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Mobility Graphs: visual analysis of Mass Mobility Dynamics via Spatio-Temporal Graphs and Clustering

IEEE TRANSACTIONS ON visualIZATION AND COMPUTER GRAPHICS 2016年第1期22卷 11-20页

作者： von Landesberger, Tatiana Brodkorb, Felix Roskosch, Philipp Andrienko, Natalia Andrienko, Gennady Kerren, Andreas Tech Univ Darmstadt Darmstadt Germany Fraunhofer IAIS Bonn Germany City Univ London London EC1V 0HB England Linnaeus Univ Vaxjo Sweden

Learning more about people mobility is an important task for official decision makers and urban planners. Mobility data sets characterize the variation of the presence of people in different places over time as well as movements (or flows) of people between the places. The analysis of mobility data is challenging due to the need to analyze and compare spatial situations (i.e., presence and flows of people at certain time moments) and to gain an understanding of the spatio-temporal changes (variations of situations over time). Traditional flow visualizations usually fail due to massive clutter. Modern approaches offer limited support for investigating the complex variation of the movements over longer time periods. We propose a visual analytics methodology that solves these issues by combined spatial and temporal simplifications. We have developed a graph-based method, called Mobility Graphs, which reveals movement patterns that were occluded in flow maps. Our method enables the visual representation of the spatio-temporal variation of movements for long time series of spatial situations originally containing a large number of intersecting flows. The interactive system supports data exploration from various perspectives and at various levels of detail by interactive setting of clustering parameters. The feasibility our approach was tested on aggregated mobility data derived from a set of geolocated Twitter posts within the Greater London city area and mobile phone call data records in Abidjan, Ivory Coast. We could show that Mobility Graphs support the identification of regular daily and weekly movement patterns of resident population.

关键词： visual analytics movement data networks graphs temporal aggregation spatial aggregation flows clustering

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A Framework for Exploring Multidimensional data with 3D Projections

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COMPUTER GRAPHICS FORUM 2011年第3期30卷 1111-1120页

作者： Poco, J. Etemadpour, R. Paulovich, F. v. Long, T. v. Rosenthal, P. Oliveira, M. C. F. Linsen, L. Minghim, R. Univ Sao Paulo Sao Carlos SP Brazil Univ Utah Salt Lake City UT 84112 USA Jacobs Univ Bremen Germany Univ Transport & Commun Hanoi Hanoi Vietnam Tech Univ Chemnitz Chemnitz Germany

visualization of high-dimensional data requires a mapping to a visual space. Whenever the goal is to preserve similarity relations a frequent strategy is to use 2D projections, which afford intuitive interactive exploration, e. g., by users locating and selecting groups and gradually drilling down to individual objects. In this paper, we propose a framework for projecting high-dimensional data to 3D visual spaces, based on a generalization of the Least-Square Projection (LSP). We compare projections to 2D and 3D visual spaces both quantitatively and through a user study considering certain exploration tasks. The quantitative analysis confirms that 3D projections outperform 2D projections in terms of precision. The user study indicates that certain tasks can be more reliably and confidently answered with 3D projections. Nonetheless, as 3D projections are displayed on 2D screens, interaction is more difficult. Therefore, we incorporate suitable interaction functionalities into a framework that supports 3D transformations, predefined optimal 2D views, coordinated 2D and 3D views, and hierarchical 3D cluster definition and exploration. For visually encoding data clusters in a 3D setup, we employ color coding of projected data points as well as four types of surface renderings. A second user study evaluates the suitability of these visual encodings. Several examples illustrate the framework's applicability for both visual exploration of multidimensional abstract (non-spatial) data as well as the feature space of multi-variate spatial data.

关键词： MULTIDIMENSIONAL databases INFORMATION visualization LEAST squares THREE-dimensional imaging IMAGE analysis

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A visual Analytics Framework for Microblog data analysis at Multiple Scales of Aggregation

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COMPUTER GRAPHICS FORUM 2016年第3期35卷 441-450页

作者： Zhang, Jiawei Ahlbrand, Benjamin Malik, Abish Chae, Junghoon Min, Zhiyu Ko, Sungahn Ebert, David S. Purdue Univ W Lafayette IN 47907 USA Univ Sci & Technol China Hefei Peoples R China Ulsan Natl Inst Sci & Technol Ulsan South Korea

Real-time microblogs can be utilized to provide situational awareness during emergency and disaster events. However, the utilization of these datasets requires the decision makers to perform their exploration and analysis across a range of data scales from local to global, while maintaining a cohesive thematic context of the transition between the different granularity levels. The exploration of different information dimensions at the varied data and human scales remains to be a non-trivial task. To this end, we present a visual analytics situational awareness environment that supports the real-time exploration of microblog data across multiple scales of analysis. We classify microblogs based on a fine-grained, crisis-related categorization approach, and visualize the spatiotemporal evolution of multiple categories by coupling a spatial lens with a glyph-based visual design. We propose a transparency-based spatial context preserving technique that maintains a smooth transition between different spatial scales. To evaluate our system, we conduct user studies and provide domain expert feedback.

关键词： Categories and Subject Descriptors (according to ACM CCS): I.3.3 [Computer Graphics]: visual Analytics Social Media analysis Disaster Management&mdash

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A Graph Algebra for Scalable visual Analytics

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IEEE COMPUTER GRAPHICS AND APPLICATIONS 2012年第4期32卷 26-33页

作者： Shaverdian, Anna A. Zhou, Hao Michailidis, George Jagadish, Hosagrahar v. Univ Michigan Dept Elect Engn & Comp Sci Ann Arbor MI 48109 USA

visual analytics (vA), which combines analytical techniques with advanced visualization features, is fast becoming a standard tool for extracting information from graph data. Researchers have developed many tools for this purpose, suggesting a need for formal methods to guide these tools\' creation. Increased data demands on computing requires redesigning vA tools to consider performance and reliability in the context of analysis of exascale datasets. Furthermore, visual analysts need a way to document their analyses for reuse and results justification. A vA graph framework encapsulated in a graph algebra helps address these needs. Its atomic operators include selection and aggregation. The framework employs a visual operator and supports dynamic attributes of data to enable scalable visual exploration of data.

关键词： Algebra data visualization Educational institutions Image color analysis visual analytics Xenon computer graphics exascale extreme-scale visual analytics graph algebra visual analytics visual analytics data visualisation COMPUTER GRAPHICS Xenon image colour analysis Algebra Educational institutions

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visual Analytics for the exploration of Tumor Tissue Characterization

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COMPUTER GRAPHICS FORUM 2015年第3期34卷 11-20页

作者： Raidou, R. G. van der Heide, U. A. Dinh, C. v. Ghobadi, G. Kallehauge, J. F. Breeuwer, M. vilanova, A. Eindhoven Univ Technol NL-5600 MB Eindhoven Netherlands Antoni van Leeuwenhoek Hosp Depat Radiotherapy Netherlands Canc Inst Amsterdam Netherlands Aarhus Univ Dept Med Phys DK-8000 Aarhus C Denmark Philips Healthcare Best Best Netherlands Delft Univ Technol NL-2600 AA Delft Netherlands

Tumors are heterogeneous tissues consisting of multiple regions with distinct characteristics. Characterization of these intra-tumor regions can improve patient diagnosis and enable a better targeted treatment. Ideally, tissue characterization could be performed non-invasively, using medical imaging data, to derive per voxel a number of features, indicative of tissue properties. However, the high dimensionality and complexity of this imaging-derived feature space is prohibiting for easy exploration and analysis - especially when clinical researchers require to associate observations from the feature space to other reference data, e.g., features derived from histopathological data. Currently, the exploratory approach used in clinical research consists of juxtaposing these data, visually comparing them and mentally reconstructing their relationships. This is a time consuming and tedious process, from which it is difficult to obtain the required insight. We propose a visual tool for: (1) easy exploration and visual analysis of the feature space of imaging-derived tissue characteristics and (2) knowledge discovery and hypothesis generation and confirmation, with respect to reference data used in clinical research. We employ, as central view, a 2D embedding of the imaging-derived features. Multiple linked interactive views provide functionality for the exploration and analysis of the local structure of the feature space, enabling linking to patient anatomy and clinical reference data. We performed an initial evaluation with ten clinical researchers. All participants agreed that, unlike current practice, the proposed visual tool enables them to identify, explore and analyze heterogeneous intra-tumor regions and particularly, to generate and confirm hypotheses, with respect to clinical reference data.

关键词： Categories and Subject Descriptors (according to ACM CCS): I.3.8 [Computer Graphics]: Applications Applications: J.3 [Computer Applications]: Life and Medical Sciences-Life and Medical Sciences

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Interactive interaction plot Supporting parameter space exploration in a design phase

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visual COMPUTER 2015年第6-8期31卷 1055-1065页

作者： Splechtna, Rainer Elshehaly, Mai Gracanin, Denis Duras, Mario Buehler, Katja Matkovic, Kresimir VRVis Res Ctr Vienna Austria Virginia Tech Ctr Human Comp Interact Blacksburg VA USA Virginia Tech Dept Comp Sci Blacksburg VA USA AVL AST Doo Zagreb Croatia

Design of experiments (DOE) is the study of how to vary control parameters to efficiently design and evaluate experiments. Main effects plot and interaction plot are two data views often used to explore differences between mean values and interactions between the DOE parameters but they are mostly limited to two parameters. We propose a new data view, interactive interaction plot, that supports exploration and analysis of high-dimensional interactions between parameters. The data view is integrated within a coordinated multiple views system. We describe the new data view using an Olympic medals data set. We also describe a case study dealing with initial selection of hybrid vehicle components. very positive feedback from automotive domain experts demonstrates the usefulness of the newly proposed approach.

关键词： Interactive visual analysis Interaction plot Main effects plot Design of experiments

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Network Narratives: data Tours for visual Network exploration and analysis 23

Network Narratives: Data Tours for Visual Network Exploratio...

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CHI conference on Human Factors in Computing Systems (CHI)

作者： Li, Wenchao Schottler, Sarah Scott-Brown, James Wang, Yun Chen, Siming Qu, Huamin Bach, Benjamin Hong Kong Univ Sci & Technol Hong Kong Peoples R China Univ Edinburgh Edinburgh Midlothian Scotland Microsoft Res Asia Beijing Peoples R China Fudan Univ Shanghai Peoples R China

ISBN: (纸本)9781450394215

This paper introduces semi-automatic data tours to aid the exploration of complex networks. Exploring networks requires significant effort and expertise and can be time-consuming and challenging. Distinct from guidance and recommender systems for visual analytics, we provide a set of goal-oriented tours for network overview, ego-network analysis, community exploration, and other tasks. Based on interviews with five network analysts, we developed a user interface (NetworkNarratives) and 10 example tours. The interface allows analysts to navigate an interactive slideshow featuring facts about the network using visualizations and textual annotations. On each slide, an analyst can freely explore the network and specify nodes, links, or subgraphs as seed elements for follow-up tours. Two studies, comprising eight expert and 14 novice analysts, show that data tours reduce exploration effort, support learning about network exploration, and can aid the dissemination of analysis results. NetworkNarratives is available online, together with detailed illustrations for each tour.

关键词： Guided exploration network visualization

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Local Affine Multidimensional Projection

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IEEE TRANSACTIONS ON visualIZATION AND COMPUTER GRAPHICS 2011年第12期17卷 2563-2571页

作者： Joia, Paulo Paulovich, Fernando v. Coimbra, Danilo Cuminato, Jose Alberto Nonato, Luis Gustavo Univ Sao Paulo BR-05508 Sao Paulo Brazil

Multidimensional projection techniques have experienced many improvements lately, mainly regarding computational times and accuracy. However, existing methods do not yet provide flexible enough mechanisms for visualization-oriented fully interactive applications. This work presents a new multidimensional projection technique designed to be more flexible and versatile than other methods. This novel approach, called Local Affine Multidimensional Projection (LAMP), relies on orthogonal mapping theory to build accurate local transformations that can be dynamically modified according to user knowledge. The accuracy, flexibility and computational efficiency of LAMP is confirmed by a comprehensive set of comparisons. LAMP's versatility is exploited in an application which seeks to correlate data that, in principle, has no connection as well as in visual exploration of textual documents.

关键词： Multidimensional Projection High Dimensional data visual data Mining

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From visual exploration to Storytelling and Back Again

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COMPUTER GRAPHICS FORUM 2016年第3期35卷 491-500页

作者： Gratzl, S. Lex, A. Gehlenborg, N. Cosgrove, N. Streit, M. Johannes Kepler Univ Linz Linz Austria Univ Utah Salt Lake City UT 84112 USA Harvard Med Sch Boston MA USA

The primary goal of visual data exploration tools is to enable the discovery of new insights. To justify and reproduce insights, the discovery process needs to be documented and communicated. A common approach to documenting and presenting findings is to capture visualizations as images or videos. Images, however, are insufficient for telling the story of a visual discovery, as they lack full provenance information and context. videos are difficult to produce and edit, particularly due to the non-linear nature of the exploratory process. Most importantly, however, neither approach provides the opportunity to return to any point in the exploration in order to review the state of the visualization in detail or to conduct additional analyses. In this paper we present CLUE (Capture, Label, Understand, Explain), a model that tightly integrates data exploration and presentation of discoveries. Based on provenance data captured during the exploration process, users can extract key steps, add annotations, and author vistories, visual stories based on the history of the exploration. These vistories can be shared for others to view, but also to retrace and extend the original analysis. We discuss how the CLUE approach can be integrated into visualization tools and provide a prototype implementation. Finally, we demonstrate the general applicability of the model in two usage scenarios: a Gapminder-inspired visualization to explore public health data and an example from molecular biology that illustrates how vistories could be used in scientific journals.

关键词： Categories and Subject Descriptors (according to ACM CCS): H.5.2 [Information Systems]: Information Interfaces and Presentation&mdash User Interfaces-Graphical user interfaces

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Geographically weighted visualization: Interactive graphics for scale-varying exploratory analysis

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IEEE TRANSACTIONS ON visualIZATION AND COMPUTER GRAPHICS 2007年第6期13卷 1161-1168页

作者： Dykes, Jason Brunsdon, Chris City Univ London Dept Informat Sci London EC1V 0HB England Univ Leicester Dept Geog Leicester LE1 7RH Leics England

We introduce a series of geographically weighted (GW) interactive graphics, or geowigs, and use them to explore spatial relationships at a range of scales. We visually encode information about geographic and statistical proximity and variation in novel ways through gw-choropleth maps, multivariate gw-boxplots, gw-shading and scalograms. The new graphic types reveal information about GW statistics at several scales concurrently. We impernent these views in prototype software containing dynamic links and GW interactions that encourage exploration and refine them to consider directional geographies. An informal evaluation uses interactive GW techniques to consider Guerry's clataset of 'moral statistics', casting doubt on correlations originally proposed through visual analysis, revealing new local anomalies and suggesting multivariate geographic relationships. Few attempts at visually synthesising geography with multivariate statistical values at multiple scales have been reported. The geowigs proposed here provide informative representations of multivariate local variation, particularly when combined with interactions that coordinate views and result in gw-shading. We argue that they are widely applicable to area and point-based geographic data and provide a set of methods to support visual analysis using GW statistics through which the effects of geography can be explored at multiple scales.

关键词： geographical weighting exploratory data analysis scale multivariate directional interaction coordinated views

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