Jiawei Yi

A k-d tree-based algorithm to parallelize Kriging interpolation of big spatial data

Parallel computing provides a promising solution to accelerate complicated spatial data processing, which has recently become increasingly computationally intense. Partitioning a big dataset into workload-balanced child data groups remains a challenge, particularly for unevenly distributed spatial data. This study proposed an algorithm based on the k-d tree method to tackle this challenge. The algorithm constructed trees based on the distribution variance of spatial data. The number of final sub-trees, unlike the conventional k-d tree method, is not always a power of two. Furthermore, the number of nodes on the left and right sub-trees is always no more than one to ensure a balanced workload. Experiments show that our algorithm is able to partition big datasets efficiently and evenly into equally sized child data groups. Speed-up ratios show that parallel interpolation can save up to 70% of the execution time of the consequential interpolation. A high efficiency of parallel computing was achieved when the datasets were divided into an optimal number of child data groups.

A representation framework for studying spatiotemporal changes and interactions of dynamic geographic phenomena

This research presented a framework to track and query spatiotemporal changes and interactions of dynamic geographic phenomena. The framework organized information of dynamic phenomena as a hierarchy of static structures, processes, and scenarios. Static structures of a dynamic phenomenon at its different evolution stages were described by its corepoint, footprint border, and composite border, which were extracted from time series remote sensing images. Time series static structures of a phenomenon were then grouped into processes to show its changes over space and time. Scenarios were used to describe a collection of interacting processes in space. We expanded the identity-based change (IBC) model by adding more primitives and operations to represent semantics of these changes and interactions. A geographic information system (GIS) database was built by integrating the expanded IBC model with our spatiotemporal framework. As demonstrated by a case study of ocean eddies in the South China Sea (SCS), query results of the behaviors and relationships of ocean eddies from the GIS database help us better understand their development and evolution, demonstrating the usefulness of this spatiotemporal framework. Spatial and semantic queries about a specific eddy from the database can further efficiently present its lifetime dynamic changes and all other eddies that interacted with it.

Automatic Identification of Oceanic Multieddy Structures From Satellite Altimeter Datasets

Very few of current eddy detection algorithms are capable of identifying multieddy structures resulted from interactions among eddies. In this study, we improve our previous hybrid detection (HD) algorithm by incorporating a new criterion to better identify multieddy structures from satellite altimeter data. The criterion defines an aspect ratio to determine if eddies have vortex overlaps and, as such, result in a composite structure of multiple eddies (a.k.a. multieddy structures). Compared with two previous studies on observed eddy-eddy interactions in eddy mergers from altimeter data, the improved HD algorithm not only successfully captures multieddy structures but also shows how eddies interact and evolve, including merging, splitting, and partial vorticity exchange. Tests of the improved HD algorithm on a series of sea-level anomaly maps in the South China Sea (SCS) from 1993 to 2012 show that single eddies, in contrast to eddies with composite structures, appear more concentrated in northwest of the Luzon and southeast of Vietnam. Tracking dual-eddy structures reveals several processes of eddy interactions in the SCS. The study demonstrates the potential value of the new HD algorithm in helping scientists to investigate characteristics of eddy-eddy interactions from satellite observations.

Mesoscale oceanic eddies in the South China Sea from 1992 to 2012: evolution processes and statistical analysis

Automated identification and tracking of mesoscale ocean eddies has recently become one research hotspot in physical oceanography. Several methods have been developed and applied to survey the general kinetic and geometric characteristics of the ocean eddies in the South China Sea (SCS). However, very few studies attempt to examine eddies’ internal evolution processes. In this study, we reported a hybrid method to trace eddies’ propagation in the SCS based on their internal structures, which are characterized by eddy centers, footprint borders, and composite borders. Eddy identification and tracking results were represented by a GIS-based spatiotemporal model. Information on instant states, dynamic evolution processes, and events of disappearance, reappearance, split, and mergence is stored in a GIS database. Results were validated by comparing against the ten Dongsha Cyclonic Eddies (DCEs) and the three long-lived anticyclonic eddies (ACEs) in the northern SCS, which were reported in previous literature. Our study confirmed the development of these eddies. Furthermore, we found more DCE-like and ACE-like eddies in these areas from 2005 to 2012 in our database. Spatial distribution analysis of disappearing, reappearing, splitting, and merging activities shows that eddies in the SCS tend to cluster to the northwest of Luzon Island, southwest of Luzon Strait, and around the marginal sea of Vietnam. Kuroshio intrusions and the complex sea floor topography in these areas are the possible factors that lead to these spatial clusters.

A Gaussian-surface-based approach to identifying oceanic multi-eddy structures from satellite altimeter datasets

This study presents a Gaussian-surface-based approach to identifying multi-eddy structures from the sea level anomaly (SLA) maps. The SLA signals of an eddy are modeled by a two-dimensional anisotropic Gaussian surface. An identification criterion is introduced to determine whether to retain or split a multi-eddy structure. Detection result of a three-eddy structure from the SLA in the South China Sea (SCS) demonstrates the effectiveness of the identification approach. Comparison with a merger event previously reported about a multi-eddy structure in northern SCS reveals the advantage of this approach over simple splitting strategy, which may lose the eddy-eddy interaction details conveyed by real multi-eddy structures.

Tracking the evolution processes and behaviors of mesoscale eddies in the South China Sea: a global nearest neighbor filter approach

The eddy tracking approach is developed using the global nearest neighbor filter (GNNF) to investigate the evolution processes and behaviors of mesoscale eddies in the South China Sea (SCS). Combining the Kalman filter and optimal data association technologies, the GNNF algorithm is able to reduce pairing errors to 0.2% in tracking synthetic eddy tracks, outperforming other existing methods. A total of 4 913 eddy tracks that last more than a week are obtained by the GNNF during 1993–2012. The analysis of a growth and a decay based on 3 445 simple eddy tracks show that eddy radius, amplitude, and vorticity smoothly increase during the first half of lifetime and decline during the second half following a parabola opening downwards. The genesis of eddies mainly clusters northwest and southwest of Luzon Island whereas the dissipations concentrate the Xisha Islands where the underwater bay traps and terminates eddies. West of the Luzon Strait, northwest of Luzon Island, and southeast of Vietnam are regions where eddy splits and mergers are frequently observed. Short disappearances mainly distribute in the first two regions. Moreover, eddy splits generally result in a decrease of the radius and the amplitude whereas eddy mergers induce growing up. Eddy intensity and vorticity, on the contrary, are strengthened in the eddy splits and diminished in mergers.

Temporal analysis of partial moving patterns identified from large trajectory datasets: A case study of Ocean eddies in the South China Sea

Trajectory data not only contains spatial locations but also rich temporal information. Discovering the temporal characteristics of trajectories is essential for understanding the dynamics of partial moving patterns. This paper presents an analysis approach to exploring temporal features of moving objects. The method first extracted representative routes from original trajectories, and then calculated the dominant time span of each line segment of the traveling routes, and finally visualized the routes on the map for subsequent analysis. Ocean eddies which are frequently observed in the South China Sea (SCS) were selected as a case study to test the utility of this approach. The time heterogeneity of the identified moving paths revealed potential seasonal movements of eddies, which demonstrated the ability of this analysis method to discover dynamic patterns from trajectories.