Hotspot Area Recognition by Using Fuzzy Density Clustering and Bidirectional Buffer

CUI Xiaojie; WANG Jiayao; GONG Xianyong; ZHAO Yao

doi:10.13203/j.whugis20180358

Volume 44 Issue 1

Jan. 2019

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Geomatics and Information Science of Wuhan University > 2019 > 44(1): 84-91. > DOI: 10.13203/j.whugis20180358

CUI Xiaojie, WANG Jiayao, GONG Xianyong, ZHAO Yao. Hotspot Area Recognition by Using Fuzzy Density Clustering and Bidirectional Buffer[J]. Geomatics and Information Science of Wuhan University, 2019, 44(1): 84-91. DOI: 10.13203/j.whugis20180358

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Hotspot Area Recognition by Using Fuzzy Density Clustering and Bidirectional Buffer

1.
Institute of Geospatial Information, Information Engineering University, Zhengzhou 450000, China
2.
Research Institute of Henan Spatio-Temporal Big Data Industrial Technology, Zhengzhou 450018, China

Funds:

The Key Projects of Consultation and Research of the Chinese Academy of Engineering 2017-XZ-13

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Author Bio:
CUI Xiaojie, PhD candidate, specializes in map distribution pattern recognition and cartographic generalization. E-mail: cuixiaojie1990@qq.com
Corresponding author:
GONG Xianyong, lecturer, PhD. E-mail: gongxygis@whu.edu.cn
Received Date: September 12, 2018
Published Date: January 04, 2019

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Abstract

Abstract

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.
- geospatial knowledge,
- hotspot area recognition,
- spatial clustering,
- fuzzy membership,
- buffer

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References

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