Enumeration area imputation methods for producing sub-municipal data in the Italian permanent population and housing census

Q3 Decision Sciences
Giancarlo Carbonetti, Giampaolo De Matteis, Marco Di Zio, Fardelli Davide, Ferrara Raffaele, Lipizzi Fabio
{"title":"Enumeration area imputation methods for producing sub-municipal data in the Italian permanent population and housing census","authors":"Giancarlo Carbonetti, Giampaolo De Matteis, Marco Di Zio, Fardelli Davide, Ferrara Raffaele, Lipizzi Fabio","doi":"10.3233/sji-220113","DOIUrl":null,"url":null,"abstract":"Over the years, official statistics have shown an increasing territorial focus on providing detailed and quality information. The Population and Housing Census has always ensured the availability of sub-municipal data useful for social, economic, and environmental decision-making processes. The new Italian Permanent Census focuses heavily on the integration of administrative and sample data and plans to provide more stable and consistent statistical data at the various territorial levels every year. Within this framework, sub-municipal data are derived from the integration of the Base Register of Individuals and the Base Register of Places. Data accuracy depends on the quality of the registers and the procedures adopted to integrate and process the input data. In this regard, Istat is working to improve geocoding information and linking procedures. One of the problems encountered is the presence of non-geocoded units due to problems in the administrative data. Istat has studied a procedure that integrates deterministic and probabilistic approaches to assign the enumeration area code to these critical units. It was conducted an experimental study to assess the quality of the imputation procedure. In this paper, we discuss the approach adopted, the evaluation process, the results obtained, and the impact on data quality.","PeriodicalId":55877,"journal":{"name":"Statistical Journal of the IAOS","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Statistical Journal of the IAOS","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3233/sji-220113","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Decision Sciences","Score":null,"Total":0}
引用次数: 0

Abstract

Over the years, official statistics have shown an increasing territorial focus on providing detailed and quality information. The Population and Housing Census has always ensured the availability of sub-municipal data useful for social, economic, and environmental decision-making processes. The new Italian Permanent Census focuses heavily on the integration of administrative and sample data and plans to provide more stable and consistent statistical data at the various territorial levels every year. Within this framework, sub-municipal data are derived from the integration of the Base Register of Individuals and the Base Register of Places. Data accuracy depends on the quality of the registers and the procedures adopted to integrate and process the input data. In this regard, Istat is working to improve geocoding information and linking procedures. One of the problems encountered is the presence of non-geocoded units due to problems in the administrative data. Istat has studied a procedure that integrates deterministic and probabilistic approaches to assign the enumeration area code to these critical units. It was conducted an experimental study to assess the quality of the imputation procedure. In this paper, we discuss the approach adopted, the evaluation process, the results obtained, and the impact on data quality.
意大利常住人口和住房普查中产生次市级数据的枚举面积估算方法
多年来,官方统计数据显示,领土越来越注重提供详细和高质量的信息。人口和住房普查始终确保提供对社会、经济和环境决策过程有用的次级城市数据。新的意大利永久人口普查主要侧重于行政数据和样本数据的整合,并计划每年在各个地区层面提供更稳定和一致的统计数据。在这一框架内,市级以下的数据来源于个人基本登记册和地方基本登记册的整合。数据的准确性取决于寄存器的质量以及集成和处理输入数据所采用的程序。在这方面,Istat正在努力改进地理编码信息和链接过程。遇到的问题之一是由于管理数据中的问题而存在未经地理编码的单元。Istat研究了一种集成确定性和概率性方法的程序,将枚举区号分配给这些关键单元。进行了一项实验研究,以评估插补程序的质量。在本文中,我们讨论了所采用的方法、评估过程、获得的结果以及对数据质量的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Statistical Journal of the IAOS
Statistical Journal of the IAOS Economics, Econometrics and Finance-Economics and Econometrics
CiteScore
1.30
自引率
0.00%
发文量
116
期刊介绍: This is the flagship journal of the International Association for Official Statistics and is expected to be widely circulated and subscribed to by individuals and institutions in all parts of the world. The main aim of the Journal is to support the IAOS mission by publishing articles to promote the understanding and advancement of official statistics and to foster the development of effective and efficient official statistical services on a global basis. Papers are expected to be of wide interest to readers. Such papers may or may not contain strictly original material. All papers are refereed.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信