Voronoi maps: An approach to individual-based environmental exposure estimation

Wan D. Bae, Shayma Alkobaisi, Wade Meyers, Sada Narayanappa, Petr Vojtechovský

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)


Estimating an individual's environmental exposure is a complicated problem that depends on the amount of time of the individual's exposure, the uncertain location of the individual, and the uncertainty in the levels of environmental factors based on available localized measurements. This problem is critical in the applications of environmental science and public health. In this paper we study the fundamental issues related to spatio-temporal uncertainty of human trajectories and environmental measurements and define a model of exposure uncertainty. We adopt a geometric data structure called the Voronoi diagram to interpolate environmental data, and utilize it in our proposed method to efficiently solve this problem. We evaluate the performance of the proposed method through experiments on both synthetic and real road networks. The experimental results show that our solution based on probabilistic routing aggregation is an efficient and extensible method for environmental exposure time estimation.

Original languageEnglish
Title of host publication2016 Symposium on Applied Computing, SAC 2016
PublisherAssociation for Computing Machinery
Number of pages8
ISBN (Electronic)9781450337397
Publication statusPublished - Apr 4 2016
Externally publishedYes
Event31st Annual ACM Symposium on Applied Computing, SAC 2016 - Pisa, Italy
Duration: Apr 4 2016Apr 8 2016

Publication series

NameProceedings of the ACM Symposium on Applied Computing


Other31st Annual ACM Symposium on Applied Computing, SAC 2016


  • Environmental exposure
  • Exposome
  • Individual-based healthcare
  • Mobile sensors
  • Trajectory uncertainty

ASJC Scopus subject areas

  • Software


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