Uncertainty quantification techniques for population density estimates derived from sparse open source data

Robert Stewart; Devin White; Marie Urban; April Morton; Clayton Webster; Miroslav Stoyanov; Eddie Bright; Budhendra L. Bhaduri

doi:10.1117/12.2015795

23 May 2013 Uncertainty quantification techniques for population density estimates derived from sparse open source data

Robert Stewart, Devin White, Marie Urban, April Morton, Clayton Webster, Miroslav Stoyanov, Eddie Bright, Budhendra L. Bhaduri

Author Affiliations +

Proceedings Volume 8747, Geospatial InfoFusion III; 874705 (2013) https://doi.org/10.1117/12.2015795
Event: SPIE Defense, Security, and Sensing, 2013, Baltimore, Maryland, United States

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.

Citation Download Citation

Robert Stewart, Devin White, Marie Urban, April Morton, Clayton Webster, Miroslav Stoyanov, Eddie Bright, and Budhendra L. Bhaduri "Uncertainty quantification techniques for population density estimates derived from sparse open source data", Proc. SPIE 8747, Geospatial InfoFusion III, 874705 (23 May 2013); https://doi.org/10.1117/12.2015795

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