Hasso-Plattner-InstitutSDG am HPI
Hasso-Plattner-InstitutDSG am HPI

Stefan Buschmann

A Software Framework for GPU-based Geo-Temporal Visualization Techniques

Spatio-temporal data denotes a category of data that contains spatial as well as temporal components. For example, time-series of geo-data, thematic maps that change over time, or tracking data of moving entities can be interpreted as spatio-temporal data. In today's automated world, an increasing number of data sources exist, which constantly generate spatio-temporal data. This includes for example traffic surveillance systems, which gather movement data about human or vehicle movements, remote-sensing systems, which frequently scan our surroundings and produce digital representations of cities and land-scapes, as well as sensor networks in different domains, such as logistics, animal behavior study, or climate research. For the analysis of spatio-temporal data, in addition to automatic statistical and data mining methods, exploratory analysis methods are employed, which are based on interactive visualization. These analysis methods let users explore a data set by interactively manipulating a visualization, thereby employing the human cognitive system and knowledge of the users to find patterns and gain insight into the data. This thesis describes a software framework for the visualization of spatio-temporal data, which consists of CPU-based techniques to enable the interactive visualization and explo-ration of large spatio-temporal data sets. The developed techniques include data manage-ment, processing, and rendering, facilitating real-time processing and visualization of large geo-temporal data sets. It includes three main contributions:
Concept and Implementation of a GPU-Based Visualization Pipeline. The developed visualization methods are based on the concept of a CPU-based visualization pipeline, in which all steps - processing, mapping, and rendering - are implemented on the GPU. With this concept, spatio-temporal data is represented directly in GPU memory, using shader programs to process and filter the data, apply mappings to visual properties, and fi-nally generate the geometric representations for a visualization during the rendering process. Data processing, filtering, and mapping are thereby executed in real-time, enabling dynamic control over the mapping and a visualization process which can be controlled interactively by a user.
Attributed 3D Trajectory Visualization. A visualization method has been developed for the interactive exploration of large numbers of 3D movement trajectories. The trajectories are visualized in a virtual geographic environment, supporting basic geometries such as lines, ribbons, spheres, or tubes. Interactive mapping can be applied to visualize the values of per-node or per-trajectory attributes, supporting shape, height, size, color, texturing, and animation as visual properties. Using the dynamic mapping system, several kind of visualization methods have been implemented, such as focus+context visualization of trajectories using interactive density maps, and space-time cube visualization to focus on the temporal aspects of individual movements.