Replication Data for: The Spatial Dynamics of Amazon Lockers in Los Angeles County

The rise of e-commerce has imposed increasing pressures on urban freight distribution systems with a significant demand for dedicated delivery services to the end consumers. Last-mile delivery, which usually happens in residential areas conducted by small vans or trucks with low speeds, raises concerns for environmental and safety issues. One of the strategies to address these problems is to set up Pick-up Point (PPs) networks or Automated Parcel (APs) systems. This research will focus on the spatial dynamics and the associated potential GHG emission reductions of Amazon Lockers, one of the most popular APs, in Los Angeles County. The location data of Amazon Lockers will be obtained by Google Map API and Python. Specifically, the questions to be answered include: (1) Describing the spatial distribution of lockers using spatial pattern analysis tool (Kernel density and Moran's I statistics); (2) Analyzing the socio-economic and built environmental factors that might affect the spatial distribution of Amazon Lockers using spatial regression models (Geographically Weight Regression); and (3) Predict and estimate the potential GHG emission reduction based on the spatial regression models. The results indicate that (1) There is a \"three-tier-clustering\" pattern based on the level of density; (2) There is a significant positive spatial autocorrelation at 99% confidence level; (3) Geographic Weighted Regression with independent variables population/internet use, income, education, walkability, transit and parking can explain 41% of the variations in dependent variables; (4) Business cooperation and spillover effects also greatly affect the locker distribution.电子商务的兴起给城市货运配送系统带来了日益严峻的压力，末端消费者对专属配送服务的需求显著攀升。最后一英里配送通常在居民区开展，由低速行驶的小型厢式货车或卡车执行作业，由此引发了环境与安全层面的诸多顾虑。针对上述问题的应对策略之一，是搭建自提点（Pick-up Point, PPs）网络或自动包裹柜（Automated Parcel, APs）系统。本研究将聚焦于洛杉矶县内最主流的自动包裹柜品类之一——亚马逊储物柜（Amazon Lockers）的空间动态特征，及其潜在的温室气体减排潜力。亚马逊储物柜的位置数据将通过谷歌地图API（Google Map API）与Python编程语言获取。具体需要解答的研究问题包括：(1) 利用空间格局分析工具（核密度分析与Moran's I统计量）刻画储物柜的空间分布特征；(2) 借助空间回归模型——地理加权回归（Geographically Weighted Regression），分析影响亚马逊储物柜空间分布的社会经济与建成环境因素；(3) 基于空间回归模型预测并估算潜在的温室气体减排量。研究结果显示：(1) 储物柜密度呈现“三级聚类”的分布格局；(2) 在99%置信水平下存在显著的正向空间自相关性；(3) 以人口/互联网使用率、收入水平、教育程度、步行友好度、公共交通与停车设施作为自变量的地理加权回归模型，能够解释因变量41%的变异；(4) 商业合作与溢出效应同样对储物柜的布局产生显著影响。