| 多层次空间同位模式自适应挖掘方法 |
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| 引用本文： | 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. |
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| 作者姓名： | 蔡建南 刘启亮 徐枫 邓敏 何占军 唐建波 |
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| 作者单位： | 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 |
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| 基金项目： | 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~~ |
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| 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。
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| 关 键 词： | 空间异质性 空间同位模式 自适应聚类 叠置分析 |
| 收稿时间： | 2015-06-29 |
| 修稿时间： | 2015-11-11 |
An Adaptive Method for Mining Hierarchical Spatial Co-location Patterns |
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| 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. |
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| Authors: | CAI Jiannan LIU Qiliang XU Feng DENG Min HE Zhanjun TANG Jianbo |
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| Institution: | Department of Geo-Informatics, School of Geosciences and Info-Physics, Central South University, Changsha 410083, ChinaAbstract |
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| 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. |
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| Keywords: | spatial heterogeneity spatial co-location pattern adaptive spatial clustering overlap analysis |
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