Census Innovation at CeLSIUS

CeLSIUS 的人口普查创新

• 批准号: ES/Z502741/1
• 负责人: Nicola Shelton
• 金额: $ 47.23万
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=ES%2FZ502741%2F1
• 关键词: Census; Innovation; CeLSIUS

This project aims to develop two aspects of Census data.1) Addressing the gap in provision with regard to access and utilising the most restricted access UK Census data (other than the longitudinal studies) by providing enhanced user information guidance, training and user support2) Creation of a fake (impossible) longitudinal England and Wales dataset to enable users to explore the data, select variables and draft code for their analysesGap in provisionThis currently exists with regard to support for using secure UK Census data other than the longitudinal studies which are currently supported by three organisations CeLSIUS in England and Wales, NILS-RSU in Northern Ireland and SLS-DSU in Scotland - collectively they form UKCenLS. Less restricted access versions of the data are currently well supported by the UK Data Service but there is no support for the most restricted access data sets other than limited user information and data storage and research clearance. CeLSIUS aims to further support two groups of data: secure origin-destination or 'flow' data (migration, commuting, student migration, and second residence sets) from 2011 and 2021/2, and de-identified individual and household microdata from 1961 to 2021.This project seeks to assess feasibility and affordability of these potential new support services and provide the required user information guidance and training in particular for the 2021/2 data where none yet exists.This service will support researchers who wish to use UK census 2021/2 migration and commuting and individual data for social science-led research, but also compare change over time.CeLSIUS has considerable experience in supporting the user of the other restricted access census data and will ensure that no data that could identify an individual becomes public.Creation of a fake longitudinal data setCurrently it is hard for users to know what variables are in the Office for National Statistics Longitudinal Study (ONS LS) and is getting harder with each Census. There are approximately 600 variables per Census and in some cases hundreds of categories for a variable.The current solutions: a) ask CeLSIUS and/or ONS b) use the data dictionary c) look at the Census forms. It is hard for users to choose the best variable options when they are so many options. This means users may select the wrong variables for their project and then must reapply for any additional variables which causes delays and places administrative burden on ONS and CeLSIUS. Also we have users who never see the data set and we do analysis for them and no users ever see the full data set so they do not always know what is best to select.We aim to create a individual level complete fake longitudinal data set to be openly available. This has imaginary people from 1971-2021 with impossible characteristics covering all options in the Censuses. This open data set will reduce burden on users and the user support team of the ONS LS. It will also be a useful tool for training.

该项目旨在开发人口普查数据的两个方面1)通过提供增强的用户信息指导、培训和用户支持，解决在获取和利用最受限的英国人口普查数据方面的差距(除了纵向研究)2)创建一个假的(不可能的)英格兰和威尔士纵向数据集，使用户能够探索数据，选择变量并起草用于他们分析的代码提供缺口目前支持使用安全的英国人口普查数据，而不是纵向研究，目前由英格兰和威尔士的摄氏度，北爱尔兰的NILS-RSU和苏格兰的SLS-DSU三个组织共同组成UKCenLS。数据访问限制较少的版本目前得到英国数据服务局的很好支持，但除了有限的用户信息和数据存储和研究许可外，不支持访问最受限的数据集。Celsius的目标是进一步支持两组数据：2011年和2021/2年的安全起点-目的地数据(迁移、通勤、学生迁移和第二居留集)，以及1961-2021年的未识别的个人和家庭微数据。该项目旨在评估这些潜在新支持服务的可行性和可负担性，并特别为尚不存在的2021/2数据提供所需的用户信息指导和培训。这项服务将支持希望使用英国人口普查2021/2迁移和通勤以及个人数据进行社会科学主导研究的研究人员，但也会比较随着时间的推移而发生的变化。CELSIUS在支持其他受限访问人口普查数据方面拥有相当多的经验，将确保任何可以识别个人身份的数据都不会公开。创建虚假的纵向数据集目前，用户很难知道国家统计办公室纵向研究(ONS LS)中有哪些变量，而且随着每次人口普查，这一数字变得越来越难。每一次人口普查大约有600个变量，在某些情况下，一个变量有数百个类别。目前的解决方案：a)询问摄氏度和/或国家统计局；b)使用数据词典；c)查看人口普查表格。有这么多的选项，用户很难选择最好的可变选项。这意味着用户可能会为他们的项目选择错误的变量，然后必须重新申请任何额外的变量，这会导致延迟，并给国家统计局和摄氏度带来行政负担。此外，我们也有用户从来没有看到数据集，我们为他们做分析，没有用户看到完整的数据集，所以他们不总是知道选择什么是最好的。我们的目标是创建一个单独级别的完整虚假纵向数据集，公开可用。这使得1971年至2021年的虚构人物具有不可能的特征，涵盖了人口普查中的所有选项。这一开放的数据集将减轻用户和国家统计局用户支持团队的负担。它也将是一种有用的培训工具。