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

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Pages (from-to)139-160
Number of pages22
JournalAnnals of Applied Statistics
Volume11
Issue number1
DOIs
StatePublished - Mar 2017

Keywords

  • Geostatistics
  • Kriging
  • Semiparametric methods
  • Spatial statistics
  • Spatio-temporal statistics

ASJC Scopus subject areas

  • Statistics and Probability
  • Modeling and Simulation
  • Statistics, Probability and Uncertainty

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