A Process-Oriented Spatiotemporal Clustering Method for Complex Trajectories of Dynamic Geographic Phenomena

Abstract

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. INDEX TERMS Spatiotemporal trajectory clustering, dynamic geographic phenomena, evolutionary behaviors, Pacific ocean, sea surface temperature anomalies.

PoSDMS: A Mining System for Oceanic Dynamics with Time Series of Raster-Formatted Datasets

“…Regarding dynamic mining technology, the PoSDMS design involved a hierarchical mining strategy based on the process semantics of the marine process-evolution sequencesnapshot state [13]. The hierarchical mining strategy provided a foundation for the designs of the module used to obtain marine process objects and their evolutions [19], the module for integrating process-oriented similarity measuring functions and expanding clustering algorithms [26], and the module for constructing process-oriented mining transaction tables and expanding association rule mining algorithms [10], and the module for visualizing oceanic dynamics and their evolutionary relationships. Taking spatial information, thematic characteristics and their evolution in time into full consideration, the processoriented mining technology improved the investigative capacity of PoSDMS to deal with oceanic dynamics.…”

“…Figure 13 shows the interface of the process-oriented trajectory clustering algorithm. Here, the clustering parameters were set the same as in Ref [26].…”

“…In recent decades, a large number of models and methods with time series of rasterformatted datasets have been proposed to obtain geographical dynamics in the form of objects, events or processes [17][18][19][20][21]. These have been proposed to analyze their dynamic characteristics [22][23][24] and explore their clustering patterns, association patterns and evolutionary patterns [8,25,26]. Differing from geographical dynamics, however, these methods and models lacked effective and useful platforms or tools with which to obtain oceanic dynamic knowledge directly from time series of raster-formatted datasets.…”

“…Generally, a scale of geographical dynamic evolution is out of step with the scale of data observation; some evolutionary relationships among geographical dynamics will be lost. To obtain true evolutionary relationships, a process-oriented idea was proposed to obtain marine anomalies [13,19,26]; to represent and analyze such dynamic changes, the graph-based model was used for urban hot islands [30], rainstorms [17,31], land use and cover changes [12] and oceanic eddies [3,9].…”

“…Popular clustering algorithms, such as K-Mean, DBSCAN (Density-Based Spatial Clustering of Applications with Noise) and SRNN (Shared Reciprocal Nearest Neighborhood), were expanded and widely used to obtain oceanic clustering patterns [35][36][37][38]. To obtain oceanic dynamic information within successive time snapshots, Liu et al proposed a process-oriented clustering method of treating spatiotemporal dynamics as a trajectory [26], with the trajectory clustering representing an evolutionary pattern. Regarding geographical association rule mining: the quantitative Apriori algorithm is a classical algorithm for obtaining richer information [39].…”