The northwestern Pacific Ocean (NWPO) is a region sensitive to global climate change and regional sea–air interactions. A number of remote-sensing images from the past three decades were used to define sensitive marine regions, which were then applied to determine the spatiotemporal association patterns of abnormal variations in marine environmental parameters using a quantitative association rule-mining method. The NWPO object 1 (NWPO-obj1) region (130°–150° E, 2°–15° N) and NWPO object 2 (NWPO-obj2) region (170°–180° E, 0°–8° N) showed more pronounced changes than elsewhere, and the monthly anomaly of sea-surface temperature (SSTA), monthly anomaly of sea-surface chlorophyll-a (chl-a), monthly anomaly of sea-level anomaly (SLAA), and El Niño Southern Oscillation (ENSO) events were closely related to one another in these two regions. In NWPO-obj1, the relation between SLAA and chl-a yields a correlation coefficient of −0.79 and the abnormal drop in SLAA was the principal factor controlling the chl-a bloom. In NWPO-obj2, the SSTA is anti-correlated with chl-a (correlation coefficient of −0.83), and the abnormal increase in SSTA might be one of the main factors leading to the extinction of chl-a. Comparing the two regions, abnormal increases in chl-a and decreases in SSTA in NWPO-obj2 were indicators of abnormal increases in SLAA in NWPO-obj1 (positive and negative correlation coefficients of 0.60 and −0.61, respectively), and the abnormal decrease of SLAA in NWPO-obj1 is correlated with the abnormal decrease of SSTA in NWPO-obj2 (correlation coefficient of 0.86), although ahead by one year. In addition, the abnormal decrease of SLAA in NWPO-obj1 was the only factor influenced by El Niño, while La Niña events had an impact on the abnormal increase of SLAA in NWPO-obj1 and the abnormal decrease of SSTA in NWPO-obj2, and also dominated their interrelationships.

There exists a sort of dynamic geographic phenomenon in the real world that has a property which is maintained from production through development to death. Using traditional storage units, e.g., point, line, and polygon, researchers face great challenges in exploring the spatial evolution of dynamic phenomena during their lifespan. Thus, this paper proposes a process-oriented two-tier graph model named PoTGM to store the dynamic geographic phenomena. The core ideas of PoTGM are as follows. 1) A dynamic geographic phenomenon is abstracted into a process with a property that is maintained from production through development to death. A process consists of evolution sequences which include instantaneous states. 2) PoTGM integrates a process graph and a sequence graph using a node–edge structure, in which there are four types of nodes, i.e., a process node, a sequence node, a state node, and a linked node, as well as two types of edges, i.e., an including edge and an evolution edge. 3) A node stores an object, i.e., a process object, a sequence object, or a state object, and an edge stores a relationship, i.e., an including or evolution relationship between two objects. Experiments on simulated datasets are used to demonstrate an at least one order of magnitude advantage of PoTGM in relation to relationship querying and to compare it with the Oracle spatial database. The applications on the sea surface temperature remote sensing products in the Pacific Ocean show that PoTGM can effectively explore marine objects as well as spatial evolution, and these behaviors may provide new references for global change research.

From the time that it first develops, a sea surface temperature anomaly (SSTA) will develop in space and time until it dissipates. Although many SST products are available, great challenges are still faced when attempting to directly explore the evolution of SSTAs. To address some of these problems, in this study, we developed a global SSTA dataset that included details of the spatial structure of SSTAs and their temporal evolution. This dataset is called GDPoSSTA. GDPoSSTA is comprised of three datasets and two relationship files and covers the period from January 1982 to December 2009. The three datasets are in SHP format and consist of a dataset of processed object-oriented SSTAs named DSPOSSTA, a dataset of sequenced object-oriented SSTA series named DSSOSSTA, and a dataset of variation object-oriented SSTA named DSVOSSTA. The two relationship files, which are in CSV format, store the evolving behavior of the SSTA sequence object and SSTA variation objects. Finally, geographic spatiotemporal statistics are derived for the DSPOSSTA and a comparison of applying TITAN to DSVOSSTA and DSPOSSTA is carried out which demonstrates the feasibility and applicability of GDPoSSTA. The GDPoSSTA dataset is available on ScienceDB platform (http://www.doi.org/10.11922/sciencedb.j00076.00090).