Abstract
The spatial pattern is a kind of typical structural knowledge that reflects the distribution characteristics of object groups. As an important semantic pattern of road networks, the city center is significant to urban analysis, cartographic generalization and spatial data matching. Previous studies mainly focus on the topological centrality calculation of road network graphs, and pay less attention to the delineation of main centers. Therefore, this study proposes an automatic recognition method of main center pattern in road networks. We firstly extract the main clusters from road nodes by improving the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) with fuzzy set theory. Moreover, the center area is generated with road meshes according to the area ratio with the covering discs of the main clusters. This proposed algorithm is applied to the road networks of a monocentric city and polycentric city respectively. The results show that our method is effective for identifying the main center pattern in the road networks. Furthermore, the contrast experiments demonstrate our method’s higher accuracy.
Highlights
The spatial pattern is a kind of typical structural knowledge, which reflects the inherent distribution characteristics and interrelations of geospatial entities
The main purpose of spatial pattern recognition is to mine structural knowledge that conforms to human spatial cognition, which can effectively improve the availability of spatial data [1,2,3]
The city center is a typical semantic pattern of road networks, which plays a vital role in the city structure
Summary
The spatial pattern is a kind of typical structural knowledge, which reflects the inherent distribution characteristics and interrelations of geospatial entities. The combination of density and centrality of the clusters was used to filter potential center areas This method is designed for the monocentric city, and it is difficult to apply in the cities with complex structures. Extracting main centers from the road networks is conducive to understanding the essential characteristic of a city and mainly helps to: (1) provide the basis for the complex pattern recognition (such as large rings); (2) find locating points for the graphic simplification; (3) support location query and navigation; (4) aid decision making in the structural and functional planning of a city.
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