4.6 Article

Fractal evolution of urban street networks in form and structure: a case study of Hong Kong

Journal

Publisher

TAYLOR & FRANCIS LTD
DOI: 10.1080/13658816.2021.1974451

Keywords

Geometric fractal; topological fractal; hierarchical fractal; fractal evolution

Funding

  1. National Natural Science Foundation of China [42171420, 42101442]
  2. Natural Science Foundation of Shanghai [21ZR1421100]

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By studying the street networks in Hong Kong from 1971 to 2018, it was found that cities evolve in a fractal manner, with the dimensions of geometric fractal, topological fractal, and hierarchical fractal increasing over time. This indicates a more mature and complex street network structure with a core-periphery pattern and progressively optimized structural elements. The findings contribute to a deeper understanding of urban development.
Cities are spatially evolving complex systems. The order and pattern beneath the apparent chaos and diverse physical forms of cities are still unclear. How the form and structure of a city evolve to improve its functions needs further exploration. To fill thisgap, we examine the geometric fractal (GF), the topological fractal (TF), and the hierarchical fractal (HF) evolution of cities by taking Hong Kong street networks from year 1971 to 2018 as an example. We find that these networks keep to be fractals both in form and structure. The values of GF, TF, and HF dimensions increase with fluctuations, revealing a more mature and complex street network. The radius-length GF dimensions demonstrate the bi-fractal property, with values ranged 1.653-1.832 and 0.677-0.892, respectively, reflecting a core-periphery pattern. The values of TF dimensions increase steadily with a wider gap to GF dimensions, indicating progressively structural optimization of street networks. These street networks keep showing fractal properties in form and structure through spatial extension, local densification, vertical stratification, hierarchies enrichment, and shortcuts construction. Moreover, street networks are GFs and TFs at the city, county, and MSA scales. The discoveries advance our understanding of urban development.

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