Multi-scale Clustering of Points Synthetically Considering Lines and Polygons Distribution
Considering the complexity and discontinuity of spatial data distribution, a clustering algorithm of points was proposed. To accurately identify and express the spatial correlation among points,lines and polygons, a Voronoi diagram that is generated by all spatial features is introduced. According t...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | zho |
| Published: |
Surveying and Mapping Press
2015-10-01
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| Series: | Acta Geodaetica et Cartographica Sinica |
| Subjects: | |
| Online Access: | http://html.rhhz.net/CHXB/html/2015-10-1152.htm |
| Summary: | Considering the complexity and discontinuity of spatial data distribution, a clustering algorithm of points was proposed. To accurately identify and express the spatial correlation among points,lines and polygons, a Voronoi diagram that is generated by all spatial features is introduced. According to the distribution characteristics of point's position, an area threshold used to control clustering granularity was calculated. Meanwhile, judging scale convergence by constant area threshold, the algorithm classifies spatial features based on multi-scale, with an <i>O</i>(<i>n</i> log <i>n</i>) running time.Results indicate that spatial scale converges self-adaptively according with distribution of points.Without the custom parameters, the algorithm capable to discover arbitrary shape clusters which be bound by lines and polygons, and is robust for outliers. |
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| ISSN: | 1001-1595 1001-1595 |