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September 30, 2025Buildings2 citationsOpen Access

Spatiotemporal Distribution and Driving Factors of Historic and Cultural Villages in China

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SJShuna JiangNLNan LüZZZhongqian Zhang

Key Points

  • Historic villages show a clustered spatial pattern, indicating the influence of urban proximity on cultural preservation.
  • The nearest neighbor index revealed significant clustering (NNI = 0.44, Z = –23.52, p = 0.00) in the southeast region of China.
  • Kernel density analysis indicates growth in village density, with significant peaks reflecting urbanization pressures over time.
  • Natural geography and socio-economic conditions are key drivers, with nighttime light intensity identified as the most influential factor (q = 0.6132).

Abstract

Historic and cultural villages in China are increasingly challenged by rapid urbanization, uneven commercial development, and fragmented preservation mechanisms. Understanding their spatiotemporal distribution and the factors shaping it is crucial for advancing the integrated development of cultural heritage conservation, ecological sustainability, and socio-economic growth. This study examines 487 historic and cultural villages using the nearest neighbor index (NNI) and kernel density analyses to reveal spatial differentiation patterns. Vector buffer analysis and the geographic detector method were further employed to identify the key drivers of village distribution. The results indicate that: (1) historic and cultural villages exhibit a distinctly clustered spatial pattern, characterized by “more in the southeast, fewer in the northwest; more in the northeast, fewer in the southwest” (NNI = 0.44, Z = –23.52, p = 0.00); (2) provincial-level spatial density demonstrates clear stratification, with high-density clusters concentrated in the Yangtze River Delta, southern Anhui, the Fujian–Zhejiang–Jiangxi junction, and along the Yellow River in Shanxi–Shaanxi–Henan. From the fifth to seventh designation batches, kernel density peaks (maximum ~0.11 × 10−2) increased significantly, reflecting stronger spatial clustering; and (3) the spatial distribution of villages is jointly shaped by natural geography, socio-economic conditions, transportation infrastructure, visitor markets, and tourism resources. Among these, nighttime light intensity was identified as the most influential individual factor (q = 0.6132), while the combination of slope aspect and per capita disposable income emerged as the dominant factor pair (q = 0.966).

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Cite This Study

Jiang et al. (2025) studied this question.

synapsesocial.com/papers/68dc1e438a7d58c25ebb24ffhttps://doi.org/10.3390/buildings15193507
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