Spatial interactions in urban scaling laws.

Area of Science:

• Urban studies
• Complex systems science
• Spatial analysis

Background:

• Traditional urban scaling law analyses often assume city independence.
• This assumption overlooks critical spatial interactions between urban areas.
• Defining city boundaries for analysis can be problematic.

Purpose of the Study:

• To develop generative models and data analysis methods that incorporate inter-city spatial interactions.
• To simultaneously infer parameters for scaling laws and spatial interactions.
• To enable rigorous model comparison and overcome boundary definition issues.

Main Methods:

• Introduction of generative models to explicitly model spatial interactions between individuals at different locations.
• Simultaneous inference of scaling law parameters and spatial interaction parameters from data.
• Application of Bayesian model comparison for rigorous evaluation.

Main Results:

• Models incorporating spatial interactions generally provide a better fit to the data.
• The inclusion of spatial interactions typically leads to a change in the exponent of the urban scaling law.
• Demonstrated effectiveness across five diverse datasets.

Conclusions:

• Accounting for spatial interactions is crucial for accurate urban scaling law analysis.
• The proposed methods offer a more robust framework for studying urban systems.
• This approach enhances our understanding of how cities grow and interact.