Using nighttime light data to identify the structure of polycentric cities and evaluate urban centers

Area of Science:

• Urban Studies
• Geospatial Analysis
• Urban Planning

Background:

• Understanding polycentric city structures is crucial for urban development, planning, and management.
• Existing research lacks methods to compare urban center levels and quantify overall development in polycentric cities.

Purpose of the Study:

• To develop and validate a novel approach for assessing urban center development levels in polycentric cities.
• To overcome limitations of traditional methods by not being restricted by administrative boundaries.

Main Methods:

• Utilized high-spatial-resolution Luojia-1A nighttime light (NTL) data to identify natural cities (NCs).
• Proposed urban center level (UCL) and urban center development index (UCDI) for quantitative comparison and assessment.
• Employed quantitative verification to confirm high accuracy in urban center identification.

Main Results:

• Successfully identified urban centers and calculated UCL and UCDI across different NTL datasets.
• Demonstrated that the proposed method is not constrained by administrative boundaries, offering more accurate urban center delineation.
• Quantitative indicators were developed to express inter-urban center level differences and overall polycentric city development.

Conclusions:

• The proposed method effectively quantifies urban center development and differences within polycentric cities.
• This approach enhances urban planning and management by providing objective, data-driven insights.
• The flexibility in defining urban centers offers a more realistic representation of urban structures.