Delineating Intra-Urban Spatial Connectivity Patterns by Travel-Activities: A Case Study of Beijing, China

TL;DR

This study models intra-urban spatial connectivity in Beijing using taxi data, revealing a gravity-based structure, a polycentric network, and semantic patterns in travel activities, enhancing understanding of urban spatial interactions.

Contribution

The paper demonstrates that intra-urban spatial connectivities follow a gravity model and uncovers semantic patterns, providing new insights into urban spatial interactions using taxi data.

Findings

Inter-TAZ interactions follow a gravity model with high fit.

The network analysis reveals Beijing's polycentric urban structure.

Semantic analysis explains deviations from the gravity model.

Abstract

Travel activities have been widely applied to quantify spatial interactions between places, regions and nations. In this paper, we model the spatial connectivities between 652 Traffic Analysis Zones (TAZs) in Beijing by a taxi OD dataset. First, we unveil the gravitational structure of intra-urban spatial connectivities of Beijing. On overall, the inter-TAZ interactions are well governed by the Gravity Model $G_{ij} = {\lambda}p_{i}p_{j}/d_{ij}$, where $p_{i}$, $p_{j}$ are degrees of TAZ $i$, $j$ and $d_{ij}$ the distance between them, with a goodness-of-fit around 0.8. Second, the network based analysis well reveals the polycentric form of Beijing. Last, we detect the semantics of inter-TAZ connectivities based on their spatiotemporal patterns. We further find that inter-TAZ connections deviating from the Gravity Model can be well explained by link semantics.