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March 2017 Static and roving sensor data fusion for spatio-temporal hazard mapping with application to occupational exposure assessment
Guilherme Ludwig, Tingjin Chu, Jun Zhu, Haonan Wang, Kirsten Koehler
Ann. Appl. Stat. 11(1): 139-160 (March 2017). DOI: 10.1214/16-AOAS995

Abstract

Rapid technological advances have drastically improved the data collection capacity in occupational exposure assessment. However, advanced statistical methods for analyzing such data and drawing proper inference remain limited. The objectives of this paper are (1) to provide new spatio-temporal methodology that combines data from both roving and static sensors for data processing and hazard mapping across space and over time in an indoor environment, and (2) to compare the new method with the current industry practice, demonstrating the distinct advantages of the new method and the impact on occupational hazard assessment and future policy making in environmental health as well as occupational health. A novel spatio-temporal model with a continuous index in both space and time is proposed, and a profile likelihood-based model fitting procedure is developed that allows fusion of the two types of data. To account for potential differences between the static and roving sensors, we extend the model to have nonhomogenous measurement error variances. Our methodology is applied to a case study conducted in an engine test facility, and dynamic hazard maps are drawn to show features in the data that would have been missed by existing approaches, but are captured by the new method.

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Guilherme Ludwig. Tingjin Chu. Jun Zhu. Haonan Wang. Kirsten Koehler. "Static and roving sensor data fusion for spatio-temporal hazard mapping with application to occupational exposure assessment." Ann. Appl. Stat. 11 (1) 139 - 160, March 2017. https://doi.org/10.1214/16-AOAS995

Information

Received: 1 November 2015; Revised: 1 October 2016; Published: March 2017
First available in Project Euclid: 8 April 2017

zbMATH: 1366.62259
MathSciNet: MR3634318
Digital Object Identifier: 10.1214/16-AOAS995

Rights: Copyright © 2017 Institute of Mathematical Statistics

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Vol.11 • No. 1 • March 2017
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