基于POI数据的玉溪市红塔区快递自提点空间格局及影响因素研究

Research on the Spatial Pattern and Influencing Factors of Express Delivery Pickup Points in Hongta District, Yuxi City Based on POI Data

摘要: 本研究以玉溪市红塔区作为研究对象，采用最近邻指数、核密度估计、标准差椭圆、局部自相关指数、泰森多边形以及地理探测器，分析快递自提点的空间分布特性及其影响因素。研究结果显示：(1) 快递自提点显示出核心集中、周边分散的布局形势，与路网的临近程度十分明显；(2) 总体显示出明显的集聚分布状态，热点高度专注于玉兴路、凤凰路等核心建成区域，不存在冷点区域；(3) 空间布局具有单核引领、轴向拓展、圈层递减的特点，高密度核心位于玉兴路、凤凰路街道，密度朝着外围山地乡镇逐渐降低，椭圆的主轴显示东北、西南走向；(4) 路网密度的解释能力最强，坡度次之，GDP与人口密度的解释能力相近；多因子交互显示非线性加强，坡度与坡向交互、坡度与路网交互的组合解释能力最强。快递自提点的空间分布状况是交通主导、地形约束、需求驱动共同影响的产物。

Abstract: This study takes Hongta District, Yuxi City as the research area, and employs nearest neighbor index, kernel density estimation, standard deviational ellipse, local spatial autocorrelation, Thiessen polygons, and geographic detector to analyze the spatial distribution characteristics of express self-pickup points and their influencing factors. The results show that: (1) The self-pickup points exhibit a layout pattern of concentrated core and dispersed periphery, with a pronounced proximity to the road network; (2) Overall, they display a significant clustered distribution, with hot spots highly concentrated in core built-up areas such as Yuxing Road and Fenghuang Road, while no cold spots are identified; (3) The spatial layout features a single-core dominance, axial expansion, and decreasing intensity in concentric rings, with the high-density core located in the subdistricts of Yuxing Road and Fenghuang Road, and density gradually declining toward peripheral mountainous townships, while the major axis of the ellipse indicates a northeast-southwest orientation; (4) Road network density exhibits the strongest explanatory power, followed by slope, while GDP and population density show comparable explanatory power; multi-factor interactions reveal nonlinear enhancement, with the combinations of slope and aspect, and slope and road network density, yielding the strongest explanatory power. The spatial distribution of express self-pickup points is the result of the combined effects of transportation dominance, topographic constraints, and demand-driven factors.