Abstract
An urban, commercial central district is often regarded as the heart of a city. Therefore, quantitative research on commercial central districts plays an important role when studying the development and evaluation of urban spatial layouts. However, conventional planar kernel density estimation (KDE) and network kernel density estimation (network KDE) do not reflect the fact that the road network density is high in urban, commercial central districts. To solve this problem, this paper proposes a new method (commercial-intersection KDE), which combines road intersections with KDE to identify commercial central districts based on point of interest (POI) data. First, we extracted commercial POIs from Amap (a Chinese commercial, navigation electronic map) based on existing classification standards for urban development land. Second, we calculated the commercial kernel density in the road intersection neighborhoods and used those values as parameters to build a commercial intersection density surface. Finally, we used the three standard deviations method and the commercial center area indicator to differentiate commercial central districts from areas with only commercial intersection density. Testing the method using Nanjing City as a case study, we show that our new method can identify seven municipal, commercial central districts and 26 nonmunicipal, commercial central districts. Furthermore, we compare the results of the traditional planar KDE with those of our commercial-intersection KDE to demonstrate our method’s higher accuracy and practicability for identifying urban commercial central districts and evaluating urban planning.
Highlights
Recent studies have found that China is one of the fastest growing countries in the world [1]
Our experiment shows that we can effectively identify current urban commercial central districts from point of interest (POI) data and road networks
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Summary
Recent studies have found that China is one of the fastest growing countries in the world [1]. Urban, commercial central districts are described as having two main characteristics: (1) a dense distribution of commercial facilities and (2) a high density of road networks. We define the urban commercial central district as an area with an obvious concentration of commercial facilities and a high density of road networks in the city. Our experiment shows that we can effectively identify current urban commercial central districts from POI data and road networks. The feasibility of the proposed method illustrates the viewpoint proposed by Huang et al [37], suggesting that we can model a city’s higher order geographic phenomena and mine a city’s dynamics and semantics (urban commercial central districts, in this paper) by integrating LBS-generated data and other multi-source data, so that people can better understand the city’s development.
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