基于粗糙集的局部同位模式挖掘算法

doi:10.13474/j.cnki.11-2246.2022.0298

摘要/Abstract

摘要： 通过空间同位模式挖掘可发现频繁发生在邻近位置的事件集合,为揭示地理现象间的共生规律提供重要的决策支持。由于空间同位模式存在空间异质性问题,已有方法不能很好地探测出空间同位模式分布的相近性区域。为此,本文从地理属性的相近性方向探测同位模式的分布区域,提出了基于粗糙集的局部空间同位模式挖掘方法。首先,从全局视角提取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,对候选同位模式的实例位置进行处理,将其分布的热点区域属性作为粗糙数据集,借助粗糙集探测局部空间同位模式自然的分布区域;最后,度量在这些局部区域的频繁程度,生成所有频繁的局部空间同位模式。通过试验与应用发现,该方法不仅可以探测局部空间同位模式分布的相近性区域,还能反映同位模式分布区域的地理属性信息。

关键词: 粗糙集, 局部空间同位模式, 地理属性, 城市设施, POI

Abstract: Through co-location pattern mining, we can find the set of events frequently occurring in nearby locations, which provides important decision support for revealing the symbiosis law between geographical phenomena.Due to the spatial heterogeneity of co-location patterns, the existing methods can not detect the distribution of co-location patterns.Therefore, in this paper, we detect the distribution regions of isotopic patterns from the direction of proximity of geographical attributes, and propose a local co-location pattern mining method based on rough sets.Firstly, infrequent co-location patterns are extracted from the global perspective as candidate local co-location patterns. Then, the locations of the candidate co-location patterns are processed, and the attributes of hot spots are used as rough data sets to detect the natural distribution regions of local co-location patterns. Finally, the frequency of these local regions is measured and all frequent local co-location patterns are generated. Through experiments and applications, it is found that this method can not only detect the similarity region of local spatial distribution of the same location pattern, but also reflect the geographical attribute information of the same location pattern distribution region.

Key words: rough set, regional co-location patterns, geographical attributes, urban facilities, POI

中图分类号:

P208

吴静, 傅优杰, 程朋根. 基于粗糙集的局部同位模式挖掘算法[J]. 测绘通报, 2022, 0(10): 80-85,104.

WU Jing, FU Youjie, CHENG Penggen. Algorithm of regional co-location patterns based on rough set[J]. Bulletin of Surveying and Mapping, 2022, 0(10): 80-85,104.

参考文献

[1] 张志兵.空间数据挖掘关键技术研究[D].武汉:华中科技大学, 2004.
[2] 李德仁,王树良,李德毅.空间数据挖掘理论与应用[M].北京:科学出版社, 2006.
[3] LIU Qiliang, LIU Wenkai, DENG Min, et al. An adaptive detection of multilevel co-location patterns based on natural neighborhoods[J]. International Journal of Geographical Information Science, 2021, 35(3):556-581.
[4] 王震宇.局部空间效应下空间关联分析模型、算法及其地理学应用[D].武汉:武汉大学, 2018.
[5] 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,NE,USA:IEEE, 2007:433-438.
[6] WAN You,ZHOU Chenghu.QuCOM:K nearest features neighborhood based qualitative spatial co-location patterns mining algorithm[C]//Proceedings of 2011 IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services.Fuzhou, China:IEEE, 2011:54-59.
[7] 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.
[8] EICK C F, PARMAR R, DING W, 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.California,USA:[s.n.],2008:1-10.
[9] 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.
[10] 徐枫,蔡建南,刘启亮,等.显著局部空间同位模式自动探测方法[J].武汉大学学报(信息科学版), 2018, 43(10):1538-1545.
[11] DING W, EICK C F, YUAN X J, et al. A framework for regional association rule mining and scoping in spatial datasets[J]. GeoInformatica, 2011, 15(1):1-28.
[12] 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.
[13] SHEKHAR S, HUANG Y. Discovering spatial co-location patterns:a summary of results[M].[S.l.]:Lecture Notes in Computer Science,2001.
[14] 麻学锋,杨雪.大湘西高级别景区时空分布特征及影响因素的空间异质性[J].自然资源学报, 2019, 34(9):1902-1916.
[15] 周涛,陆惠玲,任海玲,等.基于粗糙集的属性约简算法综述[J].电子学报, 2021, 49(7):1439-1449.
[16] JIANG H B, HU B Q. A decision-theoretic fuzzy rough set in hesitant fuzzy information systems and its application in multi-attribute decision-making[J]. Information Sciences, 2021, 579:103-127.
[17] 魏中宇.地铁建设对城市内部土地利用变化的影响:以西安地铁1、2号线为例[D].西安:陕西师范大学, 2019.
[18] 卢阳禄.基于高分影像的道路交通对建设用地功能格局的影响分析:以从化区中心城区规划区为例[D].广州:华南农业大学, 2016.
[19] 禹文豪,艾廷华,周启.设施POI的局部空间同位模式挖掘及范围界定[J].地理与地理信息科学, 2015, 31(4):6-11.
[20] 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.
[21] 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,USA:[s.n.],2011:122-132.