Spatial Scale Selection for Urban Systems: A Complexity-Heterogeneity Balancing Method.

Area of Science:

• Urban Planning and Geography
• Complex Systems Analysis
• Spatial Statistics

Background:

• Understanding urban complexity requires selecting appropriate observation scales.
• Traditional methods for scale selection are subjective and lack generalizability across diverse urban functions.
• Objective and robust methods are needed for urban spatial analysis.

Purpose of the Study:

• To develop and validate a complexity-heterogeneity balancing method for objectively identifying appropriate spatial observation scales in cities.
• To address limitations of traditional subjective and non-generalizable scale selection methods.
• To provide a framework for enhancing urban spatial analysis.

Main Methods:

• Employed renormalization group techniques to generate distribution matrices across multiple scales.
• Developed a complexity-heterogeneity balancing approach to objectively identify optimal observation scales.
• Applied the method to freight, enterprise, and restaurant distribution data in major Chinese cities.

Main Results:

• The complexity-heterogeneity balancing method successfully identified characteristic spatial scales for different urban functions.
• Results demonstrated the method's ability to reflect the unique spatial organization structures of urban functions.
• Validated the robustness of the proposed framework for urban scale determination.

Conclusions:

• The developed method offers a robust and objective framework for selecting appropriate spatial observation scales in urban analysis.
• Findings have significant implications for improving logistics optimization, industrial zoning, and commercial planning.
• Contributes to sustainable urban development by enhancing the understanding of urban functions and morphology.