Hotspot Area Recognition by Using Fuzzy Density Clustering and Bidirectional Buffer

Obtaining geospatial knowledge such as aggregation mode (i.e. hotspot) by data mining is the basis and premise of geographic information intelligent service. The aggregation mode extraction from point group is the detection of hotspots and their boundaries (hotspot areas) essentially. This paper firstly analyzes the shortcomings of the DBSCAN (density-based spatial clustering of applications with noise) -convex hull method for hotspot area recognition, and then proposes an automatic method of hotspot area generation using fuzzy density clustering and bidirectional buffer. There are two parts in this method:①Based on the theory of fuzzy sets, the fuzzy membership is calculated to improve the DBSCAN; ②The boundaries of hotspots are generated using positive-negative buffer method according to the influence radius calculated by the fuzzy membership formula. The experimental results show that this method can reflect the spatial pattern of the scientific situation. Besides, noises can be distinguished from points, thus ensuring there are no noise points in the hotspot area. Moreover, the hotspot boundaries are not only continuous and flat, also can reflect the actual shape and range of reasonable hotspot areas. Compared with the DBSCAN-convex hull method and the kernel density-contour method, the hotspot area recognized by the method proposed in this paper is better.