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  • 1.
    Jern, Mikael
    et al.
    Linköping University, Department of Science and Technology, Visual Information Technology and Applications (VITA). Linköping University, The Institute of Technology.
    Johansson, Sara
    Linköping University, Department of Science and Technology, Visual Information Technology and Applications (VITA). Linköping University, The Institute of Technology.
    Johansson, Jimmy
    Linköping University, Department of Science and Technology, Visual Information Technology and Applications (VITA). Linköping University, The Institute of Technology.
    Franzén, Johan
    Linköping University, Department of Science and Technology, Visual Information Technology and Applications (VITA). Linköping University, The Institute of Technology.
    The GAV Toolkit for Multiple Linked Views2007In: Fifth International Conference on Coordinated and Multiple Views in Exploratory Visualization, 2007. CMV '07., Los Alamitos, CA, USA: IEEE Computer Society, 2007, p. 85-97Conference paper (Refereed)
    Abstract [en]

    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.

  • 2.
    Jern, Mikael
    et al.
    Linköping University, Department of Science and Technology, Visual Information Technology and Applications (VITA). Linköping University, The Institute of Technology.
    Åström, Tobias
    Linköping University, Department of Science and Technology, Visual Information Technology and Applications (VITA). Linköping University, The Institute of Technology.
    Johansson, Sara
    Linköping University, Department of Science and Technology, Visual Information Technology and Applications (VITA). Linköping University, The Institute of Technology.
    GeoAnalytics Tools Applied to Large Geospatial Datasets2008In: Information Visualisation, 2008. IV '08. 12th International Conference, Los Alamitos, CA, USA: IEEE Computer Society, 2008, p. 362-372Conference paper (Refereed)
    Abstract [en]

    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.

  • 3. Order onlineBuy this publication >>
    Johansson Fernstad, Sara
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology.
    Algorithmically Guided Information Visualization: Explorative Approaches for High Dimensional, Mixed and Categorical Data2011Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Facilitated by the technological advances of the last decades, increasing amounts of complex data are being collected within fields such as biology, chemistry and social sciences. The major challenge today is not to gather data, but to extract useful information and gain insights from it. Information visualization provides methods for visual analysis of complex data but, as the amounts of gathered data increase, the challenges of visual analysis become more complex.

    This thesis presents work utilizing algorithmically extracted patterns as guidance during interactive data exploration processes, employing information visualization techniques. It provides efficient analysis by taking advantage of fast pattern identification techniques as well as making use of the domain expertise of the analyst. In particular, the presented research is concerned with the issues of analysing categorical data, where the values are names without any inherent order or distance; mixed data, including a combination of categorical and numerical data; and high dimensional data, including hundreds or even thousands of variables.

    The contributions of the thesis include a quantification method, assigning numerical values to categorical data, which utilizes an automated method to define category similarities based on underlying data structures, and integrates relationships within numerical variables into the quantification when dealing with mixed data sets. The quantification is incorporated in an interactive analysis pipeline where it provides suggestions for numerical representations, which may interactively be adjusted by the analyst. The interactive quantification enables exploration using commonly available visualization methods for numerical data. Within the context of categorical data analysis, this thesis also contributes the first user study evaluating the performance of what are currently the two main visualization approaches for categorical data analysis.

    Furthermore, this thesis contributes two dimensionality reduction approaches, which aim at preserving structure while reducing dimensionality, and provide flexible and user-controlled dimensionality reduction. Through algorithmic quality metric analysis, where each metric represents a structure of interest, potentially interesting variables are extracted from the high dimensional data. The automatically identified structures are visually displayed, using various visualization methods, and act as guidance in the selection of interesting variable subsets for further analysis. The visual representations furthermore provide overview of structures within the high dimensional data set and may, through this, aid in focusing subsequent analysis, as well as enabling interactive exploration of the full high dimensional data set and selected variable subsets. The thesis also contributes the application of algorithmically guided approaches for high dimensional data exploration in the rapidly growing field of microbiology, through the design and development of a quality-guided interactive system in collaboration with microbiologists.

    List of papers
    1. Interactive Quantification of Categorical Variables in Mixed Data Sets
    Open this publication in new window or tab >>Interactive Quantification of Categorical Variables in Mixed Data Sets
    2008 (English)In: Information Visualisation, 2008. IV '08. 12th International Conference / [ed] Ebad Banissi, Liz Stuart, Mikael Jern, Gennady Andrienko, Francis T. Marchese, Nasrullah Memon, Reda Alhajj, Theodor G Wyeld, Remo Aslak Burkhard, Georges Grinstein, Dennis Groth, Anna Ursyn, Carsten Maple, Anthony Faiola and Brock Craft, Los Alamitos, California: IEEE Computer Society, 2008, p. 3-10Conference paper, Published paper (Refereed)
    Abstract [en]

    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.

    Place, publisher, year, edition, pages
    Los Alamitos, California: IEEE Computer Society, 2008
    Series
    IEEE International Conference on Information Visualisation, ISSN 1550-6037
    Keywords
    Categorical data, mixed data, parallel coordinates, quantification, correspondence analysis, clustering
    National Category
    Media Engineering
    Identifiers
    urn:nbn:se:liu:diva-43480 (URN)10.1109/IV.2008.33 (DOI)000259178400001 ()73940 (Local ID)978-0-7695-3268-4 (ISBN)73940 (Archive number)73940 (OAI)
    Conference
    12th International Conference Information Visualisation, IV '08, London, UK, 9-11 July 2008
    Available from: 2009-10-10 Created: 2009-10-10 Last updated: 2014-04-22Bibliographically approved
    2. Visual Exploration of Categorical and Mixed Data Sets
    Open this publication in new window or tab >>Visual Exploration of Categorical and Mixed Data Sets
    2009 (English)In: Proceeding VAKD '09 Proceedings of the ACM SIGKDD Workshop on Visual Analytics and Knowledge Discovery: Integrating Automated Analysis with Interactive Exploration: Workshop on Visual Analytics and Knowledge Discovery, New York, USA: ACM Press, 2009, p. 21-29Conference paper, Published paper (Refereed)
    Abstract [en]

    For categorical data there does not exist any similarity measurewhich is as straight forward and general as the numericaldistance between numerical items. Due to this it is often difficultto analyse data sets including categorical variables or a combination of categorical and numerical variables (mixeddata sets). Quantification of categorical variables enablesanalysis using commonly used visual representations andanalysis techniques for numerical data. This paper presents a tool for exploratory analysis of categorical and mixed data, which uses a quantification process introduced in [Johansson2008]. The application enables analysis of mixed data sets by providingan environment for exploratory analysis using commonvisual representations in multiple coordinated views and algorithmic analysis that facilitates detection of potentially interesting patterns within combinations of categorical and numerical variables. The effectiveness of the quantificationprocess and of the features of the application is demonstratedthrough a case scenario.

    Place, publisher, year, edition, pages
    New York, USA: ACM Press, 2009
    Keywords
    Information visualization, visual exploration, quantification, categorical data, mixed data, data mining
    National Category
    Media Engineering
    Identifiers
    urn:nbn:se:liu:diva-25572 (URN)10.1145/1562849.1562852 (DOI)978-1-60558-670-0 (ISBN)
    Conference
    ACM SIGKDD Workshop on Visual Analytics and Knowledge DiscoveryACM SIGKDD Workshop on Visual Analytics and Knowledge Discovery, 28 June - 1 July, Paris, France
    Available from: 2009-10-08 Created: 2009-10-08 Last updated: 2011-12-05Bibliographically approved
    3. Visual Analysis of Mixed Data Sets Using Interactive Quantification
    Open this publication in new window or tab >>Visual Analysis of Mixed Data Sets Using Interactive Quantification
    2009 (English)In: ACM SIGKDD Explorations Newsletter, ISSN 1931-0145, Vol. 11, no 2, p. 29-38Article in journal (Refereed) Published
    Abstract [en]

    It is often diffcult to analyse data sets including a combi-nation of categorical and numerical variables (mixed datasets) since there does not exist any similarity measure whichis as straight forward and general as the numerical distancebetween numerical items. Quantication of categorical vari-ables enables analysis using commonly used visual represen-tations and analysis techniques for numerical data. Thispaper presents a tool for exploratory analysis of categoricaland mixed data which uses a quantication process intro-duced in [Johansson2008]. The application enables analysis of mixeddata sets by providing an environment for exploratory anal-ysis using common visual representations in multiple coordi-nated views and algorithmic analysis that facilitates detec-tion of potentially interesting patterns within combinationsof categorical and numerical variables. The generality andusefulness of the quantication process and of the featuresof the application is demonstrated through a case scenariousing a data set from the IEEE VAST 2008 Challenge.

    Place, publisher, year, edition, pages
    New York: ACM, 2009
    Keywords
    Information Visualization, Visual Analysis, Categorical Data, Quantification
    National Category
    Media Engineering
    Identifiers
    urn:nbn:se:liu:diva-60142 (URN)10.1145/1809400.1809406 (DOI)
    Available from: 2010-10-06 Created: 2010-10-06 Last updated: 2011-10-06
    4. A Task Based Performance Evaluation of Visualization Approaches for Categorical Data Analysis.
    Open this publication in new window or tab >>A Task Based Performance Evaluation of Visualization Approaches for Categorical Data Analysis.
    2011 (English)In: Proceedings - 15th International Conferenceon Information Visualisation, Los Alamitos, CA, USA: IEEE Computer Society, 2011, p. 80-89Conference paper, Published paper (Other academic)
    Abstract [en]

    Categorical data is common within many areas and efficient methods for analysis are needed. It is, however, often difficult to analyse categorical data since no general measure of similarity exists. One approach is to represent the categories with numerical values (quantification) prior to visualization using methods for numerical data. Another is to use visual representations specifically designed for categorical data. Although commonly used, very little guidance is available as to which method may be most useful for different analysis tasks. This paper presents an evaluation comparing the performance of employing quantification prior to visualization and visualization using a method designed for categorical data. It also provides a guidance as to which visualization approach is most useful in the context of two basic data analysis tasks: one related to similarity structures and one related to category frequency. The results strongly indicate that the quantification approach is most efficient for the similarity related task, whereas the visual representation designed for categorical data is most efficient for the task related to category frequency.

    Place, publisher, year, edition, pages
    Los Alamitos, CA, USA: IEEE Computer Society, 2011
    Keywords
    Categorical Data, Quantitative Evaluation, Usability Studies, Parallel Sets, Quantification
    National Category
    Media Engineering
    Identifiers
    urn:nbn:se:liu:diva-70855 (URN)10.1109/IV.2011.92 (DOI)978-1-4577-0868-8 (ISBN)
    Conference
    15th International Conference on Information Visualisation (IV), 2011 , 13-15 July, London, UK
    Available from: 2011-09-20 Created: 2011-09-20 Last updated: 2011-12-05
    5. Interactive Dimensionality Reduction Through User-defined Combinations of Quality Metrics
    Open this publication in new window or tab >>Interactive Dimensionality Reduction Through User-defined Combinations of Quality Metrics
    2009 (English)In: IEEE Transactions on Visualization and Computer Graphics, ISSN 1077-2626, E-ISSN 1941-0506, Vol. 15, no 6, p. 993-1000Article in journal (Refereed) Published
    Abstract [en]

    Multivariate data sets including hundreds of variables are increasingly common in many application areas. Most multivariate visualization techniques are unable to display such data effectively, and a common approach is to employ dimensionality reduction prior to visualization. Most existing dimensionality reduction systems focus on preserving one or a few significant structures in data. For many analysis tasks, however, several types of structures can be of high significance and the importance of a certain structure compared to the importance of another is often task-dependent. This paper introduces a system for dimensionality reduction by combining user-defined quality metrics using weight functions to preserve as many important structures as possible. The system aims at effective visualization and exploration of structures within large multivariate data sets and provides enhancement of diverse structures by supplying a range of automatic variable orderings. Furthermore it enables a quality-guided reduction of variables through an interactive display facilitating investigation of trade-offs between loss of structure and the number of variables to keep. The generality and interactivity of the system is demonstrated through a case scenario.

    Keywords
    Dimensionality reduction, interactivity, quality metrics, variable ordering
    National Category
    Media Engineering
    Identifiers
    urn:nbn:se:liu:diva-53092 (URN)10.1109/TVCG.2009.153 (DOI)19834164 (PubMedID)
    Available from: 2010-01-15 Created: 2010-01-15 Last updated: 2017-12-12Bibliographically approved
    6. Visual Exploration of Microbial Populations
    Open this publication in new window or tab >>Visual Exploration of Microbial Populations
    Show others...
    2011 (English)In: IEEE Symposium on Biological Data Visualization, 2011, p. 127-134Conference paper, Published paper (Other academic)
    Abstract [en]

    Studies of the ecology of microbial populations are increasingly common within many research areas as the field of microbiomics develops rapidly. The study of the ecology in sampled microbial populations generates high dimensional data sets. Although many analysis methods are available for examination of such data, a tailored tool was required to fulfill the need of interactivity and flexibility for microbiologists. In this paper, MicrobiVis is presented. It is a tool for visual exploration and interactive analysis of microbiomic populations. MicrobiVis has been designed in close collaboration with end users. It extends previous interactive systems for explorative dimensionality reduction by including a range of domain relevant features. It contributes a flexible and explorative dimensionality reduction as well as a visual and interactive environment for examination of data subsets. By combining information visualization and methods based on analytic tasks common in microbiology as a means for gaining new and relevant insights. The utility of MicrobiVis is demonstrated through a use case describinghow a microbiologist may use the system for a visual analysis of amicrobial data set. Its usability and potential is indicated throughpositive feedback from the current end users.

    Keywords
    Dimensionality reduction, information visualization, explorative analysis, microbiomics, bacterial population
    National Category
    Media Engineering
    Identifiers
    urn:nbn:se:liu:diva-70852 (URN)10.1109/BioVis.2011.6094057 (DOI)978-1-4673-0003-2 (ISBN)
    Conference
    BioVis 2011 - the 1st IEEE Symposium on Biological Data Visualization, 23-24 October 2011, Providence, RI, USA
    Available from: 2011-09-20 Created: 2011-09-20 Last updated: 2014-10-08Bibliographically approved
    7. Quality Based Guidance for Exploratory Dimensionality Reduction
    Open this publication in new window or tab >>Quality Based Guidance for Exploratory Dimensionality Reduction
    2013 (English)In: Information Visualization, ISSN 1473-8716, E-ISSN 1473-8724, Vol. 12, no 1, p. 44-64Article in journal (Refereed) Published
    Abstract [en]

    High dimensional data sets containing hundreds of variables are difficult to explore, since traditional visualization methods often are unable to represent such data effectively. Dimensionality reduction is commonly employed prior to visualization to address this difficulty, and numerous dimensionality reduction methods are available. However, few dimensionality reduction approaches take the importance of several structures into account and few provide an overview of structures existing in the full high dimensional data set. For exploratory analysis, as well as for many other tasks, several structures may be of interest and exploration of the full high dimensional data set without reduction may also be desirable.This paper presents methods for exploratory analysis and interactive dimensionality reduction, where automated methods are employed to analyse and rank the variables using a range of quality metrics, providing one or more measures of ‘interestingness’ for individual variables. Through ranking, a single value of interestingness is obtained based on several quality metrics which is usable as a threshold for the most interesting variables. An interactive environment is presented where the user is provided many possibilities to explore and gain understanding of the structures within the high dimensional data set, all based on quality metrics and ranking. Guided by this, the analyst can explore the high dimensional data set and select interactively a subset of the potentially most interesting variables, employing various interactive methods for dimensionality reduction. The effectiveness and usefulness of the system is demonstrated through a use-case analysing data from a DNA sequence-based study of bacterial populations.

    Place, publisher, year, edition, pages
    Palgrave Macmillan / SAGE Publications (UK and US), 2013
    Keywords
    High-dimensional data, dimensionality reduction, quality metrics, visual exploration, interactive visual analysis
    National Category
    Engineering and Technology
    Identifiers
    urn:nbn:se:liu:diva-70859 (URN)10.1177/1473871612460526 (DOI)000315073700003 ()
    Note

    Funding Agencies|Unilever Discover Port Sunlight||Swedish Research Council in the Linnaeus Centre CADICS||Visualization Programme||

    Available from: 2011-09-20 Created: 2011-09-20 Last updated: 2017-12-08Bibliographically approved
    Download full text (pdf)
    Algorithmically Guided Information Visualization: Explorative Approaches for High Dimensional, Mixed and Categorical Data
    Download (pdf)
    omslag
  • 4.
    Johansson Fernstad, Sara
    et al.
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology.
    Johansson, Jimmy
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology.
    A Task Based Performance Evaluation of Visualization Approaches for Categorical Data Analysis.2011In: Proceedings - 15th International Conferenceon Information Visualisation, Los Alamitos, CA, USA: IEEE Computer Society, 2011, p. 80-89Conference paper (Other academic)
    Abstract [en]

    Categorical data is common within many areas and efficient methods for analysis are needed. It is, however, often difficult to analyse categorical data since no general measure of similarity exists. One approach is to represent the categories with numerical values (quantification) prior to visualization using methods for numerical data. Another is to use visual representations specifically designed for categorical data. Although commonly used, very little guidance is available as to which method may be most useful for different analysis tasks. This paper presents an evaluation comparing the performance of employing quantification prior to visualization and visualization using a method designed for categorical data. It also provides a guidance as to which visualization approach is most useful in the context of two basic data analysis tasks: one related to similarity structures and one related to category frequency. The results strongly indicate that the quantification approach is most efficient for the similarity related task, whereas the visual representation designed for categorical data is most efficient for the task related to category frequency.

  • 5.
    Johansson Fernstad, Sara
    et al.
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology.
    Johansson, Jimmy
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology.
    Adams, Suzi
    Unilever Discover Port Sunlight, UK.
    Shaw, Jane
    Unilever Discover Port Sunlight, UK.
    Taylor, David
    Unilever Discover Port Sunlight, UK.
    Visual Exploration of Microbial Populations2011In: IEEE Symposium on Biological Data Visualization, 2011, p. 127-134Conference paper (Other academic)
    Abstract [en]

    Studies of the ecology of microbial populations are increasingly common within many research areas as the field of microbiomics develops rapidly. The study of the ecology in sampled microbial populations generates high dimensional data sets. Although many analysis methods are available for examination of such data, a tailored tool was required to fulfill the need of interactivity and flexibility for microbiologists. In this paper, MicrobiVis is presented. It is a tool for visual exploration and interactive analysis of microbiomic populations. MicrobiVis has been designed in close collaboration with end users. It extends previous interactive systems for explorative dimensionality reduction by including a range of domain relevant features. It contributes a flexible and explorative dimensionality reduction as well as a visual and interactive environment for examination of data subsets. By combining information visualization and methods based on analytic tasks common in microbiology as a means for gaining new and relevant insights. The utility of MicrobiVis is demonstrated through a use case describinghow a microbiologist may use the system for a visual analysis of amicrobial data set. Its usability and potential is indicated throughpositive feedback from the current end users.

  • 6.
    Johansson Fernstad, Sara
    et al.
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology.
    Shaw, Jane
    Unilever Discover Port Sunlight, UK.
    Johansson, Jimmy
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology.
    Quality Based Guidance for Exploratory Dimensionality Reduction2013In: Information Visualization, ISSN 1473-8716, E-ISSN 1473-8724, Vol. 12, no 1, p. 44-64Article in journal (Refereed)
    Abstract [en]

    High dimensional data sets containing hundreds of variables are difficult to explore, since traditional visualization methods often are unable to represent such data effectively. Dimensionality reduction is commonly employed prior to visualization to address this difficulty, and numerous dimensionality reduction methods are available. However, few dimensionality reduction approaches take the importance of several structures into account and few provide an overview of structures existing in the full high dimensional data set. For exploratory analysis, as well as for many other tasks, several structures may be of interest and exploration of the full high dimensional data set without reduction may also be desirable.This paper presents methods for exploratory analysis and interactive dimensionality reduction, where automated methods are employed to analyse and rank the variables using a range of quality metrics, providing one or more measures of ‘interestingness’ for individual variables. Through ranking, a single value of interestingness is obtained based on several quality metrics which is usable as a threshold for the most interesting variables. An interactive environment is presented where the user is provided many possibilities to explore and gain understanding of the structures within the high dimensional data set, all based on quality metrics and ranking. Guided by this, the analyst can explore the high dimensional data set and select interactively a subset of the potentially most interesting variables, employing various interactive methods for dimensionality reduction. The effectiveness and usefulness of the system is demonstrated through a use-case analysing data from a DNA sequence-based study of bacterial populations.

  • 7.
    Johansson, Sara
    Linköping University, Department of Science and Technology, Visual Information Technology and Applications (VITA). Linköping University, The Institute of Technology.
    Visual Exploration of Categorical and Mixed Data Sets2009In: Proceeding VAKD '09 Proceedings of the ACM SIGKDD Workshop on Visual Analytics and Knowledge Discovery: Integrating Automated Analysis with Interactive Exploration: Workshop on Visual Analytics and Knowledge Discovery, New York, USA: ACM Press, 2009, p. 21-29Conference paper (Refereed)
    Abstract [en]

    For categorical data there does not exist any similarity measurewhich is as straight forward and general as the numericaldistance between numerical items. Due to this it is often difficultto analyse data sets including categorical variables or a combination of categorical and numerical variables (mixeddata sets). Quantification of categorical variables enablesanalysis using commonly used visual representations andanalysis techniques for numerical data. This paper presents a tool for exploratory analysis of categorical and mixed data, which uses a quantification process introduced in [Johansson2008]. The application enables analysis of mixed data sets by providingan environment for exploratory analysis using commonvisual representations in multiple coordinated views and algorithmic analysis that facilitates detection of potentially interesting patterns within combinations of categorical and numerical variables. The effectiveness of the quantificationprocess and of the features of the application is demonstratedthrough a case scenario.

  • 8.
    Johansson, Sara
    et al.
    Linköping University, Department of Science and Technology, Visual Information Technology and Applications (VITA). Linköping University, The Institute of Technology.
    Jern, Mikael
    Linköping University, Department of Science and Technology, Visual Information Technology and Applications (VITA). Linköping University, The Institute of Technology.
    GeoAnalytics Visual Inquiry and Filtering Tools in Parallel Coordinates Plots2007In: Proceedings of the 15th annual ACM international symposium on Advances in geographic information systems, New York, NY, USA: Association for Computing Machinery (ACM), 2007, p. Art.no. 33-Conference paper (Refereed)
    Abstract [en]

    The complex nature of social and scientific spatial-temporal multivariate data calls for highly interactive integrated information visualization (InfoVis) and geo-visualization (GeoVis) tools and applications. Our research concentrates on improving visual user interface (VUI) methods and extending existing visual representation techniques.

    In this paper, we introduce an enhanced parallel coordinates (PC) component, integrating statistical methods for visual inquiries and filtering of spatial and multivariate data, a component that provides a fast and intuitive understanding of the distribution of data and of the relationships between data attributes. This enhanced PC component can also serve as a visual control panel for dynamically steering applications with multiple-linked and coordinated InfoVis and GeoVis views.

    The effectiveness of our proposed extended PC component is demonstrated in a tailor-made application called GeoWizard Lite which is based on our 'GeoAnalytics' visualization (GAV) framework. The GAV framework is based on the principles behind Visual Analytics (VA) and the developement is aiming to facilitate the extraction of complex patterns in large data sets via visual interaction. In this paper we also demonstrate the synergy between InfoVis and GeoVis methods through this case study exploring social science data from the Statistics Sweden data bases.

  • 9.
    Johansson, Sara
    et al.
    Linköping University, Department of Science and Technology, Visual Information Technology and Applications (VITA). Linköping University, The Institute of Technology.
    Jern, Mikael
    Linköping University, Department of Science and Technology, Visual Information Technology and Applications (VITA). Linköping University, The Institute of Technology.
    Johansson, Jimmy
    Linköping University, Department of Science and Technology, Visual Information Technology and Applications (VITA). Linköping University, The Institute of Technology.
    Interactive Quantification of Categorical Variables in Mixed Data Sets2008In: Information Visualisation, 2008. IV '08. 12th International Conference / [ed] Ebad Banissi, Liz Stuart, Mikael Jern, Gennady Andrienko, Francis T. Marchese, Nasrullah Memon, Reda Alhajj, Theodor G Wyeld, Remo Aslak Burkhard, Georges Grinstein, Dennis Groth, Anna Ursyn, Carsten Maple, Anthony Faiola and Brock Craft, Los Alamitos, California: IEEE Computer Society, 2008, p. 3-10Conference paper (Refereed)
    Abstract [en]

    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.

  • 10.
    Johansson, Sara
    et al.
    Linköping University, Department of Science and Technology, Visual Information Technology and Applications (VITA). Linköping University, The Institute of Technology.
    Johansson, Jimmy
    Linköping University, Department of Science and Technology, Visual Information Technology and Applications (VITA). Linköping University, The Institute of Technology.
    Interactive Dimensionality Reduction Through User-defined Combinations of Quality Metrics2009In: IEEE Transactions on Visualization and Computer Graphics, ISSN 1077-2626, E-ISSN 1941-0506, Vol. 15, no 6, p. 993-1000Article in journal (Refereed)
    Abstract [en]

    Multivariate data sets including hundreds of variables are increasingly common in many application areas. Most multivariate visualization techniques are unable to display such data effectively, and a common approach is to employ dimensionality reduction prior to visualization. Most existing dimensionality reduction systems focus on preserving one or a few significant structures in data. For many analysis tasks, however, several types of structures can be of high significance and the importance of a certain structure compared to the importance of another is often task-dependent. This paper introduces a system for dimensionality reduction by combining user-defined quality metrics using weight functions to preserve as many important structures as possible. The system aims at effective visualization and exploration of structures within large multivariate data sets and provides enhancement of diverse structures by supplying a range of automatic variable orderings. Furthermore it enables a quality-guided reduction of variables through an interactive display facilitating investigation of trade-offs between loss of structure and the number of variables to keep. The generality and interactivity of the system is demonstrated through a case scenario.

  • 11.
    Johansson, Sara
    et al.
    Linköping University, Department of Science and Technology, Visual Information Technology and Applications (VITA). Linköping University, The Institute of Technology.
    Johansson, Jimmy
    Linköping University, Department of Science and Technology, Visual Information Technology and Applications (VITA). Linköping University, The Institute of Technology.
    Visual Analysis of Mixed Data Sets Using Interactive Quantification2009In: ACM SIGKDD Explorations Newsletter, ISSN 1931-0145, Vol. 11, no 2, p. 29-38Article in journal (Refereed)
    Abstract [en]

    It is often diffcult to analyse data sets including a combi-nation of categorical and numerical variables (mixed datasets) since there does not exist any similarity measure whichis as straight forward and general as the numerical distancebetween numerical items. Quantication of categorical vari-ables enables analysis using commonly used visual represen-tations and analysis techniques for numerical data. Thispaper presents a tool for exploratory analysis of categoricaland mixed data which uses a quantication process intro-duced in [Johansson2008]. The application enables analysis of mixeddata sets by providing an environment for exploratory anal-ysis using common visual representations in multiple coordi-nated views and algorithmic analysis that facilitates detec-tion of potentially interesting patterns within combinationsof categorical and numerical variables. The generality andusefulness of the quantication process and of the featuresof the application is demonstrated through a case scenariousing a data set from the IEEE VAST 2008 Challenge.

  • 12.
    Johansson, Sara
    et al.
    Linköping University, Department of Science and Technology, Visual Information Technology and Applications (VITA). Linköping University, The Institute of Technology.
    Knaving, Kristina
    Linköping University, Department of Science and Technology, Visual Information Technology and Applications (VITA). Linköping University, The Institute of Technology.
    Lane, Amanda
    Unilever R&D, Port Sunlight, United Kingdom.
    Jern, Mikael
    Linköping University, Department of Science and Technology, Visual Information Technology and Applications (VITA). Linköping University, The Institute of Technology.
    Johansson, Jimmy
    Linköping University, Department of Science and Technology, Visual Information Technology and Applications (VITA). Linköping University, The Institute of Technology.
    Interactive Exploration of Ingredient Mixtures Using Multiple Coordinated Views2009In: Proceedings Information Visualization, IV 2009, Los Alamitos, CA, USA: IEEE Computer Society, 2009, p. 210-218Conference paper (Refereed)
    Abstract [en]

    The complex nature of multivariate data sets calls forhigh interactive performance and intuitive metaphors. Aspecific type of multivariate data is where the variables sum up to a constant, here defined as multicomponent data.This application paper presents an interactive applicationfor analysis of modelled multicomponent data. The aim isto find high performance variable combinations that fulfil some requested properties. The application is basedon coordinated views that include parallel coordinates, a ternary diagram, a 2D scatter plot and a line plot. It supports numerous interaction techniques enabling fast analysisof complex patterns in multicomponent data sets. The application is developed in collaboration with researchers within the fields of statistics and chemistry. An informal usability evaluation indicates that the interactive nature ofthe application clearly facilitates the analysis process.

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