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

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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Publication Details

Journal
Buildings
Published
2025-09-28
DOI
https://doi.org/10.3390/buildings15193507
Citations
1
Primary Topic
Land Use and Ecosystem Services
Type
article
Field-Weighted Citation Impact
1.11
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article

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

Shuna Jiang, Shuangqing Sheng, Hong Pan, Zhongqian Zhang et al.
1 citations
Buildings
Land Use and Ecosystem Services
1.11
article

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

Shuna Jiang, Shuangqing Sheng, Hong Pan, Zhongqian Zhang, Nan Lü, Guoyang Lu
article en
1 citations

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).

BuildingsVol. 15(19)
Southwest University of Science and Technology (CN), City University of Hong Kong (HK), Nanjing Water Conservancy Planning & Design Institute (China) (CN), China Agricultural University (CN), Shangqiu Normal University (CN)
Openalex Percentile: Top 19%
Land Use and Ecosystem Services
1.11
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