Regional surname affinity: A spatial network approach

A study of the spatial distribution characteristics of Chinese surnames

OBJECTIVES

The spatial distribution of Chinese surnames is diverse and provides rich information about the evolution of human society. This study aims to propose several indices to quantify the spatial distribution characteristics of Chinese common surnames and to explore how these distributions are related to historical evolution.

METHODS

This study uses data from China's ID information system covering 1.28 billion people across 362 cities. Based on the location quotient, several new concepts, such as "moderately concentrated cities" and "highly concentrated cities," are defined. Then indices such as range, ununiformity and spatial autocorrelation are proposed and calculated to analyze the spatial characteristics of Chinese common surnames.

RESULTS

A significant correlation is observed between the commonness of a surname and its spatial characteristics: the more common the surname, the wider its spatial range, the lower the ununiformity, and the higher the autocorrelation coefficient. These patterns reflect the complex interplay of historical, geographical, and cultural factors influencing surname spatial distribution.

CONCLUSIONS

The spatial distribution of Chinese surnames is intricately linked to their historical evolution. Most common surnames, often with deeper historical roots, exhibit wider distributions and lower ununiformity, whereas less common surnames show higher concentrations in specific areas. These quantitative results provide a comprehensive understanding of the evolutionary characteristics of Chinese surnames.

Characterizing dissimilarity of weighted networks

Measuring the dissimilarities between networks is a basic problem and wildly used in many fields. Based on method of the D-measure which is suggested for unweighted networks, we propose a quantitative dissimilarity metric of weighted network (WD-metric). Crucially, we construct a distance probability matrix of weighted network, which can capture the comprehensive information of weighted network. Moreover, we define the complementary graph and alpha centrality of weighted network. Correspondingly, several synthetic and real-world networks are used to verify the effectiveness of the WD-metric. Experimental results show that WD-metric can effectively capture the influence of weight on the network structure and quantitatively measure the dissimilarity of weighted networks. It can also be used as a criterion for backbone extraction algorithms of complex network.

Surname affinity in Santiago, Chile: A network-based approach that uncovers urban segregation

Based on a geocoded registry of more than four million residents of Santiago, Chile, we build two surname-based networks that reveal the city’s population structure. The first network is formed from paternal and maternal surname pairs. The second network is formed from the isonymic distances between the city’s neighborhoods. These networks uncover the city’s main ethnic groups and their spatial distribution. We match the networks to a socioeconomic index, and find that surnames of high socioeconomic status tend to cluster, be more diverse, and occupy a well-defined quarter of the city. The results are suggestive of a high degree of urban segregation in Santiago.

An index of Chinese surname distribution and its implications for population dynamics

Objective

We propose an index to characterize the key feature of Chinese surname distributions and investigate its implications for population structure and dynamics.

Materials and methods

The surname dataset was obtained from the National Citizen Identity Information Center, which contains 1.28 billion Chinese citizens enrolled in 2007, excluding those of Hong Kong, Macao, and Taiwan. An index, the coverage ratio of stretched exponential distribution (CRSED), is proposed based on the crossover point of stretched exponential truncated power‐law distribution, where the stretched exponential term and the power‐law term contribute equally. We use multidimensional scaling technique to demonstrate the dependence of the similarity of one prefecture to the others on the CRSED.

Results

The CRSEDs of 362 prefectures exhibit an uneven distribution. The consistency of this index is evident by strong positive correlations of CRSEDs at the three administrative levels. This new index has a strong negative correlation with the proportion of the rare surnames. The prefectures with similar CRSEDs tend to adjoin each other on the administrative map, resulting in several distinct regions, each of which shares similar terrain features or historical migrations. The prefectures with lower CRSEDs are more dissimilar to the other prefectures, while the ones with higher CRSEDs are more similar to the others.

Discussion

The population dynamics of the prefectures with higher CRSEDs are more likely dominated by migratory movements, the dominant evolutionary forces of the prefectures with lower CRSEDs can be attributed to drift and mutation.

Regional surname affinity: A spatial network approach

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.