Spatial analysis using clustering methods: Evaluating central point and median approaches

Alan T. Murray

doi:10.1007/s101090050019

Spatial analysis using clustering methods: Evaluating central point and median approaches

Journal of Geographical Systems ◽

10.1007/s101090050019 ◽

1999 ◽

Vol 1 (4) ◽

pp. 367-383 ◽

Cited By ~ 13

Author(s):

Alan T. Murray

Keyword(s):

Spatial Analysis ◽

Central Point ◽

Clustering Methods

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Applicability Evaluation of Several Spatial Clustering Methods in Spatiotemporal Data Mining of Floating Car Trajectory

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10030161 ◽

2021 ◽

Vol 10 (3) ◽

pp. 161

Author(s):

Hao-xuan Chen ◽

Fei Tao ◽

Pei-long Ma ◽

Li-na Gao ◽

Tong Zhou

Keyword(s):

Spatial Analysis ◽

Spatial Clustering ◽

Heat Index ◽

Operating Efficiency ◽

Clustering Methods ◽

Clustering Method ◽

Trajectory Mining ◽

Density Peaks ◽

Analysis Methods ◽

Taxi Trajectory

Spatial analysis is an important means of mining floating car trajectory information, and clustering method and density analysis are common methods among them. The choice of the clustering method affects the accuracy and time efficiency of the analysis results. Therefore, clarifying the principles and characteristics of each method is the primary prerequisite for problem solving. Taking four representative spatial analysis methods—KMeans, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Clustering by Fast Search and Find of Density Peaks (CFSFDP), and Kernel Density Estimation (KDE)—as examples, combined with the hotspot spatiotemporal mining problem of taxi trajectory, through quantitative analysis and experimental verification, it is found that DBSCAN and KDE algorithms have strong hotspot discovery capabilities, but the heat regions’ shape of DBSCAN is found to be relatively more robust. DBSCAN and CFSFDP can achieve high spatial accuracy in calculating the entrance and exit position of a Point of Interest (POI). KDE and DBSCAN are more suitable for the classification of heat index. When the dataset scale is similar, KMeans has the highest operating efficiency, while CFSFDP and KDE are inferior. This paper resolves to a certain extent the lack of scientific basis for selecting spatial analysis methods in current research. The conclusions drawn in this paper can provide technical support and act as a reference for the selection of methods to solve the taxi trajectory mining problem.

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