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Spatial co-location pattern mining aims to discover association rules formed by multiple types of geographic elements or events frequently adjacent to each other, which is the key for understanding the internal occurrence mechanism of complex geographic phenomena. Aiming at the shortcomings of existing spatial colocation pattern mining methods in the effective modeling of geographic data characteristics (such as the multi-scale characteristic), this paper proposes a multi-scale spatial co-location pattern mining method based on point process decomposition. Firstly, the spatial distribution of geographical elements with multiple types is modeled as a mixed spatial point process by constructing a random variable, and a non-parametric statistical index is introduced to discriminate the characteristic scale of the co-location patterns. On this basis, we define a conditional probability density distribution function to mine multi-scale spatial co-location patterns using points process decomposition. The experimental analysis results show that the proposed method can accurately depict the spatial distribution of spatial co-location patterns at different scales, and effectively reduce the subjectivity of artificially setting parameters.
