A Bayesian Machine Learning Model for Estimating Building Occupancy from Open Source Data
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 based 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 probabilistically estimates ambient occupancy in units of people/1000ft2 for over 50 building types at the national and sub-national level with the goal of providing global coverage. The challenge of global coverage led to the development of an interdisciplinary geospatial informatics system tool that provides the framework for capturing, storing, and managing open source data, handling subject matter expertise, carrying out Bayesian analytics as well as visualizing and exporting occupancy estimation results. We present the PDT project, situate the work within the larger community, and report on the progress of this multi-year project.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 probabilistically estimates ambient occupancy in units of people/1000 ft 2 for over 50 building types at the national and sub-national level with the goal of providing global coverage. The challenge of global coverage led to the development of an interdisciplinary geospatial informatics system tool that provides the framework for capturing, storing, and managing open source data, handling subject matter expertise, carrying out Bayesian analytics as well as visualizing and exporting occupancy estimation results. We present the PDT project, situate the work within the larger community, and report on the progress of this multi-year project.