共享单车出行空间异质性特征及驱动因素研究

为实现基于轨迹数据挖掘的共享单车出行空间异质性特征及其驱动因素评估，本文应用核密度分析和热点探测，获取采样分析区域并以热力值表征共享单车出行发生量，减少尺度效应的干扰；引入空间统计学的半变异函数模拟共享单车出行发生量的结构性和随机性变化规律，挖掘空间异质性特征，确定邻域效应的尺度范围；利用空间序列的斜率表征变化趋势，同时，结合改进的空间滞后和残差模型，区分土地利用、邻域效应和其他建成环境各自对共享单车出行空间异质性特征的驱动力。以北京市为案例进行分析，结果表明：北京市的共享单车出行存在中等的、 正的空间自相关性，空间异质性特征的最佳拟合模型为指数模型；空间自相关性的衰减半径为 1860 m，大于此距离时邻域效应消失；建成环境对空间异质性特征的相对驱动力最大，邻域效应对其的相对驱动力则处于中间水平，而土地利用对其的相对驱动力最小。

Bike-sharing Trips Spatial Heterogeneity and Driving Factors

This paper examines the spatial heterogeneity characteristics of bike-sharing trips and the driven factor assessment based on trajectory data mining. The kernel density analysis and hotspot detection are performed to obtain the sampled analysis area and characterize the generation of bike-sharing travel with hot values to reduce the interference of scale effects. The semi-variance function of spatial statistics is introduced to simulate the structural and stochastic variation patterns of bike-sharing travel generation, to explore the spatial heterogeneity characteristics and to determine the scale range of neighborhood effects. The slope of the spatial series is used to characterize the change trend. Meanwhile, the respective driving forces of land use, neighborhood effects and other built environment on spatial heterogeneity are distinguished through the improved spatial lag and residual models. The bike- sharing in Beijing is analyzed as a case study. The results indicate that the spatial autocorrelation of bike-sharing trips in Beijing is moderate and positive, and the spatial heterogeneity is consistent with the exponential model. The decay radius of spatial autocorrelation is 1860 meters, and the neighborhood effect becomes insignificant when the distance is greater than this threshold. The built environment has the most significant impact on the spatial heterogeneity of bike-sharing trips, the neighborhood effect is at an intermediate level, and the land use has the smallest impact on bike-sharing trips.