Marine Data Fusion for Analyzing Spatio-Temporal Ocean Region Connectivity

This thesis develops methods to automate and objectify the connectivity analysis between ocean regions. Existing methods for connectivity analysis often rely on manual integration of expert knowledge, which renders the processing of large amounts of data tedious. This thesis presents a new framework for Data Fusion that provides several approaches for automation and objectification of the entire analysis process. It identifies different complexities of connectivity analysis and shows how the Data Fusion framework can be applied and adapted to them. The framework is used in this thesis to analyze geo-referenced trajectories of fish larvae in the western Mediterranean Sea, to trace the spreading pathways of newly formed water in the subpolar North Atlantic based on their hydrographic properties, and to gauge their temporal change. These examples introduce a new, and highly relevant field of application for the established Data Science methods that were used and innovatively combined in the framework. New directions for further development of these methods are opened up which go beyond optimization of existing methods. The Marine Science, more precisely Physical Oceanography, benefits from the new possibilities to analyze large amounts of data quickly and objectively for its exact research questions. This thesis is a foray into the new field of Marine Data Science. It practically and theoretically explores the possibilities of combining Data Science and the Marine Sciences advantageously for both sides. The example of automating and objectifying connectivity analysis between marine regions in this thesis shows the added value of combining Data Science and Marine Science. This thesis also presents initial insights and ideas on how researchers from both disciplines can position themselves to thrive as Marine Data Scientists and simultaneously advance our understanding of the ocean

Hierarchical Hexagonal Clustering and Indexing

Space-filling curves (SFCs) represent an efficient and straightforward method for sparse-space indexing to transform an n-dimensional space into a one-dimensional representation. This is often applied for multidimensional point indexing which brings a better perspective for data analysis, visualization and queries. SFCs are involved in many areas such as big data analysis and visualization, image decomposition, computer graphics and geographic information systems (GISs). The indexing methods subdivide the space into logic clusters of close points and they differ in various parameters including the cluster order, the distance metrics, and the pattern shape. Beside the simple and highly preferred triangular and square uniform grids, the hexagonal uniform grids have gained high interest especially in areas such as GISs, image processing and data visualization for the uniform distance between cells and high effectiveness of circle coverage. While the linearization of hexagons is an obvious approach for memory representation, it seems there is no hexagonal SFC indexing method generally used in practice. The main limitation of hexagons lies in lacking infinite decomposition into sub-hexagons and similarity of tiles on different levels of hierarchy. Our research aims at defining a fast and robust hexagonal SFC method. The Gosper fractal is utilized to preserve the benefits of hexagonal grids and to efficiently and hierarchically linearize points in a hexagonal grid while solving the non-convex shape and recursive transformation issues of the fractal. A comparison to other SFCs and grids is conducted to verify the robustness and effectiveness of our hexagonal method

Hierarchical Hexagonal Clustering and Indexing

Space-filling curves (SFCs) represent an efficient and straightforward method for sparse-space indexing to transform an n-dimensional space into a one-dimensional representation. This is often applied for multidimensional point indexing which brings a better perspective for data analysis, visualization and queries. SFCs are involved in many areas such as big data analysis and visualization, image decomposition, computer graphics and geographic information systems (GISs). The indexing methods subdivide the space into logic clusters of close points and they differ in various parameters including the cluster order, the distance metrics, and the pattern shape. Beside the simple and highly preferred triangular and square uniform grids, the hexagonal uniform grids have gained high interest especially in areas such as GISs, image processing and data visualization for the uniform distance between cells and high effectiveness of circle coverage. While the linearization of hexagons is an obvious approach for memory representation, it seems there is no hexagonal SFC indexing method generally used in practice. The main limitation of hexagons lies in lacking infinite decomposition into sub-hexagons and similarity of tiles on different levels of hierarchy. Our research aims at defining a fast and robust hexagonal SFC method. The Gosper fractal is utilized to preserve the benefits of hexagonal grids and to efficiently and hierarchically linearize points in a hexagonal grid while solving the non-convex shape and recursive transformation issues of the fractal. A comparison to other SFCs and grids is conducted to verify the robustness and effectiveness of our hexagonal method