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multivariate visualization for atmospheric pollution

JOURNAL OF visualization 2019年第6期22卷 1093-1105页

作者： Lu, Daying Ge, Yao Wang, Laihua Zhu, Dengming Wang, Zhaoqi Yuan, Xiaoru Qufu Normal Univ Coll Software Qufu Shandong Peoples R China Chinese Acad Sci Inst Comp Technol Beijing Peoples R China Peking Univ Key Lab Machine Percept Beijing Peoples R China

multivariate visualization for atmospheric pollution is a challenging research topic. Appropriate algorithms and data structures based on modern graphics hardware are used to obtain high performance. 3D visualization of the atmospheric wind field and pollutant concentrations can easily result in visual perception problems such as occlusion and cluttering even artifacts. To solve the above issues, a K-means clustering technique is used in combination with a similarity metric between streamlines based on an iterative closest point method to cluster the initial streamlines. A small set of streamlines is then selected to represent the prominent structure of the wind field. The proper illumination model and the depth sorting method reduce the inter-occlusion between streamlines and isosurfaces to show much clearer wind field pattern and important features effectively. The atmospheric pollution data set is employed to evaluate the proposed algorithm framework.

关键词： multivariate visualization Atmospheric pollution Occlusion Streamline Isosurface

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multivariate visualization techniques in statistical process monitoring and their applications to semiconductor manufacturing

Multivariate visualization techniques in statistical process...

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Conference on Data Analysis and Modeling for Process Control III

作者： He, Q. Peter Span LLC Adv Proc Control Austin TX 78741 USA

ISBN: (纸本)0819461989

In today's semiconductor industry, massive amount of data are easily made available in sensor equipped and computer controlled processes, but at the same time, the visualization of high dimensional data has been difficult. This work gives an overview of the most commonly used multivariate visualization techniques for the visualization of high dimensional data. The visualization of process dynamics in the original variable space is discussed. This work also presents visualization of data clusters in the transformed space using projection methods, such as principal component analysis (PCA), class preserving projection (CPP) and methods proposed in this work based on Fisher discriminant analysis (FDA), and support vector machines (SVM), as well as the discussion of the visualization of the process dynamics in the transformed low dimensional space. A rapid thermal annealing process data set is used through the paper as the example data set for comparison of various visualization techniques.

关键词： multivariate visualization principal component analysis Fisher discriminant analysis support vector machines

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FreeViz - An intelligent multivariate visualization approach to explorative analysis of biomedical data

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JOURNAL OF BIOMEDICAL INFORMATICS 2007年第6期40卷 661-671页

作者： Demsar, Janez Leban, Gregor Zupan, Blaz Univ Ljubljana Fac Comp & Informat Sci SI-1000 Ljubljana Slovenia Baylor Coll Med Dept Mol & Human Genet Houston TX 77030 USA

visualization can largely improve biomedical data analysis. It plays a crucial role in explorative data analysis and may support various data mining tasks. The paper presents FreeViz, an optimization method that finds linear projection and associated scatterplot that best separates instances of different class. In a single graph, the resulting FreeViz visualization can provide a global view of the classification problem being studied, reveal interesting relations between classes and features, uncover feature interactions, and provide information about intra-class similarities. The paper gives mathematical foundations of FreeViz, and presents its utility on various biomedical data sets. (C) 2007 Elsevier Inc. All rights reserved.

关键词： explorative data analysis multivariate visualization intelligent visualisation projection search for supervised data

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Evaluation of multivariate visualization on a multivariate Task

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IEEE TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2012年第12期18卷 2114-2121页

作者： Livingston, Mark A. Decker, Jonathan W. Ai, Zhuming Naval Research Laboratory Inc. USA

multivariate visualization techniques have attracted great interest as the dimensionality of data sets grows. One premise of such techniques is that simultaneous visual representation of multiple variables will enable the data analyst to detect patterns amongst multiple variables. Such insights could lead to development of new techniques for rigorous (numerical) analysis of complex relationships hidden within the data. Two natural questions arise from this premise: Which multivariate visualization techniques are the most effective for high-dimensional data sets? How does the analysis task change this utility ranking? We present a user study with a new task to answer the first question. We provide some insights to the second question based on the results of our study and results available in the literature. Our task led to significant differences in error, response time, and subjective workload ratings amongst four visualization techniques. We implemented three integrated techniques (Data-driven Spots, Oriented Slivers, and Attribute Blocks), as well as a baseline case of separate grayscale images. The baseline case fared poorly on all three measures, whereas Data-driven Spots yielded the best accuracy and was among the best in response time. These results differ from comparisons of similar techniques with other tasks, and we review all the techniques, tasks, and results (from our work and previous work) to understand the reasons for this discrepancy.

关键词： Quantitative evaluation multivariate visualization visual task design texture perception

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Surveying design spaces with performance maps: A multivariate visualization method for parametric design and architectural design optimization

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INTERNATIONAL JOURNAL OF ARCHITECTURAL COMPUTING 2017年第1期15卷 38-53页

作者： Wortmann, Thomas Singapore Univ Technol & Design 8 Somapah Rd Singapore 487372 Singapore

This article presents a method to visualize high-dimensional parametric design spaces with applications in computational design processes and interactive optimization. The method extends Star Coordinates using a triangulation-based interpolation with Barycentric Coordinates. It supports the understanding of design problems in architectural design optimization by allowing designers to move between a high-dimensional design space and a low-dimensional Performance Map. This Performance Map displays the characteristics of the fitness landscape, develops designers' intuitions about the relationships between design parameters and performance, allows designers to examine promising design variants, and delineates promising areas for further design exploration.

关键词： Fitness landscape design space exploration multivariate visualization optimization Star Coordinates

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multivariate visualization with Data Fusion

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Information visualization 2002年第3-4期1卷 182 - 193页

作者： Pak Chung Wong Harlan Foote David L Kao Ruby Leung Jim Thomas Pacific Northwest National Laboratory Washington U.S.A.； NASA Ames Research Center California U.S.A.

We discuss a fusion-based visualization method to analyze a multivariate climate dataset and its metadata. The primary difference between a conventional visualization and a fusion-based visualization is that the former draws on a single image whereas the latter draws on multiple see-through layers, which are then overlaid on each other to form the final visualization. We propose optimized colormaps to highlight subtle features that would not be shown with conventional colormaps. We present fusion techniques that integrate multiple single-purpose visualization techniques into the same viewing space. Our highly flexible fusion approach allows scientists to explore multiple parameters concurrently by mixing and matching images without frequently reconstructing new visualizations from the data for every possible combination. Although our primary visualization application is climate modeling, we show with examples that our fundamental design - fusing layers of data images for multivariate visualization - can be generalized for other information visualization applications.

关键词： multivariate visualization data fusion color mapping flow field visualization information visualization climate modeling

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Target data projection in multivariate visualization -: An application to mine planning

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International Conference on Computational Science and Its Applications (ICCSA 2004)

作者： Soto, L Sánchez, R Amaya, J Univ Talca Dept Syst Engn Talca Chile Univ Concepcion Dept Elect Engn Concepcion 3 Chile Univ Chile Ctr Modelamiento Matemat Santiago 3 Chile

ISBN: (纸本)3540220569

visualization is a key issue for multivariate data analysis. multivariate visualization is an active research topic and many efforts have been made in order to find suitable and meaningful visual representations. In this paper we present a technique for data projection in multivariate datasets, named Target Data Projection (TDP). Through this technique a vector is created for each multivariate data item considering a subset of the available variables. A new scalar variable is generated projecting those vectors over a target vector that defines the direction of interest for visual analysis. End-users set up target vectors in order to explore particular relationships by means of application meaningful projections. Hence, it is possible to map a combination of multivariate data into one scalar variable for graphical representation and interaction. This technique has proved to be very flexible and useful in mine planning providing valuable information for decision making.

关键词： multivariate visualization mine planning multiple visualization workspaces target data projection human-computer interaction

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GraphScape: Integrated multivariate network visualization

GraphScape: Integrated multivariate network visualization

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Asia/Pacific Symposium on Visualisation 2007

作者： Xu, Kai Cunningham, Andrew Hong, Seok-Hee Thomas, Bruce H. National ICT Australia Australia University of South Australia Australia National ICT Australia University of Sydney Australia

ISBN: (纸本)9781424408085

In this paper, we introduce a new method, GraphScape, to visualize multivariate networks, i.e., graphs with multivariate data associated with their nodes. GraphScape adopts a landscape metaphor with network structure displayed on a 2D plane and the surface height in the third dimension represents node attribute. More than one attribute can be visualized simultaneously by using multiple surfaces. In addition, GraphScape can be easily combined with existing methods to further increase the total number of attributes visualized. One of the major goals of GraphScape is to reveal multivariate graph clustering, which is based on both network structure and node attributes. This is achieved by a new layout algorithm and an innovative way of constructing attribute surface, which also allows visual clustering at different scales through interaction. A simplified attribute surface model is also proposed to reduce computation requirement when visualizing large networks. GraphScape is applied to networks of three different size (20, 100, and 1500) to demonstrate its effectiveness.

关键词： graph visualization multivariate visualization

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Bimodal visualization of Industrial X-Ray and Neutron Computed Tomography Data

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IEEE TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2025年第4期31卷 2196-2210页

作者： Huang, Xuan Miao, Haichao Kim, Hyojin Townsend, Andrew Champley, Kyle Tringe, Joseph Pascucci, Valerio Bremer, Peer-Timo Univ Utah SCI Inst Salt Lake City UT 84112 USA Lawrence Livermore Natl Lab Ctr Appl Sci Comp Livermore CA 94550 USA Lawrence Livermore Natl Lab Nondestruct Characterizat Inst Livermore CA 94550 USA

Advanced manufacturing creates increasingly complex objects with material compositions that are often difficult to characterize by a single modality. Our collaborating domain scientists are going beyond traditional methods by employing both X-ray and neutron computed tomography to obtain complementary representations expected to better resolve material boundaries. However, the use of two modalities creates its own challenges for visualization, requiring either complex adjustments of bimodal transfer functions or the need for multiple views. Together with experts in nondestructive evaluation, we designed a novel interactive bimodal visualization approach to create a combined view of the co-registered X-ray and neutron acquisitions of industrial objects. Using an automatic topological segmentation of the bivariate histogram of X-ray and neutron values as a starting point, the system provides a simple yet effective interface to easily create, explore, and adjust a bimodal visualization. We propose a widget with simple brushing interactions that enables the user to quickly correct the segmented histogram results. Our semiautomated system enables domain experts to intuitively explore large bimodal datasets without the need for either advanced segmentation algorithms or knowledge of visualization techniques. We demonstrate our approach using synthetic examples, industrial phantom objects created to stress bimodal scanning techniques, and real-world objects, and we discuss expert feedback.

关键词： Neutrons Histograms visualization Transfer functions Computed tomography Rendering (computer graphics) Image segmentation multivariate visualization visualization application image segmentation volume visualization

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Exploratory visualization of multivariate data with variable quality

Exploratory visualization of multivariate data with variable...

引用

IEEE Symposium on Visual Analytics Science and Technology

作者： Xie, Zaixian Huang, Shiping Ward, Matthew O. Rundensteiner, Elke A. Worcester Polytech Inst Dept Comp Sci Worcester MA 01609 USA

ISBN: (纸本)1424405912

Real-world data is known to be imperfect, suffering from various forms of defects such as sensor variability, estimation errors, uncertainty, human errors in data entry, and gaps in data gathering. Analysis conducted on variable quality data can lead to inaccurate or incorrect results. An effective visualization system must make users aware of the quality of their data by explicitly conveying not only the actual data content. but also its quality attributes. While some research has been conducted on visualizing uncertainty in spatiotemporal data and univariate data. little work has been reported on extending this capability into multivariate data visualization. In this paper we describe our approach to the problem of visually exploring multivariate data with variable quality. As a foundation, we propose a general approach to defining quality measures for tabular data in which data may experience quality problems at three granularities: individual data values, complete records, and specific dimensions. We then present two approaches to visual mapping of quality information into display space. In particular, one solution embeds the quality measures as explicit values into the original dataset by regarding value quality and record quality as new data dimensions. The other solution is to superimpose the quality information within the data visualizations using additional visual variables. We also report on user studies conducted to assess alternate mappings of quality attributes to visual variables for the second method. In addition. we describe case studies that expose some of the advantages and disadvantages of these two approaches.

关键词： uncertainty visualization multivariate visualization data quality

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