Real-time Processing and Visualization of Massive Air-Traffic Data in Digital Landscapes Digital Landscape Architecture 2015, Dessau Stefan Buschmann, Matthias Trapp, and Jürgen Döllner Hasso-Plattner-Institut, Universität Potsdam 05.06.2015
Movement Data ◮ Movement data ◮ Traffic data (e.g., road, naval, or air-traffic) ◮ Pedestrian movements ◮ Animal movements ◮ Features of movement data ◮ Spatio-temporal geodata ◮ Often represented by spatial trajectories ◮ Large data sets (in both, the spatial and temporal dimension) ◮ Advancing technology for real-time acquisition, transfer, and storage ◮ Visualization of massive movement data in digital landscapes ◮ Visualization of dynamic phenomena ◮ Embedded into 3D virtual environments such as digital landscape models, city models, or virtual globes ◮ Interactive visualization, exploration, and analysis of 3D movement data ◮ Visual Analytics Buschmann, S. Real-time Processing and Visualization of Massive Air-Traffic Data in Digital Landscapes 2 • •
Movement Data ◮ Movement data ◮ Traffic data (e.g., road, naval, or air-traffic) ◮ Pedestrian movements ◮ Animal movements ◮ Features of movement data ◮ Spatio-temporal geodata ◮ Often represented by spatial trajectories ◮ Large data sets (in both, the spatial and temporal dimension) ◮ Advancing technology for real-time acquisition, transfer, and storage ◮ Visualization of massive movement data in digital landscapes ◮ Visualization of dynamic phenomena ◮ Embedded into 3D virtual environments such as digital landscape models, city models, or virtual globes ◮ Interactive visualization, exploration, and analysis of 3D movement data ◮ Visual Analytics Buschmann, S. Real-time Processing and Visualization of Massive Air-Traffic Data in Digital Landscapes 3 • •
Movement Data ◮ Movement data ◮ Traffic data (e.g., road, naval, or air-traffic) ◮ Pedestrian movements ◮ Animal movements ◮ Features of movement data ◮ Spatio-temporal geodata ◮ Often represented by spatial trajectories ◮ Large data sets (in both, the spatial and temporal dimension) ◮ Advancing technology for real-time acquisition, transfer, and storage ◮ Visualization of massive movement data in digital landscapes ◮ Visualization of dynamic phenomena ◮ Embedded into 3D virtual environments such as digital landscape models, city models, or virtual globes ◮ Interactive visualization, exploration, and analysis of 3D movement data ◮ Visual Analytics Buschmann, S. Real-time Processing and Visualization of Massive Air-Traffic Data in Digital Landscapes 4 • •
Visualization of Movement Data ◮ InfoVis ◮ Visualization of complex spatio-temporal data ◮ Visualization of attribute values ◮ GIS Traffic volumes in the city of Potsdam (Google Maps, https://maps.google.de). ◮ Analytical view ◮ Often embedded in a map context ◮ Temporal aspects ◮ Color mapping ◮ Space-Time Cube ◮ Animation Tominski, C., Schumann, H., Andrienko, G. & Andrienko, N.: Stacking-Based Visualization of Trajectory Attribute Data, IEEE Transactions on Visualization and Computer Graphics(18, 12), 2012. Buschmann, S. Real-time Processing and Visualization of Massive Air-Traffic Data in Digital Landscapes 5 • •
Visualization of Movement Data ◮ InfoVis ◮ Visualization of complex spatio-temporal data ◮ Visualization of attribute values ◮ GIS Traffic volumes in the city of Potsdam (Google Maps, https://maps.google.de). ◮ Analytical view ◮ Often embedded in a map context ◮ Temporal aspects ◮ Color mapping ◮ Space-Time Cube ◮ Animation Tominski, C., Schumann, H., Andrienko, G. & Andrienko, N.: Stacking-Based Visualization of Trajectory Attribute Data, IEEE Transactions on Visualization and Computer Graphics(18, 12), 2012. Buschmann, S. Real-time Processing and Visualization of Massive Air-Traffic Data in Digital Landscapes 6 • •
Visualization of Movement Data ◮ InfoVis ◮ Visualization of complex spatio-temporal data ◮ Visualization of attribute values ◮ GIS Traffic volumes in the city of Potsdam (Google Maps, https://maps.google.de). ◮ Analytical view ◮ Often embedded in a map context ◮ Temporal aspects ◮ Color mapping ◮ Space-Time Cube ◮ Animation Tominski, C., Schumann, H., Andrienko, G. & Andrienko, N.: Stacking-Based Visualization of Trajectory Attribute Data, IEEE Transactions on Visualization and Computer Graphics(18, 12), 2012. Buschmann, S. Real-time Processing and Visualization of Massive Air-Traffic Data in Digital Landscapes 7 • •
Digital Landscapes ◮ 3D virtual environments ◮ Digital landscape models ◮ Terrain models ◮ Vegetation models ◮ 3D virtual city models ◮ Features ◮ Complex geometry ◮ Costly rendering ◮ Scenery for InfoVis? ◮ Visualize dynamic phenomena ◮ Support interactive 3D virtual city model of the city of Nuremberg (image created by 3D exploration and analysis Content Logistics, 2015). Buschmann, S. Real-time Processing and Visualization of Massive Air-Traffic Data in Digital Landscapes 8 • •
Digital Landscapes ◮ 3D virtual environments ◮ Digital landscape models ◮ Terrain models ◮ Vegetation models ◮ 3D virtual city models ◮ Features ◮ Complex geometry ◮ Costly rendering ◮ Scenery for InfoVis? ◮ Visualize dynamic phenomena ◮ Support interactive 3D virtual city model of the city of Nuremberg (image created by 3D exploration and analysis Content Logistics, 2015). Buschmann, S. Real-time Processing and Visualization of Massive Air-Traffic Data in Digital Landscapes 9 • •
Digital Landscapes ◮ 3D virtual environments ◮ Digital landscape models ◮ Terrain models ◮ Vegetation models ◮ 3D virtual city models ◮ Features ◮ Complex geometry ◮ Costly rendering ◮ Scenery for InfoVis? ◮ Visualize dynamic phenomena ◮ Support interactive 3D virtual city model of the city of Nuremberg (image created by 3D exploration and analysis Content Logistics, 2015). Buschmann, S. Real-time Processing and Visualization of Massive Air-Traffic Data in Digital Landscapes 10 • •
Visualization of Movement Data in Virtual Landscapes ◮ Challenges ◮ Handle massive amounts of trajectories in high-resolution data sets ◮ Geometric complex , high detailed 3D scenes for digital landscapes ◮ Maintain interactivity for exploration and mapping Example of dynamic spatio-temporal data: frequency data based on aggregation of traffic volumes. ◮ Goals ◮ Avoid additional creation and storage of large geometry ◮ Reduce integration costs (e.g., costly updates of geometry) Visualization of frequency data using a 3D city model as context and scenery. Buschmann, S. Real-time Processing and Visualization of Massive Air-Traffic Data in Digital Landscapes 11 • •
Visualization of Movement Data in Virtual Landscapes ◮ Challenges ◮ Handle massive amounts of trajectories in high-resolution data sets ◮ Geometric complex , high detailed 3D scenes for digital landscapes ◮ Maintain interactivity for exploration and mapping Example of dynamic spatio-temporal data: frequency data based on aggregation of traffic volumes. ◮ Goals ◮ Avoid additional creation and storage of large geometry ◮ Reduce integration costs (e.g., costly updates of geometry) Visualization of frequency data using a 3D city model as context and scenery. Buschmann, S. Real-time Processing and Visualization of Massive Air-Traffic Data in Digital Landscapes 12 • •
Our Approach (1/2) ◮ GPU-based rendering pipeline ◮ Interactive spatio-temporal filtering ◮ Generic mapping of trajectory attributes to geometric representations and appearance ◮ Real-time rendering within 3D virtual environments ◮ Advantages ◮ Processing and rendering of massive data sets ◮ Maintaining small memory footprint ◮ Configurable on-the-fly geometry generation Comparison of a traditional forward-rendering visualization pipeline (top) with our GPU-based mapping approach (bottom). Buschmann, S. Real-time Processing and Visualization of Massive Air-Traffic Data in Digital Landscapes 13 • •
Our Approach (2/2) ◮ On-the-fly geometry generation ◮ Input data is represented and managed entirely on the GPU ◮ Real-time mapping of data attributes to visual properties, such as type of geometry, width/radius, color, texture mapping, and animation ◮ Interactive configuration of the mapping can be applied based on data attributes , classification , or user interaction ◮ Applications ◮ Real-time adjustment of mapping options ◮ Interactive spatial and temporal exploration ◮ Interactive generation of density maps Supported basic geometry types for attribute mapping: (1) lines, (2) tubes, (3) ribbons, and (4) spheres. Buschmann, S. Real-time Processing and Visualization of Massive Air-Traffic Data in Digital Landscapes 14 • •
Real-Time Trajectory Rendering ◮ Interactive trajectory rendering ◮ Real-time exploration of massive trajectory data sets ◮ Spatial, temporal, and attribute-based filtering ◮ Interactive mapping ◮ Visualization of attributes using mapping configurations Buschmann, S. Real-time Processing and Visualization of Massive Air-Traffic Data in Digital Landscapes 15 • •
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