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dc.contributor.authorShi, Yongbinen_US
dc.contributor.authorLi, Leen_US
dc.contributor.authorWang, Youguien_US
dc.contributor.authorChen, Jiaweien_US
dc.contributor.authorYuan, Yidaen_US
dc.contributor.authorStanley, H. Eugeneen_US
dc.date.accessioned2020-02-27T20:31:02Z
dc.date.available2020-02-27T20:31:02Z
dc.date.issued2019-03-01
dc.identifierhttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000458409000002&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=6e74115fe3da270499c3d65c9b17d654
dc.identifier.citationYongbin Shi, Le Li, Yougui Wang, Jiawei Chen, Yida Yuan, H.E. Stanley. 2019. "Regional surname affinity: A spatial network approach." American Journal Of Physical Anthropology, Volume 168, Issue 3, pp. 428 - 437. https://doi.org/10.1002/ajpa.23755
dc.identifier.issn0002-9483
dc.identifier.issn1096-8644
dc.identifier.urihttps://hdl.handle.net/2144/39576
dc.description.abstractOBJECTIVE We investigate surname affinities among areas of modern‐day China, by constructing a spatial network, and making community detection. It reports a geographical genealogy of the Chinese population that is result of population origins, historical migrations, and societal evolutions. MATERIALS AND METHODS We acquire data from the census records supplied by China's National Citizen Identity Information System, including the surname and regional information of 1.28 billion registered Chinese citizens. We propose a multilayer minimum spanning tree (MMST) to construct a spatial network based on the matrix of isonymic distances, which is often used to characterize the dissimilarity of surname structure among areas. We use the fast unfolding algorithm to detect network communities. RESULTS We obtain a 10‐layer MMST network of 362 prefecture nodes and 3,610 edges derived from the matrix of the Euclidean distances among these areas. These prefectures are divided into eight groups in the spatial network via community detection. We measure the partition by comparing the inter‐distances and intra‐distances of the communities and obtain meaningful regional ethnicity classification. DISCUSSION The visualization of the resulting communities on the map indicates that the prefectures in the same community are usually geographically adjacent. The formation of this partition is influenced by geographical factors, historic migrations, trade and economic factors, as well as isolation of culture and language. The MMST algorithm proves to be effective in geo‐genealogy and ethnicity classification for it retains essential information about surname affinity and highlights the geographical consanguinity of the population.en_US
dc.description.sponsorshipNational Natural Science Foundation of China, Grant/Award Numbers: 61773069, 71731002; National Social Science Foundation of China, Grant/Award Number: 14BSH024; Foundation of China of China Scholarships Council, Grant/Award Numbers: 201606045048, 201706040188, 201706040015; DOE, Grant/Award Number: DE-AC07-05Id14517; DTRA, Grant/Award Number: HDTRA1-14-1-0017; NSF, Grant/Award Numbers: CHE-1213217, CMMI-1125290, PHY-1505000 (61773069 - National Natural Science Foundation of China; 71731002 - National Natural Science Foundation of China; 14BSH024 - National Social Science Foundation of China; 201606045048 - Foundation of China of China Scholarships Council; 201706040188 - Foundation of China of China Scholarships Council; 201706040015 - Foundation of China of China Scholarships Council; DE-AC07-05Id14517 - DOE; HDTRA1-14-1-0017 - DTRA; CHE-1213217 - NSF; CMMI-1125290 - NSF; PHY-1505000 - NSF)en_US
dc.format.extentp. 428 - 437en_US
dc.languageEnglish
dc.language.isoen_US
dc.publisherWILEYen_US
dc.relation.ispartofAmerican Journal Of Physical Anthropology
dc.rights© 2018 The Authors. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.en_US
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectScience & technologyen_US
dc.subjectLife sciences & biomedicineen_US
dc.subjectAnthropologyen_US
dc.subjectEvolutionary biologyen_US
dc.subjectCommunity detectionen_US
dc.subjectEthnicity classificationen_US
dc.subjectIsonymic distanceen_US
dc.subjectMultilayer minimum spanning treeen_US
dc.subjectSpatial networken_US
dc.subjectArgentinaen_US
dc.subjectAlgorithmsen_US
dc.subjectAnthropologyen_US
dc.subjectAsian continental ancestry groupen_US
dc.subjectChinaen_US
dc.subjectDemographyen_US
dc.subjectEthnic groupsen_US
dc.subjectHumansen_US
dc.subjectModels, statisticalen_US
dc.subjectNamesen_US
dc.subjectEvolutionary biologyen_US
dc.subjectArchaeologyen_US
dc.titleRegional surname affinity: a spatial network approachen_US
dc.typeArticleen_US
dc.description.versionPublished versionen_US
dc.identifier.doi10.1002/ajpa.23755
pubs.elements-sourceweb-of-scienceen_US
pubs.notesEmbargo: Not knownen_US
pubs.organisational-groupBoston Universityen_US
pubs.organisational-groupBoston University, College of Arts & Sciencesen_US
pubs.organisational-groupBoston University, College of Arts & Sciences, Department of Physicsen_US
pubs.publication-statusPublisheden_US
dc.identifier.mycv414649


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© 2018 The Authors. 

This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Except where otherwise noted, this item's license is described as © 2018 The Authors. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.