显著空间同位模式的多尺度挖掘方法

空间同位模式挖掘对于揭示地理现象间的共生、依赖规律具有重要价值。然而,空间同位模式挖掘中参数阈值缺乏先验知识,若设置不合理,挖掘结果中会遗漏重要的模式或包含冗余的、甚至错误的模式。为此,本文提出了一种基于模式重建的显著空间同位模式多尺度挖掘方法。首先,定义了互邻近距离指标,该指标可用来确定距离阈值的有效取值范围。进而,以模式重建为基础构建零模型,借助统计检验的方法来发现显著的空间同位模式,从而避免了兴趣度阈值的设置。最后,对空间同位模式进行多尺度挖掘,并引入生存期的概念对同位模式多尺度挖掘结果进行有效性评价。试验结果表明:本文方法可有效降低算法参数设置的主观性,从而提升空间同位模式挖掘结果的准确性和稳健性。

A Multi-scale Method for Mining Significant Spatial Co-location Patterns

Spatial co-location patterns discovery aims to detect spatial features whose instances are frequently located in geographic proximity.Such patterns can reveal unknown regularity in geographic phenomena and they are helpful for decision-making.However,due to the little prior knowledge,it is difficult to specify thresholds for neighbor distance and prevalence index.As a result,the outcomes of most algorithms always include insignificant or even erroneous patterns.A pattern-reconstruction-based approach was proposed to discover only significant co-location patterns.Firstly,we introduce a new definition of MNND,which can identify the lower and upper bounds of neighbor distance threshold.Then,a nul l model was constructed based on the pattern reconstruction.On basis of that,selection of prevalence threshold is replaced by hypothesis testing.Finally,significant colocation patterns were mined at multiple distances and the results were evaluated by the notion of lifetime.The experimental results show that our approach could avoid the subjectivity in determining those thresholds,thereby improving the correctness and robustness.