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Spatio-temporal models for generating a map of high resolution NO2 level
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 Title & Authors
Spatio-temporal models for generating a map of high resolution NO2 level
Yoon, Sanghoo; Kim, Mingyu;
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Recent times have seen an exponential increase in the amount of spatial data, which is in many cases associated with temporal data. Recent advances in computer technology and computation of hierarchical Bayesian models have enabled to analyze complex spatio-temporal data. Our work aims at modeling data of daily average nitrogen dioxide (NO2) levels obtained from 25 air monitoring sites in Seoul between 2003 and 2010. We considered an independent Gaussian process model and an auto-regressive model and carried out estimation within a hierarchical Bayesian framework with Markov chain Monte Carlo techniques. A Gaussian predictive process approximation has shown the better prediction performance rather than a Hierarchical auto-regressive model for the illustrative NO2 concentration levels at any unmonitored location.
Auto-regressive model;bayesian inference;gaussian predictive process;nitrogen dioxide;space-time modelling;
 Cited by
통계적모형을 통한 고해상도 일별 평균기온 산정,윤상후;

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