Historical Patterns of Regional Population Migration in China: A Novel Surname‐Based Analysis

OBJECTIVES: This study aims to explore historical population migration patterns in China using a novel surname-based method called GNCurve. By applying this method to Chinese prefectures, the study identifies typical migration patterns and investigates how historical migration has shaped regional population structures. MATERIALS AND METHODS: The data used in this study are from China's National Citizen Identity Information Center (NCIIC), which includes surname and prefecture information for 1.28 billion individuals. We propose and apply the GNCurve method, which integrates surname distance with geographic distance, to analyze historical population migration across 328 Chinese prefectures. Then, a cluster analysis of these GNCurves is performed to classify migration patterns into several distinct categories. RESULTS: The analysis identifies five categories of typical historical migration patterns: reclaimed immigration (RI), strong diffusion (SD), long-distance migration (LDM), infrequent migration (IM), and social isolation (SI). We find that prefectures that fall into the same category tend to be clustered geographically, collectively providing a nationwide picture of Chinese population migration dynamics. DISCUSSION: The GNCurve method presents a novel surname-based approach to studying regional historical migration patterns and provides a unique perspective on how geographical, historical, and socioeconomic factors have influenced the current population structure. Additionally, GNCurve is potentially a tool for investigating historical migration patterns in other countries and regions.