The analysis of names from the 2011 Census of Population

伦敦大学学院之前进行的研究表明，一个名字通常提供了一个开放的、可访问的声明，说明其承载者的文化、种族和语言特征（例如Mateos et al 2011）。父母对前（名）名的选择可能会进一步揭示这些特征，而不断变化的时尚往往使名字成为年龄和其他地理和社会特征的有效指标。这些信息已被用于开发人名的工作分类，并已成功地用于增加用于审计目的的不完整数据记录-例如，在衡量不同种族群体的国民保健制度预防保健倡议的成功。但是，这些分类是利用不完整的地址登记册（例如公开版的选民名册）和电话薄编制的。在这类研究中迄今使用的数据源有许多缺点，限制了结果分类在应用于新数据集时的有用性：1。作为分类基础的数据来源提供了对总体人口的不完整且可能有偏见的表述。例如，公共选民登记册不包括（年轻人和移民）非选民或（隐私敏感的）“选择退出”的个人，公共电话号码簿覆盖面不广，姓名也很少。2. 名字的年龄分布的商业分类通常仅限于16岁以上的人群，并且补充国家统计局的出生姓名数据（例如www.ons.gov.uk/ons/rel/vsob1/baby-names--england-and-wales/2012/stb-baby-names-2012.html）容易出错，因为年幼的孩子可能会移居国外，移民可能会带着年幼的孩子。因此，这些来源不允许在任何特定时刻对居住在英国的人口进行包容性快照。虽然“众包”验证是可能的（例如www.onomap.org），但没有综合的方法来比较预测和客观（例如年龄）或自我分配（例如种族）特征。4. 很少有人关注如何对“难以接触到”的群体进行分类，比如加勒比人，他们的种族可能只能通过名字和姓氏之间的微妙联系来确定。5. 聚类过程在很大程度上是无空间的，这在很大程度上是因为地理覆盖的不均匀和缺乏高度精细的位置信息。本研究将通过使用最佳可用的二级数据集来解决这些缺点，以开发丰富的分类并进行敏感性分析，以完善和改进其在英国的普遍应用。通过扩展Mateos等人（2011）的方法，将个人层面的人口普查数据用于将姓氏和姓氏分类为文化、种族和语言群体。至关重要的是，这一分类的结果将首次与个人和家庭有关种族、民族身份、出生国、第一和第二语言以及国籍的数据进行比较。这将有可能调查分类中明显错误的原因，并查明这些错误集中的小地理区域。根据这些结果，通过一个反复的程序，将使用安全的网上设施来改进分类。将家庭和个人分类结果与人口普查身份措施方面的自我分配进行比较，将有可能使分类工具对文化同化指标敏感，无论是通过异族通婚还是居住时间，从地方到国家都是如此。“姓氏地区”也将用于为2011年国家统计局的产出地区分类添加区域背景。

Previous research conducted at UCL has demonstrated that a name very often provides an open and accessible statement of the cultural, ethnic and linguistic characteristics of its bearer (e.g. Mateos et al 2011). Additional light may be shed upon these characteristics by parental choice of fore-(given) name, while changing fashions often render forenames a valid indicator of age and other geographic and social characteristics. This information has been used to develop working classifications of names, and they have been successfully used to augment incomplete data records for audit purposes - for example in gauging the success of NHS preventive care initiatives across different ethnic groups. However, these classifications have been developed using incomplete address registers (such as the public version of the Electoral Roll) and telephone directories.There are a number of shortcomings to the data sources hitherto used in this kind of research that limit the usefulness of the resulting classifications when applied to new datasets:1. The data sources underlying the classifications provide incomplete and probably biased representations of the population-at-large. For example, public electoral registers do not include (young and immigrant) non-voters or (privacy sensitive) 'opt out' individuals, and public telephone directories provide less than universal coverage and few given names. 2. Commercial classifications of the age profiles of given names are typically restricted to the 16+ age cohorts, and supplementation with ONS birth name data (e.g. www.ons.gov.uk/ons/rel/vsob1/baby-names--england-and-wales/2012/stb-baby-names-2012.html) is error prone because young children may move abroad and immigrants may bring young children with them. Thus these sources do not allow an inclusive snapshot of the population resident in the UK at any specific moment in time.3. Whilst 'crowd sourced' validation is possible (e.g. www.onomap.org), there is no comprehensive means of comparing predicted and objective (e.g. age) or self-assigned (e.g. ethnicity) characteristics. 4. Little focus has been developed upon refining attempts to classify 'hard to reach' groups, such as Caribbeans, whose ethnicity can likely only be ascertained through subtle associations between forename-surname pairings. 5. The clustering procedure has been largely aspatial, in significant part because of unevenness of geographical coverage and the absence of highly granular location information. This research will address these shortcomings through use of the best available secondary dataset for developing an enriched classification and conducting sensitivity analysis to refine and improve its universal application across the UK. Individual level Census data will be used in order to develop a classification of given and family names into cultural, ethnic and linguistic groups, by extending the methodology of Mateos et al (2011). Crucially, and for the first time, the results of this classification will be compared to individual and household data on Ethnicity, National Identity, Country of Birth, First and Second Language Spoken and Nationality. This will make it possible to investigate the causes of apparent errors in the classification, and to identify the small geographic areas in which they are concentrated. Through an iterative procedure, secure online facilities will be used to improve the classification in the light of these results.Comparison of household and individual classification results with self assignments in terms of Census measures of identity will make it possible to make the classification tool sensitive to indicators of cultural assimilation, whether through inter-marriage or duration of residence, at scales from the local to the national.The 'surname regions' will also be used to add regional context to the 2011 ONS Output Area Classification.