A Novel Process-Oriented Graph Storage for Dynamic Geographic Phenomena

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

Many effective and advanced methods have been developed to explore oceanic dynamics using time series of raster-formatted datasets; however, they have generally been designed at a scale suitable for data observation and used independently of each other, despite the potential advantages of combining different modules into an integrated system at a scale suited for dynamic evolution. From raster-formatted datasets to marine knowledge, we developed and integrated several mining algorithms at a dynamic evolutionary scale and combined them into six modules: a module of raster-formatted dataset pretreatment; a module of process-oriented object extraction; a module of process-oriented representation and management (process-oriented graph database); a module of process-oriented clustering; a module of process-oriented association rule mining; and a module of process-oriented visualization. On the basis of such modules, we developed a process-oriented spatiotemporal dynamic mining system named PoSDMS (Process-oriented Spatiotemporal Dynamics Mining System). PoSDMS was designed to have the capacity to deal with at least six environments of marine anomalies with 40 years of raster-formatted datasets, including their extraction, representation, storage, clustering, association and visualization. The effectiveness of the integrated system was evaluated in a case study of sea surface temperature datasets during the period from January 1982 to December 2021 in global oceans. The main contribution of this study was the development of a mining system at a scale suited for dynamic evolution, providing an analyzing platform or tool to deal with time series of raster-formatted datasets to aid in obtaining marine knowledge.

A Process-Oriented Approach to Identify Evolutions of Sea Surface Temperature Anomalies with a Time-Series of a Raster Dataset

It is important to consider where, when, and how the evolution of sea surface temperature anomalies (SSTA) plays significant roles in regional or global climate changes. In the comparison of where and when, there is a great challenge in clearly describing how SSTA evolves in space and time. In light of the evolution from generation, through development, and to the dissipation of SSTA, this paper proposes a novel approach to identifying an evolution of SSTA in space and time from a time-series of a raster dataset. This method, called PoAIES, includes three key steps. Firstly, a cluster-based method is enhanced to explore spatiotemporal clusters of SSTA, and each cluster of SSTA at a time snapshot is taken as a snapshot object of SSTA. Secondly, the spatiotemporal topologies of snapshot objects of SSTA at successive time snapshots are used to link snapshot objects of SSTA into an evolution object of SSTA, which is called a process object. Here, a linking threshold is automatically determined according to the overlapped areas of the snapshot objects, and only those snapshot objects that meet the specified linking threshold are linked together into a process object. Thirdly, we use a graph-based model to represent a process object of SSTA. A node represents a snapshot object of SSTA, and an edge represents an evolution between two snapshot objects. Using a number of child nodes from an edge’s parent node and a number of parent nodes from the edge’s child node, a type of edge (an evolution relationship) is identified, which shows its development, splitting, merging, or splitting/merging. Finally, an experiment on a simulated dataset is used to demonstrate the effectiveness and the advantages of PoAIES, and a real dataset of satellite-SSTA is used to verify the rationality of PoAIES with the help of ENSO’s relevant knowledge, which may provide new references for global change research.

A Process-Oriented Method for Tracking Rainstorms with a Time-Series of Raster Datasets

Extreme rainstorms have important socioeconomic consequences, but understanding their fine spatial structures and temporal evolution still remains challenging. In order to achieve this, in view of an evolutionary property of rainstorms, this paper designs a process-oriented algorithm for identifying and tracking rainstorms, named PoAIR. PoAIR uses time-series of raster datasets and consists of three steps. The first step combines an accumulated rainfall time-series and spatial connectivity to identify rainstorm objects at each time snapshot. Secondly, PoAIR adopts the geometrical features of eccentricity, rectangularity, roundness, and shape index, as well as the thematic feature of the mean rainstorm intensity, to match the same rainstorm objects in successive snapshots, and then tracks the same rainstorm objects during a rainstorm evolution sequence. In the third step, an evolutionary property of a rainstorm sequence is used to extrapolate its spatial location and geometrical features at the next time snapshot and reconstructs a rainstorm process by linking rainstorm sequences with an area-overlapping threshold. Experiments on simulated datasets demonstrate that PoAIR performs better than the Thunderstorm Identification, Tracking, Analysis and Nowcasting algorithm (TITAN) in both rainfall tracking and identifying the splitting, merging, and merging-splitting of rainstorm objects. Additionally, applications of PoAIR to Integrated Multi-satellitE Retrievals for Global Precipitation Mission (GPM/IMERG) final products covering mainland China show that PoAIR can effectively track rainstorm objects.