陕西省传统村落可达性特征及影响因素的空间分异

夏伊凡; 黄晓燕; 康晨晨; 徐旭

doi:10.16152/j.cnki.xdxbzr.2024-02-014

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陕西省传统村落可达性特征及影响因素的空间分异

地理研究 | 更新时间：2024-04-19

- 陕西省传统村落可达性特征及影响因素的空间分异
- Spatial differentiation of accessibility characteristics and influencing factors of traditional villages in Shaanxi Province
- 西北大学学报（自然科学版） 2024年54卷第2期页码：288-299
- 作者机构：
  
  1.陕西师范大学　西北国土资源研究中心，陕西　西安　710119
  2.陕西师范大学　西北城镇化与国土环境空间模拟重点实验室，陕西　西安　710119
  3.温州大学　建筑工程学院，浙江　温州　325035
- 作者简介：
  
  夏伊凡，女，从事城市交通地理研究。
  黄晓燕，女，教授，博士生导师，从事城市地理与城乡规划研究，hxiaoy@snnu.edu.cn。
- 基金信息：
  
  国家自然科学基金(41831284);国家重点研发计划课题(2019YFD1100901);浙江省软科学项目(2021C35117)
- DOI：10.16152/j.cnki.xdxbzr.2024-02-014
  中图分类号： C912.82; K901
- 纸质出版日期：2024-04-25，
  
  收稿日期：2023-11-08，
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夏伊凡, 黄晓燕, 康晨晨, 等. 陕西省传统村落可达性特征及影响因素的空间分异[J]. 西北大学学报（自然科学版）, 2024,54(2):288-299.

XIA Yifan, HUANG Xiaoyan, KANG Chenchen, et al. Spatial differentiation of accessibility characteristics and influencing factors of traditional villages in Shaanxi Province[J]. Journal of Northwest University (Natural Science Edition), 2024,54(2):288-299.
夏伊凡, 黄晓燕, 康晨晨, 等. 陕西省传统村落可达性特征及影响因素的空间分异[J]. 西北大学学报（自然科学版）, 2024,54(2):288-299. DOI： 10.16152/j.cnki.xdxbzr.2024-02-014.

XIA Yifan, HUANG Xiaoyan, KANG Chenchen, et al. Spatial differentiation of accessibility characteristics and influencing factors of traditional villages in Shaanxi Province[J]. Journal of Northwest University (Natural Science Edition), 2024,54(2):288-299. DOI： 10.16152/j.cnki.xdxbzr.2024-02-014.

摘要

传统村落是推进乡村振兴和新型城镇化建设的重要载体。以陕西省182个国家级传统村落为研究对象，分析其空间分布特征，并运用ArcGIS网络分析和地理加权回归模型，深入剖析县级和镇级尺度下传统村落可达性特征及影响因素的空间分异。研究结果表明：①陕西省国家级传统村落呈集聚型空间分布，在空间上形成了4个主要集聚区。②传统村落可达性整体水平较低，呈现以关中为中心的阶梯状递减特征。③不同空间尺度下的传统村落可达性与影响因素存在着较强的空间不稳定性。县级尺度下，高程与传统村落可达性基本呈正相关关系，但在镇级尺度下，两者正负向关系并存，且呈现以榆林西北部和渭南中部为中心向外围圈层逐渐增强的特点。县级尺度下，近水体最短距离与传统村落可达性水平呈负相关关系，但在镇级尺度下，其对可达性水平的影响呈现出从陕南和陕北向中部逐步递减的趋势。④高程、坡度和近水体最短距离是影响陕西省国家级传统村落可达性的主要因素，且在不同地区具有不同的影响效应。传统村落可达性特征和影响因素的研究可以为不同地区传统村落可达性的改进和传统村落保护利用策略的制定提供科学依据。

Abstract

Traditional villages are an important carrier for promoting rural revitalization and new urbanization construction. The spatial distribution characteristics of 182 national-level traditional villages in Shaanxi Province were analyzed as research objects. Using ArcGIS network analysis and GWR model

the spatial differentiation of traditional village accessibility characteristics and influencing factors at the county and town scales were analyzed in depth. The results of the study show that: ① The national-level traditional villages in Shaanxi Province are characterized by agglomeration spatial distribution

forming four main agglomeration area in space. ② The overall level of accessibility is low

showing a stepwise decline centered on Guanzhong. ③ There is strong spatial instability in the accessibility and influencing factors of traditional village accessibility at different spatial scales. At the county level

the elevation has a positive correlation with the accessibility of traditional villages. However

at the town level

both positive and negative relationships coexist

and are characterized by a gradual increase from the northwestern part of Yulin and the central part of Weinan to the peripheral circles. At the county level

the distance to neighboring rivers is negatively correlated with the level of accessibility of traditional villages. However

at the town level

its impact on accessibility shows a decreasing trend from southern and northern Shaanxi to the central part of the country. ④ Elevation

slope

and the distance to neighboring rivers are the main factors in the accessibility of national-level traditional villages in Shaanxi Province and have different influencing effects in different areas. Research on the accessibility of traditional villages can help reveal the ways to improve the accessibility of traditional villages in different regions

and can provide a scientific basis for the formulation of future conservation and utilization strategies for traditional villages.

关键词

传统村落可达性GWR模型不同尺度陕西省

Keywords

traditional villagesaccessibilitygeographically weighted regression modeldifferent scalesShaanxi Province

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