How to cite this paper
Putri, T., Jaya, I., Toharudin, T & Kristiani, F. (2024). Sensitivity analysis of the PC hyperprior for range and standard deviation components in Bayesian Spatiotemporal high-resolution prediction: An application to PM2.5 prediction in Jakarta, Indonesia.International Journal of Data and Network Science, 8(2), 871-880.
Refrences
Adesina, O. S., Olatayo, T. O., Agboola, O. O., & Oguntunde, P. E. (2018). Bayesian Dirichlet process mixture prior for count data. International Journal of Mechanical Engineering and Technology, 9(12), 630–646.
Armstrong, M., & Boufassa, A. (1988). Comparing the robustness of ordinary kriging and lognormal kriging: Outlier re-sistance. Mathematical Geology, 20(4), 447–457. https://doi.org/10.1007/BF00892988
Bai, L., Wang, J., Ma, X., & Lu, H. (2018). Air Pollution Forecasts: An Overview. International Journal of Environmental Research and Public Health, 15(4), 780. https://doi.org/10.3390/ijerph15040780
Bhattacharya, A., Pati, D., Pillai, N. S., & Dunson, D. B. (2014). Dirichlet-Laplace priors for optimal shrinkage. https://doi.org/https://doi.org/10.48550/arXiv.1401.5398
Blangiardo, M. (2015). Spatial modeling. Spatial and Spatio-Temporal Bayesian Models with R - INLA, 173–234. https://doi.org/10.1002/9781118950203.ch6
Cai, Z., Jermaine, C., Vagena, Z., Logothetis, D., & Perez, L. L. (2013). The Pairwise Gaussian Random Field for High-Dimensional Data Imputation. 2013 IEEE 13th International Conference on Data Mining, 61–70. https://doi.org/10.1109/ICDM.2013.149
Chen, J., Miao, C., Yang, D., Liu, Y., Zhang, H., & Dong, G. (2023). Estimation of fine-resolution PM 2 . 5 concentrations using the INLA-SPDE method. 14(April).
Cressie, N., & Wikle, C. K. (2012). Space‐Time Kalman Filter. In Encyclopedia of Environmetrics. Wiley. https://doi.org/10.1002/9780470057339.vas037
Evans, M., & Jang, G. H. (2011). Weak Informativity and the Information in One Prior Relative to Another. Statistical Science, 26(3). https://doi.org/10.1214/11-STS357
Fassò, A., Cameletti, M., & Fass, A. (2007). Web Working Papers by The Italian Group of Environmental Statistics A gen-eral spatio-temporal model for environmental data A general spatio-temporal model for environmental data. February.
Fitriani, R., & Gede Nyoman Mindra Jaya, I. (2020). Spatial modeling of confirmed COVID-19 pandemic in East Java Province by geographically weighted negative binomial regression. Communications in Mathematical Biology and Neuroscience, 2020, 1–17. https://doi.org/10.28919/cmbn/4874
Fuglstad, G.-A., Simpson, D., Lindgren, F., & Rue, H. (2019). Constructing Priors that Penalize the Complexity of Gaussi-an Random Fields. Journal of the American Statistical Association, 114(525), 445–452. https://doi.org/10.1080/01621459.2017.1415907
Fund Defense, E. (2020). Policies to Reduce Pollution and protect Health.
Gelman, A. (2006). Prior distributions for variance parameters in hierarchical models (comment on article by Browne and Draper). Bayesian Analysis, 1(3). https://doi.org/10.1214/06-BA117A
Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013). Bayesian Data Analysis. Chap-man and Hall/CRC. https://doi.org/10.1201/b16018
Gladson, L., Garcia, N., Bi, J., Liu, Y., Lee, H. J., & Cromar, K. (2022). Evaluating the Utility of High-Resolution Spatio-temporal Air Pollution Data in Estimating Local PM2.5 Exposures in California from 2015–2018. Atmosphere, 13(1), 85. https://doi.org/10.3390/atmos13010085
Gómez-Rubio, V. (2020). Bayesian Inference with INLA. Chapman and Hall/CRC. https://doi.org/10.1201/9781315175584
Handcock, M. S. (1994). Measuring the Uncertainty in Kriging (pp. 436–447). https://doi.org/10.1007/978-94-011-0824-9_46
Jaya, I. G. N. M., & Folmer, H. (2022). Spatiotemporal high-resolution prediction and mapping: methodology and applica-tion to dengue disease. Journal of Geographical Systems, 24(4), 527–581. https://doi.org/10.1007/s10109-021-00368-0
Jaya, I. G. N. M., Ruchjana, B. N., Tantular, B., Zulhanif, & Andriyana, Y. (2018). Simulation and application of the spa-tial autoregressive geographically weighted regression model (SAR-GWR). ARPN Journal of Engineering and Applied Sciences, 13(1), 377–385.
Li, L., Losser, T., Yorke, C., & Piltner, R. (2014). Fast Inverse Distance Weighting-Based Spatiotemporal Interpolation: A Web-Based Application of Interpolating Daily Fine Particulate Matter PM2.5 in the Contiguous U.S. Using Parallel Programming and k-d Tree. International Journal of Environmental Research and Public Health, 11(9), 9101–9141. https://doi.org/10.3390/ijerph110909101
Liang, D., & Kumar, N. (2013). Time-space Kriging to address the spatiotemporal misalignment in the large datasets. At-mospheric Environment, 72, 60–69. https://doi.org/10.1016/j.atmosenv.2013.02.034
Lindgren, F., & Rue, H. (2015). Bayesian Spatial Modelling with R - INLA. Journal of Statistical Software, 63(19). https://doi.org/10.18637/jss.v063.i19
Lindgren, F., Rue, H., & Lindström, J. (2011). An Explicit Link between Gaussian Fields and Gaussian Markov Random Fields: The Stochastic Partial Differential Equation Approach. Journal of the Royal Statistical Society Series B: Statis-tical Methodology, 73(4), 423–498. https://doi.org/10.1111/j.1467-9868.2011.00777.x
Liu, J., Wan, G., Liu, W., Li, C., Peng, S., & Xie, Z. (2023). High-dimensional spatiotemporal visual analysis of the air quality in China. Scientific Reports, 13(1), 5462. https://doi.org/10.1038/s41598-023-31645-1
Lloyd, C., & Atkinson, P. (2001). Assessing uncertainty in estimates with ordinary and indicator kriging. Computers & Geosciences, 27(8), 929–937. https://doi.org/10.1016/S0098-3004(00)00132-1
Lloyd, C. D., & Atkinson, P. M. (2002). Non‐stationary Approaches for Mapping Terrain and Assessing Prediction Uncer-tainty. Transactions in GIS, 6(1), 17–30. https://doi.org/10.1111/1467-9671.00092
Lu, G. Y., & Wong, D. W. (2008). An adaptive inverse-distance weighting spatial interpolation technique. Computers & Geosciences, 34(9), 1044–1055. https://doi.org/10.1016/j.cageo.2007.07.010
Manisalidis, I., Stavropoulou, E., Stavropoulos, A., & Bezirtzoglou, E. (2020). Environmental and Health Impacts of Air Pollution: A Review. Frontiers in Public Health, 8. https://doi.org/10.3389/fpubh.2020.00014
Marhamah, E., & Jaya, I. G. N. M. (2020). Modeling positive COVID-19 cases in Bandung City by means geographically weighted regression. Communications in Mathematical Biology and Neuroscience. https://doi.org/10.28919/cmbn/4991
Martins, T. G., Simpson, D., Lindgren, F., & Rue, H. (2013). Bayesian computing with INLA: New features. Computation-al Statistics & Data Analysis, 67, 68–83. https://doi.org/10.1016/j.csda.2013.04.014
Molina, R., Vega, M., Mateos, J., & Katsaggelos, A. K. (2008). Variational posterior distribution approximation in Bayes-ian super resolution reconstruction of multispectral images. Applied and Computational Harmonic Analysis, 24(2), 251–267. https://doi.org/10.1016/j.acha.2007.03.006
Nakanishi, Y., Kaneta, T., & Nishino, S. (2022). A Review of Monitoring Construction Equipment in Support of Construc-tion Project Management. Frontiers in Built Environment, 7. https://doi.org/10.3389/fbuil.2021.632593
Nurfaizah, A. (2022). Stasiun Pemantauan Kualitas Udara Jakarta Masih Minim. Kompas. https://www.kompas.id/baca/metro/2022/12/10/jakarta-perlu-tambah-alat-pemantaun-kualitas-udara
Rasmussen, C. E., & Williams, C. K. I. (2005). Gaussian Processes for Machine Learning. The MIT Press. https://doi.org/10.7551/mitpress/3206.001.0001
Rue, H., Martino, S., & Chopin, N. (2009). Approximate Bayesian Inference for Latent Gaussian models by using Integrat-ed Nested Laplace Approximations. Journal of the Royal Statistical Society Series B: Statistical Methodology, 71(2), 319–392. https://doi.org/10.1111/j.1467-9868.2008.00700.x
Sidén, P. (2020). Scalable Bayesian spatial analysis with Gaussian Markov random fields (Issue Dissertation). www.liu.se
Simpson, D., Rue, H., Riebler, A., Martins, T. G., & Sørbye, S. H. (2017). Penalising model component complexity: A principled, practical approach to constructing priors. Statistical Science, 32(1), 1–28. https://doi.org/10.1214/16-STS576
Song, H.-R., Fuentes, M., & Ghosh, S. (2008). A comparative study of Gaussian geostatistical models and Gaussian Mar-kov random field models1. Journal of Multivariate Analysis, 99(8), 1681–1697. https://doi.org/10.1016/j.jmva.2008.01.012
Sørbye, S. H., & Rue, H. (2014). Scaling intrinsic Gaussian Markov random field priors in spatial modelling. Spatial Sta-tistics, 8, 39–51. https://doi.org/10.1016/j.spasta.2013.06.004
Sørbye, S. H., & Rue, H. (2017). Penalised Complexity Priors for Stationary Autoregressive Processes. Journal of Time Series Analysis, 38(6), 923–935. https://doi.org/10.1111/jtsa.12242
Sprenger, J. (2018). The objectivity of Subjective Bayesianism. European Journal for Philosophy of Science, 8(3), 539–558. https://doi.org/10.1007/s13194-018-0200-1
Sun, X.-L., Wu, Y.-J., Zhang, C., & Wang, H.-L. (2019). Performance of median kriging with robust estimators of the variogram in outlier identification and spatial prediction for soil pollution at a field scale. Science of The Total Envi-ronment, 666, 902–914. https://doi.org/10.1016/j.scitotenv.2019.02.231
Varentsov, M., Esau, I., & Wolf, T. (2020). High-Resolution Temperature Mapping by Geostatistical Kriging with Exter-nal Drift from Large-Eddy Simulations. Monthly Weather Review, 148(3), 1029–1048. https://doi.org/10.1175/MWR-D-19-0196.1
Ventrucci, M., & Rue, H. (2016). Penalized complexity priors for degrees of freedom in Bayesian P-splines. Statistical Modelling, 16(6), 429–453. https://doi.org/10.1177/1471082X16659154
Wang, Y., Huang, C., Hu, J., & Wang, M. (2022). Development of high-resolution spatio-temporal models for ambient air pollution in a metropolitan area of China from 2013 to 2019. Chemosphere, 291, 132918. https://doi.org/10.1016/j.chemosphere.2021.132918
Armstrong, M., & Boufassa, A. (1988). Comparing the robustness of ordinary kriging and lognormal kriging: Outlier re-sistance. Mathematical Geology, 20(4), 447–457. https://doi.org/10.1007/BF00892988
Bai, L., Wang, J., Ma, X., & Lu, H. (2018). Air Pollution Forecasts: An Overview. International Journal of Environmental Research and Public Health, 15(4), 780. https://doi.org/10.3390/ijerph15040780
Bhattacharya, A., Pati, D., Pillai, N. S., & Dunson, D. B. (2014). Dirichlet-Laplace priors for optimal shrinkage. https://doi.org/https://doi.org/10.48550/arXiv.1401.5398
Blangiardo, M. (2015). Spatial modeling. Spatial and Spatio-Temporal Bayesian Models with R - INLA, 173–234. https://doi.org/10.1002/9781118950203.ch6
Cai, Z., Jermaine, C., Vagena, Z., Logothetis, D., & Perez, L. L. (2013). The Pairwise Gaussian Random Field for High-Dimensional Data Imputation. 2013 IEEE 13th International Conference on Data Mining, 61–70. https://doi.org/10.1109/ICDM.2013.149
Chen, J., Miao, C., Yang, D., Liu, Y., Zhang, H., & Dong, G. (2023). Estimation of fine-resolution PM 2 . 5 concentrations using the INLA-SPDE method. 14(April).
Cressie, N., & Wikle, C. K. (2012). Space‐Time Kalman Filter. In Encyclopedia of Environmetrics. Wiley. https://doi.org/10.1002/9780470057339.vas037
Evans, M., & Jang, G. H. (2011). Weak Informativity and the Information in One Prior Relative to Another. Statistical Science, 26(3). https://doi.org/10.1214/11-STS357
Fassò, A., Cameletti, M., & Fass, A. (2007). Web Working Papers by The Italian Group of Environmental Statistics A gen-eral spatio-temporal model for environmental data A general spatio-temporal model for environmental data. February.
Fitriani, R., & Gede Nyoman Mindra Jaya, I. (2020). Spatial modeling of confirmed COVID-19 pandemic in East Java Province by geographically weighted negative binomial regression. Communications in Mathematical Biology and Neuroscience, 2020, 1–17. https://doi.org/10.28919/cmbn/4874
Fuglstad, G.-A., Simpson, D., Lindgren, F., & Rue, H. (2019). Constructing Priors that Penalize the Complexity of Gaussi-an Random Fields. Journal of the American Statistical Association, 114(525), 445–452. https://doi.org/10.1080/01621459.2017.1415907
Fund Defense, E. (2020). Policies to Reduce Pollution and protect Health.
Gelman, A. (2006). Prior distributions for variance parameters in hierarchical models (comment on article by Browne and Draper). Bayesian Analysis, 1(3). https://doi.org/10.1214/06-BA117A
Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013). Bayesian Data Analysis. Chap-man and Hall/CRC. https://doi.org/10.1201/b16018
Gladson, L., Garcia, N., Bi, J., Liu, Y., Lee, H. J., & Cromar, K. (2022). Evaluating the Utility of High-Resolution Spatio-temporal Air Pollution Data in Estimating Local PM2.5 Exposures in California from 2015–2018. Atmosphere, 13(1), 85. https://doi.org/10.3390/atmos13010085
Gómez-Rubio, V. (2020). Bayesian Inference with INLA. Chapman and Hall/CRC. https://doi.org/10.1201/9781315175584
Handcock, M. S. (1994). Measuring the Uncertainty in Kriging (pp. 436–447). https://doi.org/10.1007/978-94-011-0824-9_46
Jaya, I. G. N. M., & Folmer, H. (2022). Spatiotemporal high-resolution prediction and mapping: methodology and applica-tion to dengue disease. Journal of Geographical Systems, 24(4), 527–581. https://doi.org/10.1007/s10109-021-00368-0
Jaya, I. G. N. M., Ruchjana, B. N., Tantular, B., Zulhanif, & Andriyana, Y. (2018). Simulation and application of the spa-tial autoregressive geographically weighted regression model (SAR-GWR). ARPN Journal of Engineering and Applied Sciences, 13(1), 377–385.
Li, L., Losser, T., Yorke, C., & Piltner, R. (2014). Fast Inverse Distance Weighting-Based Spatiotemporal Interpolation: A Web-Based Application of Interpolating Daily Fine Particulate Matter PM2.5 in the Contiguous U.S. Using Parallel Programming and k-d Tree. International Journal of Environmental Research and Public Health, 11(9), 9101–9141. https://doi.org/10.3390/ijerph110909101
Liang, D., & Kumar, N. (2013). Time-space Kriging to address the spatiotemporal misalignment in the large datasets. At-mospheric Environment, 72, 60–69. https://doi.org/10.1016/j.atmosenv.2013.02.034
Lindgren, F., & Rue, H. (2015). Bayesian Spatial Modelling with R - INLA. Journal of Statistical Software, 63(19). https://doi.org/10.18637/jss.v063.i19
Lindgren, F., Rue, H., & Lindström, J. (2011). An Explicit Link between Gaussian Fields and Gaussian Markov Random Fields: The Stochastic Partial Differential Equation Approach. Journal of the Royal Statistical Society Series B: Statis-tical Methodology, 73(4), 423–498. https://doi.org/10.1111/j.1467-9868.2011.00777.x
Liu, J., Wan, G., Liu, W., Li, C., Peng, S., & Xie, Z. (2023). High-dimensional spatiotemporal visual analysis of the air quality in China. Scientific Reports, 13(1), 5462. https://doi.org/10.1038/s41598-023-31645-1
Lloyd, C., & Atkinson, P. (2001). Assessing uncertainty in estimates with ordinary and indicator kriging. Computers & Geosciences, 27(8), 929–937. https://doi.org/10.1016/S0098-3004(00)00132-1
Lloyd, C. D., & Atkinson, P. M. (2002). Non‐stationary Approaches for Mapping Terrain and Assessing Prediction Uncer-tainty. Transactions in GIS, 6(1), 17–30. https://doi.org/10.1111/1467-9671.00092
Lu, G. Y., & Wong, D. W. (2008). An adaptive inverse-distance weighting spatial interpolation technique. Computers & Geosciences, 34(9), 1044–1055. https://doi.org/10.1016/j.cageo.2007.07.010
Manisalidis, I., Stavropoulou, E., Stavropoulos, A., & Bezirtzoglou, E. (2020). Environmental and Health Impacts of Air Pollution: A Review. Frontiers in Public Health, 8. https://doi.org/10.3389/fpubh.2020.00014
Marhamah, E., & Jaya, I. G. N. M. (2020). Modeling positive COVID-19 cases in Bandung City by means geographically weighted regression. Communications in Mathematical Biology and Neuroscience. https://doi.org/10.28919/cmbn/4991
Martins, T. G., Simpson, D., Lindgren, F., & Rue, H. (2013). Bayesian computing with INLA: New features. Computation-al Statistics & Data Analysis, 67, 68–83. https://doi.org/10.1016/j.csda.2013.04.014
Molina, R., Vega, M., Mateos, J., & Katsaggelos, A. K. (2008). Variational posterior distribution approximation in Bayes-ian super resolution reconstruction of multispectral images. Applied and Computational Harmonic Analysis, 24(2), 251–267. https://doi.org/10.1016/j.acha.2007.03.006
Nakanishi, Y., Kaneta, T., & Nishino, S. (2022). A Review of Monitoring Construction Equipment in Support of Construc-tion Project Management. Frontiers in Built Environment, 7. https://doi.org/10.3389/fbuil.2021.632593
Nurfaizah, A. (2022). Stasiun Pemantauan Kualitas Udara Jakarta Masih Minim. Kompas. https://www.kompas.id/baca/metro/2022/12/10/jakarta-perlu-tambah-alat-pemantaun-kualitas-udara
Rasmussen, C. E., & Williams, C. K. I. (2005). Gaussian Processes for Machine Learning. The MIT Press. https://doi.org/10.7551/mitpress/3206.001.0001
Rue, H., Martino, S., & Chopin, N. (2009). Approximate Bayesian Inference for Latent Gaussian models by using Integrat-ed Nested Laplace Approximations. Journal of the Royal Statistical Society Series B: Statistical Methodology, 71(2), 319–392. https://doi.org/10.1111/j.1467-9868.2008.00700.x
Sidén, P. (2020). Scalable Bayesian spatial analysis with Gaussian Markov random fields (Issue Dissertation). www.liu.se
Simpson, D., Rue, H., Riebler, A., Martins, T. G., & Sørbye, S. H. (2017). Penalising model component complexity: A principled, practical approach to constructing priors. Statistical Science, 32(1), 1–28. https://doi.org/10.1214/16-STS576
Song, H.-R., Fuentes, M., & Ghosh, S. (2008). A comparative study of Gaussian geostatistical models and Gaussian Mar-kov random field models1. Journal of Multivariate Analysis, 99(8), 1681–1697. https://doi.org/10.1016/j.jmva.2008.01.012
Sørbye, S. H., & Rue, H. (2014). Scaling intrinsic Gaussian Markov random field priors in spatial modelling. Spatial Sta-tistics, 8, 39–51. https://doi.org/10.1016/j.spasta.2013.06.004
Sørbye, S. H., & Rue, H. (2017). Penalised Complexity Priors for Stationary Autoregressive Processes. Journal of Time Series Analysis, 38(6), 923–935. https://doi.org/10.1111/jtsa.12242
Sprenger, J. (2018). The objectivity of Subjective Bayesianism. European Journal for Philosophy of Science, 8(3), 539–558. https://doi.org/10.1007/s13194-018-0200-1
Sun, X.-L., Wu, Y.-J., Zhang, C., & Wang, H.-L. (2019). Performance of median kriging with robust estimators of the variogram in outlier identification and spatial prediction for soil pollution at a field scale. Science of The Total Envi-ronment, 666, 902–914. https://doi.org/10.1016/j.scitotenv.2019.02.231
Varentsov, M., Esau, I., & Wolf, T. (2020). High-Resolution Temperature Mapping by Geostatistical Kriging with Exter-nal Drift from Large-Eddy Simulations. Monthly Weather Review, 148(3), 1029–1048. https://doi.org/10.1175/MWR-D-19-0196.1
Ventrucci, M., & Rue, H. (2016). Penalized complexity priors for degrees of freedom in Bayesian P-splines. Statistical Modelling, 16(6), 429–453. https://doi.org/10.1177/1471082X16659154
Wang, Y., Huang, C., Hu, J., & Wang, M. (2022). Development of high-resolution spatio-temporal models for ambient air pollution in a metropolitan area of China from 2013 to 2019. Chemosphere, 291, 132918. https://doi.org/10.1016/j.chemosphere.2021.132918