Performance period: 1/1/2019 to 12/31/2019

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.