Natural Hazards

, Volume 81, Issue 3, pp 1929–1956 | Cite as

A Bayesian machine learning model for estimating building occupancy from open source data

  • Robert Stewart
  • Marie Urban
  • Samantha Duchscherer
  • Jason Kaufman
  • April Morton
  • Gautam Thakur
  • Jesse Piburn
  • Jessica Moehl
Original Paper
First Online:
Received:
Accepted:

Abstract

Understanding building occupancy is critical to a wide array of applications including natural hazards loss analysis, green building technologies, and population distribution modeling. Due to the expense of directly monitoring buildings, scientists rely in addition on a wide and disparate array of ancillary and open source information including subject matter expertise, survey data, and remote sensing information. These data are fused using data harmonization methods, which refer to a loose collection of formal and informal techniques for fusing data together to create viable content for building occupancy estimation. In this paper, we add to the current state of the art by introducing the population data tables (PDT), a Bayesian model and informatics system for systematically arranging data and harmonization techniques into a consistent, transparent, knowledge learning framework that retains in the final estimation uncertainty emerging from data, expert judgment, and model parameterization. PDT aims to estimate ambient occupancy in units of people/1000 ft2 for a number of building types at the national and sub-national level with the goal of providing global coverage. We present the PDT model, situate the work within the larger community, and report on the progress of this multi-year project.

Keywords

Population Building Occupancy Bayesian Uncertainty Open source Elicitation 
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Notes

Acknowledgments

This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, 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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Copyright information

© Springer Science+Business Media Dordrecht 2016

Authors and Affiliations

  • Robert Stewart
    • 1
  • Marie Urban
    • 1
  • Samantha Duchscherer
    • 2
  • Jason Kaufman
    • 2
  • April Morton
    • 2
  • Gautam Thakur
    • 1
  • Jesse Piburn
    • 1
  • Jessica Moehl
    • 2
  1. 1.Oak Ridge National LaboratoryOak RidgeUSA
  2. 2.Oak Ridge Associated UniversitiesOak RidgeUSA

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