Journal of the Korean Data and Information Science Society

The Korean Data and Information Science Society (한국데이터정보과학회)
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Spatio-temporal models for generating a map of high resolution NO2 level

  • Yoon, Sanghoo (Department of Computer Science and Statistics, Daegu University) ;
  • Kim, Mingyu (WISE institute, Hankuk University of Foreign Studies)
  • Received : 2016.03.04
  • Accepted : 2016.05.17
  • Published : 2016.05.31
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Abstract

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.

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References

  1. Ahn, D. S., Han, J. H., Yoon, T. H., Kim, C. H. and Rho, M. S. (2015). Small area estimations for disease mapping by using spatial model. Journal of the Korean Data & Information Science Society, 26, 101-109. https://doi.org/10.7465/jkdi.2015.26.1.101
  2. Bakar, K. S. and Sahu, S. K. (2015). spTimer: Spatio-temporal Bayesian modelling using R. Journal of Statistical Software, 63, 1-32.
  3. Choi, H., Lim, D. H., Kim, J. H., Son, B. K., Lim, J. H. and Hong Y. C. (2000). Study on the interrelationship of air pollution and respiratory diseases in Inchon city via children who visited the emergency room of Inha university hospital. Korean Journal of Pediatrics, 43, 1372-1379.
  4. Cressie, N. (1994). An approach to statistical spatial-temporal modeling of meteorological fields: Comment. Journal of the American Statistical Association, 89, 379-382.
  5. Gelfand, A. E., Banerjee, S. and Gamerman, D. (2005). Spatial process modeling for univariate and multivariate dynamic spatial data. Environmetrics, 16, 465-479. https://doi.org/10.1002/env.715
  6. Gelfand, A. E. and Smith, A. F. M. (1990). Sampling-based approaches to calculating marginal densities. Journal of the American Statistical Association, 85, 398-409. https://doi.org/10.1080/01621459.1990.10476213
  7. Gelman, A., Carlin, J. B., Stern, H. S. and Rubin, D.B. (2004). Bayesian Data Analysis., 2nd edition, Chapmand & Hall/CRC, Boca Raton.
  8. Goodall, C. and Mardia, K. V. (1994). Challenges in multivariate spatio-temporal modeling. Proceedings of the XVIIth International Biometric Conference, Hamilton, Ontario, Canada, 82 august 1994, 1-17.
  9. Jacob, D. J., Munger, J. W., Waldman, J. M. and Hoffmann, M. R. (1986). The H2SO4-HNO3-NH3 system at high humidities and in fogs: 1. Spatial and temporal patterns in the San Joaquin Valley of California. Journal of Geophysical Research: Atmospheres, 91, 1073-1088. https://doi.org/10.1029/JD091iD01p01073
  10. Kwon, H. J. and Cho, S. H. (1999). Air pollution and daily mortality in Seoul. Journal of Preventive Medicine and Public Health, 32, 191-199.
  11. Kyriakidis, P. C. and Journel, A. G. (1999). Geostatistical space-time models: A review. Mathematical Geology, 31, 651-684. https://doi.org/10.1023/A:1007528426688
  12. Lee, H. J. (2005). Development of time series models for ozone at Jung Dong in Korea. Journal of Natural Science in Pyongtaek University, 51-63.
  13. Lee, H. J. (2006). Time series models for ozone at Osan city in Korea. Pyoungtaek Review, 20, 187-201.1
  14. Lee, H. J. (2009). Analysis of statistical models for ozone concentrations at the Paju city in Korea. Journal of the Korean Data & Information Science Society, 20, 1075-1082.
  15. Lee, W. J. and Park, C. Y. (2015). Prediction of apartment prices per unit in Daegu-Gyeongbuk areas by spatial regression models, Journal of the Korean Data & Information Science Society, 26, 561-568. https://doi.org/10.7465/jkdi.2015.26.3.561
  16. Lu, C. and Tian, H. (2007). Spatial and temporal patterns of nitrogen deposition in China: Synthesis of observational data. Journal of Geophysical Research: Atmospheres (1984-2012), 112.
  17. Prasad, A. K., Singh, R. P. and Kafatos, M. (2012). Influence of coal-based thermal power plants on the spatialtemporal variability of tropospheric NO 2 column over India. Environmental Monitoring and Assessment, 184, 1891-1907. https://doi.org/10.1007/s10661-011-2087-6
  18. Sahu, S. K. and Bakar, K. S. (2012). Hierarchical Bayesian autoregressive models for large space time data with applications to ozone concentration modelling. Applied Stochastic Models in Business and Industry, 28, 395-415. https://doi.org/10.1002/asmb.1951
  19. Sahu, S. K. and Mardia, K. V. (2005). Recent trends in modeling spatio-temporal data. Proceedings of the Special Meeting on Statistics and Environment Organized by the Society Italiana di Statistica held in University Di Messina, September 21-23, 2005, Invited Papers, 69-83. Published by the University Di Messina, Messina, Italy.
  20. Sitnov, S. A. (2009). Analysis of spatial-temporal variability of tropospheric NO 2 column over Moscow megapolis using OMI spectrometer (Aura satellite) data. Doklady Earth Sciences, 429, 1511-1517. https://doi.org/10.1134/S1028334X09090219
  21. Stroud, J. R., Muller, P. and Sanso, B. (2001). Dynamic models for spatio-temporal data. Journal of the Royal Statistical Society B, 63, 673-689. https://doi.org/10.1111/1467-9868.00305
  22. Wikle, C. K. and Cressie, N. (1999). A dimension-reduced approach to space-time Kalman filtering. Biometrika, 86, 815-829. https://doi.org/10.1093/biomet/86.4.815
  23. Wikle, C. K. (2003). Hierarchical models in environmental science. International Statistical Review, 71, 181-199.

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