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Regional surname affinity: A spatial network approach.

Yongbin Shi1,2, Le Li1,2, Yougui Wang1,2

  • 1School of Systems Science, Beijing Normal University, Beijing 100875, China.

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|December 27, 2018
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Summary
This summary is machine-generated.

This study maps Chinese population genealogy using surname data from 1.28 billion citizens. It reveals eight distinct regional groups based on geographical and historical factors, highlighting population origins and migrations.

Keywords:
community detectionethnicity classificationisonymic distancemultilayer minimum spanning treespatial network

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Area of Science:

  • Population genetics
  • Computational social science
  • Spatial network analysis

Background:

  • Understanding population structure is key to human history.
  • Chinese surnames offer insights into historical migrations and societal evolution.
  • Previous studies lacked a large-scale, geographically resolved analysis of surname affinities.

Purpose of the Study:

  • To construct a geographical genealogy of the Chinese population.
  • To identify regional population clusters based on surname data.
  • To explore the drivers of observed population structures.

Main Methods:

  • Utilized surname and regional data from 1.28 billion Chinese citizens.
  • Constructed a spatial network using a multilayer minimum spanning tree (MMST) based on isonymic distances.
  • Applied community detection algorithms to identify population groups.

Main Results:

  • A 10-layer MMST network with 362 nodes and 3,610 edges was generated.
  • Community detection identified eight distinct geographical groups of Chinese prefectures.
  • The detected communities showed meaningful regional ethnicity classification, with adjacent areas typically belonging to the same group.

Conclusions:

  • The MMST algorithm effectively reveals geo-genealogy and ethnicity classifications.
  • Population partitions are shaped by geography, historical migrations, trade, and cultural factors.
  • Surname affinities provide a robust framework for understanding population consanguinity and historical dynamics.