多层次空间同位模式自适应挖掘方法

doi:10.11947/j.AGCS.2016.20150337

测绘学报 ›› 2016, Vol. 45 ›› Issue (4): 475-485.doi: 10.11947/j.AGCS.2016.20150337

多层次空间同位模式自适应挖掘方法

蔡建南, 刘启亮, 徐枫, 邓敏, 何占军, 唐建波

中南大学地球科学与信息物理学院地理信息系, 湖南长沙 410083

收稿日期:2015-06-29 修回日期:2015-11-11 出版日期:2016-04-20 发布日期:2016-04-28
通讯作者: 刘启亮,E-mail:qiliang.liu@csu.edu.cn E-mail:qiliang.liu@csu.edu.cn
作者简介:蔡建南(1992-),男,硕士生,研究方向为时空关联规则挖掘。
基金资助:
湖南省自然科学杰出青年基金(14JJ1007);国家自然科学基金(41471385);资源与环境信息系统国家重点实验室开放基金

An Adaptive Method for Mining Hierarchical Spatial Co-location Patterns

CAI Jiannan, LIU Qiliang, XU Feng, DENG Min, HE Zhanjun, TANG Jianbo

Department of Geo-Informatics, School of Geosciences and Info-Physics, Central South University, Changsha 410083, ChinaAbstract

Received:2015-06-29 Revised:2015-11-11 Online:2016-04-20 Published:2016-04-28
Supported by:
The Hunan Provincial Science Fund for Distinguished Young Scholars(No.14JJ1007);The National Natural Science Foundation of China(No.41471385);State Key Laboratory of Resources and Environmental Information System

摘要/Abstract

摘要： 空间同位模式挖掘旨在从空间数据中发现频繁发生在邻近位置的事件集合,对于揭示地理现象间的共生规律具有重要价值。由于地理现象的空间异质特质,空间同位模式也存在区域性分异的特点,在不同空间层次上的分析结果各异。然而,现有方法仅从全局视角挖掘空间同位模式,发现局部空间同位模式依然是一个亟待解决的难题。为此,本文基于由整体到局部的思想,提出了一种多层次空间同位模式自适应挖掘方法。首先,从全局视角提取频繁的空间同位模式,将全局不频繁的空间同位模式作为候选的局部空间同位模式;然后,通过对候选局部同位模式进行自适应聚类自动识别其局部分布区域,并在这些局部区域内度量候选模式的频繁程度;进而,提出了一种叠置推绎的方法,从频繁子模式的局部区域中进一步推绎获得超模式的局部分布区域,最终生成所有频繁的局部空间同位模式集合。通过试验分析与比较发现,本文方法不仅可以发现全局的空间同位模式,还能有效提取具有区域性分布特征的局部空间同位模式,可以从多个空间层次上反映地理事件间的共生规则。

关键词: 空间异质性, 空间同位模式, 自适应聚类, 叠置分析

Abstract: Mining spatial co-location patterns plays a key role in spatial data mining. Spatial co-location patterns refer to subsets of features whose objects are frequently located in close geographic proximity. Due to spatial heterogeneity, spatial co-location patterns are usually not the same across geographic space. However, existing methods are mainly designed to discover global spatial co-location patterns, and not suitable for detecting regional spatial co-location patterns. On that account, an adaptive method for mining hierarchical spatial co-location patterns is proposed in this paper. Firstly, global spatial co-location patterns are detected and other non-prevalent co-location patterns are identified as candidate regional co-location patterns. Then, for each candidate pattern, adaptive spatial clustering method is used to delineate localities of that pattern in the study area, and participation ratio is utilized to measure the prevalence of the candidate co-location pattern. Finally, an overlap operation is developed to deduce localities of (k+1)-size co-location patterns from localities of k-size co-location patterns. Experiments on both simulated and real-life datasets show that the proposed method is effective for detecting hierarchical spatial co-location patterns.

Key words: spatial heterogeneity, spatial co-location pattern, adaptive spatial clustering, overlap analysis

中图分类号:

P208

蔡建南, 刘启亮, 徐枫, 邓敏, 何占军, 唐建波. 多层次空间同位模式自适应挖掘方法[J]. 测绘学报, 2016, 45(4): 475-485.

CAI Jiannan, LIU Qiliang, XU Feng, DENG Min, HE Zhanjun, TANG Jianbo. An Adaptive Method for Mining Hierarchical Spatial Co-location Patterns[J]. Acta Geodaetica et Cartographica Sinica, 2016, 45(4): 475-485.

参考文献

[1] 李德仁, 张良培, 夏桂松. 遥感大数据自动分析与数据挖掘[J]. 测绘学报, 2014, 43(12):1211-1216. DOI:10.13485/j.cnki.11-2089.2014.0187. LI Deren, ZHANG Liangpei, XIA Guisong. Automatic Analysis and Mining of Remote Sensing Big Data[J]. Acta Geodaetica et Cartographica Sinica, 2014, 43(12):1211-1216. DOI:10.13485/j.cnki.11-2089.2014.0187.
[2] 江冲亚, 李满春, 刘永学. 海岸带水体遥感信息全自动提取方法[J]. 测绘学报, 2011, 40(3):332-337. JIANG Chongya, LI Manchun, LIU Yongxue. Full-automatic Method for Coastal Water Information Extraction from Remote Sensing Image[J]. Acta Geodaetica et Cartographica Sinica, 2011, 40(3):332-337.
[3] 胡庆武,王明,李清泉. 利用位置签到数据探索城市热点与商圈[J]. 测绘学报, 2014, 43(3):314-321. DOI:10.13485/j.cnki.11-2089.2014.0045. HU Qingwu, WANG Ming, LI Qingquan. Urban Hotspot and Commercial Area Exploration with Check-in Data[J]. Acta Geodaetica et Cartographica Sinica, 2014, 43(3):314-321. DOI:10.13485/j.cnki.11-2089.2014.0045.
[4] 谢超, 陈毓芬, 王英杰. Apriori算法在ACViS中用户行为监测数据挖掘中的应用研究[J]. 测绘学报, 2010, 39(4):397-403. XIE Chao, CHEN Yufen, WANG Yingjie. Application of Apriori Algorithm in User Action Monitoring Data Mining in Adaptive Cartographic Visualization System[J]. Acta Geodaetica et Cartographica Sinica, 2010, 39(4):397-403.
[5] 田晶, 艾廷华, 丁绍军. 基于C4.5算法的道路网网格模式识别[J]. 测绘学报, 2012, 41(1):121-126. TIAN Jing, AI Tinghua, DING Shaojun. Grid Pattern Recognition in Road Networks Based on C4.5 Algorithm[J]. Acta Geodaetica et Cartographica Sinica, 2012, 41(1):121-126.
[6] SHEKHAR S, HUANG Y. Co-location Rules Mining:A Summary of Results[C]//Proceedings of the 7th International Symposium on Spatio and Temporal Databases. Redondo Beach:[s.n.], 2001.
[7] YOO J S, SHEKHAR S, SMITH J, et al. A Partial Join Approach for Mining Co-location Patterns[C]//Proceedings of the 12th Annual ACM International Workshop on Geographic Information Systems. New York:ACM, 2004:241-249.
[8] AGRAWAL R,SRIKANT R.Fast Algorithms for Mining Association Rules[C]//Proceedings of the 20th International Conference on Very Large Data Bases. Santiago:VLDB, 1994:487-499.
[9] KOPERSKI K, HAN Jiawei. Discovery of Spatial Association Rules in Geographic Information Databases[C]//EGENHOFER M J, HERRING J R. Advances in Spatial Databases. Berlin:Springer, 1995:47-66.
[10] 李光强, 邓敏, 朱建军. 基于Voronoi图的空间关联规则挖掘方法研究[J]. 武汉大学学报(信息科学版), 2008, 33(12):1242-1245. LI Guangqiang, DENG Min, ZHU Jianjun. Spatial Association Rules Mining Methods Based on Voronoi Diagram[J]. Geomatics and Information Science of Wuhan University, 2008, 33(12):1242-1245.
[11] YOO J S, SHEKHAR S. A Joinless Approach for Mining Spatial Colocation Patterns[J]. IEEE Transactions on Knowledge and Data Engineering, 2006, 18(10):1323-1337.
[12] XIAO Xiangye, XIE Xing, LUO Qiong, et al. Density Based Co-location Pattern Discovery[C]//Proceedings of the 16th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. New York:ACM, 2008:29.
[13] WANG Jinfeng, LI Xinhu, CHRISTAKOS G, et al. Geographical Detectors-based Health Risk Assessment and Its Application in the Neural Tube Defects Study of the Heshun Region, China[J]. International Journal of Geographical Information Science, 2010, 24(1):107-127.
[14] MILLER H J, HAN Jiawei. Geographic Data Mining and Knowledge Discovery[M]. 2nd ed. New York:CRC Press, 2009.
[15] CELIK M, KANG J M, SHEKHAR S. Zonal Co-location Pattern Discovery with Dynamic Parameters[C]//Proceedings of the 7th IEEE International Conference on Data Mining. Omaha:IEEE, 2007:433-438.
[16] DING Wei, EICK C F, YUAN Xiaojing, et al. A Framework for Regional Association Rule Mining and Scoping in Spatial Datasets[J]. Geoinformatica, 2011, 15(1):1-28.
[17] QIAN Feng, CHIEW K, HE Qinming, et al. Mining Regional Co-location Patterns with kNNG[J]. Journal of Intelligent Information Systems, 2014, 42(3):485-505.
[18] EICK C F, PARMAR R, DING Wei, et al. Finding Regional Co-location Patterns for Sets of Continuous Variables in Spatial Datasets[C]//Proceedings of the 16th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. New York:ACM, 2008.
[19] 沙宗尧, 李晓雷. 异质环境下的空间关联规则挖掘[J]. 武汉大学学报(信息科学版), 2009, 34(12):1480-1484. SHA Zongyao, LI Xiaolei. Algorithm of Mining Spatial Association Data under Spatially Heterogeneous Environment[J]. Geomatics and Information Science of Wuhan University, 2009, 34(12):1480-1484.
[20] MOHAN P, SHEKHAR S, SHINE J A, et al. A Neighborhood Graph Based Approach to Regional Co-location Pattern Discovery:A Summary of Results[C]//Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. New York:ACM, 2011:122-132.
[21] 边馥苓, 万幼. k-邻近空间关系下的同位模式挖掘算法[J]. 武汉大学学报(信息科学版), 2009, 34(3):331-334, 338. BIAN Fuling, WAN You. A Novel Spatial Co-location Pattern Mining Algorithm Based on k-nearest Feature Relationship[J]. Geomatics and Information Science of Wuhan University, 2009, 34(3):331-334, 338.
[22] WAN You,ZHOU Chenghu.QuCOM:K Nearest Features Neighborhood Based Qualitative Spatial Co-location Patterns Mining Algorithm[C]//Proceedings of the IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services. Fuzhou:IEEE, 2011:54-59.
[23] 刘启亮, 邓敏, 石岩, 等. 一种基于多约束的空间聚类方法[J]. 测绘学报, 2011, 40(4):509-516. LIU Qiliang, DENG Min, SHI Yan, et al. A Novel Spatial Clustering Method Based on Multi-constraints[J]. Acta Geodaetica et Cartographica Sinica, 2011, 40(4):509-516.
[24] YOO J S, BOW M. Mining Spatial Colocation Patterns:A Different Framework[J]. Data Mining and Knowledge Discovery, 2012, 24(1):159-194.
[25] BARUA S, SANDER J. Mining Statistically Significant Co-location and Segregation Patterns[J]. IEEE Transactions on Knowledge and Data Engineering, 2014, 26(5):1185-1199.
[26] ESTIVILL-CASTRO V,LEE I.Argument Free Clustering for Large Spatial Point-data Sets via Boundary Extraction from Delaunay Diagram[J]. Computers, Environment and Urban Systems, 2002, 26(4):315-334.
[27] BOUCHER D H, JAMES S, KEELER K H. The Ecology of Mutualism[J]. Annual Review of Ecology and Systematics, 1982, 13:315-347.
[28] HUBÁLEK Z. Coefficients of Association and Similarity, Based on Binary (Presence-absence) Data:An Evaluation[J]. Biological Reviews, 1982, 57(4):669-689.
[29] 郑明月. 空间聚类规则在洪河湿地植被类型分布梯度变化中的应用[D]. 哈尔滨:哈尔滨师范大学, 2013. ZHENG Mingyue. The Application of Spatial Clustering Rules in the Honghe Wetland Vegetation Form Distribution Change of Gradient[D]. Harbin:Harbin Normal University, 2013.
[30] 娄彦景, 赵魁义. 洪河自然保护区湿地主要植被类型物种多样性与表层土壤性质的相关性研究[J]. 湿地科学, 2008, 6(1):45-50. LOU Yanjing, ZHAO Kuiyi. Correlation between Plant Species Diversity of Main Vegetation Types and Surface Soil Properties in Wetland of Honghe Nature Reserve[J]. Wetland Science, 2008, 6(1):45-50.

多层次空间同位模式自适应挖掘方法

An Adaptive Method for Mining Hierarchical Spatial Co-location Patterns

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 3

编辑推荐

Metrics

本文评价

[1]	邓敏, 谌恺祺, 石岩, 陈袁芳, 郭艺文. 多尺度空间同位模式挖掘的点过程分解方法[J]. 测绘学报, 2022, 51(2): 258-268.
[2]	刘文凯, 刘启亮, 蔡建南. 自然邻域支持下的空间同位模式挖掘方法[J]. 测绘学报, 2019, 48(1): 95-105.
[3]	谢忠; 魏东琦; 吴亮; 郭际元 . 简单矢量数据多边形裁剪问题的图模型 [J]. 测绘学报, 2009, 38(4): 0-310.