{"title":"重新定义尼日利亚的气候带","authors":"I. Agada, S. I. Aondoakaa, E. J. Eweh","doi":"10.9734/psij/2023/v27i4796","DOIUrl":null,"url":null,"abstract":"Aims: This study aimed at identifying climatic zones over Nigeria. \nDuration of Study: Daily air temperature data for the 36 states and FCT in Nigeria were obtained from National Aeronautic and Space Administration (NASA) for the period of thirty-seven (37) years (1984-2020). \nMethod: Several hierarchical clustering procedures–single linkages, complete linkage, average distance within clusters, average distance between clusters, centroid clustering, median linkage and Ward’s method were used in this study. \nResults: Based on the findings, median linkage, complete linkage and Ward’s clustering method solutions seems more realistic. The application of cluster analysis, revealed five climate zones (cluster) over Nigeria. Cluster 1 covers Plateau state only while cluster 2 covers three south-west states and one north-west state (Kaduna). Cluster 3 covers all the states in south-east, south-south (except Cross River), North-central (except Kogi and Kwara), Bauchi in North-west and ogun and Oyo in South-west. Cluster 4 covers one state in south-south (Cross River), South-south (Lagos), two states in North-central (Kogi and Kwara), three states in North-east (Adamawa, Gombe and Taraba) and four states in North-west (Jigawa, Kano, Kastina and Zamfara). Lastly cluster 5 covers two states in North-east (Borno and Yobe) and North-west (Kebbi and Sokoto). \nConclusion: Our findings clearly show that cluster analysis can be applied to identify similar weather/climate state from air temperature data.","PeriodicalId":124795,"journal":{"name":"Physical Science International Journal","volume":"1994 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Re-defining the Climatic Zones over Nigeria\",\"authors\":\"I. Agada, S. I. Aondoakaa, E. J. Eweh\",\"doi\":\"10.9734/psij/2023/v27i4796\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Aims: This study aimed at identifying climatic zones over Nigeria. \\nDuration of Study: Daily air temperature data for the 36 states and FCT in Nigeria were obtained from National Aeronautic and Space Administration (NASA) for the period of thirty-seven (37) years (1984-2020). \\nMethod: Several hierarchical clustering procedures–single linkages, complete linkage, average distance within clusters, average distance between clusters, centroid clustering, median linkage and Ward’s method were used in this study. \\nResults: Based on the findings, median linkage, complete linkage and Ward’s clustering method solutions seems more realistic. The application of cluster analysis, revealed five climate zones (cluster) over Nigeria. Cluster 1 covers Plateau state only while cluster 2 covers three south-west states and one north-west state (Kaduna). Cluster 3 covers all the states in south-east, south-south (except Cross River), North-central (except Kogi and Kwara), Bauchi in North-west and ogun and Oyo in South-west. Cluster 4 covers one state in south-south (Cross River), South-south (Lagos), two states in North-central (Kogi and Kwara), three states in North-east (Adamawa, Gombe and Taraba) and four states in North-west (Jigawa, Kano, Kastina and Zamfara). Lastly cluster 5 covers two states in North-east (Borno and Yobe) and North-west (Kebbi and Sokoto). \\nConclusion: Our findings clearly show that cluster analysis can be applied to identify similar weather/climate state from air temperature data.\",\"PeriodicalId\":124795,\"journal\":{\"name\":\"Physical Science International Journal\",\"volume\":\"1994 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-08-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Physical Science International Journal\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.9734/psij/2023/v27i4796\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Physical Science International Journal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.9734/psij/2023/v27i4796","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Aims: This study aimed at identifying climatic zones over Nigeria.
Duration of Study: Daily air temperature data for the 36 states and FCT in Nigeria were obtained from National Aeronautic and Space Administration (NASA) for the period of thirty-seven (37) years (1984-2020).
Method: Several hierarchical clustering procedures–single linkages, complete linkage, average distance within clusters, average distance between clusters, centroid clustering, median linkage and Ward’s method were used in this study.
Results: Based on the findings, median linkage, complete linkage and Ward’s clustering method solutions seems more realistic. The application of cluster analysis, revealed five climate zones (cluster) over Nigeria. Cluster 1 covers Plateau state only while cluster 2 covers three south-west states and one north-west state (Kaduna). Cluster 3 covers all the states in south-east, south-south (except Cross River), North-central (except Kogi and Kwara), Bauchi in North-west and ogun and Oyo in South-west. Cluster 4 covers one state in south-south (Cross River), South-south (Lagos), two states in North-central (Kogi and Kwara), three states in North-east (Adamawa, Gombe and Taraba) and four states in North-west (Jigawa, Kano, Kastina and Zamfara). Lastly cluster 5 covers two states in North-east (Borno and Yobe) and North-west (Kebbi and Sokoto).
Conclusion: Our findings clearly show that cluster analysis can be applied to identify similar weather/climate state from air temperature data.