利用层次Voronoi图进行点群综合

doi:10.13485/j.cnki.11-2089.2014.0166

测绘学报 ›› 2014, Vol. 43 ›› Issue (12): 1300-1306.doi: 10.13485/j.cnki.11-2089.2014.0166

• 学术论文 • 上一篇

利用层次Voronoi图进行点群综合

李佳田, 康顺, 罗富丽

昆明理工大学国土资源工程学院, 云南昆明 650093

收稿日期:2013-06-03 修回日期:2014-01-01 出版日期:2014-12-20 发布日期:2014-12-23
作者简介:李佳田(1975-),男,博士,副教授,研究方向为GIS不规则空间剖分模型与算法.ljtwcx@163.com
基金资助:
国家自然科学基金(41161061;40901197)

Point Group Generalization Method Based on Hierarchical Voronoi Diagram

LI Jiatian, KANG Shun, LUO Fuli

Faculty of Land Resources Engineering, Kunming University of Science and Technology, Kunming 650093, China

Received:2013-06-03 Revised:2014-01-01 Online:2014-12-20 Published:2014-12-23

摘要/Abstract

摘要： 通过距离权重描述点的重要程度,采用改进的k-means算法得到点群的聚类中心,进而以聚类中心为基础,构建了层次加权Voronoi图与Voronoi层次树结构.以点群的分布范围、排列方式与密度为度量,给出了基于Voronoi层次树结构的点群综合方法,确保了点群综合前后在空间形态分布上的一致性.结合地理统计学计算,对综合方法作了进一步的量化评估与优化.经验证,本文方法是可行、有效的.

关键词: 点群综合, 权重, 空间聚类, 层次Voronoi图, 树结构

Abstract: The importance of the point is described by distance weight and the clustering center point of a point group is obtained by modified k-means algorithm. Furthermore, the clustering center is taken as base to construct hierarchical weighted Voronoi diagram and hierarchical tree structure. Distribution scope, arrangement, and density of the group is taken as the measurement to construct the point generalization method based on hierarchical Voronoi diagram tree structure, thus ensuring the consistency in spatial morphology before and after. Combination with geological statistics calculation, this generalization method is estimated and optimized. Finally, the practicability and availability of this method is confirmed through concrete experiment.

Key words: point group generalization, weight, spatial clustering, hierarchical Voronoi diagram, tree structure

中图分类号:

P283

李佳田, 康顺, 罗富丽. 利用层次Voronoi图进行点群综合[J]. 测绘学报, 2014, 43(12): 1300-1306.

LI Jiatian, KANG Shun, LUO Fuli. Point Group Generalization Method Based on Hierarchical Voronoi Diagram[J]. Acta Geodaetica et Cartographica Sinica, 2014, 43(12): 1300-1306.

参考文献

[1] MA Yongli. Cartography Course[M]. Nanjing: Nanjing University Press,2003. (马永立. 地图学教程[M]. 南京: 南京大学出版社, 2003.)
[2] YU Yanping, SHEN Jie, SHANG Zaiying. A Study on the Time Complexity of Point Cluster Selection and Simplification Algorithms[J]. Journal of Nanjing Normal University: Natural Science Edition, 2012, 35(1): 111-116. (于艳平,沈婕,尚在颖. 点群选取与化简算法时间复杂度研究[J]. 南京师范大学报: 自然科学版, 2012, 35(1): 111-116.)
[3] WANG Jiayao, DENG Hongyan. A Model of Cartographical Generalization Based on Genetic Algorithm[J]. Geomatics and Information Science of Wuhan University, 2005, 30(7): 565-569. (王家耀,邓红艳. 基于遗传算法的制图综合模型研究[J]. 武汉大学学报: 信息科学版, 2005, 30(7): 565-569.)
[4] QIAN Haizong, WU Fang, ZHANG Linlin, et al. Quality Assessment of Point Group Geometry Generalization with Polarization Transformation[J]. Acta Geodaetica et Cartographica Sinica, 2005, 34(4): 261-269. (钱海忠,武芳,张琳琳,等. 基于极化变换的点群综合几何质量评估[J]. 测绘学报,2005, 34(4): 261-269.)
[5] LU Yi, ZHAI Jingsheng, DU Jinghai, et al. Recognition, Measurement and Generalization for Point Cluster Features in Digital Nautical Chart[J]. Geomatics and Information Science of Wuhan University, 2001, 26(2): 133-139. (陆毅,翟京生,杜景海,等. 数字海图点群状特征的识别、量测与综合[J]. 武汉大学学报: 信息科学版,2001, 26(2): 133-139.)
[6] GUO Qingsheng. Structuralizing Point Features and Its's Application in Map Generalization[J]. Journal of the PLA Institute of Surveying and Mapping, 1999, 16(3): 210-213. (郭庆胜. 点状要素群的结构化及其在综合中的应用[J]. 解放军测绘学院学报,1999, 16(3): 210-213.)
[7] AI Tinghua, LIU Yaolin. A Method of Point Cluster Simplification with Spatial Distribution Properties Preserved[J]. Acta Geodaetica et Cartographica Sinica, 2002, 31(2): 175-181. (艾延华,刘耀林. 保持空间分布特征的群点化简方法[J]. 测绘学报, 2002, 31(2): 175-181.)
[8] ELENA D, MICHEL M D. Encyclopedia of Distance[M]. [S. l]: Luthaze Press, 2009.
[9] GUO Qingsheng, ZHENG Chunyan, HU Huake. Hierarchical Clustering Method of Group of Points Based on the Neighborhood Graph[J]. Acta Geodaetica et Cartographica Sinica, 2008, 37(2): 256-261. (郭庆胜,郑春燕,胡华科. 基于邻近图的点群层次聚类方法的研究[J]. 测绘学报, 2008, 37(2): 256-261.)
[10] DAVIES D L, BOULDIN D W. A Cluster Separation Measure[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1979, 1(2): 224-227.
[11] CHEN Jun. Voronoi-based Dynamic Spatial Data Model[M]. Beijing: Surveying and Mapping Press, 2002. (陈军. Voronoi动态空间数据模型[M]. 北京: 测绘出版社, 2002.)
[12] ZHAO Renliang. Voronoi Methods for Computing Spatial Relations in GIS[M]. Beijing: Surveying and Mapping Press, 2006. (赵仁亮. 基于Voronoi图的GIS空间关系计算[M]. 北京: 测绘出版社, 2006.)
[13] BALZER M, DEUSSEN O. Voronoi Treemaps[C]// IEEE Symposium on Information Visualization 2005: INFOVIS. New York: IEEE, 2005: 49-56.
[14] DONG P L. Generating and Updating Multiplicatively Weighted Voronoi Diagrams for Point, Line and Polygon Features in GIS[J]. Computers & Geosciences, 2008, 34(4): 411-421.
[15] AURENHAMMER F, EDELSBRUNNER H. An Optimal Algorithm for Constructing the Weighted Voronoi Diagram in the Plane [J]. Pattern Recognition, 1984, 17(2): 251-257.
[16] LIU Tao, DU Qingyun, YAN Haowen. Spatial Similarity Assessment of Point Clusters[J]. Geomatics and Information Science of Wuhan University, 2011, 36(10): 1149-1153. (刘涛,杜清运,闫浩文. 空间点群目标相似度计算[J]. 武汉大学学报: 信息科学版, 2011, 36(10): 1149-1153.)
[17] BONAN L, FREDERICO F. TDD: A Comprehensive Model for Qualitative Spatial Similarity Assessment [J]. Spatial Cognition and Computation, 2006, 6(1): 31-62.
[18] CAI Y X, GUO Q S. Point Set Generalization Based on the Kohonet Net[J]. Geo-spatial Information Science, 2008, 11(3): 221-227.
[19] LI Yulong, ZHU Huahua. Automated Recognition of Point Cluster Scope with Voronoi Diagram[J]. Journal of Engineering Graphics, 2007, 28(3): 73-77. (李玉龙,朱华华. 应用Voronoi图的点群范围自动识别[J]. 工程图学学报, 2007, 28(3): 73-77.)
[20] PATRICIA F, RAY L, JOHN R. A Comparison of Geometric Approaches to Assessing Spatial Similarity for GIR [J]. International Journal of Geographical Information Science, 2008, 22(3): 337-360.

利用层次Voronoi图进行点群综合

Point Group Generalization Method Based on Hierarchical Voronoi Diagram

PDF

可视化

被引次数

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 14

编辑推荐

Metrics

本文评价

[1]	刘呈熠, 武芳, 巩现勇, 行瑞星, 杜佳威. 空间知识挖掘的自然面群聚集度聚类方法[J]. 测绘学报, 2021, 50(4): 544-555.
[2]	伍贻威, 王世超. 不通过自由纸面时建立时间基准的方法与性能分析[J]. 测绘学报, 2021, 50(3): 343-355.
[3]	伍贻威. 国际自由原子时原始权重算法初步分析[J]. 测绘学报, 2020, 49(4): 415-422.
[4]	郭庆胜, 魏智威, 王勇, 王琳. 特征分类与邻近图相结合的建筑物群空间分布特征提取方法[J]. 测绘学报, 2017, 46(5): 631-638.
[5]	刘萌, 邬群勇, 邱端昇, 孙梅, 张强. 签到位置数据的密度峰值快速搜索与聚类方法[J]. 测绘学报, 2017, 46(4): 516-525.
[6]	杨伟, 艾廷华. 运用约束Delaunay三角网从众源轨迹线提取道路边界[J]. 测绘学报, 2017, 46(2): 237-245.
[7]	李志林, 刘启亮, 唐建波. 尺度驱动的空间聚类理论[J]. 测绘学报, 2017, 46(10): 1534-1548.
[8]	余龙飞, 胡伍生, 韩理想, 郑敦勇. 基于混合罚函数法的多尺度电离层层析确权方法[J]. 测绘学报, 2016, 45(S2): 156-164.
[9]	余莉, 甘淑, 袁希平, 杨明龙. 综合线面特征分布的点目标多尺度聚类方法[J]. 测绘学报, 2015, 44(10): 1152-1159.
[10]	李正泉, 吴尧祥. 顾及方向遮蔽性的反距离权重插值法[J]. 测绘学报, 2015, 44(1): 91-98.
[11]	杨康,李满春,刘永学,程亮,陈焱明. 基于累积相似度表面的空间权重矩阵构建方法[J]. 测绘学报, 2012, 41(2): 259-265,.
[12]	王美玲,程林. 浮动车地图匹配算法研究[J]. 测绘学报, 2012, 41(1): 133-0.
[13]	刘启亮,邓敏,石岩,彭东亮. 一种基于多约束的空间聚类方法[J]. 测绘学报, 2011, 40(4): 0-441.
[14]	耿协鹏杜晓初胡鹏. 基于栅格距离变换的扩展对象空间聚类方法[J]. 测绘学报, 2009, 38(2): 0-174.