Estimating building occupancy: a machine learning system for day, night, and episodic events

Marie Urban (), Robert Stewart, Scott Basford, Zachary Palmer and Jason Kaufman
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Marie Urban: Oak Ridge National Laboratory
Robert Stewart: Oak Ridge National Laboratory
Scott Basford: Oak Ridge National Laboratory
Zachary Palmer: Oak Ridge National Laboratory
Jason Kaufman: Oak Ridge National Laboratory

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2023, vol. 116, issue 2, No 44, 2417-2436

Abstract: Abstract Building occupancy research increasingly emphasizes understanding the social and physical dynamics of how people occupy space. Opportunities in the open source domain including social media, Volunteered Geographic Information, crowdsourcing, and sensor data have proliferated, resulting in the exploration of building occupancy dynamics at varying spatiotemporal scales. At Oak Ridge National Laboratory, research into building occupancies through the development of a global learning framework that accommodates exploitation of open source authoritative sources, including governmental census and surveys, journal articles, real estate databases, and more, to report national and subnational building occupancies across the world continues through the Population Density Tables (PDT) project. This probabilistic learning system accommodates expert knowledge, experience, and open-source data to capture local, socioeconomic, and cultural information about human activity. It does so through a systematic process of data harmonization techniques in the development of observation models for over 50 building types to dynamically update baseline estimates and report probabilistic diurnal and episodic building occupancy estimates. This discussion will explore how PDT is implemented at scale and expanded based on the development of observation model classes and will explain how to interpret and spatially apply the reported probability occupancy estimates and uncertainty.

Keywords: Building occupancy; Bayesian learning; Probability estimates; Uncertainty quantification; Systematic process; Population modeling (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s11069-022-05772-3

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