Discovering spatiotemporal hot spot region and mining patterns from moving trajectory random sampling

The moving trajectory by random sampling distributes unevenly in time dimension. After projecting the three-dimensional spatiotemporal trajectory data into one-dimensional time domain, a spatiotemporal hot spot region discovery and moving pattern mining methods are proposed based on automatic detection of intensive time intervals. Through detecting intensive time intervals dynamically with a bottom-up clustering strategy, the spatiotemporal hot spot regions are discovered in corresponding time intervals. A depth-first algorithm is designed to mine the set of frequency moving patterns. Finally, based on synthetic moving trajectory dataset, the effectiveness and scalability of the proposed algorithms are verified.