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

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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

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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

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Movement Data

◮

Movement data

◮

Animal movements

◮

Features of movement data

◮

Large data sets (in both, the spatial and temporal dimension)

◮

Visualization of massive movement data in digital landscapes

◮

Visualization of

dynamic phenomena

◮

Embedded into

such as digital landscape models,

◮

Interactive

visualization, exploration, and analysis of 3D movement data

◮

Visual Analytics

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Movement Data

◮

Movement data

◮

Animal movements

◮

Features of movement data

◮

Large data sets (in both, the spatial and temporal dimension)

◮

Visualization of massive movement data in digital landscapes

◮

Visualization of

dynamic phenomena

◮

Embedded into

such as digital landscape models,

◮

Interactive

visualization, exploration, and analysis of 3D movement data

◮

Visual Analytics

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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), 2012.

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Visualization of Movement Data

◮

InfoVis

◮

Visualization of attribute

values

◮

GIS

◮

Analytical view

◮

context

◮

Temporal aspects

◮

Color mapping

◮

Space-Time Cube

◮

Animation

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Visualization of Movement Data

◮

InfoVis

◮

Visualization of attribute

values

◮

GIS

◮

Analytical view

◮

context

◮

Temporal aspects

◮

Color mapping

◮

Space-Time Cube

◮

Animation

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Digital Landscapes

◮

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).

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Digital Landscapes

◮

Digital landscape models

◮

Terrain models

◮

Vegetation models

◮

Features

◮

Complex geometry

◮

Costly rendering

◮

Visualize dynamic

phenomena

◮

Support interactive

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Digital Landscapes

◮

Digital landscape models

◮

Terrain models

◮

Vegetation models

◮

Features

◮

Complex geometry

◮

Costly rendering

◮

Visualize dynamic

phenomena

◮

Support interactive

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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.

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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.

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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

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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

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Real-Time Trajectory Rendering

◮

Interactive trajectory rendering

◮

Real-time exploration of massive trajectory data sets

◮

Spatial, temporal, and attribute-based filtering

◮

Interactive mapping

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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).

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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).

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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).

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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

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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.

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Detail-And-Overview

◮

Overview visualization

by

means of a density map

◮

Detailed inspection

of

individual trajectories within

the context

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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

◮

Examine temporal features and

relationships for a number of

trajectories

STC visualization of approaching and departing aircrafts.

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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

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Thank You!

Dipl.-Inform. Stefan Buschmann

[email protected]

Computer Graphics Systems

Prof. Dr. Jürgen Döllner

Hasso-Plattner-Institut für Softwaresystemtechnik GmbH

www.hpi3d.de

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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

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Our Approach

◮

GPU data representation

◮

Geometry creation and attribute mapping

The actual geometry is created on-the-fly and passed on for rendering.

◮

Real-time rendering

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Our Approach

◮

Dynamic data pulling

◮

Real-time rendering

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Our Approach

◮

Visualization configurations

◮

Real-time rendering

(30)

Our Approach

◮

Geometry creation and attribute mapping

◮

Real-time rendering

The generated geometry is rendered according to the configuration.

30

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Our Approach

◮

Visualization configurations

◮

Real-time rendering

The generated geometry is rendered according to the configuration.