同位模式挖掘是空间数据挖掘的热点问题之一，应用领域广泛。已有的同位模式挖掘方法一般采用统计或数据挖掘的方式，要求对复杂的数学公式、算法及相关参数等有深刻的理解，主要针对同质的欧式空间中地理现象。而城市空间中人为地理现象大多发生在网络空间，鉴于此，本文提出了一种网络空间同位模式可视化挖掘方法。该方法利用视觉语言表达网络空间现象之间的影响和交互作用。首先，利用网络空间核密度估计表达网络空间现象的分布情况和影响范围，为网络空间现象的同位模式挖掘提供支持，并建立单个地理现象分布情况与颜色之间的映射；然后基于色光加色混合原理获得两个地理现象相互影响的认知，借以挖掘空间同位模式。本文提出的方法属于形象思维，具有直观，形象和易感受等特点。
Mining co-location pattern is one of the hottest topics of current research in the spatial data mining community. The existing co-location mining methods belong to spatial statistics or data mining approaches, requiring much understanding of complex mathematical or statistical algorithms and parameters; and they consider events as taking place in a homogeneous and isotropic context in Euclidean space, whereas the physical movement in an urban space is usually constrained by a road network. This paper proposes a visualization method to mine co-location pattern along networks. The visual language is used to represent mutual influence between two geographic phenomena along networks. Firstly, taking Tobler's first law of geography into consideration, we use a network kernel density estimation method to express distribution pattern of geographic phenomena along networks, and construct a mapping between the distribution pattern of geographic phenomenon and color. Secondly, based on the law of additive color mixing, two colors representing two geographic phenomena are mixed to get cognition of the interaction between the two geographic phenomena. This method makes use of visual thinking, and it is intuitive and can be easily understood.
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