48th IEEE conference on decision and control and 28th Chinese control conference, Shanghai, China, December 16–18, 2009
Morton A (2013) A process model for capturing museum population dynamics. Master’s thesis, Department of Mathematics, California State Polytechnic University
Noulas A, Scellato S, Lambiotte R, Pontil M, Mascolo C (2012) A tale of many cities: universal patterns in human urban mobility. PLoS ONE 7(5):e37027. doi:10.1371/journal.pone.0037027
Qiu F, Cromley R (2013) Areal interpolation and dasymetric modeling. Geogr Anal 45:213–215. doi:10.1111/gean.12016
Schlich R, Axhausen K (2003) Habitual travel behaviour: Evidence from a six-week travel diary. Transportation 30(1):13–36
Stewart RN, Urban M, Morton A, Duchscherer S (2016) A Bayesian machine learning model for estimating building occupancy from open source data. Nat Hazards. doi:10.1007/s11069-016-2164-9
Zandbergen P, Ignizio D (2010) Comparison of dasymetric mapping techniques for small-area population estimates. Cartogr Geogr Inf Sci 37(3):199–214
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This manuscript has been authored by employees of UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy. Accordingly, the United States Government retains, and the publisher, by accepting the article for publication, acknowledges that the United States Government retains, a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript or allow others to do so, for United States Government purposes.
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Stewart, R. et al. (2017). Can Social Media Play a Role in the Development of Building Occupancy Curves?. In: Griffith, D., Chun, Y., Dean, D. (eds) Advances in Geocomputation. Advances in Geographic Information Science. Springer, Cham. https://doi.org/10.1007/978-3-319-22786-3_6
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