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The Spatial Dynamics of Amazon Lockers in Los Angeles County 2019 The Spatial Dynamics of Amazon Lockers in Los Angeles County Project Number 5.4c Final Report | October 2019 Jiawen Fang Genevieve Giuliano, PhD An-Min Wu, PhD Jiawen Fang MetroFreight October 2019 TECHNICAL REPORT DOCUMENTATION PAGE 1. Report No. 2. Government Accession No. 3. Recipient’s Catalog No. MF-5.4c N/A N/A 4. Title and Subtitle 5. Report Date The Spatial Dynamics of Amazon Lockers in Los Angeles County October 1, 2019 6. Performing Organization Code N/A 7. Author(s) 8. Performing Organization Report No. Jiawen Fang, USC https://orcid.org/0000-0002-6339-134X MF-5.4c Genevieve Giuliano, USC https://orcid.org/0000-0002-9257-826 An-Min Wu, USC 9. Performing Organization Name and Address 10. Work Unit No. University of Southern California N/A 650 Childs Way, RGL 216 11. Contract or Grant No. Los Angeles, CA 90089 USDOT Grant 69A3551747109 12. Sponsoring Agency Name and Address 13. Type of Report and Period Covered Office of the Assistant Secretary for Research and Technology Final report (Feb. 2019–Oct. 2019) 1200 New Jersey Avenue, SE 14. Sponsoring Agency Code Washington, DC 20590 USDOT OST-R 15. Supplementary Notes Report URL: https://www.metrans.org/research/the-spatial-dynamics-of-amazon-lockers-in-los-angeles-county 16. Abstract 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. 17. Key Words 18. Distribution Statement E-commerce; urban freight; last mile delivery; sustainability; safety No restrictions. 19. Security Classif. (of this report) 20. Security Classif. (of this page) 21. No. of Pages 22. Price Unclassified Unclassified 30 N/A Form DOT F 1700.7 (8-72) Reproduction of completed page authorized 1 Jiawen Fang MetroFreight October 2019 TABLE OF CONTENTS 0 Acknowledgements .......................................................................................................................... 3 1 Introduction ...................................................................................................................................... 4 2 Literature Review.............................................................................................................................. 5 2.1 The Environmental Benefits of PP Networks ............................................................................... 5 2.2 The Factors that Affect the Design of PP Networks ..................................................................... 5 2.3 Building up Sustainable Networked Delivery System .................................................................. 6 2.4 Research Gaps .............................................................................................................................. 6 3 Theoretical Model............................................................................................................................. 7 4 Data Collection.................................................................................................................................. 8 5 Methods .......................................................................................................................................... 10 5.1 Spatial Point Pattern Analysis - Kernel Density and Ripley's K Function.................................... 10 5.2 Spatial Autocorrelation – Moran’s I statistics and Hot Spot Analysis ........................................ 10 5.3 Spatial Regression - OLS and GWR ............................................................................................. 11 6 Findings ........................................................................................................................................... 11 6.1 Spatial Point Pattern Analysis .................................................................................................... 11 6.2 Spatial Autocorrelation .............................................................................................................. 14 6.3 Spatial Regression ...................................................................................................................... 16 6.3.1 Comparative Results of OLS and GWR ................................................................................... 16 6.4 Other Potential Factors beyond the Model ............................................................................... 19 7 Conclusions ..................................................................................................................................... 21 8 Limitations and Future Research .................................................................................................... 22 9 References ...................................................................................................................................... 23 10 Data Management Plan .................................................................................................................. 25 10.1 Products of Research.................................................................................................................. 25 10.2 Data Format and Content........................................................................................................... 25 10.3 Data Access and Sharing ............................................................................................................ 25 10.4 Reuse and Redistribution ........................................................................................................... 25 11 Appendix ......................................................................................................................................... 26 11.1 Python Scripts for Collecting Amazon Locker Location Data ..................................................... 26 11.2 R Scripts for K-function............................................................................................................... 26 2 Jiawen Fang MetroFreight October 2019 0 ACKNOWLEDGEMENTS This research was supported by the Volvo Research and Educational Foundation through the MetroFreight Center of Excellence. The report is also affiliated with the Pacific Southwest Region University Transportation Center (PSR UTC). The Pacific Southwest Region University Transportation Center (UTC) is the Region 9 University Transportation Center funded under the US Department of Transportation’s University Transportation Centers Program. Established in 2016, the Pacific Southwest Region UTC (PSR) is led by the University of Southern California and includes seven partners: Long Beach State University; University of California, Davis; University of California, Irvine; University of California, Los Angeles; University of Hawaii; Northern Arizona University; Pima Community College. U.S. DEPARTMENT OF TRANSPORTATION (USDOT) DISCLAIMER The contents of this report reflect the views of the authors, who are responsible for the facts and the accuracy of the information presented herein. This document is disseminated in the interest of information exchange. The report is funded, partially or entirely, by a grant from the U.S. Department of Transportation’s University Transportation Centers Program. However, the U.S. Government assumes no liability for the contents or use thereof. 3 Jiawen Fang MetroFreight October 2019 THE SPATIAL DYNAMICS OF AMAZON LOCKERS IN L.A. COUNTY By Jiawen Fang 1 INTRODUCTION 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. Trucks with low speeds would consume more energy and emit more pollution. Residents, especially in dense neighborhoods, are concerned that delivery trucks impose great threats on their safety. One of the strategies to address these problems is to set up Pick-up Point
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