Multi-Scale Massive Points Fast Clustering Based on Hierarchical Density Spanning Tree
Spatial clustering is dependent on spatial scales. With the widespread use of web maps, a fast clustering method for multi-scale spatial elements has become a new requirement. Therefore, to cluster and display elements rapidly at different spatial scales, we propose a method called Multi-Scale Massi...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | ISPRS International Journal of Geo-Information |
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| Online Access: | https://www.mdpi.com/2220-9964/12/1/24 |
| _version_ | 1797441663019253760 |
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| author | Song Chen Fuhao Zhang Zhiran Zhang Siyi Yu Agen Qiu Shangqin Liu Xizhi Zhao |
| author_facet | Song Chen Fuhao Zhang Zhiran Zhang Siyi Yu Agen Qiu Shangqin Liu Xizhi Zhao |
| author_sort | Song Chen |
| collection | DOAJ |
| description | Spatial clustering is dependent on spatial scales. With the widespread use of web maps, a fast clustering method for multi-scale spatial elements has become a new requirement. Therefore, to cluster and display elements rapidly at different spatial scales, we propose a method called Multi-Scale Massive Points Fast Clustering based on Hierarchical Density Spanning Tree. This study refers to the basic principle of Clustering by Fast Search and Find of Density Peaks aggregation algorithm and introduces the concept of a hierarchical density-based spanning tree, combining the spatial scale with the tree links of elements to propose the corresponding pruning strategy, and finally realizes the fast multi-scale clustering of elements. The first experiment proved the time efficiency of the method in obtaining clustering results by the distance-scale adjustment of parameters. Accurate clustering results were also achieved. The second experiment demonstrated the feasibility of the method at the aggregation point element and showed its visual effect. This provides a further explanation for the application of tree-link structures. |
| first_indexed | 2024-03-09T12:28:22Z |
| format | Article |
| id | doaj.art-0e94d90fd3f442a897db340bea0c77e2 |
| institution | Directory Open Access Journal |
| issn | 2220-9964 |
| language | English |
| last_indexed | 2024-03-09T12:28:22Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj.art-0e94d90fd3f442a897db340bea0c77e22023-11-30T22:32:02ZengMDPI AGISPRS International Journal of Geo-Information2220-99642023-01-011212410.3390/ijgi12010024Multi-Scale Massive Points Fast Clustering Based on Hierarchical Density Spanning TreeSong Chen0Fuhao Zhang1Zhiran Zhang2Siyi Yu3Agen Qiu4Shangqin Liu5Xizhi Zhao6Chinese Academy of Surveying and Mapping, Beijing 100830, ChinaChinese Academy of Surveying and Mapping, Beijing 100830, ChinaKey Laboratory of Monitoring, Evaluation and Early Warning of Territorial Spatial Planning Implementation, Ministry of Natural Resources, Chongqing 401147, ChinaSchool of Economic and Management, Shanghai University of Sport, 650 Qingyuanhuan Rd, Shanghai 200438, ChinaChinese Academy of Surveying and Mapping, Beijing 100830, ChinaChinese Academy of Surveying and Mapping, Beijing 100830, ChinaChinese Academy of Surveying and Mapping, Beijing 100830, ChinaSpatial clustering is dependent on spatial scales. With the widespread use of web maps, a fast clustering method for multi-scale spatial elements has become a new requirement. Therefore, to cluster and display elements rapidly at different spatial scales, we propose a method called Multi-Scale Massive Points Fast Clustering based on Hierarchical Density Spanning Tree. This study refers to the basic principle of Clustering by Fast Search and Find of Density Peaks aggregation algorithm and introduces the concept of a hierarchical density-based spanning tree, combining the spatial scale with the tree links of elements to propose the corresponding pruning strategy, and finally realizes the fast multi-scale clustering of elements. The first experiment proved the time efficiency of the method in obtaining clustering results by the distance-scale adjustment of parameters. Accurate clustering results were also achieved. The second experiment demonstrated the feasibility of the method at the aggregation point element and showed its visual effect. This provides a further explanation for the application of tree-link structures.https://www.mdpi.com/2220-9964/12/1/24multi-scale clusteringpoint clusteringCFSFDP algorithmdensity hierarchytree structure |
| spellingShingle | Song Chen Fuhao Zhang Zhiran Zhang Siyi Yu Agen Qiu Shangqin Liu Xizhi Zhao Multi-Scale Massive Points Fast Clustering Based on Hierarchical Density Spanning Tree ISPRS International Journal of Geo-Information multi-scale clustering point clustering CFSFDP algorithm density hierarchy tree structure |
| title | Multi-Scale Massive Points Fast Clustering Based on Hierarchical Density Spanning Tree |
| title_full | Multi-Scale Massive Points Fast Clustering Based on Hierarchical Density Spanning Tree |
| title_fullStr | Multi-Scale Massive Points Fast Clustering Based on Hierarchical Density Spanning Tree |
| title_full_unstemmed | Multi-Scale Massive Points Fast Clustering Based on Hierarchical Density Spanning Tree |
| title_short | Multi-Scale Massive Points Fast Clustering Based on Hierarchical Density Spanning Tree |
| title_sort | multi scale massive points fast clustering based on hierarchical density spanning tree |
| topic | multi-scale clustering point clustering CFSFDP algorithm density hierarchy tree structure |
| url | https://www.mdpi.com/2220-9964/12/1/24 |
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