Hot Spot Analysis of Tourist Attractions Based on Stay Point Spatial Clustering
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초록
The wide application of various integrated locationbased services (LBS social) and tourism application (app) has generated a large amount of trajectory space data. The trajectory data are used to identify popular tourist attractions with high density of tourists, and they are of great significance to smart service and emergencymanagement of scenic spots. A hot spot analysis method is proposed, based on spatial clustering of trajectory stop points. The DBSCAN algorithm is studied with fast clustering speed, noise processing and clustering of arbitrary shapes in space. The shortage of parameters is manually selected, and an improved method is proposedto adaptively determine parameters based on statistical distribution characteristics of data. DBSCAN clustering analysis and contrast experiments are carried out for three different datasets of artificial synthetic twodimensional dataset, fourdimensional Iris real dataset and scenic track retention point. The experiment resultsshow that the method can automatically generate reasonable clustering division, and it is superior to traditional algorithms such as DBSCAN and kmeans. Finally, based on the spatial clustering results of the trajectory stay points, the GetisOrd Gi* hotspot analysis and mapping are conducted in ArcGIS software. The hot spots ofdifferent tourist attractions are classified according to the analysis results, and the distribution of popular scenic spots is determined with the actual heat of the scenic spots.
Abstract
The wide application of various integrated locationbased services (LBS social) and tourism application (app) has generated a large amount of trajectory space data. The trajectory data are used to identify popular tourist attractions with high density of tourists, and they are of great significance to smart service and emergencymanagement of scenic spots. A hot spot analysis method is proposed, based on spatial clustering of trajectory stop points. The DBSCAN algorithm is studied with fast clustering speed, noise processing and clustering of arbitrary shapes in space. The shortage of parameters is manually selected, and an improved method is proposedto adaptively determine parameters based on statistical distribution characteristics of data. DBSCAN clustering analysis and contrast experiments are carried out for three different datasets of artificial synthetic twodimensional dataset, fourdimensional Iris real dataset and scenic track retention point. The experiment resultsshow that the method can automatically generate reasonable clustering division, and it is superior to traditional algorithms such as DBSCAN and kmeans. Finally, based on the spatial clustering results of the trajectory stay points, the GetisOrd Gi* hotspot analysis and mapping are conducted in ArcGIS software. The hot spots ofdifferent tourist attractions are classified according to the analysis results, and the distribution of popular scenic spots is determined with the actual heat of the scenic spots.
참고문헌 (12)
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