基于多尺度地理加权回归模型的城市建成环境对共享单车空间分布影响研究

Built environment effects on the spatio-temporal distribution of shared bikes based on multi-scale geographic weighted regression

近年来共享单车逐渐融入人们的出行模式,在解决短距离出行需求的同时,也成为了普通大众参与实现“碳中和”的重要途径之一。尽管学界对共享单车的时空特征进行了深入的探索,但关于城市建成环境对共享单车空间分布影响的实证研究方法仍以全局回归或地理加权回归为主,在对影响因素空间作用尺度上的研究仍存在明显不足。本文划定北京市六环内方形区域为研究范围,基于2019年5月7日摩拜单车数据,引入学界较为前沿的多尺度地理加权回归模型对城市建成环境中影响共享单车使用的因素进行识别。结果表明:①验证了多尺度地理加权回归模型在单车出行数据中的解释力,模型拟合优度高于经典地理加权回归模型。②各变量间空间作用尺度存在一定分异。其中,区位、公司企业与公共设施尺度较小,存在较大的空间异质性;距公交车站距离、休闲娱乐和土地利用混合度尺度接近全局,影响程度在空间上变化平缓。③在本案例中,距公交车站距离、公司企业、公共设施、休闲娱乐、土地利用混合度为影响共享单车空间分布的关键要素。其中,区位、土地利用混合度和公司企业等要素对共享单车空间分布影响最为显著,对上述要素进行空间规划或治理时,需留意其对单车分布量的边际效应,合理预留停车空间,改善单车出行环境,高效响应出行需求。

Bike-sharing has emerged as a transformative mode of transportation in China,providing an effective solution for short-distance travel demands.Despite the growing attention paid to the spatio-temporal characteristics of shared bikes in recent years,few empirical studies have focused on the impact of the urban built environment on the spatio-temporal distribution of shared bikes.To address this gap,this study employs Mobike data from May 17th,2019,and takes the area within the Sixth Ring Road of Beijing as the study area.The study introduces a cutting-edge statistical method,the multi-scale geographic weighted regression(MGWR)model,to identify the factors that impact the use of shared bikes in the urban built environment,including distance to the bus station,company,public facilities,leisure and entertainment,and land-use mix.The MGWR model is chosen for its capability to customize the spatial scale of each spatial process and provide more robust regression results than traditional geographic weighted regression.The results of this study demonstrate that the MGWR model is a valuable tool for identifying the factors that impact the use of shared bikes in the urban built environment.The study finds that distance to the bus station,company,public facilities,leisure and entertainment,and land-use mix are the key indicators that affect the spatial and temporal distribution of shared bikes.Among these factors,location,degree of space utilization,and company factors have the most significant impact on the spatio-temporal distribution of shared bikes.In conclusion,this study provides valuable insights into the factors that impact the spatiotemporal distribution of shared bikes in the urban built environment of Beijing.The use of the MGWR model allows for a more accurate and customized analysis of the spatial scale of each spatial process,which leads to more robust regression results.The findings of this study have important implications for urban planners and policymakers in effectively managing and planning bike-sharing systems in Beijing and other cities with similar urban built environments.The study recommends that the location of shared bikes be considered in relation to the urban built environment,along with the degree of space utilization and company factors in planning and managing bike-sharing systems.