How to cite this paper
Falah, A., Ruchjana, B., Abdullah, A & Rejito, J. (2023). Clustering spatial autoregressive kriging model for climate: A bibliometric analysis approach.International Journal of Data and Network Science, 7(2), 637-646.
Refrences
Aria, M., & Cuccurullo, C. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975. https://doi.org/10.1016/j.joi.2017.08.007
Banzhaf, E., Bulley, H. N., Inkoom, J. N., & Elze, S. (2022). Mapping Open Data and Big Data to Address Climate Resilience of Urban Informal Settlements in Sub-Saharan Africa. 1–13.
Carvalho, M. J., Melo-Gonçalves, P., Teixeira, J. C., & Rocha, A. (2016). Regionalization of Europe based on a K-Means Cluster Analysis of the climate change of temperatures and precipitation. Physics and Chemistry of the Earth, 94, 22–28. https://doi.org/10.1016/j.pce.2016.05.001
Efe, B., Gözet, E., Özgür, E., Lupo, A. R., & Deniz, A. (2022). Spatiotemporal Variation of Tourism Climate Index for Türkiye during 1981–2020. Climate, 10(10), 1–32. https://doi.org/10.3390/cli10100151
Ellegaard, O., & Wallin, J. A. (2015). The bibliometric analysis of scholarly production: How great is the impact? Scientometrics, 105(3), 1809–1831. https://doi.org/10.1007/s11192-015-1645-z
Felix, A. Y., Vinay, G. S. S., & Akhik, G. (2019). K-Means cluster using rainfall and storm prediction in machine learning technique. Journal of Computational and Theoretical Nanoscience, 16(8), 3265–3269. https://doi.org/10.1166/jctn.2019.8174
Ghavidel, Y., Baghbanan, P., & Farajzadeh, M. (n.d.). The spatial analysis of thunderstorm hazard in Iran. 10(5). https://doi.org/10.1007/s12517-017-2902-7
Gonzalez-Gonzalez, M. A., & Guertin, D. P. (2021). Seasonal bean yield forecast for non-irrigated croplands through climate and vegetation index data: Geospatial effects. International Journal of Applied Earth Observation and Geoinformation, 105(November), 102623. https://doi.org/10.1016/j.jag.2021.102623
Greasby, T. A., & Sain, S. R. (2011). Multivariate Spatial Analysis of Climate Change Projections. Journal of Agricultural, Biological, and Environmental Statistics, 16(4), 571–585. https://doi.org/10.1007/s13253-011-0072-8
Hennon, C. C., Papin, P. P., Zarzar, C. M., & ... (2013). Tropical cloud cluster climatology, variability, and genesis productivity. In Journal of …. journals.ametsoc.org. https://journals.ametsoc.org/view/journals/clim/26/10/jcli-d-12-00387.1.xml?tab_body=abstract-display
Kouchaksaraei, H. R., & Karl, H. (2019). Service function chaining across openstack and kubernetes domains. DEBS 2019 - Proceedings of the 13th ACM International Conference on Distributed and Event-Based Systems, 240–243. https://doi.org/10.1145/3328905.3332505
Kuo, P. F., Huang, T. E., & Putra, I. G. B. (2021). Comparing kriging estimators using weather station data and local greenhouse sensors. Sensors, 21(5), 1–15. https://doi.org/10.3390/s21051853
Li, Y., Hernandez, J. H., Aviles, M., Knappett, P. S. K., Giardino, J. R., Miranda, R., Puy, M. J., Padilla, F., & Morales, J. (n.d.). Empirical Bayesian Kriging method to evaluate inter-annual water-table evolution in the Cuenca Alta del Río Laja aquifer, Guanajuato, México. 582, 124517. https://doi.org/https://doi.org/10.1016/j.jhydrol.2019.124517
Mahlstein, I., & Knutti, R. (2010). Regional climate change patterns identified by cluster analysis. Climate Dynamics, 35(4), 587–600. https://doi.org/10.1007/s00382-009-0654-0
Marchetti, Y., Nguyen, H., Braverman, A., & Cressie, N. (n.d.). Spatial data compression via adaptive dispersion clustering. 117, 138–153. https://doi.org/https://doi.org/10.1016/j.csda.2017.08.004
Masri, S., Jin, Y., & Wu, J. (2022). Compound Risk of Air Pollution and Heat Days and the Influence of Wildfire by SES across California, 2018–2020: Implications for Environmental Justice in the Context of Climate Change. Climate, 10(10), 2018–2020. https://doi.org/10.3390/cli10100145
Massimo, A., & Corrado, C. (2020). Bilionshiny biliomertrix. https://www.bibliometrix.org/biblioshiny/
Mojiri, A., Waghei, Y., Sani, H. R. N., & Borzadaran, G. R. M. (2018). Comparison of predictions by kriging and spatial autoregressive models. Communications in Statistics: Simulation and Computation, 47(6), 1785–1795. https://doi.org/10.1080/03610918.2017.1324980
Ohana-Levi, N., Bahat, I., Peeters, A., Shtein, A., Netzer, Y., Cohen, Y., & Ben-Gal, A. (n.d.). A weighted multivariate spatial clustering model to determine irrigation management zones. 162, 719–731. https://doi.org/https://doi.org/10.1016/j.compag.2019.05.012
Orton, T. G., Pringle, M. J., Bishop, T. F. A., Menzies, N. W., & Dang, Y. P. (n.d.). Increment-averaged kriging for 3-D modelling and mapping soil properties: Combining machine learning and geostatistical methods. 361, 114094. https://doi.org/https://doi.org/10.1016/j.geoderma.2019.114094
Ouyang, Y., & Panda, S. S. (2022). Linking Climate-Change Impacts on Hydrological Processes. Climate, 10(7), 1–5. https://doi.org/10.3390/cli10070096
Palmatier, R. W., Houston, M. B., & Hulland, J. (2018). Review articles: purpose, process, and structure. Journal of the Academy of Marketing Science, 46(1), 1–5. https://doi.org/10.1007/s11747-017-0563-4
Paramasivam, C. R., & Venkatramanan, S. (2019). An introduction to various spatial analysis techniques. GIS and Geostatistical Techniques for Groundwater Science, June, 23–30. https://doi.org/10.1016/B978-0-12-815413-7.00003-1
Peirce, A. M., Espira, L. M., & Larson, P. S. (2022). Climate Change Related Catastrophic Rainfall Events and Non-Communicable Respiratory Disease: A Systematic Review of the Literature. Climate, 10(7). https://doi.org/10.3390/cli10070101
Pinar Aslantas Bostan, Z. (2006). Exploring the Mean Annual Precipitation and Temperature Values Over Turkey By Using Environmental Variables. June 2014, 1–6.
Qiu, B., Zhou, M., Qiu, Y., Liu, S., Ou, G., Ma, C., Tu, J., & Li, S. (2022). An Integrated Spatial Autoregressive Model for Analyzing and Simulating Urban Spatial Growth in a Garden City, China. International Journal of Environmental Research and Public Health, 19(18). https://doi.org/10.3390/ijerph191811732
Querner, P., Sterflinger, K., Derksen, K., Leissner, J., Landsberger, B., Hammer, A., & Brimblecombe, P. (2022). Climate Change and Its Effects on Indoor Pests (Insect and Fungi) in Museums. Climate, 10(7), 1–10. https://doi.org/10.3390/cli10070103
Requia, W. J., Coull, B. A., & Koutrakis, P. (2019). The influence of spatial patterning on modeling PM2.5 constituents in Eastern Massachusetts. Science of the Total Environment, 682, 247–258. https://doi.org/10.1016/j.scitotenv.2019.05.012
Ribeiro, M. C., Pinho, P., Branquinho, C., Llop, E., & Pereira, M. J. (2016). Geostatistical uncertainty of assessing air quality using high-spatial-resolution lichen data: A health study in the urban area of Sines, Portugal. Science of the Total Environment, 562, 740–750. https://doi.org/10.1016/j.scitotenv.2016.04.081
Sadiq, S., Saboor, A., Mohsin, A. Q., Khalid, A., & Tanveer, F. (2019). Ricardian analysis of climate change–agriculture linkages in Pakistan. Climate and Development, 11(8), 679–686. https://doi.org/10.1080/17565529.2018.1531746
Samrat, B. K., & Alok, K. B. (2017). Climate sensitivities and farmland values in Nepal: A spatial panel Ricardian approach. Journal of Development and Agricultural Economics, 9(6), 145–161. https://doi.org/10.5897/jdae2017.0822
Shekhar, S., Evans, M. R., Kang, J. M., & ... (2011). Identifying patterns in spatial information: A survey of methods. Reviews: Data Mining …. https://onlinelibrary.wiley.com/doi/abs/10.1002/widm.25
Silva, M. V. da, Pandorfi, H., Jardim, A. M. da R. F., Oliveira-Júnior, J. F. de, Divincula, J. S. da, Giongo, P. R., Silva, T. G. F. da, Almeida, G. L. P. de, Moura, G. B. de A., & Lopes, P. M. O. (2021). Spatial modeling of rainfall patterns and groundwater on the coast of northeastern Brazil. Urban Climate, 38(February). https://doi.org/10.1016/j.uclim.2021.100911
McKee, T. B., Doesken, N. J., & Kleist, J. (1993). Analysis of Standardized Precipitation Index (SPI) data for drought assessment. Water (switzerland), 26(2), 1-72. https://doi.org/10.1088/1755-1315/5
Troiani, F., Piacentini, D., Della Seta, M., & Galve, J. P. (2017). Stream Length-gradient Hotspot and Cluster Analysis (SL-HCA) to fine-tune the detection and interpretation of knickzones on longitudinal profiles. Catena, 156(March), 30–41. https://doi.org/10.1016/j.catena.2017.03.015
Van Eck, N. J., & Waltman, L. (2019). Manual for VOSviwer version 1.6.10. CWTS Meaningful Metrics, January, 1–53. https://www.vosviewer.com/documentation/Manual_VOSviewer_1.6.10.pdf
Yang, X., Xie, X., Liu, D. L., Ji, F., & Wang, L. (2015). Spatial Interpolation of Daily Rainfall Data for Local Climate Impact Assessment over Greater Sydney Region. Advances in Meteorology, 2015. https://doi.org/10.1155/2015/563629
Yokoi, S., Takayabu, Y. N., Nishii, K., Nakamura, H., Endo, H., Ichikawa, H., Inoue, T., Kimoto, M., Kosaka, Y., Miyasaka, T., Oshima, K., Sato, N., Tsushima, Y., & Watanabe, M. (2011). Application of cluster analysis to climate model performance metrics. Journal of Applied Meteorology and Climatology, 50(8), 1666–1675. https://doi.org/10.1175/2011JAMC2643.1
Zhang, C., Tang, Y., Xu, X., & Kiely, G. (2011). Towards spatial geochemical modelling: Use of geographically weighted regression for mapping soil organic carbon contents in Ireland. Applied Geochemistry, 26(7), 1239–1248. https://doi.org/10.1016/j.apgeochem.2011.04.014
Zomer, R. J., Trabucco, A., Bossio, D. A., & Verchot, L. V. (2008). Climate change mitigation: A spatial analysis of global land suitability for clean development mechanism afforestation and reforestation. Agriculture, Ecosystems and Environment, 126(1–2), 67–80. https://doi.org/10.1016/j.agee.2008.01.014
Banzhaf, E., Bulley, H. N., Inkoom, J. N., & Elze, S. (2022). Mapping Open Data and Big Data to Address Climate Resilience of Urban Informal Settlements in Sub-Saharan Africa. 1–13.
Carvalho, M. J., Melo-Gonçalves, P., Teixeira, J. C., & Rocha, A. (2016). Regionalization of Europe based on a K-Means Cluster Analysis of the climate change of temperatures and precipitation. Physics and Chemistry of the Earth, 94, 22–28. https://doi.org/10.1016/j.pce.2016.05.001
Efe, B., Gözet, E., Özgür, E., Lupo, A. R., & Deniz, A. (2022). Spatiotemporal Variation of Tourism Climate Index for Türkiye during 1981–2020. Climate, 10(10), 1–32. https://doi.org/10.3390/cli10100151
Ellegaard, O., & Wallin, J. A. (2015). The bibliometric analysis of scholarly production: How great is the impact? Scientometrics, 105(3), 1809–1831. https://doi.org/10.1007/s11192-015-1645-z
Felix, A. Y., Vinay, G. S. S., & Akhik, G. (2019). K-Means cluster using rainfall and storm prediction in machine learning technique. Journal of Computational and Theoretical Nanoscience, 16(8), 3265–3269. https://doi.org/10.1166/jctn.2019.8174
Ghavidel, Y., Baghbanan, P., & Farajzadeh, M. (n.d.). The spatial analysis of thunderstorm hazard in Iran. 10(5). https://doi.org/10.1007/s12517-017-2902-7
Gonzalez-Gonzalez, M. A., & Guertin, D. P. (2021). Seasonal bean yield forecast for non-irrigated croplands through climate and vegetation index data: Geospatial effects. International Journal of Applied Earth Observation and Geoinformation, 105(November), 102623. https://doi.org/10.1016/j.jag.2021.102623
Greasby, T. A., & Sain, S. R. (2011). Multivariate Spatial Analysis of Climate Change Projections. Journal of Agricultural, Biological, and Environmental Statistics, 16(4), 571–585. https://doi.org/10.1007/s13253-011-0072-8
Hennon, C. C., Papin, P. P., Zarzar, C. M., & ... (2013). Tropical cloud cluster climatology, variability, and genesis productivity. In Journal of …. journals.ametsoc.org. https://journals.ametsoc.org/view/journals/clim/26/10/jcli-d-12-00387.1.xml?tab_body=abstract-display
Kouchaksaraei, H. R., & Karl, H. (2019). Service function chaining across openstack and kubernetes domains. DEBS 2019 - Proceedings of the 13th ACM International Conference on Distributed and Event-Based Systems, 240–243. https://doi.org/10.1145/3328905.3332505
Kuo, P. F., Huang, T. E., & Putra, I. G. B. (2021). Comparing kriging estimators using weather station data and local greenhouse sensors. Sensors, 21(5), 1–15. https://doi.org/10.3390/s21051853
Li, Y., Hernandez, J. H., Aviles, M., Knappett, P. S. K., Giardino, J. R., Miranda, R., Puy, M. J., Padilla, F., & Morales, J. (n.d.). Empirical Bayesian Kriging method to evaluate inter-annual water-table evolution in the Cuenca Alta del Río Laja aquifer, Guanajuato, México. 582, 124517. https://doi.org/https://doi.org/10.1016/j.jhydrol.2019.124517
Mahlstein, I., & Knutti, R. (2010). Regional climate change patterns identified by cluster analysis. Climate Dynamics, 35(4), 587–600. https://doi.org/10.1007/s00382-009-0654-0
Marchetti, Y., Nguyen, H., Braverman, A., & Cressie, N. (n.d.). Spatial data compression via adaptive dispersion clustering. 117, 138–153. https://doi.org/https://doi.org/10.1016/j.csda.2017.08.004
Masri, S., Jin, Y., & Wu, J. (2022). Compound Risk of Air Pollution and Heat Days and the Influence of Wildfire by SES across California, 2018–2020: Implications for Environmental Justice in the Context of Climate Change. Climate, 10(10), 2018–2020. https://doi.org/10.3390/cli10100145
Massimo, A., & Corrado, C. (2020). Bilionshiny biliomertrix. https://www.bibliometrix.org/biblioshiny/
Mojiri, A., Waghei, Y., Sani, H. R. N., & Borzadaran, G. R. M. (2018). Comparison of predictions by kriging and spatial autoregressive models. Communications in Statistics: Simulation and Computation, 47(6), 1785–1795. https://doi.org/10.1080/03610918.2017.1324980
Ohana-Levi, N., Bahat, I., Peeters, A., Shtein, A., Netzer, Y., Cohen, Y., & Ben-Gal, A. (n.d.). A weighted multivariate spatial clustering model to determine irrigation management zones. 162, 719–731. https://doi.org/https://doi.org/10.1016/j.compag.2019.05.012
Orton, T. G., Pringle, M. J., Bishop, T. F. A., Menzies, N. W., & Dang, Y. P. (n.d.). Increment-averaged kriging for 3-D modelling and mapping soil properties: Combining machine learning and geostatistical methods. 361, 114094. https://doi.org/https://doi.org/10.1016/j.geoderma.2019.114094
Ouyang, Y., & Panda, S. S. (2022). Linking Climate-Change Impacts on Hydrological Processes. Climate, 10(7), 1–5. https://doi.org/10.3390/cli10070096
Palmatier, R. W., Houston, M. B., & Hulland, J. (2018). Review articles: purpose, process, and structure. Journal of the Academy of Marketing Science, 46(1), 1–5. https://doi.org/10.1007/s11747-017-0563-4
Paramasivam, C. R., & Venkatramanan, S. (2019). An introduction to various spatial analysis techniques. GIS and Geostatistical Techniques for Groundwater Science, June, 23–30. https://doi.org/10.1016/B978-0-12-815413-7.00003-1
Peirce, A. M., Espira, L. M., & Larson, P. S. (2022). Climate Change Related Catastrophic Rainfall Events and Non-Communicable Respiratory Disease: A Systematic Review of the Literature. Climate, 10(7). https://doi.org/10.3390/cli10070101
Pinar Aslantas Bostan, Z. (2006). Exploring the Mean Annual Precipitation and Temperature Values Over Turkey By Using Environmental Variables. June 2014, 1–6.
Qiu, B., Zhou, M., Qiu, Y., Liu, S., Ou, G., Ma, C., Tu, J., & Li, S. (2022). An Integrated Spatial Autoregressive Model for Analyzing and Simulating Urban Spatial Growth in a Garden City, China. International Journal of Environmental Research and Public Health, 19(18). https://doi.org/10.3390/ijerph191811732
Querner, P., Sterflinger, K., Derksen, K., Leissner, J., Landsberger, B., Hammer, A., & Brimblecombe, P. (2022). Climate Change and Its Effects on Indoor Pests (Insect and Fungi) in Museums. Climate, 10(7), 1–10. https://doi.org/10.3390/cli10070103
Requia, W. J., Coull, B. A., & Koutrakis, P. (2019). The influence of spatial patterning on modeling PM2.5 constituents in Eastern Massachusetts. Science of the Total Environment, 682, 247–258. https://doi.org/10.1016/j.scitotenv.2019.05.012
Ribeiro, M. C., Pinho, P., Branquinho, C., Llop, E., & Pereira, M. J. (2016). Geostatistical uncertainty of assessing air quality using high-spatial-resolution lichen data: A health study in the urban area of Sines, Portugal. Science of the Total Environment, 562, 740–750. https://doi.org/10.1016/j.scitotenv.2016.04.081
Sadiq, S., Saboor, A., Mohsin, A. Q., Khalid, A., & Tanveer, F. (2019). Ricardian analysis of climate change–agriculture linkages in Pakistan. Climate and Development, 11(8), 679–686. https://doi.org/10.1080/17565529.2018.1531746
Samrat, B. K., & Alok, K. B. (2017). Climate sensitivities and farmland values in Nepal: A spatial panel Ricardian approach. Journal of Development and Agricultural Economics, 9(6), 145–161. https://doi.org/10.5897/jdae2017.0822
Shekhar, S., Evans, M. R., Kang, J. M., & ... (2011). Identifying patterns in spatial information: A survey of methods. Reviews: Data Mining …. https://onlinelibrary.wiley.com/doi/abs/10.1002/widm.25
Silva, M. V. da, Pandorfi, H., Jardim, A. M. da R. F., Oliveira-Júnior, J. F. de, Divincula, J. S. da, Giongo, P. R., Silva, T. G. F. da, Almeida, G. L. P. de, Moura, G. B. de A., & Lopes, P. M. O. (2021). Spatial modeling of rainfall patterns and groundwater on the coast of northeastern Brazil. Urban Climate, 38(February). https://doi.org/10.1016/j.uclim.2021.100911
McKee, T. B., Doesken, N. J., & Kleist, J. (1993). Analysis of Standardized Precipitation Index (SPI) data for drought assessment. Water (switzerland), 26(2), 1-72. https://doi.org/10.1088/1755-1315/5
Troiani, F., Piacentini, D., Della Seta, M., & Galve, J. P. (2017). Stream Length-gradient Hotspot and Cluster Analysis (SL-HCA) to fine-tune the detection and interpretation of knickzones on longitudinal profiles. Catena, 156(March), 30–41. https://doi.org/10.1016/j.catena.2017.03.015
Van Eck, N. J., & Waltman, L. (2019). Manual for VOSviwer version 1.6.10. CWTS Meaningful Metrics, January, 1–53. https://www.vosviewer.com/documentation/Manual_VOSviewer_1.6.10.pdf
Yang, X., Xie, X., Liu, D. L., Ji, F., & Wang, L. (2015). Spatial Interpolation of Daily Rainfall Data for Local Climate Impact Assessment over Greater Sydney Region. Advances in Meteorology, 2015. https://doi.org/10.1155/2015/563629
Yokoi, S., Takayabu, Y. N., Nishii, K., Nakamura, H., Endo, H., Ichikawa, H., Inoue, T., Kimoto, M., Kosaka, Y., Miyasaka, T., Oshima, K., Sato, N., Tsushima, Y., & Watanabe, M. (2011). Application of cluster analysis to climate model performance metrics. Journal of Applied Meteorology and Climatology, 50(8), 1666–1675. https://doi.org/10.1175/2011JAMC2643.1
Zhang, C., Tang, Y., Xu, X., & Kiely, G. (2011). Towards spatial geochemical modelling: Use of geographically weighted regression for mapping soil organic carbon contents in Ireland. Applied Geochemistry, 26(7), 1239–1248. https://doi.org/10.1016/j.apgeochem.2011.04.014
Zomer, R. J., Trabucco, A., Bossio, D. A., & Verchot, L. V. (2008). Climate change mitigation: A spatial analysis of global land suitability for clean development mechanism afforestation and reforestation. Agriculture, Ecosystems and Environment, 126(1–2), 67–80. https://doi.org/10.1016/j.agee.2008.01.014