Emergence of urban growth patterns from human mobility behavior

Area of Science:

• Urban morphology and spatial analysis
• Computational social science
• Human mobility patterns

Background:

• Cities exhibit fractal-like structures governed by scaling laws, often modeled using correlated percolation.
• The micro-foundations of spatially correlated urban growth, particularly the role of human behavior, remain poorly understood.
• Large-scale human mobility data offers new insights into urban dynamics.

Purpose of the Study:

• To introduce a computational urban growth model grounded in human mobility behavior.
• To investigate the mechanisms responsible for spatially correlated urban growth and fractal-like morphologies.
• To connect empirical findings in urban growth patterns with human movement characteristics.

Main Methods:

• Development of a computational urban growth model incorporating human mobility.
• Comparison of the model's outputs with three empirical urban datasets.
• Analysis of social interactions and long-term memory effects in human movement data.

Main Results:

• The proposed model successfully captures spatially correlated urban growth.
• Strong social interactions and long-term memory effects in human movements are identified as key drivers.
• The model aligns with established urban growth laws and fractal urban morphology.

Conclusions:

• Human mobility behavior, specifically social interactions and memory effects, fundamentally shapes urban form.
• The developed model provides a micro-foundation for understanding fractal-like urban growth.
• This research bridges the gap between human mobility studies and urban morphology.