Mining Co-location Pattern of Network Spatial Phenomenon Based on the Law of Additive Color Mixing

doi:10.11947/j.AGCS.2017.20160324

Abstract

Abstract: 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.

Key words: co-location pattern, visualization, network, additive color mixing

CLC Number:

P208

AI Tinghua, ZHOU Mengjie, LI Xiaoming. Mining Co-location Pattern of Network Spatial Phenomenon Based on the Law of Additive Color Mixing[J]. Acta Geodaetica et Cartographica Sinica, 2017, 46(6): 753-759.

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