Scenario Driven Building Occupancy Estimates

Authors: Marie Urban*, Oak Ridge National Laboratory, Sarah Walters, Oak Ridge National Laboratory
Topics: Population Geography, Quantitative Methods, Behavioral Geography
Keywords: building occupancy, learning system, probability estimates, COVID-19
Session Type: Virtual Paper
Presentation File: No File Uploaded

The COVID-19 pandemic has brought about significant changes in population mobility as a result of government-mandated guidance directed towards reducing social contact in hopes of minimizing the spread of the virus. Such guidance restricts the number of people attending social events, restaurants, office work, and has brought about more online learning for k-12 schools and universities, as well as increased virtual attendance at professional conferences and meetings. With these changes in behavior, new approaches are necessary for population modeling influenced by not only pandemics, but extreme weather, natural disasters and conflict for building energy research, disaster preparedness and response, and national security. The Population Density Table (PDT) project at Oak Ridge National Laboratory (ORNL) is a global learning system that reports ambient building occupancy probability estimates (ppl/1000 sq ft2) for over 50 building types at a national and subnational level. An array of observation models capture snapshots of building occupancy influenced by local socio-cultural data such as school attendance, unemployment, visitors, and more. The challenge is to develop scenario-based occupancies for known hazards such as the recent COVID-19 pandemic. Building occupancies are dynamic and directly reflect the directives as social contact measures were/are implemented at different stages across the US and the world in response to the spread of the virus. The goal of this discussion is to develop scenarios for different stages of contact through updates to existing observation models and specifically capturing building occupancy dynamics during COVID for select areas.

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