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Proceedings Paper

Uncertainty quantification techniques for population density estimates derived from sparse open source data
Author(s): Robert Stewart; Devin White; Marie Urban; April Morton; Clayton Webster; Miroslav Stoyanov; Eddie Bright; Budhendra L. Bhaduri
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Paper Abstract

The Population Density Tables (PDT) project at Oak Ridge National Laboratory (www.ornl.gov) is developing population density estimates for specific human activities under normal patterns of life based largely on information available in open source. Currently, activity-based density estimates are based on simple summary data statistics such as range and mean. Researchers are interested in improving activity estimation and uncertainty quantification by adopting a Bayesian framework that considers both data and sociocultural knowledge. Under a Bayesian approach, knowledge about population density may be encoded through the process of expert elicitation. Due to the scale of the PDT effort which considers over 250 countries, spans 50 human activity categories, and includes numerous contributors, an elicitation tool is required that can be operationalized within an enterprise data collection and reporting system. Such a method would ideally require that the contributor have minimal statistical knowledge, require minimal input by a statistician or facilitator, consider human difficulties in expressing qualitative knowledge in a quantitative setting, and provide methods by which the contributor can appraise whether their understanding and associated uncertainty was well captured. This paper introduces an algorithm that transforms answers to simple, non-statistical questions into a bivariate Gaussian distribution as the prior for the Beta distribution. Based on geometric properties of the Beta distribution parameter feasibility space and the bivariate Gaussian distribution, an automated method for encoding is developed that responds to these challenging enterprise requirements. Though created within the context of population density, this approach may be applicable to a wide array of problem domains requiring informative priors for the Beta distribution.

Paper Details

Date Published: 23 May 2013
PDF: 16 pages
Proc. SPIE 8747, Geospatial InfoFusion III, 874705 (23 May 2013); doi: 10.1117/12.2015795
Show Author Affiliations
Robert Stewart, Oak Ridge National Lab. (United States)
The Univ. of Tennessee (United States)
Devin White, Oak Ridge National Lab. (United States)
The Univ. of Tennessee (United States)
Marie Urban, Oak Ridge National Lab. (United States)
April Morton, Oak Ridge National Lab. (United States)
Clayton Webster, Oak Ridge National Lab. (United States)
Miroslav Stoyanov, Oak Ridge National Lab. (United States)
Eddie Bright, Oak Ridge National Lab. (United States)
Budhendra L. Bhaduri, Oak Ridge National Lab. (United States)
The Univ. of Tennessee (United States)


Published in SPIE Proceedings Vol. 8747:
Geospatial InfoFusion III
Matthew F. Pellechia; Richard J. Sorensen; Kannappan Palaniappan, Editor(s)

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