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2024 Vol.42, Issue 6 Preview Page

Article

Spatiotemporal Changes of the Korean Parcel Delivery Network through Spatial Flow Cluster Analysis

공간유동 군집 분석을 통한 한국 택배화물 네트워크의 시공간적 변화 분석

Paul Hayoung JUNG1
,
So-Young JANG2*
정 해영1
,
장 소영2*
1Assistant Professor, Asia Pacific School of Logistics, INHA University, Inhcheon, 22212, Korea
2Associate Research Fellow, Department of Logistics, Korea Transport Institute, Sejong 30147, Korea
1인하대학교 아태물류학부 조교수
2한국교통연구원 물류연구본부 부연구위원
*Corresponding Author
31 December 2024. pp. 739-758
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Abstract

Recently, the parcel delivery industry has emerged as an essential service due to the rapid growth of online distribution and e-commerce, while it follows the surge in parcel volumes during the COVID-19 pandemic. This study analyzes the structure of the parcel delivery network in Korea and the spatiotemporal changes in major regions using parcel delivery data from 2019 to 2023. By applying the Berglund-Karlström’s Gij statistical method for spatial flow cluster analysis, we quantitatively evaluated the interactions between origins and destinations and identified network characteristics for each product category. The results revealed a strong hub-and-spoke structure in the Seoul metropolitan area across multiple product categories. Particularly in the furniture/interior, books/music, and fashion apparel sectors, the metropolitan area functions as a major logistics hub. A decentralized network structure was also observed in the food sector, with major departure points also forming outside the metropolitan area. During the pandemic, the increase in non-physical consumption led to a significant surge in parcel volumes and affected the network structure and distribution patterns. This study provides an evidence-based ground for nationwide logistics policy formulation by comprehensively understanding the spatiotemporal characteristics and changing trends of the parcel delivery network. Policy implications include the need to transition from a single hub structure centered on the metropolitan area to a multi-hub system, strengthen infrastructure in logistics hub regions connected to the global e-commerce market, and establish customized logistics strategies tailored to the characteristics of each product category. Future research should incorporate data from various courier companies, consider external factors, and utilize advanced spatial flow analysis techniques to analyze network changes more precisely.

Keywords
e-commerce
logistics policy
parcel delivery network
spatial flow cluster analysis
spatiotemporal analysis

현대 사회에서 택배산업은 온라인 유통과 전자상거래의 급성장으로 필수적인 서비스로 부상하였으며, 특히 코로나19 팬데믹 이후 택배 물동량이 급증하였다. 본 연구는 2019년부터 2023년까지의 CJ대한통운 택배 물동량 데이터를 활용하여 전국 단위의 택배 화물 네트워크 구조와 주요 권역의 시공간적 변화를 분석하였다. Berglund-Karlström의 Gij 통계 기반 공간유동 군집 분석 기법을 적용하여 출발지와 도착지 간의 상호작용을 정량적으로 평가하고, 상품군별 네트워크 특성을 파악하였다. 분석 결과, 수도권을 중심으로 한 허브 앤 스포크(Hub-and-Spoke) 구조가 여러 상품군에서 강하게 나타났으며, 특히 가구/인테리어, 도서/음반, 패션의류 부문에서 수도권이 주요 물류 허브로 기능하고 있음을 확인하였다. 반면, 식품 부문에서는 분산형 네트워크 구조가 나타나 수도권 외 지역에서도 주요 출발지가 형성되었다. 또한 팬데믹 기간 동안 비대면 소비 증가로 택배 물동량이 급증하였으며, 이는 네트워크 구조와 물동량 분포에 영향을 미쳤다. 본 연구는 택배 화물 네트워크의 시공간적 특성과 변화 양상을 종합적으로 파악함으로써, 향후 물류 정책 수립에 필요한 실질적 기초 자료를 제공한다. 정책적 시사점으로는 수도권 중심의 단일 허브 구조에서 다중 허브 체계로의 전환, 글로벌 이커머스 시장과 연계된 물류 허브 지역의 인프라 강화, 상품군별 특성에 맞는 맞춤형 물류 전략 수립의 필요성이 제기된다. 향후 연구에서는 다양한 택배사 데이터를 포함하고, 외부 요인을 고려하며, 최신 공간유동 분석 기법을 활용하여 네트워크 변화를 더욱 정교하게 분석할 필요가 있다.

키워드
이커머스
물류 정책
택배화물 네트워크
시공간적 분석
공간유동 군집 분석
References
1

Berglund S., Karlström A. (1999), Identifying Local Spatial Association in Flow Data, Journal of Geographical Systems, 1(3), 219-236.

10.1007/s101090050013
2

Kang M. G., Lee S. B., Nam D. (2024), An Optimal Urban Microfulfillment Center Location Model Utilizing Mobility Big Data for Delivery Vehicles, Journal of Logistics Science Technology, 5(1), 76-98.

3

Kim B.-K., Park J.-S., Ryu H., Nam D. (2023a), A Study on the Estimation and Prediction of Daily Delivery Service Volume in the Metropolitan Area: Focuses on the Case of Gyeonggi-Do, Korean Journal of Logistics, 31(2), 47-58.

4

Kim J., Nam D., Jang S. (2023b), A Study on Classifying Under-Serviced Areas With Consideration of Regional Equity in Customer Logistics Services, Korean Journal of Logistics, 31(6), 17-30.

5

Kim Y., Ryu H., Nam D., Kwon M., Choi H. (2024), Analysis of Satisfaction-Dissatisfaction Factors for Digital Shared Logistics Services for Small Business Owners, Journal of Transport Research, 31(2), 65-75.

6

Lee S., Hwang T., Lee W. D., Hwang C.S. (2023), Measuring Spatial Associations of Intercity Flows Between Depopulation Regions Considering Nearest Neighborhoods, Journal of the Korean Geographical Society, 58(6), 644-656.

7

Lee S., Lee G. (2021), Development and Application of the Flow Cluster Detection Method: A Case of Public Transit Trips in Seoul, Journal of the Korean Cartographic Association, 21(2), 129-144.

10.16879/jkca.2021.21.2.129
8

Lee Y. S., Kim M. Y., Kim S. T., Lee Y. H. (2005), A Study on Supply Chain Network Design of Small Package Delivery Service, Journal of the Korean Society of Supply Chain Management, 5(2), 43-52.

9

Lim H. (2013), Delimitation of Local Parcel Delivery Service Areas Using Regional Partitioning Method, Korean Journal of Logistics, 21(3), 83-94.

10.15735/kls.2013.21.3.006
10

Oh K., Kim S., Choi H. S., Lee H. (2022), Delivery Service Demand Analysis Using Social Network Analysis, Information Systems Review, 24(4).

10.14329/isr.2022.24.4.001
11

Park I.-S., Lee S.-H. (2018), A Study on the Predictions Model of Courier Service Volume in Korea, Korean Business Education Review, 33(1), 47-65.

10.23839/kabe.2018.33.1.47
12

Tao R., Chen Y., Thill J.-C. (2023), A Space-Time Flow LISA Approach for Panel Flow Data, Computers, Environment and Urban Systems, 106, 102042.

10.1016/j.compenvurbsys.2023.102042
13

Tao R., Depken C., Thill J.-C., Kashiha M. (2017), Flow HDBSCAN: A Hierarchical and Density-Based Spatial Flow Clustering Method, Proceedings of the 3rd ACM SIGSPATIAL Workshop on Smart Cities and Urban Analytics.

10.1145/3152178.3152189
14

Tao R., Thill J.-C. (2016), Spatial Cluster Detection in Spatial Flow Data, Geographical Analysis, 48(4), 355-372.

10.1111/gean.12100
15

Tao R., Thill J.-C. (2018), Flow AMOEBA: Identifying Regions of Anomalous Spatial Interactions, Geographical Analysis, 51(1), 1-20.

10.1111/gean.12161
16

Tao R., Thill J.-C. (2019), Flow Cross K-function: A Bivariate Flow Analytical Method, International Journal of Geographical Information Science, 33(10), 2055-2071.

10.1080/13658816.2019.1608362
17

Tao R., Thill J.-C. (2020), BiFlowLISA: Measuring Spatial Association for Bivariate Flow Data, Computers, Environment and Urban Systems, 83, 101519.

10.1016/j.compenvurbsys.2020.101519
Information
  • Publisher :Korean Society of Transportation
  • Publisher(Ko) :대한교통학회
  • Journal Title :Journal of Korean Society of Transportation
  • Journal Title(Ko) :대한교통학회지
  • Volume : 42
  • No :6
  • Pages :739-758
  • Received Date : 2024-11-11
  • Revised Date : 2024-11-18
  • Accepted Date : 2024-11-23
  • DOI :https://doi.org/10.7470/jkst.2024.42.6.739
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