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May 23, 2024Ecological Informatics65 citationsOpen Access

Investigating the spatial distribution mechanisms of traditional villages from the human geography region: A case study of Jiangnan, China

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SBShibo BiJDJiacheng DuTZTian Zheng

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Abstract

Investigating the spatial distribution and adaptation mechanisms of traditional villages is crucial for their development and human well-being. However, relatively few studies have focused on the interaction and comprehensive influence of historical-cultural, natural-geographic and socio-economic factors on traditional villages in Jiangnan region. Employing geographically weighted regression (GWR) and Geodetector methods, we developed an integrated framework. We found that (1) Traditional villages exhibit a "one-core, multiple-periphery" clustering pattern (nearest neighbor index <1), with significant high-high and low-low local spatial clustering characteristics and preferring gentle and relatively Complex terrains to adapt to the need to avoid warfare. (2) Urbanization negatively affects traditional village density patterns, while natural-geographic complexity has a positive impact. Their interaction nonlinearly amplifies this effect, with the urbanization-GDP interplay being particularly significant (q = 0.765). (3) Factors exhibit significant spatial heterogeneity in their impact on traditional village density, with the explanatory power increasing by 20% (GWR/R2 = 0.65). The urbanization rate and slope are the most critical socio-economic and natural-geographic factors, respectively (local regression coefficient: 23.31 and 11.58). Our study transcends administrative boundaries, revealing how historical-cultural, natural-geographical, and socio-economic factors collectively influence the distribution of traditional villages. This not only provides a new theoretical foundation for the conservation and development of traditional villages but also provides a new methodology basis for research on traditional villages.

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Bi et al. (2024) studied this question.

synapsesocial.com/papers/68e68ab2b6db643587612865https://doi.org/10.1016/j.ecoinf.2024.102649
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