自然邻域支持下的空间同位模式挖掘方法

doi:10.11947/j.AGCS.2019.20170653

测绘学报 ›› 2019, Vol. 48 ›› Issue (1): 95-105.doi: 10.11947/j.AGCS.2019.20170653

自然邻域支持下的空间同位模式挖掘方法

刘文凯^1,2, 刘启亮^1,2, 蔡建南^1,2

1. 中南大学有色金属成矿预测与地质环境监测教育部重点实验室, 湖南长沙 410083;
2. 中南大学地理信息系, 湖南长沙 410083

收稿日期:2017-11-16 修回日期:2018-06-25 出版日期:2019-01-20 发布日期:2019-01-31
通讯作者: 刘启亮 E-mail:qiliang.liu@csu.edu.cn
作者简介:刘文凯(1993-),男,硕士生,研究方向为时空关联规则挖掘。E-mail:wenkai.liu@csu.edu.cn
基金资助:
国家重点研发计划（2017YFB0503601）；国家自然科学基金（41730105；41601410）；中南大学创新驱动计划（2018CX015）；中南大学中央高校基本科研业务费专项资金（2018zzts678）

Discovery of co-location patterns based on natural neighborhood

LIU Wenkai^1,2, LIU Qiliang^1,2, CAI Jiannan^1,2

1. Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring, Central South University, Ministry of Education, Changsha 410083, China;
2. Department of Geo-Informatics, Central South University, Changsha 410083, China

Received:2017-11-16 Revised:2018-06-25 Online:2019-01-20 Published:2019-01-31
Supported by:
The National Key Research and Development Programof China (No. 2017YFB0503601);The National Natural Science Foundation of China (Nos. 41730105;41601410);The Innovation-driven Project of Central South University (No. 2018CX015);The Fundamental Research Funds for the Central Universities of Central South University(No. 2018zzts678)

摘要/Abstract

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。

关键词: 空间同位模式, 自然邻域, Delaunay三角网, 自适应

Abstract:

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.

Key words: co-location pattern, natural neighborhood, delaunay triangulation network, adaptive

中图分类号:

P208

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.

LIU Wenkai, LIU Qiliang, CAI Jiannan. Discovery of co-location patterns based on natural neighborhood[J]. Acta Geodaetica et Cartographica Sinica, 2019, 48(1): 95-105.

参考文献

[1] 王香红, 栾兆擎, 闫丹丹, 等. 洪河沼泽湿地17种植物的生态位[J]. 湿地科学, 2015, 13(1):49-54. WANG Xianghong, LUAN Zhaoqing, YAN Dandan, et al. Niche of 17 kinds of plants in marshes in Honghe[J]. Wetland Science, 2015, 13(1):49-54.
[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:a general 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 co-location 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 co-location 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 the 19th 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, RYBIИSKI 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:a guide 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, ÖZDEMIREL 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.

自然邻域支持下的空间同位模式挖掘方法

Discovery of co-location patterns based on natural neighborhood

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

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]	晏雄锋, 袁拓, 杨敏, 孔博, 刘鹏程. 建筑物形状特征分析表达与自适应化简方法[J]. 测绘学报, 2022, 51(2): 269-278.
[3]	刘万科, 农旗, 陶贤露, 朱锋, 胡捷. 非完整约束的OD/SINS自适应组合导航方法[J]. 测绘学报, 2022, 51(1): 9-17.
[4]	靳凯迪, 柴洪洲, 宿楚涵, 向民志. 顾及有色噪声的光纤陀螺随机噪声自适应滤波方法[J]. 测绘学报, 2022, 51(1): 80-86.
[5]	贾象阳, 黄先锋, 牛文渊, 张帆, 高云龙, 杨冲. 顾及噪声密度函数差异的自适应回归算法及其在人工标靶提取中的应用[J]. 测绘学报, 2021, 50(2): 226-234.
[6]	宋越, 李厚朴, 翟国君. 机载激光测深波形去噪算法对比分析[J]. 测绘学报, 2021, 50(2): 270-278.
[7]	邝英才, 吕志平, 王方超, 李林阳, 杨凯淳. GNSS/声学联合定位的自适应滤波算法[J]. 测绘学报, 2020, 49(7): 854-864.
[8]	姚连璧, 秦长才, 张邵华, 陈启超, 阮东旭, 聂顺根. 车载激光点云的道路标线提取及语义关联[J]. 测绘学报, 2020, 49(4): 480-488.
[9]	姜三, 江万寿. Delaunay三角网约束下的影像稳健匹配方法[J]. 测绘学报, 2020, 49(3): 322-333.
[10]	徐旺, 游雄, 张威巍, 邓晨. 2D地图的建筑物场景结构提取方法及其在城市增强现实中的应用[J]. 测绘学报, 2020, 49(12): 1619-1629.
[11]	颜金彪, 吴波, 彭馨. 顾及Gestalt邻近与简化原则的平面点集形状重建[J]. 测绘学报, 2020, 49(11): 1485-1496.
[12]	张春森, 张奇源, 郭丙轩, 薛万唱. 倾斜影像自适应初始物方面元优化匹配算法[J]. 测绘学报, 2020, 49(1): 108-116.
[13]	陈敏, 朱庆, 何海清, 严少华, 赵怡涛. 面向城区宽基线立体像对视角变化的结构自适应特征点匹配[J]. 测绘学报, 2019, 48(9): 1129-1140.
[14]	高扬骏, 吕志伟, 周朋进, 贾铮洋, 张伦东, 丛佃伟. 北斗中长基线三频模糊度解算的自适应抗差滤波算法[J]. 测绘学报, 2019, 48(3): 295-302.
[15]	李成名, 殷勇, 武鹏达, 刘晓丽. 一种聚集性面群中毗邻区自动识别与处理方法[J]. 测绘学报, 2019, 48(3): 363-373.