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A Dataset for Multiple Structural Representations of Urban Road Maps Across Chinese Cities
Hong Zhang1,2,3,4, Yukan Jin5
1Institute for Global Innovation Studies, East China Normal University, Shanghai, China.
Scientific Data
|August 5, 2026
Summary
This study introduces a novel dataset of multi-representational urban road networks for 297 Chinese cities. This standardized framework enhances urban morphology and spatial cognition research.
Area of Science:
- Urban Studies
- Geographic Information Science
- Network Science
Background:
- Urban road networks are crucial for city structure and function but existing data lack standardized multi-representational formats for in-depth analysis.
- General datasets like OpenStreetMap require significant processing for systematic urban studies, often lacking geometric, topological, semantic, and cognitive perspectives simultaneously.
Purpose of the Study:
- To present a comprehensive dataset of urban road networks with multiple structural representations for systematic urban studies.
- To provide a standardized framework capturing geometric, topological, semantic, and cognitive properties of road networks.
- To facilitate research in urban morphology, spatial cognition, accessibility, space syntax, and complex network analysis.
Main Methods:
- Developed a dataset of simplified and topologically reconstructed road networks for 293 prefecture-level cities and 4 provincial-level municipalities in China.
- Created four complementary graph models: segment-based primal, segment-based dual, stroke-based dual, and mixed dual graphs.
- Integrated named streets with strokes in mixed dual graphs for enhanced semantic and cognitive representation.
Main Results:
- The dataset exhibits high geometric fidelity (R² = 0.991) and strong topological consistency, retaining 99% of original connectivity.
- The multi-representational approach captures diverse properties of urban road networks within a unified framework.
- Data is provided in GIS-compatible Shapefile and GraphML formats, ensuring accessibility and reproducibility.
Conclusions:
- The presented dataset offers a robust, open-access foundation for advanced urban road network analysis.
- This resource supports research beyond general navigation, focusing on morphological and structural aspects of urban environments.
- The standardized multi-representational approach advances systematic studies of urban form and spatial organization.
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