Mikael Jern

Space, time and visual analytics

Visual analytics aims to combine the strengths of human and electronic data processing. Visualisation, whereby humans and computers cooperate through graphics, is the means through which this is achieved. Seamless and sophisticated synergies are required for analysing spatio-temporal data and solving spatio-temporal problems. In modern society, spatio-temporal analysis is not solely the business of professional analysts. Many citizens need or would be interested in undertaking analysis of information in time and space. Researchers should find approaches to deal with the complexities of the current data and problems and find ways to make analytical tools accessible and usable for the broad community of potential users to support spatio-temporal thinking and contribute to solving a large range of problems.

Revealing structure within clustered parallel coordinates displays

In order to gain insight into multivariate data, complex structures must be analysed and understood. Parallel coordinates is an excellent tool for visualizing this type of data but has its limitations. This paper deals with one of its main limitations - how to visualize a large number of data items without hiding the inherent structure they constitute. We solve this problem by constructing clusters and using high precision textures to represent them. We also use transfer functions that operate on the high precision textures in order to highlight different aspects of the cluster characteristics. Providing predefined transfer functions as well as the support to draw customized transfer functions makes it possible to extract different aspects of the data. We also show how feature animation can be used as guidance when simultaneously analysing several clusters. This technique makes it possible to visually represent statistical information about clusters and thus guides the user, making the analysis process more efficient.

Revealing structure in visualizations of dense 2D and 3D parallel coordinates

Parallel coordinates is a well-known technique used for visualization of multivariate data. When the size of the data sets increases the parallel coordinates display results in an image far too cluttered to perceive any structure. We tackle this problem by constructing high-precision textures to represent the data. By using transfer functions that operate on the high-precision textures, it is possible to highlight different aspects of the entire data set or clusters of the data. Our methods are implemented in both standard 2D parallel coordinates and 3D multi-relational parallel coordinates. Furthermore, when visualizing a larger number of clusters, a technique called ‘feature animation’ may be used as guidance by presenting various cluster statistics. A case study is also performed to illustrate the analysis process when analysing large multivariate data sets using our proposed techniques.

The GAV Toolkit for Multiple Linked Views

Implementing InfoVis multivariate data tools, timelinked coordinated views and visual dynamic queries with conditioning from scratch is not a simple programming task. Our research objective is to develop a generic GeoAnalytics visualization (GAV) component toolkit, based on the principles behind visual analytics (VA), for dynamically exploring time-varying, geographically referenced and multivariate attributes simultaneously. GAV includes components based on a synergy of technologies from information visualization, geovisualization and scientific visualization. Our research concentrates on improving visual user interfaces (VUI) methods and trying to extend existing visual representation techniques. The effectiveness of our proposed component toolkit and framework is demonstrated in two customized applications GeoWizard analysing multivariate energy usage data for Swedish municipalities and MD-Explorer exploring multivariate data using novel interactive ternary diagrams. We use parallel coordinates with embedded visual inquiry methods that serves as a visual control panel for dynamically linked and coordinated views. Finally, discoveries made during the visual exploration process can be captured and organized in a format for later recall and communication to others.

A web-enabled visualization toolkit for geovisual analytics

A framework and class library (GAV Flash) implemented in Adobe’s ActionScript is introduced, designed with the intention to significantly shorten the time and effort needed to develop customized web-enabled applications for geovisual analytics tasks. Through an atomic-layered component architecture, GAV Flash provides a collection of interactive geographic and information visualization representations for exploring high-dimensional spaces that are extended with motion behaviour. Versatile interaction methods are drawn from many data visualization research areas and optimized for dynamic web visualization of spatio-temporal and multivariate data. Based on layered-component thinking and the use of programming interface mechanism, the GAV Flash architecture is open and facilitates the creation of new or improved versions of existing components so that ideas can be tried out or optimized rapidly in a fully functional environment. GAV Flash is not only a tool for interactive visualization, it also supports storytelling around visual analytics in which visual representations serve not only as a discovery tool for individuals but also as a means to share stories among users fostering a social style of collaborative data analysis. A mechanism ‘snapshot’ for saving the explorative results of a reasoning process is introduced, which aids collaboration and publication of gained insight and knowledge embedded as dynamic visualizations in blogs or web pages with associative metadata or ‘storytelling’.

Integration of unsupervised clustering, interaction and parallel coordinates for the exploration of large multivariate data

Parallel coordinates are widely used in many applications for visualization of multivariate data. Because of the nature of parallel coordinates, the visualization technique is often used for data overview. However, when the number of tuples to be visualized becomes very large, this technique makes it difficult to distinguish the overall structure. In This work we present a novel technique which uses a classification approach, the self-organizing map (an unsupervised learning algorithm), to solve this problem by creating an initial clustering of the data. By initially only visualizing the resulting representational clusters, the inherited global structure can be shown. Using linked views and allowing the user to perform drill-down and filtering on these representations reveals the single data items without loss of context.

GeoAnalytics Tools Applied to Large Geospatial Datasets

Geovisual analytics focuses on finding location-related patterns and relationship. Many approaches exist but generally do not scale well with large spatial datasets. We propose three enhancements that facilitate scalable geovisual analytics of voluminous geospatial data based on geographic mapping coordinated and linked with parallel coordinates (PC): 1) texture-based geographic mapping that exploits GPU-based rendering performance applied to overview + detail views, 2) statistical methods embedded in PC, 3) aggregated dynamic grid maps that integrate with PC. In this context, we have extended our previous introduced psilaGeoAnalyticspsila Visualization (GAV) framework and class library with a novel implementation of the standard PC using an atomic layered component architecture that allows new ideas to be implemented and assessed without having to rewrite a complete functional PC component. We demonstrate our proposed enhancements applied to a large geospatial dataset containing more than 10,000 Swedish zip (postal) code regions described by more than three million (X, Y) boundary coordinates and includes many associated demographics and statistical attributes.

Interactive Quantification of Categorical Variables in Mixed Data Sets

Data sets containing a combination of categorical and continuous variables (mixed data sets) are difficult to analyse since no generalized similarity measure exists for categorical variables. Quantification of categorical variables makes it possible to represent this type of data using techniques designed for numerical data. This paper presents a quantification process of categorical variables in mixed data sets that incorporates information on relationships among the continuous variables into the process, as well as utilizing the domain knowledge of a user. An interactive visualization environment using parallel coordinates as a visual interface is provided, where the user is able to control the quantification process and analyse the result. The efficiency of the approach is demonstrated using two mixed data sets.

Visual user interface for PDAs

The need for visualisation applications developed for small handheld devices such as PDAs and intelligent mobiles are growing. A visual user interface VUI model based on zooming user interface techniques (ZUI), to adapt two complete different visualisation application areas; online brand shopping and flood warning system for PDAs, is presented. The online brand shopping was evaluated in a benchmark usability study comparing it to traditional PC-based eNet shopping. We also give a blueprint to inform researchers and software engineers about our experience in developing visualisation applications for PDAs with existing development platforms.

A web-enabled Geovisual Analytics tool applied to OECD Regional Data

Recent advances in web-enabled graphics technologies have the potential to make a dramatic impact on developing highly interactive Geovisual Analytics applications for the Internet. An emerging and challenging application domain is geovisualization of regional (sub-national) statistics. Higher integration driven by institutional processes and economic globalisation is eroding national borders and creating competition along regional lines in the world market. Sound information at sub-national level and benchmarking of regions across countries, therefore, has increased in importance in the policy agenda of many countries. In this paper, we introduce “OECD eXplorer” – an interactive tool for analyzing and communicating gained insights and discoveries about spatial-temporal and multivariate OECD regional data. This database is a potential treasure chest for policy-makers, researchers and citizens to gain a better understanding of a region’s structure and performance and to carry out analysis of territorial trends and disparities based on sound information comparable across countries. Many approaches and tools have been developed in spatial-related knowledge discovery but generally they do not scale well with dynamic visualization of larger spatial data on the Internet. In this context, we introduce a web-compliant Geovisual Analytics toolkit that supports a broad collection of functional components for analysis and validation, hypothesis generation, communicating and finally collaborating gained insights and knowledge based on a snapshot mechanism that captures, re-uses and shares task-related explorative events. An important ambition is to develop a generic highly interactive web “eXplorer” platform that can be the foundation for easy customization of similar dynamic web applications using different geographical boundaries and indicators and be publicly available. Given this global dimension, the dream of building a repository “statistical Wiki” of progress indicators, where experts and public users can use these generic tools to compare situations for two or more countries, regions or local communities, could be accomplished.