M. Barcelos, F. Bernardini, A. Barcelos, Guido Vaz Silva
{"title":"基于金融流动性指标聚类的城市排名","authors":"M. Barcelos, F. Bernardini, A. Barcelos, Guido Vaz Silva","doi":"10.1145/3085228.3085288","DOIUrl":null,"url":null,"abstract":"Nowadays, many cities around the world adopted different definitions of Smart Cities. Many organizations proposed collections of indicators for evaluating how smart the cities are. One problem related to applying these indicators is how to compare fairly the cities, considering that they are quite different in some countries or regions, like in Brazil. Therefore, grouping similar cities should be interesting, though there are different methods for grouping them. This work proposes a method for grouping cities based on their financial features, supported by clustering and regression techniques. This work also present an evaluation of the proposed method using real data from cities located in Rio de Janeiro state in Brazil. The results show the feasibility of the method, although obtaining the specifically type of used data is yet a challenge.1","PeriodicalId":416111,"journal":{"name":"Proceedings of the 18th Annual International Conference on Digital Government Research","volume":"21 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"City Ranking Based on Financial Flux Indicator Clustering\",\"authors\":\"M. Barcelos, F. Bernardini, A. Barcelos, Guido Vaz Silva\",\"doi\":\"10.1145/3085228.3085288\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Nowadays, many cities around the world adopted different definitions of Smart Cities. Many organizations proposed collections of indicators for evaluating how smart the cities are. One problem related to applying these indicators is how to compare fairly the cities, considering that they are quite different in some countries or regions, like in Brazil. Therefore, grouping similar cities should be interesting, though there are different methods for grouping them. This work proposes a method for grouping cities based on their financial features, supported by clustering and regression techniques. This work also present an evaluation of the proposed method using real data from cities located in Rio de Janeiro state in Brazil. The results show the feasibility of the method, although obtaining the specifically type of used data is yet a challenge.1\",\"PeriodicalId\":416111,\"journal\":{\"name\":\"Proceedings of the 18th Annual International Conference on Digital Government Research\",\"volume\":\"21 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-06-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 18th Annual International Conference on Digital Government Research\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3085228.3085288\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 18th Annual International Conference on Digital Government Research","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3085228.3085288","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
City Ranking Based on Financial Flux Indicator Clustering
Nowadays, many cities around the world adopted different definitions of Smart Cities. Many organizations proposed collections of indicators for evaluating how smart the cities are. One problem related to applying these indicators is how to compare fairly the cities, considering that they are quite different in some countries or regions, like in Brazil. Therefore, grouping similar cities should be interesting, though there are different methods for grouping them. This work proposes a method for grouping cities based on their financial features, supported by clustering and regression techniques. This work also present an evaluation of the proposed method using real data from cities located in Rio de Janeiro state in Brazil. The results show the feasibility of the method, although obtaining the specifically type of used data is yet a challenge.1