Uncover the Dynamic Community Structure of Instant Delivery Network
Fuente:
arXiv
Guardado en:
| Autores principales: | , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866917838889943040 |
|---|---|
| author | Zhang, Chengbo Li, Yonglin Xiao, Zuopeng |
| author_facet | Zhang, Chengbo Li, Yonglin Xiao, Zuopeng |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_10002 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Uncover the Dynamic Community Structure of Instant Delivery Network Zhang, Chengbo Li, Yonglin Xiao, Zuopeng Physics and Society 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. |
| title | Uncover the Dynamic Community Structure of Instant Delivery Network |
| topic | Physics and Society |
| url | https://arxiv.org/abs/2411.10002 |