LAUSR

There exists a kind of trajectories of dynamic geographic phenomena, which have splitting, merging, or merging-splitting branches. Clustering these complex trajectories may help to more deeply explore and analyze the evolution mechanism of geographic phenomena. However, few methods explore the clustering patterns of such trajectories. Thus, we propose a Process-oriented Spatiotemporal Clustering Method (PoSCM) for clustering complex trajectories with multiple branches. The PoSCM includes the following three parts: the first represents the trajectories with a “process-sequence-node” structure inspired by a process-oriented semantic model; the second designs a hierarchical similarity measurement method to calculate the similarity of space, time, thematic attributes and evolution structure between any two trajectories; the last uses a density-based clustering algorithm to mine the trajectories’ clustering patterns. Simulation experiments are used to evaluate PoSCM and to demonstrate the advantages by comparing against that of the VF2 algorithm. A case study of sea surface temperature abnormal variation (SSTAV) trajectories in the Pacific Ocean is addressed. The clustering results not only validate well-known knowledge but also provide some new insights about the evolution characteristics of SSTAVs during El Niño Southern Oscillation (ENSO); these insights may provide new references for further study on global climate change.