HiSpatialCluster: A novel high-performance software tool for clustering massive spatial points

doi:10.1111/tgis.12463

	HiSpatialCluster: A novel high-performance software tool for clustering massive spatial points
	Chen, Yiran 1,2; Huang, Zhou 1,2; Pei, Tao 3; Liu, Yu 1,2
刊名	TRANSACTIONS IN GIS
	2018-10-01
卷号	22 期号:5 页码:1275-1298
ISSN号	1361-1682
DOI	10.1111/tgis.12463
通讯作者	Huang, Zhou(huangzhou@pku.edu.cn)
英文摘要	In the era of big data, spatial clustering is a very important means for geo-data analysis. When clustering big geo-data such as social media check-in data, geotagged photos, and taxi trajectory points, traditional spatial clustering algorithms are facing more challenges. On the one hand, existing spatial clustering tools cannot support the clustering of massive point sets; on the other hand, there is no perfect solution for self-adaptive spatial clustering. In order to achieve clustering of millions or even billions of points adaptively, a new spatial clustering toolHiSpatialClusterwas proposed, in which the CFSFDP (clustering by fast search and finding density peaks) idea to find cluster centers and the DBSCAN (density-based spatial clustering of applications with noise) idea of density-connect filtering for classification are introduced. The tool's source codes and other resources have been released on Github, and experimental evaluation was performed through clustering massive taxi trajectory points and Flickr geotagged photos in Beijing, China. The spatial clustering results were compared with those through K-means and DBSCAN as well. As a spatial clustering tool, HiSpatialCluster is expected to play a fundamental role in big geo-data research. First, this tool enables clustering adaptively on massive point datasets with uneven spatial density distribution. Second, the density-connect filter method is applied to generate homogeneous analysis units from geotagged data. Third, the tool is accelerated by both parallel CPU and GPU computing so that millions or even billions of points can be clustered efficiently.
资助项目	National Key Research and Development Program of China[2017YFB0503602] ; National Natural Science Foundation of China[41771425] ; National Natural Science Foundation of China[41625003]
WOS关键词	ALGORITHM ; DATABASES ; PHOTOS
WOS研究方向	Geography
语种	英语
出版者	WILEY
WOS记录号	WOS:000447163200012
资助机构	National Key Research and Development Program of China ; National Natural Science Foundation of China
内容类型	期刊论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/52672]
专题	中国科学院地理科学与资源研究所
通讯作者	Huang, Zhou
作者单位	1.Peking Univ, Inst Remote Sensing & Geog Informat Syst, Beijing 100871, Peoples R China 2.Peking Univ, Beijing Key Lab Spatial Informat Integrat & Its A, Beijing 100871, Peoples R China 3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
推荐引用方式 GB/T 7714	Chen, Yiran,Huang, Zhou,Pei, Tao,et al. HiSpatialCluster: A novel high-performance software tool for clustering massive spatial points[J]. TRANSACTIONS IN GIS,2018,22(5):1275-1298.
APA	Chen, Yiran,Huang, Zhou,Pei, Tao,&Liu, Yu.(2018).HiSpatialCluster: A novel high-performance software tool for clustering massive spatial points.TRANSACTIONS IN GIS,22(5),1275-1298.
MLA	Chen, Yiran,et al."HiSpatialCluster: A novel high-performance software tool for clustering massive spatial points".TRANSACTIONS IN GIS 22.5(2018):1275-1298.