{"title":"意大利社会不平等的多维统计分析","authors":"Paola Perchinunno , Samuela L'Abbate , Corrado Crocetta , Leonardo Salvatore Alaimo","doi":"10.1016/j.seps.2024.102005","DOIUrl":null,"url":null,"abstract":"<div><p>The analysis of social inequalities is a topic of current interest and is studied as a factor in the evolution and measurement of the level of well-being. A fundamental prerequisite for a correct statistical analysis of this phenomenon is the need to share a univocal definition of the concept of social inequalities. This work starts from the need to identify territorial areas and/or population subgroups characterized by situations of hardship or strong social exclusion through the construction of indicators that can estimate situations of social inequalities in small areas. Scientific research options have been oriented towards the establishment of a multidimensional approach, sometimes renouncing dichotomous logic to go as far as fuzzy classifications in which each unit simultaneously belongs and does not belong to the selected category. Multidimensional statistical analysis methodologies (TFR method) and Density Based Spatial Clustering methods (DBSCAN) will therefore be used to aggregate adjacent spatial units with high intensity of social inequalities.</p></div>","PeriodicalId":22033,"journal":{"name":"Socio-economic Planning Sciences","volume":"95 ","pages":"Article 102005"},"PeriodicalIF":6.2000,"publicationDate":"2024-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Multidimensional statistical analysis of social inequalities in Italy\",\"authors\":\"Paola Perchinunno , Samuela L'Abbate , Corrado Crocetta , Leonardo Salvatore Alaimo\",\"doi\":\"10.1016/j.seps.2024.102005\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>The analysis of social inequalities is a topic of current interest and is studied as a factor in the evolution and measurement of the level of well-being. A fundamental prerequisite for a correct statistical analysis of this phenomenon is the need to share a univocal definition of the concept of social inequalities. This work starts from the need to identify territorial areas and/or population subgroups characterized by situations of hardship or strong social exclusion through the construction of indicators that can estimate situations of social inequalities in small areas. Scientific research options have been oriented towards the establishment of a multidimensional approach, sometimes renouncing dichotomous logic to go as far as fuzzy classifications in which each unit simultaneously belongs and does not belong to the selected category. Multidimensional statistical analysis methodologies (TFR method) and Density Based Spatial Clustering methods (DBSCAN) will therefore be used to aggregate adjacent spatial units with high intensity of social inequalities.</p></div>\",\"PeriodicalId\":22033,\"journal\":{\"name\":\"Socio-economic Planning Sciences\",\"volume\":\"95 \",\"pages\":\"Article 102005\"},\"PeriodicalIF\":6.2000,\"publicationDate\":\"2024-06-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Socio-economic Planning Sciences\",\"FirstCategoryId\":\"96\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0038012124002040\",\"RegionNum\":2,\"RegionCategory\":\"经济学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ECONOMICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Socio-economic Planning Sciences","FirstCategoryId":"96","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0038012124002040","RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ECONOMICS","Score":null,"Total":0}
Multidimensional statistical analysis of social inequalities in Italy
The analysis of social inequalities is a topic of current interest and is studied as a factor in the evolution and measurement of the level of well-being. A fundamental prerequisite for a correct statistical analysis of this phenomenon is the need to share a univocal definition of the concept of social inequalities. This work starts from the need to identify territorial areas and/or population subgroups characterized by situations of hardship or strong social exclusion through the construction of indicators that can estimate situations of social inequalities in small areas. Scientific research options have been oriented towards the establishment of a multidimensional approach, sometimes renouncing dichotomous logic to go as far as fuzzy classifications in which each unit simultaneously belongs and does not belong to the selected category. Multidimensional statistical analysis methodologies (TFR method) and Density Based Spatial Clustering methods (DBSCAN) will therefore be used to aggregate adjacent spatial units with high intensity of social inequalities.
期刊介绍:
Studies directed toward the more effective utilization of existing resources, e.g. mathematical programming models of health care delivery systems with relevance to more effective program design; systems analysis of fire outbreaks and its relevance to the location of fire stations; statistical analysis of the efficiency of a developing country economy or industry.
Studies relating to the interaction of various segments of society and technology, e.g. the effects of government health policies on the utilization and design of hospital facilities; the relationship between housing density and the demands on public transportation or other service facilities: patterns and implications of urban development and air or water pollution.
Studies devoted to the anticipations of and response to future needs for social, health and other human services, e.g. the relationship between industrial growth and the development of educational resources in affected areas; investigation of future demands for material and child health resources in a developing country; design of effective recycling in an urban setting.