Acta Scientiarum Naturalium Universitatis Pekinensis ›› 2021, Vol. 57 ›› Issue (4): 671-678.DOI: 10.13209/j.0479-8023.2021.019

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Spatial Distribution and Influencing Factors of Unsafe Charging for Electric Bicycles in Urban Areas

LIAO Cong1,2, WU Lun1,2, CAI Heng1,2, CHEN Yueyi1,2, TIAN Yuan1,2,†   

  1. 1. Institute of Remote Sensing and Geographical Information Systems, Peking University, Beijing 100871 2. Beijing Key Laboratory of Spatial Information Integration and Its Applications, Beijing 100871
  • Received:2020-06-05 Revised:2020-10-20 Online:2021-07-20 Published:2021-07-20
  • Contact: TIAN Yuan, E-mail: tianyuanpku(at)


廖聪1,2, 邬伦1,2, 蔡恒1,2, 陈跃毅1,2, 田原1,2,†   

  1. 1. 北京大学遥感与地理信息系统研究所, 北京 100871 2. 空间信息集成与 3S 工程应用北京市重点实验室, 北京 100871
  • 通讯作者: 田原, E-mail: tianyuanpku(at)
  • 基金资助:
    国家重点研发计划(2018YFB0505500, 2018YFB0505504)资助


By using spatial hot spot analysis and spatial error regression model, the authors analyzed the spatial distribution pattern and the influencing factors of unsafe charging behavior for e-bikes in Guangming district of Shenzhen, with the daily inspection data collected in urban grid management. Urban spatial features, such as land use types, employment opportunities, facility and traffic conditions were found to have a close correlation with unsafe charging behaviors at different significance levels. There was a certain spatial autocorrelation of the unsafe charging behavior of e-bikes. Most of the unsafe charging concentrated in a few grids, and 9.2% of the grids in the case area covered 84.94% of the total number of unsafe charging behaviors. Variables related to travel demand and traffic conditions have a stimulating effect on people's behavior of illegal charging of e-bikes. There was a positive correlation between the proportion of residential land, the number of enterprises and the road density within the urban grid and the unsafe charging behaviors of e-bikes. The standardized regression coefficients were 0.09, 0.03 and 0.02 respectively. There was a negative correlation between the proportion of public facilities and public building land in urban grid, the distance from home to primary schools and the unsafe charging behaviors of ebikes, and the standardized regression coefficients were ?0.02 and ?0.01. Higher coverage rate of bus stop would reduce the use of e-bikes, and lead to less unsafe charging behaviors. The results can be used to direct the optimization of patrol routes in grid management and improve the ability of pre-perception and fine prevention of urban fire accidents. 

Key words: fire hazard, short-distance travel, urban spatial feature, spatial autocorrelation, grid management 


利用深圳市光明区网格化城市管理系统上报的电动自行车违规充电隐患数据, 采用空间热点分析和空间误差回归模型, 分析违规充电隐患的空间分布规律及其影响因素。结果表明, 用地类型、就业情况、生活设施和交通条件等城市空间要素与电动自行车违规充电行为有不同程度的相关性: 1) 电动自行车违规充电行为存在一定程度的空间自相关, 大多数违规充电行为集中在少数网格中, 案例区9.2%的网格囊括84.94%的总违规充电隐患数量; 2) 与出行需求和交通条件相关的变量对电动自行车违规充电行为具有正向刺激作用, 住宅用地比例、企业数量和道路密度等指标的标准化回归系数分别为0.09, 0.03和0.02; 3) 城市网格中公用设施、公用建筑用地比例以及到小学的距离与电动车违规充电行为负相关, 标准化相关系数为?0.02和 ?0.01; 4) 更高的公交站点覆盖率会减少电动自行车的使用行为, 相应地, 也导致违规充电行为减少。研究结果可以指导网格化管理中的巡查路线优化, 提升城市火灾隐患的事前感知和精细化防控能力。

关键词: 火灾隐患, 短距离出行, 城市空间要素, 空间自相关, 网格化管理