Clustering spatial autoregressive kriging model for climate: A bibliometric analysis approach
Climate change is caused by temperature, rainfall, and wind variation in locations that last a long time. This change can be described and predicted using a spatial model, one of which is the Clustering Spatial Autoregressive (SAR) Kriging model. Therefore, this research aims to conduct a b...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Growing Science
2023-01-01
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| Series: | International Journal of Data and Network Science |
| Online Access: | http://www.growingscience.com/ijds/Vol7/ijdns_2023_30.pdf |
| _version_ | 1797862285471907840 |
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| author | Annisa Nur Falah Budi Nurani Ruchjana Atje Setiawan Abdullah Juli Rejito |
| author_facet | Annisa Nur Falah Budi Nurani Ruchjana Atje Setiawan Abdullah Juli Rejito |
| author_sort | Annisa Nur Falah |
| collection | DOAJ |
| description | Climate change is caused by temperature, rainfall, and wind variation in locations that last a long time. This change can be described and predicted using a spatial model, one of which is the Clustering Spatial Autoregressive (SAR) Kriging model. Therefore, this research aims to conduct a bibliometric analysis in a spatial and Clustering SAR Kriging model on climate change. It presents a Systematic Literature Review (SLR) with the development of the Clustering SAR Kriging model, incorporating articles from the Google Scholar, ScienceDirect, Dimensions AI, and Scopus databases from 2011-2021. Furthermore, two stages of analysis have been conducted, first, bibliometric analysis was performed for mapping and thematic evolution using VOSviewer software and R-biblioshiny. This analysis generated 185 papers after conducting a duplication check and developed a network of research on evolutionary subject matters at this stage. Second, research subjects were analyzed using the Clustering SAR Kriging model. More screening criteria were followed, and 18 articles were obtained for the SLR analysis. Furthermore, the development of the Clustering SAR Kriging model was observed for the prediction and description of climate change. The results are predicted to benefit applicable businesses to predict climate phenomena in unobserved places. |
| first_indexed | 2024-04-09T22:16:46Z |
| format | Article |
| id | doaj.art-f7a1983e607e49fda2fb11f634a322b5 |
| institution | Directory Open Access Journal |
| issn | 2561-8148 2561-8156 |
| language | English |
| last_indexed | 2024-04-09T22:16:46Z |
| publishDate | 2023-01-01 |
| publisher | Growing Science |
| record_format | Article |
| series | International Journal of Data and Network Science |
| spelling | doaj.art-f7a1983e607e49fda2fb11f634a322b52023-03-23T03:34:23ZengGrowing ScienceInternational Journal of Data and Network Science2561-81482561-81562023-01-017263764610.5267/j.ijdns.2023.3.008Clustering spatial autoregressive kriging model for climate: A bibliometric analysis approachAnnisa Nur FalahBudi Nurani RuchjanaAtje Setiawan AbdullahJuli Rejito Climate change is caused by temperature, rainfall, and wind variation in locations that last a long time. This change can be described and predicted using a spatial model, one of which is the Clustering Spatial Autoregressive (SAR) Kriging model. Therefore, this research aims to conduct a bibliometric analysis in a spatial and Clustering SAR Kriging model on climate change. It presents a Systematic Literature Review (SLR) with the development of the Clustering SAR Kriging model, incorporating articles from the Google Scholar, ScienceDirect, Dimensions AI, and Scopus databases from 2011-2021. Furthermore, two stages of analysis have been conducted, first, bibliometric analysis was performed for mapping and thematic evolution using VOSviewer software and R-biblioshiny. This analysis generated 185 papers after conducting a duplication check and developed a network of research on evolutionary subject matters at this stage. Second, research subjects were analyzed using the Clustering SAR Kriging model. More screening criteria were followed, and 18 articles were obtained for the SLR analysis. Furthermore, the development of the Clustering SAR Kriging model was observed for the prediction and description of climate change. The results are predicted to benefit applicable businesses to predict climate phenomena in unobserved places.http://www.growingscience.com/ijds/Vol7/ijdns_2023_30.pdf |
| spellingShingle | Annisa Nur Falah Budi Nurani Ruchjana Atje Setiawan Abdullah Juli Rejito Clustering spatial autoregressive kriging model for climate: A bibliometric analysis approach International Journal of Data and Network Science |
| title | Clustering spatial autoregressive kriging model for climate: A bibliometric analysis approach |
| title_full | Clustering spatial autoregressive kriging model for climate: A bibliometric analysis approach |
| title_fullStr | Clustering spatial autoregressive kriging model for climate: A bibliometric analysis approach |
| title_full_unstemmed | Clustering spatial autoregressive kriging model for climate: A bibliometric analysis approach |
| title_short | Clustering spatial autoregressive kriging model for climate: A bibliometric analysis approach |
| title_sort | clustering spatial autoregressive kriging model for climate a bibliometric analysis approach |
| url | http://www.growingscience.com/ijds/Vol7/ijdns_2023_30.pdf |
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