{"title":"基于ArcGIS制图和聚类分析的移民外流与汇款流入效应比较研究","authors":"Md. Ashraful Islam, M. Rokonuzzaman","doi":"10.58970/ijsb.2176","DOIUrl":null,"url":null,"abstract":"This study’s goal is to determine how remittances affect migration. Data from 45 nations is utilized for this analysis, which is separated into six regions: The Middle East and North Africa (MENA), the Association of Southeast Asian Nations (ASEAN), East Africa, South Africa, and West Africa. Utilizing descriptive statistics, significant graphs, and cluster analysis, this investigation is thoroughly and properly completed. The data displayed in ArcGIS maps also demonstrate the emigration and remittance outflows of several Asian and African nations between 1985 and 2017. By considering five clusters, cluster analysis is used to analyze similar nations in terms of how remittances affect migration. Three, eight, thirteen, nineteen, and three of these 45 nations make up Clusters I through V, respectively. Cluster V is made up of Bangladesh, India, and China. As II and III are the most distant clusters and II and III are the closest clusters, the cluster distance between III and V is low and between II and III is very high. The absence of time-series migration data is the key constant in this investigation. To achieve successful outcomes, simulation studies might be used. In order to obtain better results, anyone can also include discrimination analysis in their analysis. ArcGIS is also used to create visually appealing data distribution visualizations, such as maps, charts, and graphs with legends and annotations.","PeriodicalId":297563,"journal":{"name":"International Journal of Science and Business","volume":"13 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Comparative Study on Migration Outflows and the Effects of Remittance Inflows Utilizing ArcGIS Mapping and Cluster Analysis\",\"authors\":\"Md. Ashraful Islam, M. Rokonuzzaman\",\"doi\":\"10.58970/ijsb.2176\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This study’s goal is to determine how remittances affect migration. Data from 45 nations is utilized for this analysis, which is separated into six regions: The Middle East and North Africa (MENA), the Association of Southeast Asian Nations (ASEAN), East Africa, South Africa, and West Africa. Utilizing descriptive statistics, significant graphs, and cluster analysis, this investigation is thoroughly and properly completed. The data displayed in ArcGIS maps also demonstrate the emigration and remittance outflows of several Asian and African nations between 1985 and 2017. By considering five clusters, cluster analysis is used to analyze similar nations in terms of how remittances affect migration. Three, eight, thirteen, nineteen, and three of these 45 nations make up Clusters I through V, respectively. Cluster V is made up of Bangladesh, India, and China. As II and III are the most distant clusters and II and III are the closest clusters, the cluster distance between III and V is low and between II and III is very high. The absence of time-series migration data is the key constant in this investigation. To achieve successful outcomes, simulation studies might be used. In order to obtain better results, anyone can also include discrimination analysis in their analysis. ArcGIS is also used to create visually appealing data distribution visualizations, such as maps, charts, and graphs with legends and annotations.\",\"PeriodicalId\":297563,\"journal\":{\"name\":\"International Journal of Science and Business\",\"volume\":\"13 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Science and Business\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.58970/ijsb.2176\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Science and Business","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.58970/ijsb.2176","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Comparative Study on Migration Outflows and the Effects of Remittance Inflows Utilizing ArcGIS Mapping and Cluster Analysis
This study’s goal is to determine how remittances affect migration. Data from 45 nations is utilized for this analysis, which is separated into six regions: The Middle East and North Africa (MENA), the Association of Southeast Asian Nations (ASEAN), East Africa, South Africa, and West Africa. Utilizing descriptive statistics, significant graphs, and cluster analysis, this investigation is thoroughly and properly completed. The data displayed in ArcGIS maps also demonstrate the emigration and remittance outflows of several Asian and African nations between 1985 and 2017. By considering five clusters, cluster analysis is used to analyze similar nations in terms of how remittances affect migration. Three, eight, thirteen, nineteen, and three of these 45 nations make up Clusters I through V, respectively. Cluster V is made up of Bangladesh, India, and China. As II and III are the most distant clusters and II and III are the closest clusters, the cluster distance between III and V is low and between II and III is very high. The absence of time-series migration data is the key constant in this investigation. To achieve successful outcomes, simulation studies might be used. In order to obtain better results, anyone can also include discrimination analysis in their analysis. ArcGIS is also used to create visually appealing data distribution visualizations, such as maps, charts, and graphs with legends and annotations.