乡村振兴背景下村庄分类与发展策略研究——以河南省封丘县3个乡镇为例
Research on Village Classification and Development Strategies in the Context of Rural Revitalization:A Case Study of Three Townships in Fengqiu County,Henan Province
杨宇豪 1乔家君 2王卫雯 1朱肖勇 1敦英杰1
作者信息
- 1. 河南大学 地理与环境学院,河南 开封 475004
- 2. 河南大学 地理与环境学院,河南 开封 475004;河南大学 黄河中下游数字地理技术教育部重点实验室,河南 开封 475004
- 折叠
摘要
因地制宜、分类指导是编制村庄规划、推动乡村振兴的重要举措.选取封丘县 123 个行政村,从村庄规模、产业基础、城乡区位、用地适宜性、特色保护和基础设施建设6 个方面评价村庄发展潜力,结合POI数据进行核密度分析,利用村庄分类模型,采用定性与定量相结合的方法确定五类村庄的划分阈值.结果表明:(1)利用"先评价、后分类"的分类体系,分类结果相对合理,证明定性与定量相结合的分析方法的科学性和有效性.(2)研究区地处平原,自然环境条件差别极小,影响村庄类型的主要因素集中于村庄规模、产业基础和区位条件.(3)村庄类型的主体为整治改善类村庄,此类村庄的发展将是实现乡村振兴战略的重点.
Abstract
This study selected 123 administrative villages in Fengqiu County of Henan Province and evaluated their development potential from six aspects:Village size,industrial base,urban-rural location,land suitability,feature preservation,and infrastructure development.By combining POI data for kernel density analysis and utili-zing the village classification model,a combination of qualitative and quantitative methods was employed to deter-mine the classification thresholds for five types of villages.Results are showed:(1)The classification system of"assessment first,classification later"yields relatively reasonable results,demonstrating the scientificity and effec-tiveness of the qualitative and quantitative analysis method used in this study.(2)The study area is located in a plain with minimal differences in natural environmental conditions,and the main factors affecting village types are concentrated on village size,industrial base,and location conditions.(3)The majority of village types are classi-fied as those requiring renovation and improvement,and the development of such villages will be the focus of reali-zing the rural revitalization strategy.
关键词
村庄分类/乡村振兴/村庄规划/多源数据/河南省封丘县Key words
village classification/rural revitalization/village planning/multi-source data/Fengqiu County of Henan Province引用本文复制引用
基金项目
国家自然科学基金(42371223)
教育部人文社会科学研究项目(23YJC790141)
河南省自然科学基金(242300420591)
出版年
2024
NETL
NSTL
万方数据