Mining spatiotemporal dominant co-location patterns

Spatial co-location pattern mining is an important branch of spatial data mining, which can identify spatial features that prevalently occur in proximity. Based on spatial co-location patterns, the research of dominant relationships mining within co-location patterns further considers the influence relationship among features. However, relying solely on spatial data to analyze the positions and distribution of features for mining dominant relationships is insufficient and may lead to incorrect patterns. To address this limitation, this paper introduces the temporal factor into the research of dominant relationships mining and proposes the spatiotemporal dominant co-location pattern mining (STDCPM). At first, we define the concepts of spatiotemporal dominant relationship from both temporal and spatial dimensions, and then propose the spatiotemporal dominant participation index to assess the prevalence of spatiotemporal dominant co-location patterns. Furthermore, we design two algorithms, the spatiotemporal dominant co-location pattern mining algorithm with level-by-level search and its improved version, i.e., the spatiotemporal dominant co-location pattern mining approach based on dual pruning and refining set (STDCPM-DPR), to ensure efficient mining in spatiotemporal datasets. The time complexity, correctness, and completeness of proposed algorithms are discussed. Extensive experiments on real-world datasets demonstrate the effectiveness of STDCPM and the efficiency of STDCPM-DPR algorithm.