结合调查数据和商业数据,研究罗利-达勒姆地区华人移民的空间分布。

IF 1.5 3区 数学 Q2 SOCIAL SCIENCES, MATHEMATICAL METHODS
Eric A Bai, Botao Ju, Madeleine Beckner, Jerome P Reiter, M Giovanna Merli, Ted Mouw
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引用次数: 0

摘要

许多人口调查不提供受访者的居住地址信息,而是提供诸如邮政编码或更高的集合等粗略的地理位置信息。然而,精细分辨率的地理可以有利于表征社区,特别是相对罕见的人口,如移民。获得此类信息的一种方法是通过匹配两个文件的共同变量,将调查记录与包括居住地址在内的辅助数据库中的记录联系起来。我们提出了一种基于概率记录链接的方法,使罗利-达勒姆中国移民研究中的调查参与者能够与居住记录信息提供商InfoUSA的记录相匹配。这两个文件使用了不同的中文姓名罗马化做法,我们通过一种新颖的、可推广的策略来构建记录的罗马化姓名的两两比较向量来解决这个问题。利用全贝叶斯记录联系模型,研究了北卡罗莱纳州罗利-达勒姆地区中国移民的地理空间分布特征。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Studying Chinese immigrants' spatial distribution in the Raleigh-Durham area by linking survey and commercial data using romanized names.

Many population surveys do not provide information on respondents' residential addresses, instead offering coarse geographies like zip code or higher aggregations. However, fine resolution geography can be beneficial for characterizing neighbourhoods, especially for relatively rare populations such as immigrants. One way to obtain such information is to link survey records to records in auxiliary databases that include residential addresses by matching on variables common to both files. We present an approach based on probabilistic record linkage that enables matching survey participants in the Chinese Immigrants in Raleigh-Durham Study to records from InfoUSA, an information provider of residential records. The two files use different Chinese name romanization practices, which we address through a novel and generalizable strategy for constructing records' pairwise comparison vectors for romanized names. Using a fully Bayesian record linkage model, we characterize the geospatial distribution of Chinese immigrants in the Raleigh-Durham area of North Carolina.

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来源期刊
CiteScore
2.90
自引率
5.00%
发文量
136
审稿时长
>12 weeks
期刊介绍: Series A (Statistics in Society) publishes high quality papers that demonstrate how statistical thinking, design and analyses play a vital role in all walks of life and benefit society in general. There is no restriction on subject-matter: any interesting, topical and revelatory applications of statistics are welcome. For example, important applications of statistical and related data science methodology in medicine, business and commerce, industry, economics and finance, education and teaching, physical and biomedical sciences, the environment, the law, government and politics, demography, psychology, sociology and sport all fall within the journal''s remit. The journal is therefore aimed at a wide statistical audience and at professional statisticians in particular. Its emphasis is on well-written and clearly reasoned quantitative approaches to problems in the real world rather than the exposition of technical detail. Thus, although the methodological basis of papers must be sound and adequately explained, methodology per se should not be the main focus of a Series A paper. Of particular interest are papers on topical or contentious statistical issues, papers which give reviews or exposés of current statistical concerns and papers which demonstrate how appropriate statistical thinking has contributed to our understanding of important substantive questions. Historical, professional and biographical contributions are also welcome, as are discussions of methods of data collection and of ethical issues, provided that all such papers have substantial statistical relevance.
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