突泉县贫困地域差异比较研究——以三个行政村为例 A Comparative Study on the Difference of Poverty Areas in Tuquan County—Taking Three Administrative Villages as Examples

Abstract

突泉县位于中国十四个集中连片特困地区——大兴安岭南麓山区，鉴于突泉县特殊的地域类型，分为北部山区，中部丘陵地区，以及南部平原地区，因此突泉县贫困问题具有特殊性。以突泉县三个地区根据实地调研抽选三个行政村为主要研究对象，基于人地关系地域系统理论，结合空间自相关方法，分析贫困村分布规律和贫困地域特征，探究突泉县农村贫困的主导因素，旨在为突泉县精准扶贫工作提供依据。研究结果表明：突泉县农村贫困具有高度空间聚类的特点，在村域空间单元水平上，Moran’s I指数的值为0.38 > 0，说明贫困村空间分布呈现出聚类分布的特征，即贫困程度相似的村域总体上倾向于聚集分布。其中Z得分为12.54，则说明随机产生此聚类模式的可能性小于1%。P检验也为0，说明通过检验。且分为高值聚类和低值聚类，在高值聚类区分布在中部丘陵地区，低值聚类区主要分布在南部平原区。北部山区主要为随机分布。可以初步说明贫困受自然地理条件与区位条件影响很大。 Tuquan County is located in the fourteen concentrated contiguous areas of China—Daxinganling Mountains in the southern foothills. Due to the special type of Tuquan County, it is divided into the northern mountainous area, the central hilly areas, and the southern plains, so the problem of poverty in Tuquan County has particularity. Taking Tuquan County as an example, three villages were selected as research objects according to field survey. Based on the geographical system theory of human-land relationship and the spatial autocorrelation method, the distribution of poverty-stricken villages and the characteristics of poverty-stricken areas were analyzed. The dominant factor of poverty is to provide the basis for the accurate poverty alleviation work in Tuquan County. The results show that the rural poverty in Tuquan County is characterized by a high degree of spatial clustering. The value of Moran’s I index is 0.38 > 0 in the spatial unit level of the village, indicating that the spatial distribution of poverty-stricken villages presents the char-acteristics of cluster distribution, namely, poverty. The villages with similar degrees generally tend to gather and distribute. The Z score is 12.54, which indicates that the probability of randomly generating this clustering pattern is less than 1%. P test is also 0, indicating that the test passed. It is divided into high-value clustering and low-value clustering. It is distributed in the middle hilly region in the high value clustering area and low value clustering area mainly in the southern plain area. The northern mountains are mostly randomly distributed. It can be preliminarily explained that poverty is greatly affected by the geographical conditions and geographical conditions.