A spatial interaction incorporated betweenness centrality measure

Area of Science:

• Network Science
• Spatial Analysis
• Geography

Background:

• Betweenness centrality (BC) is a standard metric for identifying critical network nodes.
• BC assumes equal importance of all shortest paths, which is unrealistic for spatial networks with varying interactions.
• Existing methods overlook the influence of spatial interactions on network functionality.

Purpose of the Study:

• To propose a novel metric, spatial interaction incorporated betweenness centrality (SIBC), for spatial networks.
• To evaluate SIBC's effectiveness in identifying critical nodes and edges.
• To demonstrate SIBC's utility in enhancing network robustness and efficiency.

Main Methods:

• Developed SIBC by weighting shortest paths based on spatial interaction intensity.
• Applied two forms of SIBC to the Shenzhen street network and China's intercity network.
• Conducted robustness analysis to assess SIBC's performance in improving network efficiency.

Main Results:

• SIBC significantly outperforms traditional BC in identifying critical nodes and edges in spatial networks.
• The proposed SIBC metric effectively combines network structure with spatial interaction intensity.
• SIBC demonstrates superior performance in robustness analysis, indicating improved network efficiency.

Conclusions:

• Introducing spatial configuration into network analysis models is crucial for understanding network functionality.
• SIBC offers a more accurate and effective approach to identifying critical elements in spatial networks.
• The SIBC metric has practical applications in optimizing network design and improving resilience.