TY - GEN
T1 - Voronoi maps
T2 - 31st Annual ACM Symposium on Applied Computing, SAC 2016
AU - Bae, Wan D.
AU - Alkobaisi, Shayma
AU - Meyers, Wade
AU - Narayanappa, Sada
AU - Vojtechovský, Petr
N1 - Funding Information:
This material is based upon works supported in part by the Information and Communication Technology Fund of United Arab Emirates under award number 21T042 and in part by the National Science Foundation under award number CNIC-1338378.
Publisher Copyright:
© 2016 ACM.
PY - 2016/4/4
Y1 - 2016/4/4
N2 - 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.
AB - 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.
KW - Environmental exposure
KW - Exposome
KW - Individual-based healthcare
KW - Mobile sensors
KW - Trajectory uncertainty
UR - http://www.scopus.com/inward/record.url?scp=84975860043&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84975860043&partnerID=8YFLogxK
U2 - 10.1145/2851613.2851715
DO - 10.1145/2851613.2851715
M3 - Conference contribution
AN - SCOPUS:84975860043
T3 - Proceedings of the ACM Symposium on Applied Computing
SP - 596
EP - 603
BT - 2016 Symposium on Applied Computing, SAC 2016
PB - Association for Computing Machinery
Y2 - 4 April 2016 through 8 April 2016
ER -