| 摘要: |
| 目的 为研究喀斯特地区贫困空间分异特征及其影响因素，文章以典型喀斯特区安顺市为研究区域，基于2015年安顺市村域贫困发生率数据，选择9个地理因素研究其对贫困空间分异的影响。方法 综合运用核密度分析、空间自相关分析、地理探测器等方法，定量评估了喀斯特地区农村贫困空间异质性的成因。结果 （1）安顺市南部较北部贫困程度高、贫困规模大，且存在空间贫困陷阱；（2）从各因子对贫困发生率的解释力看，耕地占比（q=0.28）、平均高程（q=0.2）和平均坡度（q=0.19）是决定贫困发生率空间异质性的主导因子。平均高程与耕地占比因子耦合作用对贫困发生率空间异质性的解释力最大（q=0.34）；（3）分贫困程度后，除石漠化的决定力增强外，其他各因子的决定力均减少。平均高程对轻度和重度贫困的决定力均最大（q=0.12，q=0.09）。结论 在典型喀斯特地区，耕地资源和高程是贫困空间异质性的主要原因，石漠化对贫困的影响随贫困程度的增加而增强。因子间的耦合作用程度在不同贫困程度区域的差异显著，重度贫困地区的致贫因素更为复杂。因此，喀斯特贫困地区实施乡村振兴战略，应综合考虑不同贫困程度区域贫困形成机制，基于案例推理构建各贫困程度地区的定量化情景模拟模型是今后工作的重点。 |
| 关键词: 喀斯特 贫困 空间分异 地理探测器 安顺 |
| DOI：10.7621/cjarrp.1005-9121.20210811 |
| 分类号:K901 |
| 基金项目:贵州省科技计划重大专项“石漠化防治生态衍生产业扶贫模式与技术示范”（黔科合平台人才[2017]5411号）；国家自然科学基金项目“典型岩溶区土壤侵蚀规律及其与石漠化耦合关系研究”(41561066）；贵州省科技计划课题“石漠化综合治理监测评价技术及‘三位一体’集成研究示范”（黔科合SY字[2013]3160号） |
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| SPATIAL DISTRIBUTION AND ITS DETERMINANTS OF POVERTY IN TYPICAL KARST AREA BASED ON GEODETECTORS |
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Zhao Rong1,2, Xiong Kangning1,2, Chen Qiwei1,2,3
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1.Karst Research Institute, Guizhou Normal University, Guiyang 550001, Guizhou, China;2.Technology Research Center for Karst Rocky Desertification Rehabilitation, Guiyang 550001, Guizhou, China;3.School of Geography and Resources, Guizhou Education University, Guiyang 550018, China
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| Abstract: |
| To study the spatial heterogeneity of rural poverty and its formation mechanism in typical Karst area. Based on the data of Anshun village-level impoverishment rate in 2015, we took Anshun city as the research area, and used the methods of kernel density analysis, spatial autocorrelation analysis and Geodetector model to evaluate the cause of spatial heterogeneity of rural poverty in Karst area. The results showed that: (1) The poverty degree and scale in the south of Anshun city were higher than those in the north, and there were spatial poverty traps. (2)Geodetector found that the dominant factors of poverty spatial heterogeneity in Anshun were farmland area proportion (q=0.28), average elevation (q=0.2) and average slop (q=0.19). (3) After divided the degree of poverty, the decisive power of factors was decreased. But the power of rocky desertification was increased. Average elevation was the dominant factors of light (q=0.12) and severe poverty (q=0.09). Coupled q-value of two factors in various poverty degree are different. In order to realize the strategy of rural revitalization in poor Karst areas, the poverty impact mechanism in different poverty levels should be considered comprehensively. Constructing quantitative scenario simulation model by case-based reasoning will be the focus of future work. |
| Key words: Karst poverty spatial differentiation geodetector Anshun |
