【摘要】 针对公开人口分布数据难以精确刻画小区域内人口分布特征及其在同种地类不同区域存在人口差异性等问题，提出了一种顾及空间异质性的人口分布数据精细划分方法。该方法的核心思想是以格网为基础单元，分区建立人口与辅助数据间的关联，从而构建人口分布精细划分模型。采用首尾分割法实现分区策略，利用人口密度约束地类权重，在地类权重、地类人口密度权重与面积权重三重约束下对人口重新分配；在此基础上，利用Dasymetric法对优化的人口分布数据进行精细划分。结果表明，该方法的平均相对误差为5.71%,决定系数为0.98,不仅提高了人口分布数据的精度和分辨率，同时有效解决了当前公开人口分布数据存在的问题，打破了对人口统计数据的依赖，为人口精细化研究提供了新的思路。更多还原

【Abstract】 For the public population distribution data, it is difficult to accurately describe the population distribution characteristics in a small area and the population differences in different regions of the same land type. A fine division method of population distribution data considering spatial heterogeneity is proposed. The core idea of this method is to establish the relationship between population and auxiliary data based on the grid as the basic unit, to construct a fine division model of population distribution. By using the head/tail breaks method to realize the zoning strategy, the population density is used to constrain the land class weight, and the population is redistributed under the triple constraints of land type weight, land type population density weight, and area weight. On this basis, the Dasymetric method is used to divide the optimized population distribution data finely. The results show that the mean relative error is 5.71%,and the coefficient of determination is 0.98,which not only improves the accuracy and resolution of population distribution data, but also effectively solves the problems existing in the current public population distribution data, breaks the dependence on demographic data, and provides a new idea for population refinement research.更多还原