Abstract
In the context of the mid-late development of China’s urbanization, promoting sustainable urban development and giving full play to urban potential have become a social focus, which is of enormous practical significance for the study of urban spatial pattern. Based on such Internet data as a map’s Point of Interest (POI), this paper studies the spatial distribution pattern and clustering characteristics of POIs of four categories of service facilities in Chengdu of Sichuan Province, including catering, shopping, transportation, scientific, educational, and cultural services, by means of spatial data mining technologies such as dimensional autocorrelation analysis and DBSCAN clustering. Global spatial autocorrelation is used to study the correlation between an index of a certain element and itself (univariate) or another index of an adjacent element (bivariate); partial spatial autocorrelation is used to identify characteristics of spatial clustering or spatial anomaly distribution of geographical elements. DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is able to detect clusters of any shape without prior knowledge. The final step is to carry out quantitative analysis and reveal the distribution characteristics and coupling effects of spatial patterns. According to the results, (1) the spatial distribution of POIs of all service facilities is significantly polarized, as they are concentrated in the old city, and the trend of suburbanization is indistinctive, showing three characteristics, namely, central driving, traffic accessibility, and dependence on population activity; (2) the spatial distribution of POIs of the four categories of service facilities is featured by the pattern of “one center, multiple clusters,” where “one center” mainly covers the area within the first ring road and partial region between the first ring road and the third ring road, while “multiple clusters” are mainly distributed in the well-developed areas in the second circle of Chengdu, such as Wenjiang District and Shuangliu District; and (3) there is a significant correlation between any two categories of POIs. Highly mixed multifunctional areas are mainly distributed in the urban center, while service industry is less aggregated in urban fringe areas, and most of them are single-functional or dual-functional regions.
Highlights
At present, China’s social economy has entered a period of rapid development, and the process of urbanization has accelerated significantly
E calculation results in Table 2 show that the Z values of various types of Point of Interest (POI) are greater than 1.96, i.e., the confidence is greater than 95%, while Moran’s I index of the four service industry facilities’ POIs are all positive, indicating that the spatial distribution of the four types of POIs is generally clustered
L-L cluster area indicates that the number of service industry facilities in the area is small with the number of service industry facilities decreasing to the adjacent area, and the layout is relatively scattered. ere are clusters in the POI of transportation service facilities, showing a clustered edge distribution. e H-L cluster area indicates that there are more service industry facilities in the area, but there are fewer service industry facilities in the adjacent area. e L-H cluster area indicates that the number of service industry facilities in the area is small, but the number in the adjacent area is large
Summary
China’s social economy has entered a period of rapid development, and the process of urbanization has accelerated significantly. At this stage, China still witnesses a rapid development with an urbanization rate of 30%–70%. E urban spatial pattern will affect further improvement of the level of urbanization and affect the development capacity of the regional economy [5]. The informatization level of urban infrastructure service facilities has been greatly improved, making it easier to obtain complete spatial data such as public services and life services. Point of Interest (POI), as an emerging spatial data source, is able to explore the overall spatial pattern of the city, while at the same time, conduct spatial identification and quantitative research on the city center to finely identify the high spatial distribution within the city. e feature field is of great significance [7]
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