Abstract
Urban form can be reflected by many city elements, such as streets. A street network serves as the backbone of a city and reflects a city’s physical structure. A street network’s topological measures and statistical distributions have been widely investigated in recent years, but previous studies have seldom characterized the heavy-tailed distribution of street connectivities from a fractal perspective. The long-tail distribution of street connectivities can be fractal under the new, third definition: a set or pattern is fractal if the scaling of far more small things than large ones recurs at least twice. The number of recurred scaling patterns of far more less-connected streets than well-connected ones greatly helps in measuring the scaling hierarchy of a street network. Moreover, it enables us to examine the potential fractality of urban street networks at the national scale. In this connection, the present study aims to contribute to urban morphology in China through the investigation of the ubiquity of fractal cities from the lens of street networks. To do this, we generate hundreds of thousands of natural streets from about 4.5 million street segments over 298 Chinese cities and adopted power-law detection as well as three fractal metrics that emerged from the third definition of fractal. The results show that almost all cities bear a fractal structure in terms of street connectivities. Furthermore, our multiple regression analysis suggests that the fractality of street networks is positively correlated with urban socioeconomic status and negatively correlated with energy consumption. Therefore, the fractal metrics can be a useful supplement to traditional street-network configuration measures such as street lengths.
Highlights
It is widely recognized that geographic features such as mountains and rivers are neither smooth nor regular, so they cannot be precisely described on the basis of Euclidean geometry
If we take a closer look at the coefficient β, it should be noted that the positive effect of l∀Streets to urban socioeconomic status is stronger than any of three fractal metrics
The paper applies an integration of three fractal metrics—ht-index, CRG, and RA—for fractal or scaling analysis of street connectivities
Summary
It is widely recognized that geographic features such as mountains and rivers are neither smooth nor regular, so they cannot be precisely described on the basis of Euclidean geometry. Human activities inside an urban space, especially those manifested by location-based social media data, agglomerate into a fractal-like structure that contains a minority of high-density locations and a majority of low-density ones, which are ordered spatially from the city center to the periphery [16,17]. This paper is motivated to use the third definition of fractal, which unties the power law constraint by considering other types of heavy-tailed distribution as the support of fractality, to identify the universal fractal pattern of street networks at a wide spatial extent. Another triggering factor of this research is the geospatial big data.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
