Abstract
A country can be well-comprehended through its core cities. Similarly, we can learn about a city from its hotspots, as they manifest the concentration of urban infrastructures and human activities. Following this philosophy, this paper studies the intra-urban form and function from a complexity science perspective by exploring the power law distribution of hotspot sizes and related socio-economic attributes. To detect hotspots, we rely on spatial clustering of geospatial big data sets, including street data from OpenStreetMap platform and nighttime light (NTL) data from the visible infrared imaging radiometer suite (VIIRS) imagery. Unlike conventional spatial units, which are imposed by governments or authorities (such as census block), the delineation of hotspots is done in a totally bottom-up manner and, more importantly, can help us examine precisely the scaling pattern of urban morphological and functional aspects. This results in two types of urban hotspots—street-based and NTL-based hotspots—being generated across 20 major cities in China. We find that Zipf’s law of hotspot sizes (both types) holds remarkably well for each city, as do the city-size distributions at the country level, indicating a statistically self-similar structure of geographic space. We further find that the urban scaling law can be effectively detected when using NTL-based hotspots as basic units. Furthermore, the comparison between two types of hotspots enables us to gain in-depth insights of urban planning and urban economic development.
Highlights
To derive the hotspots from the street nodes, we established big triangulated irregular network (TIN) models for each city, whose TIN edges range from tens to hundreds of thousands (Table 1)
The heavy-tailed distribution statistics were striking for each TIN model, as the average edge length was classified effectively between short and long TIN edges according to their imbalanced ratios
In natural and societal phenomena, it has been widely adopted that the scaling pattern and power-law statistics are signs of sustainability [49]
Summary
The urbanization in China has been unprecedentedly rapid as well in the past few decades [2], reaching 60.6% nationally in 2019 [3]. City-related research has attracted scientists from a variety of subjects and has, inevitably, become cross-disciplinary, including geography, economics, computer science, and physics, etc. To converge these disciplines, scholars have called for a new science of cities in the past few decades, in which they view cities as an organized complexity [4], for studying cities’ fractal shapes, complex structures, and nonlinear dynamics (e.g., [5,6,7,8,9,10,11])
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