Real-time Processing and Visualization of Massive Air-Traffic Data in Digital Landscapes

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1 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

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 2

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 3

4 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

5 Visualization of Movement Data InfoVis Visualization of complex spatio-temporal data Visualization of attribute values GIS Analytical view Often embedded in a map context Temporal aspects Color mapping Space-Time Cube Animation Traffic volumes in the city of Potsdam (Google Maps, https://maps.google.de). Tominski, C., Schumann, H., Andrienko, G. & Andrienko, N.: Stacking-Based Visualization of Trajectory Attribute Data, IEEE Transactions on Visualization and Computer Graphics(18, 12), Buschmann, S. Real-time Processing and Visualization of Massive Air-Traffic Data in Digital Landscapes 5

6 Visualization of Movement Data InfoVis Visualization of complex spatio-temporal data Visualization of attribute values GIS Analytical view Often embedded in a map context Temporal aspects Color mapping Space-Time Cube Animation Traffic volumes in the city of Potsdam (Google Maps, https://maps.google.de). Tominski, C., Schumann, H., Andrienko, G. & Andrienko, N.: Stacking-Based Visualization of Trajectory Attribute Data, IEEE Transactions on Visualization and Computer Graphics(18, 12), Buschmann, S. Real-time Processing and Visualization of Massive Air-Traffic Data in Digital Landscapes 6

7 Visualization of Movement Data InfoVis Visualization of complex spatio-temporal data Visualization of attribute values GIS Analytical view Often embedded in a map context Temporal aspects Color mapping Space-Time Cube Animation Traffic volumes in the city of Potsdam (Google Maps, https://maps.google.de). Tominski, C., Schumann, H., Andrienko, G. & Andrienko, N.: Stacking-Based Visualization of Trajectory Attribute Data, IEEE Transactions on Visualization and Computer Graphics(18, 12), Buschmann, S. Real-time Processing and Visualization of Massive Air-Traffic Data in Digital Landscapes 7

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 exploration and analysis 3D virtual city model of the city of Nuremberg (image created by 3D Content Logistics, 2015). Buschmann, S. Real-time Processing and Visualization of Massive Air-Traffic Data in Digital Landscapes 8

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 exploration and analysis 3D virtual city model of the city of Nuremberg (image created by 3D Content Logistics, 2015). Buschmann, S. Real-time Processing and Visualization of Massive Air-Traffic Data in Digital Landscapes 9

10 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 exploration and analysis 3D virtual city model of the city of Nuremberg (image created by 3D Content Logistics, 2015). Buschmann, S. Real-time Processing and Visualization of Massive Air-Traffic Data in Digital Landscapes 10

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 Goals Avoid additional creation and storage of large geometry Reduce integration costs (e.g., costly updates of geometry) Example of dynamic spatio-temporal data: frequency data based on aggregation of traffic volumes. 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

12 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 Goals Avoid additional creation and storage of large geometry Reduce integration costs (e.g., costly updates of geometry) Example of dynamic spatio-temporal data: frequency data based on aggregation of traffic volumes. 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

13 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

14 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

15 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

16 Real-Time Aggregation and Density Maps Real-time aggregation of trajectories Generate density maps at arbitrary spatial and temporal scales Real-time exploration Spatial, temporal, and attribute-based filtering Visualization of differences and changes over time Visualization of density maps of moving objects: aggregated view on air-traffic movements over the time period of a week (left), comparison of two time periods using distinct color channels red and blue (right). Buschmann, S. Real-time Processing and Visualization of Massive Air-Traffic Data in Digital Landscapes 16

17 Visualization of Massive Trajectory Data Sets (2/2) Visualize large numbers of trajectories Interactive exploration and filtering Use mapping configurations to visually distinguish classes of trajectories (e.g., approaching and departing air planes, or aircraft types) Visualization of approaching (red) and departing (blue) aircrafts, depicting direction (texture mapping and animation) and velocity (texture stretching, animation speed, and color). Buschmann, S. Real-time Processing and Visualization of Massive Air-Traffic Data in Digital Landscapes 17

18 Visualization of Massive Trajectory Data Sets (2/2) Visualize large numbers of trajectories Interactive exploration and filtering Use mapping configurations to visually distinguish classes of trajectories (e.g., approaching and departing air planes, or aircraft types) Visualization of different aircraft types: the weight class of aircrafts is depicted by diameter and color (from red for large aircrafts to green for light aircrafts). Buschmann, S. Real-time Processing and Visualization of Massive Air-Traffic Data in Digital Landscapes 18

19 Individual Trajectory Visualization Detailed visualization of individual trajectories Visualization of trajectory attributes by attribute mapping and classification Use various geometric primitives to distinguish between different features Classification based on the time-stamp of each sample points: Detailed visualization (speed and acceleration) of trajectories in the vicinity of an airport, discrete visualization of far-away sample points. Buschmann, S. Real-time Processing and Visualization of Massive Air-Traffic Data in Digital Landscapes 19

20 Exploration and Interaction Image-based selection of trajectories by user input Highlighting of selected trajectories using distinct visual styles Choose mapping styles to display selected trajectories in more detail, or visualize different sets of attributes Highlighting of a trajectory representing a missed-approach on an airport, visualizing the current speed using color, texture, and animation. Buschmann, S. Real-time Processing and Visualization of Massive Air-Traffic Data in Digital Landscapes 20

21 Detail-And-Overview Overview visualization by means of a density map Detailed inspection of individual trajectories within the context Detail-and-overview Visualization. Buschmann, S. Real-time Processing and Visualization of Massive Air-Traffic Data in Digital Landscapes 21

22 Temporal Exploration Space-Time-Cube (STC): map the time attribute to the visual z-axis Understand the temporal order of events, but omit the 3D characteristics of movements STC visualization of approaching and departing aircrafts. Examine temporal features and relationships for a number of trajectories Detailed spatio-temporal examination of a single trajectory. Buschmann, S. Real-time Processing and Visualization of Massive Air-Traffic Data in Digital Landscapes 22

23 Demonstration Buschmann, S. Real-time Processing and Visualization of Massive Air-Traffic Data in Digital Landscapes 23

24 Conclusions Generic technique for visualizing large movement data for a number of use cases Air traffic impact using landscape/city models Pedestrian movements Animal movements Car traffic Support interactive Visual Analytics / Big Data Analytics of large spatio-temporal data in digital landscapes Use of digital landscapes as a computational model and scenery for data analytics What role can Exploratory Visual Analytics play for GeoDesign? Predictive Analytics Prescriptive Analytics Buschmann, S. Real-time Processing and Visualization of Massive Air-Traffic Data in Digital Landscapes 24

25 Thank You! Dipl.-Inform. Stefan Buschmann Computer Graphics Systems Prof. Dr. Jürgen Döllner Hasso-Plattner-Institut für Softwaresystemtechnik GmbH Thanks to DFS Deutsche Flugsicherung GmbH for the provided data. Buschmann, S. Real-time Processing and Visualization of Massive Air-Traffic Data in Digital Landscapes 25

26 Our Approach GPU data representation Central attribute storage buffer that is streamed to the GPU. Visualization configurations Define how attribute values are mapped to visual properties. Dynamic data pulling Fetch attribute data based on selected configuration. Geometry creation and attribute mapping The actual geometry is created on-the-fly and passed on for rendering. Real-time rendering The generated geometry is rendered according to the configuration. Buschmann, S. Real-time Processing and Visualization of Massive Air-Traffic Data in Digital Landscapes 26

27 Our Approach GPU data representation Central attribute storage buffer that is streamed to the GPU. Visualization configurations Define how attribute values are mapped to visual properties. Dynamic data pulling Fetch attribute data based on selected configuration. Geometry creation and attribute mapping The actual geometry is created on-the-fly and passed on for rendering. Real-time rendering The generated geometry is rendered according to the configuration. Buschmann, S. Real-time Processing and Visualization of Massive Air-Traffic Data in Digital Landscapes 27

28 Our Approach GPU data representation Central attribute storage buffer that is streamed to the GPU. Visualization configurations Define how attribute values are mapped to visual properties. Dynamic data pulling Fetch attribute data based on selected configuration. Geometry creation and attribute mapping The actual geometry is created on-the-fly and passed on for rendering. Real-time rendering The generated geometry is rendered according to the configuration. Buschmann, S. Real-time Processing and Visualization of Massive Air-Traffic Data in Digital Landscapes 28

29 Our Approach GPU data representation Central attribute storage buffer that is streamed to the GPU. Visualization configurations Define how attribute values are mapped to visual properties. Dynamic data pulling Fetch attribute data based on selected configuration. Geometry creation and attribute mapping The actual geometry is created on-the-fly and passed on for rendering. Real-time rendering The generated geometry is rendered according to the configuration. Buschmann, S. Real-time Processing and Visualization of Massive Air-Traffic Data in Digital Landscapes 29

30 Our Approach GPU data representation Central attribute storage buffer that is streamed to the GPU. Visualization configurations Define how attribute values are mapped to visual properties. Dynamic data pulling Fetch attribute data based on selected configuration. Geometry creation and attribute mapping The actual geometry is created on-the-fly and passed on for rendering. Real-time rendering The generated geometry is rendered according to the configuration. Buschmann, S. Real-time Processing and Visualization of Massive Air-Traffic Data in Digital Landscapes 30

31 Our Approach GPU data representation Central attribute storage buffer that is streamed to the GPU. Visualization configurations Define how attribute values are mapped to visual properties. Dynamic data pulling Fetch attribute data based on selected configuration. Geometry creation and attribute mapping The actual geometry is created on-the-fly and passed on for rendering. Real-time rendering The generated geometry is rendered according to the configuration. Buschmann, S. Real-time Processing and Visualization of Massive Air-Traffic Data in Digital Landscapes 31

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