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    Regional surname affinity: a spatial network approach

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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.
    Date Issued
    2019-03-01
    Publisher Version
    10.1002/ajpa.23755
    Author(s)
    Shi, Yongbin
    Li, Le
    Wang, Yougui
    Chen, Jiawei
    Yuan, Yida
    Stanley, H. Eugene
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    Permanent Link
    https://hdl.handle.net/2144/39576
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    Published version
    Citation (published version)
    Yongbin 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
    Abstract
    OBJECTIVE 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.
    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.
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    • CAS: Physics: Scholarly Papers [356]
    • BU Open Access Articles [3730]


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