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Temporal group linkage and evolution analysis for census data

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dc.contributor.authorChristen, Victor
dc.contributor.authorGroß, Anika
dc.contributor.authorFisher, Jeffrey
dc.contributor.authorWang, Qing
dc.contributor.authorChristen, Peter
dc.contributor.authorRahm, Erhard
dc.contributor.editorMarkl, V
dc.contributor.editorOrlando, S
dc.contributor.editorMitschang, B
dc.contributor.editorAndritsos, P
dc.contributor.editorSattler, K U
dc.contributor.editorBreß, S
dc.coverage.spatialVenice, Italy
dc.date.accessioned2021-09-22T23:55:16Z
dc.date.available2021-09-22T23:55:16Z
dc.date.createdMarch 21-24 2017
dc.date.issued2017
dc.date.updated2024-01-21T07:15:40Z
dc.description.abstractThe 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.
dc.description.sponsorshipThis work was partially funded by the Australian Research Council (ARC) under Discovery Project DP160101934, and Universities Australia and the German Academic Exchange Service (DAAD).en_AU
dc.format.mimetypeapplication/pdfen_AU
dc.identifier.isbn9783893180738en_AU
dc.identifier.issn2367-2005en_AU
dc.identifier.urihttp://hdl.handle.net/1885/248689
dc.language.isoen_AUen_AU
dc.provenanceDistribution of this paper is permitted under the terms of the Creative Commons license CC-by-nc-nd 4.0en_AU
dc.publisherUniversity of Konstanz
dc.relationhttp://purl.org/au-research/grants/arc/DP160101934
dc.relation.ispartofAdvances in Database Technology - Proceedings of the 201th International Conference on Extending Database Technology
dc.relation.ispartofseries20th International Conference on Extending Database Technology (EDBT 2017)en_AU
dc.rights© 2017, Copyright is with the authors
dc.rights.licenseCreative Commons license CC-by-nc-nd 4.0en_AU
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/en_AU
dc.sourceAdvances in Database Technology - Proceedings of the 201th International Conference on Extending Database Technology
dc.source.urihttps://openproceedings.org/html/pages/2017_edbt.htmlen_AU
dc.titleTemporal group linkage and evolution analysis for census data
dc.typeConference paper
dcterms.accessRightsOpen Accessen_AU
local.bibliographicCitation.lastpage631en_AU
local.bibliographicCitation.startpage620en_AU
local.contributor.affiliationChristen, Victor, University Leipzigen_AU
local.contributor.affiliationGroß, Anika, University Leipzigen_AU
local.contributor.affiliationFisher, Jeffrey, College of Engineering and Computer Science, ANUen_AU
local.contributor.affiliationWang, Qing, College of Engineering and Computer Science, ANUen_AU
local.contributor.affiliationChristen, Peter, College of Engineering and Computer Science, ANUen_AU
local.contributor.affiliationRahm, Erhard, University of Leipzigen_AU
local.contributor.authoruidFisher, Jeffrey, u4814620en_AU
local.contributor.authoruidWang, Qing, u5170295en_AU
local.contributor.authoruidChristen, Peter, u4021539en_AU
local.description.notesImported from ARIESen_AU
local.description.refereedYes
local.identifier.absfor080109 - Pattern Recognition and Data Miningen_AU
local.identifier.absseo970108 - Expanding Knowledge in the Information and Computing Sciencesen_AU
local.identifier.ariespublicationu1046907xPUB30en_AU
local.identifier.doi10.5441/002/edbt.2017.83en_AU
local.identifier.scopusID2-s2.0-85046399235
local.publisher.urlhttps://openproceedings.org/en_AU
local.type.statusPublished Versionen_AU

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