自然邻域支持下的空间同位模式挖掘方法
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摘要
空间同位模式指频繁发生在邻近空间位置的事件集合,此类模式对于深入理解不同空间要素间的交互关系具有重要意义。空间同位模式挖掘的一个核心内容是空间要素邻近关系构建,然而现有方法在空间要素分布不均匀时难以准确地描述要素间的邻近关系,容易导致挖掘结果的遗漏或误判。为此,本文提出了一种基于自然邻域的空间同位模式挖掘方法。首先从同位模式的产生机理分析入手,过滤同位模式挖掘中的干扰要素;进而,从距离邻近性、密度变化一致性和关系紧密性的原则出发,自适应地构建空间要素实例的自然邻近关系;最后,以自然邻域为基础,基于图的连通性从整体到局部发现多层次同位模式。试验分析与比较发现,本文方法能够有效发现空间要素分布不均匀情况下的同位模式,而且降低了人为设置邻域参数对挖掘结果的影响。
Discovery of co-location patterns is crucial to understanding the interaction among different spatial features.The construction of neighborhood relationship among spatial features plays a key role in co-location pattern mining,however,existing methods are difficult to construct appropriate neighborhood relationship when the spatial features distribute unevenly.This limitation is very likely to make the omission and/or misjudgment of co-location patterns.To address this issue,a co-location pattern mining method based on natural neighborhood is proposed in this study.After removing the randomly distributed spatial features,natural neighborhood relationship among different spatial features is adaptively constructed on basis of three principles,i.e.geographic proximity,the consistency of density and compactness of neighboring relationship.The multi-level co-location patterns are discovered based on the delaunay triangulation network.The experimental results showed that the proposed method could discover the co-location patterns among unevenly distributed spatial features completely and accurately,and no user-specified parameters are required for the construction of natural neighborhood.
Discovery of co-location patterns is crucial to understanding the interaction among different spatial features.The construction of neighborhood relationship among spatial features plays a key role in co-location pattern mining,however,existing methods are difficult to construct appropriate neighborhood relationship when the spatial features distribute unevenly.This limitation is very likely to make the omission and/or misjudgment of co-location patterns.To address this issue,a co-location pattern mining method based on natural neighborhood is proposed in this study.After removing the randomly distributed spatial features,natural neighborhood relationship among different spatial features is adaptively constructed on basis of three principles,i.e.geographic proximity,the consistency of density and compactness of neighboring relationship.The multi-level co-location patterns are discovered based on the delaunay triangulation network.The experimental results showed that the proposed method could discover the co-location patterns among unevenly distributed spatial features completely and accurately,and no user-specified parameters are required for the construction of natural neighborhood.
引文
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[2] WANG Fahui,HU Yujie,WANG Shuai,et al.Local indicator of colocation quotient with a statistical significance test:examining spatial association of crime and facilities[J].The Professional Geographer,2017,69(1):22-31.
[3] YU Wenhao,AI Tinghua,HE Yakun,et al.Spatial co-location pattern mining of facility points-of-interest improved by network neighborhood and distance decay effects[J].International Journal of Geographical Information Science,2017,31(2):280-296.
[4] WANG Junmei,HSU W,LEE M L.A framework for mining topological patterns in spatio-temporal databases[C]∥Proceedings of the 14th ACM International Conference on Information and Knowledge Management.Bremen,Germany:ACM,2005:429-436.
[5] HUANG Y,SHEKHAR S,XIONG H.Discovering colocation patterns from spatial data sets:ageneral approach[J].IEEE Transactions on Knowledge and Data Engineering,2004,16(12):1472-1485.
[6] 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.Washington DC:ACM,2004:241-249.
[7] 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.
[8] 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.Irvine,California:ACM,2008:102-114.DOI:10.1145/1463434.1463471.
[9] YOO J S,BOW M.Mining spatial colocation patterns:a different framework[J].Data Mining and Knowledge Discovery,2012,24(1):159-194.
[10] QIAN Feng,HE Qinming,CHIEW K,et al.Spatial colocation pattern discovery without thresholds[J].Knowledge and Information Systems,2012,33(2):419-445.
[11] DENG Min,HE Zhanjun,LIU Qiliang,et al.Multi-scale approach to mining significant spatial co-location patterns[J].Transactions in GIS,2017,21(5):1023-1039.
[12] BARUA S,SANDER J.Mining statistically significant colocation and segregation patterns[J].IEEE Transactions on Knowledge and Data Engineering,2014,26(5):1185-1199.
[13] 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.
[14] 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.
[15] 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.
[16] 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 the19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems.Chicago,Illinois:ACM,2011:122-132.
[17]蔡建南,刘启亮,徐枫,等.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.DOI:10.11947/j.AGCS.2016.20150337.CAI Jiannan,LIU Qiliang,XU Feng,et al.An adaptive method for mining hierarchical spatial co-location patterns[J].Acta Geodaetica et Cartographica Sinica,2016,45(4):475-485.DOI:10.11947/j.AGCS.2016.20150337.
[18]边馥苓,万幼.k-邻近空间关系下的空间同位模式挖掘算法[J].武汉大学学报(信息科学版),2009,34(3):331-334.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.
[19] BEMBENIK R,RYBISKI H.FARICS:A method of mining spatial association rules and collocations using clustering and delaunay diagrams[J].Journal of Intelligent Information Systems,2009,33(1):41-64.
[20] SUNDARAM V M,THNAGAVELU A,PANEER P.Discovering co-location patterns from spatial domain using a delaunay approach[J].Procedia Engineering,2012,38(12):2832-2845.
[21] FORTIN M J,DALE M R T.Spatial analysis:aguide for ecologists[M]. Cambridge:Cambridge University Press,2005.
[22] CAI Jiannan,DENG Min,LIU Qiliang,et al.Nonparametric significance test for discovery of network-constrained spatial co-location patterns[J].Geographical Analysis,2018,65(9):126-137.
[23] BUYONG T.Spatial data analysis for geographic information science[M].Kuala Lumpur, Malaysia:Penerbit UTM,2007.
[24] CLARK P J,EVANS F C.Distance to nearest neighbor as a measure of spatial relationships in populations[J].Ecology,1954,35(4):445-453.
[25] INKAYA T,KAYALIGIL S,ZDEMIREL N E.An adaptive neighbourhood construction algorithm based on density and connectivity[J].Pattern Recognition Letters,2015,52(1):17-24.
[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]龙冬平,柳林,冯嘉欣,等.社区环境对入室盗窃和室外盗窃影响的对比分析——以ZG市ZH半岛为例[J].地理学报,2017,72(2):341-355.LONG Dongping,LIU Lin,FENG Jiaxin,et al.Comparisons of the community environment effects on burglary and outdoor-theft:a case study of ZH peninsula in ZG city[J].Acta Geographica Sinica,2017,72(2):341-355.