The Topology ToolKit

This system paper presents the Topology ToolKit (TTK), a software platform designed for topological data analysis in scientific visualization. TTK provides a unified, generic, efficient, and robust implementation of key algorithms for the topological analysis of scalar data, including: critical points, integral lines, persistence diagrams, persistence curves, merge trees, contour trees, Morse-Smale complexes, fiber surfaces, continuous scatterplots, Jacobi sets, Reeb spaces, and more. TTK is easily accessible to end users due to a tight integration with ParaView. It is also easily accessible to developers through a variety of bindings (Python, VTK/C++) for fast prototyping or through direct, dependence-free, C++, to ease integration into pre-existing complex systems. While developing TTK, we faced several algorithmic and software engineering challenges, which we document in this paper. In particular, we present an algorithm for the construction of a discrete gradient that complies to the critical points extracted in the piecewise-linear setting. This algorithm guarantees a combinatorial consistency across the topological abstractions supported by TTK, and importantly, a unified implementation of topological data simplification for multi-scale exploration and analysis. We also present a cached triangulation data structure, that supports time efficient and generic traversals, which self-adjusts its memory usage on demand for input simplicial meshes and which implicitly emulates a triangulation for regular grids with no memory overhead. Finally, we describe an original software architecture, which guarantees memory efficient and direct accesses to TTK features, while still allowing for researchers powerful and easy bindings and extensions. TTK is open source (BSD license) and its code, online documentation and video tutorials are available on TTK's website

Digital Urban - The Visual City

Nothing in the city is experienced by itself for a city’s perspicacity is the sum of its surroundings. To paraphrase Lynch (1960), at every instant, there is more than we can see and hear. This is the reality of the physical city, and thus in order to replicate the visual experience of the city within digital space, the space itself must convey to the user a sense of place. This is what we term the “Visual City”, a visually recognisable city built out of the digital equivalent of bricks and mortar, polygons, textures, and most importantly data. Recently there has been a revolution in the production and distribution of digital artefacts which represent the visual city. Digital city software that was once in the domain of high powered personal computers, research labs and professional software are now in the domain of the public-at-large through both the web and low-end home computing. These developments have gone hand in hand with the re-emergence of geography and geographic location as a way of tagging information to non-proprietary web-based software such as Google Maps, Google Earth, Microsoft’s Virtual Earth, ESRI’s ArcExplorer, and NASA’s World Wind, amongst others. The move towards ‘digital earths’ for the distribution of geographic information has, without doubt, opened up a widespread demand for the visualization of our environment where the emphasis is now on the third dimension. While the third dimension is central to the development of the digital or visual city, this is not the only way the city can be visualized for a number of emerging tools and ‘mashups’ are enabling visual data to be tagged geographically using a cornucopia of multimedia systems. We explore these social, textual, geographical, and visual technologies throughout this chapter

Stacking-based visualization of trajectory attribute data

Visualizing trajectory attribute data is challenging because it involves showing the trajectories in their spatio-temporal context as well as the attribute values associated with the individual points of trajectories. Previous work on trajectory visualization addresses selected aspects of this problem, but not all of them. We present a novel approach to visualizing trajectory attribute data. Our solution covers space, time, and attribute values. Based on an analysis of relevant visualization tasks, we designed the visualization solution around the principle of stacking trajectory bands. The core of our approach is a hybrid 2D/3D display. A 2D map serves as a reference for the spatial context, and the trajectories are visualized as stacked 3D trajectory bands along which attribute values are encoded by color. Time is integrated through appropriate ordering of bands and through a dynamic query mechanism that feeds temporally aggregated information to a circular time display. An additional 2D time graph shows temporal information in full detail by stacking 2D trajectory bands. Our solution is equipped with analytical and interactive mechanisms for selecting and ordering of trajectories, and adjusting the color mapping, as well as coordinated highlighting and dedicated 3D navigation. We demonstrate the usefulness of our novel visualization by three examples related to radiation surveillance, traffic analysis, and maritime navigation. User feedback obtained in a small experiment indicates that our hybrid 2D/3D solution can be operated quite well

Dynamic Composite Data Physicalization Using Wheeled Micro-Robots

This paper introduces dynamic composite physicalizations, a new class of physical visualizations that use collections of self-propelled objects to represent data. Dynamic composite physicalizations can be used both to give physical form to well-known interactive visualization techniques, and to explore new visualizations and interaction paradigms. We first propose a design space characterizing composite physicalizations based on previous work in the fields of Information Visualization and Human Computer Interaction. We illustrate dynamic composite physicalizations in two scenarios demonstrating potential benefits for collaboration and decision making, as well as new opportunities for physical interaction. We then describe our implementation using wheeled micro-robots capable of locating themselves and sensing user input, before discussing limitations and opportunities for future work