@inproceedings{e0e6278494184bef9bb207aec809bf55,
title = "Temporal group linkage and evolution analysis for census data",
abstract = "The temporal linkage of census data allows the detailed analysis of population-related changes in an area of interest. It should not only link records about the same person but also support the linkage of groups of related persons such as households. In this paper, we thus propose a new approach to both temporal record and group (household) linkage for census data and study its application for change analysis. The approach utilizes the relationships between individuals to determine the similarity of groups and their members within a graph-based method. The approach is also iterative by first identifying high quality matches that are subsequently extended by matches found with less restrictive similarity criteria. A comprehensive evaluation using historical census data from the UK indicates a high effectiveness of the proposed approach. Furthermore, the linkage enables an insightful analysis of household changes determined by so-called evolution patterns.",
author = "Victor Christen and Anika Groβ and Jeffrey Fisher and Qing Wang and Peter Christen and Erhard Rahm",
note = "Publisher Copyright: {\textcopyright} 2017, Copyright is with the authors.; 20th International Conference on Extending Database Technology, EDBT 2017 ; Conference date: 21-03-2017 Through 24-03-2017",
year = "2017",
doi = "10.5441/002/edbt.2017.83",
language = "English",
series = "Advances in Database Technology - EDBT",
publisher = "OpenProceedings.org",
pages = "620--631",
editor = "Bernhard Mitschang and Volker Markl and Sebastian Bress and Periklis Andritsos and Kai-Uwe Sattler and Salvatore Orlando",
booktitle = "Advances in Database Technology - EDBT 2017",
}