Uncover the Dynamic Community Structure of Instant Delivery Network

TL;DR

This paper analyzes the spatiotemporal evolution of instant delivery networks in Beijing, revealing dynamic community patterns and key influencing factors, with implications for urban planning and platform optimization.

Contribution

It introduces a novel dynamic community detection approach for large-scale delivery networks and uncovers their temporal evolution and influencing factors.

Findings

Identified 309 dynamic communities with daily formation and dissolution patterns.

Key factors like building area and population influence community stability.

Online retail and services contribute to community instability.

Abstract

The rise of instant delivery services has reshaped urban spatial structures through the interaction between suppliers and consumers. However, limited research has explored the spatiotemporal dynamics of delivery network structures. This study constructs a time-dependent, multi-layer instant delivery network in the case city of Beijing using a large-scale dataset from Eleme, organized into 500m grid units. A dynamic community detection method identifies evolving community structures over time. The results reveal 309 dynamic communities, with an average size of 13.78 square kilometers. Communities form in the morning, expand, stabilize, then contract, and disappear by night. Key factors influencing stability include building area and residential population, while online retail and service facilities contribute to instability. These findings offer insights into the spatial structure of instant delivery networks and the factors driving their dynamics, with practical implications for optimizing platform strategies, resource allocation, and urban transportation planning.