Advanced Record Linkage Methods and Privacy Aspects for Population Reconstruction—A Survey and Case Studies

Christen, Peter; Vatsalan, Dinusha; Fu, Zhichun

doi:10.1007/978-3-319-19884-2_5

Cited by 9 publications

(6 citation statements)

References 77 publications

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“…This chapter extends an earlier shorter workshop paper on the same topic (Christen 2014). In the following section, we provide a brief overview of advanced methods and techniques that have been developed in recent years.…”

Section: Introductionsupporting

confidence: 64%

Population Reconstruction

Kirby¹,

Carson²,

Dunlop³

et al. 2015

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PrefacePeople shape societies. They are linked to each other by family ties and networks with social, economic and religious dimensions. People live together in households and form communities. Some own a house, land and other properties, often related to their profession. And all this is in continuous change. People are born, marry, have children and die, and they change houses and addresses, and build careers. For the study of a society in all aspects, people are at the heart of the problem and should be known in the context of their complex relationships. Even today, it is not easy to get this information in an all-enfolding way, but for populations in the past, it is a real challenge. And that is what this book is about. The book addresses the problems that are encountered, and solutions that have been proposed, when we aim to identify people and to reconstruct populations under conditions where information is scarce, ambiguous, fuzzy and sometimes erroneous.It is not a single discipline that is involved in such an endeavour. Historians, social scientists, and linguists represent the humanities through their knowledge of the complexity of the past, the limitations of sources and the possible interpretations of information. The availability of big data from digitised archives and the need of complex analyses to identify individuals require the involvement of computer scientists. With contributions from all these fields, often in direct cooperation, this book is at the heart of digital humanities and hopefully a source of inspiration for future investigations.The process from handwritten registers to a reconstructed digitised population has three major phases which shape the three sections of this book. The first phase is that of data transcription and digitisation while structuring the information in a meaningful and efficient way. Little of this phase can be automated. With archives that comprise easily tens of millions of records, the help of volunteers for transcription and digitisation is indispensable, but requires a rigorous management. Experiences from Denmark demonstrate the complexity of this task in Chap. 1. Spelling variation, aliases, abbreviations, errors and typos all generate difficulties in further processing and require data cleaning. Similarity measures can be helpful to v

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Section: Introductionsupporting

confidence: 64%

Population Reconstruction

Kirby¹,

Carson²,

Dunlop³

et al. 2015

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“…Record linkage has been mostly studied for other applications, but recently, with the rise in the number of indexed genealogical datasets, several studies have applied record linkage techniques for genealogical data [7,8,11,18,19,23,25,27]. The most closely related to our work are the papers by Efremova et al [11], Christen [7], and Kouki et al [19].…”

Section: Related Workmentioning

confidence: 87%

Computationally Inferred Genealogical Networks Uncover Long-Term Trends in Assortative Mating

Malmi

Gionis

Solin

2018

Proceedings of the 2018 World Wide Web Conference on World Wide Web - WWW '18

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Genealogical networks, also known as family trees or population pedigrees, are commonly studied by genealogists wanting to know about their ancestry, but they also provide a valuable resource for disciplines such as digital demography, genetics, and computational social science. These networks are typically constructed by hand through a very time-consuming process, which requires comparing large numbers of historical records manually. We develop computational methods for automatically inferring large-scale genealogical networks. A comparison with human-constructed networks attests to the accuracy of the proposed methods. To demonstrate the applicability of the inferred large-scale genealogical networks, we present a longitudinal analysis on the mating patterns observed in a network. This analysis shows a consistent tendency of people choosing a spouse with a similar socioeconomic status, a phenomenon known as assortative mating. Interestingly, we do not observe this tendency to consistently decrease (nor increase) over our study period of 150 years.

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“…The identification of the survivors in the 1812 municipal register enabled us to determine that these people did not die during the period studied. If we would have considered as survivors all those that had not been located as deceased, we would have over-estimated the survival of the groups most prone to migration (Christen et al, 2015;Efremova et al, 2015;Ruggles, 1992Ruggles, , 2002. The fact that parish records provided information about the name and surname of the next of kin 5 has been a fundamental element for identification, given that it enabled us to significantly reduce the overlinks derived from shared onomastics.…”

Section: Sources and The Construction Of The Databasementioning

confidence: 99%

Inequality in mortality in pre-industrial southern Europe during an epidemic episode: socio-economic determinants (eighteenth - nineteenth centuries Spain)

Haro

Pujadas-Mora

Gómez

2021

Economics & Human Biology

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The objective of this study is to gain more comprehensive knowledge about social inequality in mortality in pre-industrial periods. With this aim, we have reconstructed the life courses of the inhabitants of the town of Vera in south-east Spain for the period 1797-1812 in order to estimate the influence of socio-economic status on ordinary and extraordinary mortality, given that, during this period, the town suffered from several epidemic outbreaks of yellow fever. As a result of these outbreaks, around a quarter of the town's population died. The results obtained indicate social inequality in mortality at least from the end of the eighteenth century. Although the differences are higher in mortality caused by non-infectious diseases or ill-defined causes, the coefficients also show a certain social gradient in mortality derived from infectious diseases. However, with respect to this latter type of mortality, the place of residence -seems to have a greater influence on the chances of survival than socio-economic status.

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Advanced Record Linkage Methods and Privacy Aspects for Population Reconstruction—A Survey and Case Studies

Cited by 9 publications

References 77 publications

Population Reconstruction

Population Reconstruction

Computationally Inferred Genealogical Networks Uncover Long-Term Trends in Assortative Mating

Inequality in mortality in pre-industrial southern Europe during an epidemic episode: socio-economic determinants (eighteenth - nineteenth centuries Spain)

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