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| 1 | 黄土丘陵沟壑区农村居民点发展类型识别——以吴起县为例显示文摘利用最小累积阻力(Minimal cumulative resistance,MCR)模型,从生态和建设2个角度出发,确定居民点布局适宜性,辅以社会网络分析法研究居民点空间网络关系,从居民点整体适宜性和个体重要性出发,识别陕西省延安市吴起县的居民点发展类型。结果表明:(1)地形和交通条件为决定黄土丘陵沟壑区居民点空间布局的关键约束力,居民点集聚程度整体不高,以带状分布为主,散中有聚。(2)适宜性分区结果表现出“整体集聚、部分分散”的特征,位于适宜建设区内的农村居民点占62.83%,部分居民点分布影响区域生态稳定性。(3)现有居民点网络结构不均匀,需培育发展潜力较大的村庄节点,促进村镇地区均衡发展。(4)基于适宜性及网络分析结果,划分“直接城镇类”“优先发展类”“有条件发展类”“限制扩张类”4种类型并提出相应的发展重点。研究结果可为黄土丘陵沟壑区农村居民点合理规划和发展提供参考。 | 李嘉会 吴金华 王祯 白雨霞 | 2023 | 干旱区地理2023，46，3： | 1 |
| 2 | 大都市郊区不同类型乡村空间形态特征与居民感知差异显示文摘城市化进程的加快促使乡村成为资本青睐和占用的空间,并不断外化于村落空间形态等物质空间中。而乡村形态分化与居民感知差异微观透视了城乡关系的变化与村民生活方式的重塑,是村落规划、土地整治、农村居民点重构等的前提与基础。该研究基于空间句法模型,选取北京市半截河村和莲花池村为案例,探讨大都市郊区不同类型乡村空间形态与居民空间感知差异,识别乡村空间形态与感知差异的形成机制。研究发现:1)不同类型乡村空间形态存在较大差异,传统农业型乡村发展具有内聚性,村落空间形态同构性明显;休闲旅游型乡村发展外向性特征明显,村落空间形态与格局不断分化与重塑。2)不同类型使用者乡村空间感知分化显著,传统农业型乡村村民感知延续传统,生活空间感知频率远高于生产空间;休闲旅游型乡村村民对生产空间感知程度显著高于生活空间,外来群体意象图简单,空间元素多处出现断点。3)乡村空间形态和感知差异是多重因素相互制约的结果。最后,针对不同类型乡村空间形态差异及村民感知偏差,从完善内外交通网络、合理规划村落空间、维护乡村地方性以及分类引导资本投资四方面提出了政策建议,以期为大都市郊区农村居民点重构与村庄发展规划提供理论借鉴。 | 张娟 燕静 孙瑞瑞 王茂军 蔡蓓蕾 | 2023 | 农业工程学报2023，39，20： | 0 |
| 3 | Spatial-temporal differentiation and influencing factors of rural settlements in mountainous areas: an example of Liangshan Yi Autonomous Prefecture, Southwestern China显示文摘Rural settlement is the basic spatial unit for compact communities in rural area. Scientific exploration of spatial-temporal differentiation and its influencing factors is the premise of spatial layout rationalization. Based on land use data of Liangshan Yi Autonomous Prefecture(hereinafter referred to as Liangshan Prefecture) in Sichuan Province, China from 1980 to 2020, compactness index, fractal dimension, imbalance index, location entropy and the optimal parameters-based geographical detector(OPGD) model are used to analyze the spatial-temporal evolution of the morphological characteristics of rural settlements, and to explore the influence of natural geographical factors, socioeconomic factors, and policy factors on the spatial differentiation of rural settlements. The results show that:(1) From 1980 to 2020, the rural settlements area in Liangshan Prefecture increased by 15.96 km^(2). In space, the rural settlements are generally distributed in a local aggregation, dense in the middle and sparse around the periphery. In 2015, the spatial density and expansion index of rural settlements reached the peak.(2) From 1980 to 2020, the compactness index decreased from 0.7636 to 0.7496, the fractal dimension increased from 1.0283 to 1.0314, and the fragmentation index decreased from 0.1183 to 0.1047. The spatial morphological structure of rural settlements tended to be loose, the shape contour tended to be complex, the degree of fragmentation decreased, and the spatial distribution was significantly imbalanced.(3) The results of OPGD detection in 2015 show that the influence of each factor is slope(0.2371) > traffic accessibility(0.2098) > population(0.1403) > regional GDP(0.1325) > elevation(0.0987) > poverty alleviation(0). The results of OPGD detection in 2020 show that the influence of each factor is slope(0.2339) > traffic accessibility(0.2198) > population(0.1432) > regional GDP(0.1219) > poverty alleviation(0.0992) > elevation(0.093). Natural geographical factors(slope and elevation) are the basic factors affecting the spatial distribution of rural settlements, and rural settlements are widely distributed in the river valley plain and the second half mountain area. Socioeconomic factors(traffic accessibility, population, and regional GDP) have a greater impact on the spatial distribution of rural settlements, which is an important factor affecting the spatial distribution of rural settlements. Policy factors such as poverty alleviation relocation have an indispensable impact on the spatial distribution of rural settlements. The research results can provide decisionmaking basis for the spatial arrangement of rural settlements in Liangshan Prefecture, and optimize the implementation of rural revitalization policies. | WANG Yumeng DENG Qingchun YANG Haiqing LIU Hui YANG Feng ZHAO Yakai | 2024 | Journal of Mountain Science2024，21，1： | 0 |
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