Interactive Information Visualization Project Report

Transcription

1 Interactive Information Visualization Project Report Using real-time physical simulation to perform multiple dynamic queries on multivariate data Mathieu Le Goc

2 Dataset description The dataset chosen for this project contains information about different places in Paris, such as restaurants, bars, hotels and food shops. For each place, the dataset provides usual information such as the name, the address, the phone number or the neighbourhood. Additionally, the dataset gives extra data such as the community rating, a short description or a link to the place s Yelp webpage. Part of this data is used to create the visualization. Visualization and interaction choices Inspiration The visualization principles used in this project are inspired from two different research papers. The first one is Dust & Magnet: multivariate information visualization using a magnet metaphor from Yi et al. In this paper, each data points is represented by a physical particle. The user can apply filters materialized by magnets, in order to organise multivariate data. The second paper is Kinetica: Naturalistic Multi-touch Data Visualization from Rzeszotarski et al. It is an implementation of Dust and Magnets in real time on a tablet using multitouch gestures. The real time simulation brings more realistic animations. Interaction The interaction in this application is very simple since it only relies on the mouse. The user either has to click, drag and drop to move, modify, assign or enable magnets. Physical Simulation The simulation is running in real time at 1000Hz to provide a smooth and appealing simulation. Data points are represented by small spheres with a fixed radius. At each cycle of the simulation, the different forces are calculated to create a realistic rendering. From these forces are deduced acceleration speed and position using physical models. These forces include collisions forces between particles and with magnets, but also attraction and repulsion forces between magnets and particles. These last ones are the forces that are going to rule the visualization. A simple spring force model is used to create the linear attraction of the magnet. The further the particle is the stronger the force is. This choice is particularly adapted to multiple magnets queries. Indeed, if a particle is attracted by several magnets, the particle is just going to sit at the barycentre of these magnets. Magnets can be resized, and this size is used as weight for the magnet, to increase its influence on the particles and hence emphasize its characteristic. Concerning the repulsion force, the Coulomb law is used to model a realistic push back of the particles by the magnets. Thus the closer the particle is to the magnet the stronger the force is. This helps to have a clean space around the magnets, without disturbing the rest of the environment.

3 Initial view of the visualization without filter Basic information (name, rating, neighbourhood, type) when the mouse is over the particle Menu to create and modify queries

4 Reading the Visualization Data encoding Two different color encodings are available in this application. The first one encodes the rating of the place into using a diverging colour scale. This provides a rough idea of the quality of the establishment without having to open the page. The second one encodes the type of the place as a color, to help focusing the filtering on more specific characteristics. The different colours were generated by the website ColorBrewer. Data filtering Different techniques are possible to filter the data out. The main one relies and the metaphor of physical entities, where magnets attract corresponding particles and repulse others. To perform a query, the user needs first to place empty magnets in the interaction space by dragging it from the menu on the left side into the window. He or she can then assign a value to this magnet by also dragging it from the menu and releasing it on the magnet. This value will determine which particles will be attracted by it and as soon as the value is assigned, the magnet starts to have an effect on the particles. It becomes then easy to create clusters simply by placing different magnets in the window, for example a bar magnet and a restaurant magnet. Magnets placed without value This magnet attracts all the particle having the bar characteristic Multiple queries can be performed at the same time to create clusters with several specific criteria. This is achieved by placing several magnets in the window. They create a barycentric coordinate system where the coordinates of each particle are dictated by the correspondance with the magnets and the strength of these last ones, as shown below. It is possible to move and resize the magnets to adjust the visualization layout.

5 French bars French restaurants This shows two groups of places using three different magnets, the French bars and the French restaurants Technical aspects This application was developed using Processing. The only library used in this project is G4P, providing GUI controls such as sliders or checkboxes. The physical simulation as well as the rest of the application was entirely coded.

6 Discussion and future work This project implements a real time physical simulation to create an interactive visualization of multivariate data. Applying the Dust and Magnets metaphor, the visualization represents data with particles, and various magnets can be applied to them. The user can perform dynamic queries by adding magnets and interact with them. The real time simulation enhance the user experience by providing a playful visualization, encouraging exploration. However, this visualization presents a few limitations. First, the particle representation limits the quantity of data points on a given screen surface. Increasing the size of the window obviously enables the use of bigger datasets, however it still bounds the number to thousands of points but no more in order to preserve readability. Besides, the real time physical simulation requires a given amount of computation per particle per cycle, which increases with the number of data points. Finally, the lack of space in the window can also limit the number of magnets used at the same time. Indeed, data clusters can become less obvious with many magnets (i.e. 10 or more), because they tend to get too close to each other, or even merge with neighbour clusters. However, this visualization works well to perform simple queries and isolate easily and quickly places that match the user s criteria. Possible future work could implement different types of force models to introduce new behaviours, hence new ways to read the visualization. It could also include more e v o l v e d interaction techniques.

7 References Aviz s website providing guidelines and datasets: Processing website for the coding references: Quarks place hosting the GUI library: ColorBrewer tool to generate colours scales: Papers: Dust & Magnet: Multivariate Information Visualization Using a Magnet Metaphor, Yi et al. IV Kinetica: Naturalistic Multi-touch Data Visualization, Rzeszotarski et al. CHI