Data Visualization and Computational Art History
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Computer Graphics and Visualization
European Research Consortium for Informatics and Mathematics Number 44 January 2001 www.ercim.org Special Theme: Computer Graphics and Visualization Next Issue: April 2001 Next Special Theme: Metacomputing and Grid Technologies CONTENTS KEYNOTE 36 Physical Deforming Agents for Virtual Neurosurgery by Michele Marini, Ovidio Salvetti, Sergio Di Bona 3 by Elly Plooij-van Gorsel and Ludovico Lutzemberger 37 Visualization of Complex Dynamical Systems JOINT ERCIM ACTIONS in Theoretical Physics 4 Philippe Baptiste Winner of the 2000 Cor Baayen Award by Anatoly Fomenko, Stanislav Klimenko and Igor Nikitin 38 Simulation and Visualization of Processes 5 Strategic Workshops – Shaping future EU-NSF collaborations in in Moving Granular Bed Gas Cleanup Filter Information Technologies by Pavel Slavík, František Hrdliãka and Ondfiej Kubelka THE EUROPEAN SCENE 39 Watching Chromosomes during Cell Division by Robert van Liere 5 INRIA is growing at an Unprecedented Pace and is starting a Recruiting Drive on a European Scale 41 The blue-c Project by Markus Gross and Oliver Staadt SPECIAL THEME 42 Augmenting the Common Working Environment by Virtual Objects by Wolfgang Broll 6 Graphics and Visualization: Breaking new Frontiers by Carol O’Sullivan and Roberto Scopigno 43 Levels of Detail in Physically-based Real-time Animation by John Dingliana and Carol O’Sullivan 8 3D Scanning for Computer Graphics by Holly Rushmeier 44 Static Solution for Real Time Deformable Objects With Fluid Inside by Ivan F. Costa and Remis Balaniuk 9 Subdivision Surfaces in Geometric -
Manual Graphing Tools 2019.Pdf
INSTITUTE Manual on V-Dem Graphing Tools March 2019 Copyright © V-Dem Institute, University of Gothenburg. All rights reserved. Manual on the V-Dem Graphing Tools The V-Dem Graphing Tools is a new platform for making data visualization intuitive, accessible and easy to use. They allow users to analyze 450+ indicators and indices of democracy on all the countries in the world from 1900 to the present day. The reliable, precise nature of the indicators as well as their lengthy historical coverage should be useful not only to scholars studying democracy, but also to governments, practitioners and NGOs. In this document you can find tips on how to use 12 different Graphing Tools. Featured Tools We recommend that you start exploring data with the variable and country graphs, interactive maps and motion chart. These simple and user-friendly interfaces allow you to explore 450 aspects of democracy for all countries in the world over the last 100 years. 1. Variable Graph: multiple countries for one index or one variable at a time 2. Country Graph: multiple variables and/or indices for one country over time 3. Interactive Maps: generates maps for any V-Dem indicator for any year 4. Motion Charts: visualizes how the relationship between two variables changes over time Charts & Comparisons Tools These brand-new tools make it possible to create even more detailed and nuanced charts, complex graphics and heat maps, thematic and regional comparisons. 5. Variable Radar Chart: multiple variables and/or indices for one country over time 6. Country Radar Chart: multiple countries for one indicator/index 7. -
Google Chart Tools ( Allow You to Embed Many Different Kinds of Charts and Maps in Web Pages
Turning Data into Information – Tools, Tips, and Training A Summer Series Sponsored by Erin Gore, Institutional Data Council Chair; Berkeley Policy Analysts Roundtable, Business Process Analysis Working Group (BPAWG) and Cal Assessment Network (CAN) Web Data Visualization Tools Presented by Russ Acker, Office of Planning & Analysis [email protected], 510.642.1300, skype: ucb-opa Google Chart Tools (http://code.google.com/apis/charttools/) allow you to embed many different kinds of charts and maps in web pages. This software is free and doesn’t require you to have a Google account; all you need is a website and some data. (A little HTML/JavaScript knowledge doesn’t hurt either, but you can figure it out without too much trouble.) The charts can read data that’s embedded directly in the page, retrieved from a database, or in some cases, stored in a Google Docs spreadsheet. There are two major categories within Google Chart Tools: Image charts are simple and dynamically generated, but not interactive once produced. These are basically a picture of a chart that is created on the fly each time the web page gets viewed. Interactive charts are also dynamically generated, but include interactive features, such as zooming and movement. Google currently implements many of these as Flash objects, which means those particular interactive charts won’t work on iPhones or iPads. We’ll be looking at both kinds of charts in this session. Tableau Software has a similar tool, called Tableau Public, for embedding interactive charts in web pages. It’s free, very powerful, and probably easier to implement than Google Chart Tools, but it does require the use of a Windows-only application to import data, and is not very well documented. -
Math 253: Mathematical Methods for Data Visualization – Course Introduction and Overview (Spring 2020)
Math 253: Mathematical Methods for Data Visualization – Course introduction and overview (Spring 2020) Dr. Guangliang Chen Department of Math & Statistics San José State University Math 253 course introduction and overview What is this course about? Context: Modern data sets often have hundreds, thousands, or even millions of features (or attributes). ←− large dimension Dr. Guangliang Chen | Mathematics & Statistics, San José State University2/30 Math 253 course introduction and overview This course focuses on the statistical/machine learning task of dimension reduction, also called dimensionality reduction, which is the process of reducing the number of input variables of a data set under consideration, for the following benefits: • It reduces the running time and storage space. • Removal of multi-collinearity improves the interpretation of the parameters of the machine learning model. • It can also clean up the data by reducing the noise. • It becomes easier to visualize the data when reduced to very low dimensions such as 2D or 3D. Dr. Guangliang Chen | Mathematics & Statistics, San José State University3/30 Math 253 course introduction and overview There are two different kinds of dimension reduction approaches: • Feature selection approaches try to find a subset of the original features variables. Examples: subset selection, stepwise selection, Ridge and Lasso regression. ←− Already covered in Math 261A • Feature extraction transforms the data in the high-dimensional space to a space of fewer dimensions. ←− Focus of this course Examples: principal component analysis (PCA), ISOmap, and linear discriminant analysis (LDA). Dr. Guangliang Chen | Mathematics & Statistics, San José State University4/30 Math 253 course introduction and overview Dimension reduction methods to be covered in this course: • Linear projection methods: – PCA (for unlabled data), – LDA (for labled data) • Nonlinear embedding methods: – Multidimensional scaling (MDS), ISOmap – Locally linear embedding (LLE) – Laplacian eigenmaps Dr. -
Volume Rendering
Volume Rendering 1.1. Introduction Rapid advances in hardware have been transforming revolutionary approaches in computer graphics into reality. One typical example is the raster graphics that took place in the seventies, when hardware innovations enabled the transition from vector graphics to raster graphics. Another example which has a similar potential is currently shaping up in the field of volume graphics. This trend is rooted in the extensive research and development effort in scientific visualization in general and in volume visualization in particular. Visualization is the usage of computer-supported, interactive, visual representations of data to amplify cognition. Scientific visualization is the visualization of physically based data. Volume visualization is a method of extracting meaningful information from volumetric datasets through the use of interactive graphics and imaging, and is concerned with the representation, manipulation, and rendering of volumetric datasets. Its objective is to provide mechanisms for peering inside volumetric datasets and to enhance the visual understanding. Traditional 3D graphics is based on surface representation. Most common form is polygon-based surfaces for which affordable special-purpose rendering hardware have been developed in the recent years. Volume graphics has the potential to greatly advance the field of 3D graphics by offering a comprehensive alternative to conventional surface representation methods. The object of this thesis is to examine the existing methods for volume visualization and to find a way of efficiently rendering scientific data with commercially available hardware, like PC’s, without requiring dedicated systems. 1.2. Volume Rendering Our display screens are composed of a two-dimensional array of pixels each representing a unit area. -
How to Use the Motion Chart: a NAEP Example
How to Use the Motion Chart: A NAEP Example The Motion Chart provides a unique way to see data across time and by a variety of factors. The text below describes one example of how to view the results for eighth-graders in Boston and Charlotte by selecting specific settings for the X and Y axes, as well as the color and size of the circles. The example shown below is just one of many potential ways to analyze data using the Motion Chart, and therefore it is not intended to provide a broad summary of the findings that a user can explore with this tool. Analyze where districts plot for two student groups You can determine the X and Y axes by clicking on the arrows and selecting a variable (e.g., average scale scores for all students). For instance, the chart below shows the average scores in 2003 for all students on the X axis, and the average score for students eligible for the National School Lunch Program (NSLP) on the Y axis. In this example, the cursor is hovering over the circle representing Boston, showing the average scale score for all students in 2003 as 261.8 on the X axis, and the score for students eligible for the NSLP as 256.4 on the Y axis. From just a quick glance at the chart, it’s clear that the blue circle representing Charlotte has a higher average score for all students but a similar score for students eligible for the NSLP. 1 Use color and size to create context The color and size of the circles on the chart can be selected to show all the same variables that can be shown in the X and Y axes. -
Graph Visualization and Navigation in Information Visualization 1
HERMAN ET AL.: GRAPH VISUALIZATION AND NAVIGATION IN INFORMATION VISUALIZATION 1 Graph Visualization and Navigation in Information Visualization: a Survey Ivan Herman, Member, IEEE CS Society, Guy Melançon, and M. Scott Marshall Abstract—This is a survey on graph visualization and navigation techniques, as used in information visualization. Graphs appear in numerous applications such as web browsing, state–transition diagrams, and data structures. The ability to visualize and to navigate in these potentially large, abstract graphs is often a crucial part of an application. Information visualization has specific requirements, which means that this survey approaches the results of traditional graph drawing from a different perspective. Index Terms—Information visualization, graph visualization, graph drawing, navigation, focus+context, fish–eye, clustering. involved in graph visualization: “Where am I?” “Where is the 1 Introduction file that I'm looking for?” Other familiar types of graphs lthough the visualization of graphs is the subject of this include the hierarchy illustrated in an organisational chart and Asurvey, it is not about graph drawing in general. taxonomies that portray the relations between species. Web Excellent bibliographic surveys[4],[34], books[5], or even site maps are another application of graphs as well as on–line tutorials[26] exist for graph drawing. Instead, the browsing history. In biology and chemistry, graphs are handling of graphs is considered with respect to information applied to evolutionary trees, phylogenetic trees, molecular visualization. maps, genetic maps, biochemical pathways, and protein Information visualization has become a large field and functions. Other areas of application include object–oriented “sub–fields” are beginning to emerge (see for example Card systems (class browsers), data structures (compiler data et al.[16] for a recent collection of papers from the last structures in particular), real–time systems (state–transition decade). -
From Surface Rendering to Volume
What is Computer Graphics? • Computational process of generating images from models and/or datasets using computers • This is called rendering (computer graphics was traditionally considered as a rendering method) • A rendering algorithm converts a geometric model and/or dataset into a picture Department of Computer Science CSE564 Lectures STNY BRK Center for Visual Computing STATE UNIVERSITY OF NEW YORK What is Computer Graphics? This process is also called scan conversion or rasterization How does Visualization fit in here? Department of Computer Science CSE564 Lectures STNY BRK Center for Visual Computing STATE UNIVERSITY OF NEW YORK Computer Graphics • Computer graphics consists of : 1. Modeling (representations) 2. Rendering (display) 3. Interaction (user interfaces) 4. Animation (combination of 1-3) • Usually “computer graphics” refers to rendering Department of Computer Science CSE564 Lectures STNY BRK Center for Visual Computing STATE UNIVERSITY OF NEW YORK Computer Graphics Components Department of Computer Science CSE364 Lectures STNY BRK Center for Visual Computing STATE UNIVERSITY OF NEW YORK Surface Rendering • Surface representations are good and sufficient for objects that have homogeneous material distributions and/or are not translucent or transparent • Such representations are good only when object boundaries are important (in fact, only boundary geometric information is available) • Examples: furniture, mechanical objects, plant life • Applications: video games, virtual reality, computer- aided design Department of -
Inventing Graphing: Meta- Representational Expertise In
JOURNAL OF MATHEMATICAL BEHAVIOR 10, 117-160 (1991) Inventing Graphing: Meta Representational Expertise in Children ANDREA A. mSEssA DAVID HAMMER BRUCE SHERIN University of California, Berkeley TINA KOLPAKOWSKI The Bentley School, Oakland, CA We examine a cooperative activity of a sixth-grade class. The activity took place over 5 days and focused on inventing adequate static representations of motion. In generating, critiquing, and refining numerous representations, we find indications of strong meta representational competence. In addition to conceptual and design competence, we focus on the structure of activities and find in them an intricate blend of (I) the children's conceptual and interactional skills, (2) their interest in, and sense of ownership over, the inventions, and (3) the teacher's initiation and organization of activities, which is deli cately balanced with her letting the activities evolve according to student-set directions. 1. INTRODUCTION In November 1989, 8 sixth-grade students in a school in Oakland, California invented graphing as a means of representing motion. Now, of course, we mean that they "reinvented" graphing. In fact, we know that most of them already knew at least something about graphing. But the more we look at the data, the more we are convinced that these children did genuine and important creative work and that their accomplishment warrants study as an exceptional example of student-directed learning. We would like to understand We thank the following for comments on drafts and for contributing to the ongoing discussion of inventing graphing: Steve Adams, Don Ploger, Susan Newman, Jack Smith. We are immensely grateful to the 8 sixth-grade students who did the creative work presented here. -
Seven Things You Should Know About Data Visualization II
7 things you should know about... Data Visualization II Scenario What is it? One requirement of Vera’s master’s program in sociol Data visualization is the use of tools to represent data in the form ogy is a thesis, which includes a presentation of findings of charts, maps, tag clouds, animations, or any graphical means before her peers for review, discussion, and recommen that make content easier to understand. The past two years has dations. In addition to her own clinical project, which seen a blossoming of visualization applications, as well as of tech traces correlations between environmental factors and nologies and infrastructure to support increasingly sophisticated learning disabilities in the United States, Vera gathers visual representations of data. The greatest change, however, may 12 years of data on learning disabilities from organiza be in access to data. Electronic sensors, for example, have made tions in the United States, Canada, New Zealand, the weather information available on a previously unimaginable scale. Netherlands, South Africa, Australia, and Great Britain. While geographic information systems (GIS) have for years allowed As she begins looking over the paper with an eye toward individuals to gather, transform, and analyze data, new tools have presentation, she becomes convinced that her statis 1become widely available that easily create unique mashups of dis tics, alone, will not clearly articulate the implications of parate sources of data, as evidenced by the increasing number of her research to an audience of listeners, so she starts applications that employ Google Earth and Google Maps. Growing looking for ways to present the information visually. -
Scalable Exact Visualization of Isocontours in Road Networks Via Minimum-Link Paths∗
Journal of Computational Geometry jocg.org SCALABLE EXACT VISUALIZATION OF ISOCONTOURS IN ROAD NETWORKS VIA MINIMUM-LINK PATHS∗ Moritz Baum,y Thomas Bläsius,z Andreas Gemsa,y Ignaz Rutter,yx and Franziska Wegnery Abstract. Isocontours in road networks represent the area that is reachable from a source within a given resource limit. We study the problem of computing accurate isocontours in realistic, large-scale networks. We propose isocontours represented by polygons with minimum number of segments that separate reachable and unreachable components of the network. Since the resulting problem is not known to be solvable in polynomial time, we introduce several heuristics that run in (almost) linear time and are simple enough to be implemented in practice. A key ingredient is a new practical linear-time algorithm for minimum-link paths in simple polygons. Experiments in a challenging realistic setting show excellent performance of our algorithms in practice, computing near-optimal solutions in a few milliseconds on average, even for long ranges. 1 Introduction How far can I drive my battery electric vehicle, given my position and the current state of charge? This question expresses range anxiety (the fear of getting stranded) caused by limited battery capacities and a sparse charging infrastructure. An answer in the form of a map that visualizes the reachable region helps to find charging stations in range and to overcome range anxiety. This reachable region is bounded by curves that represent points of constant energy consumption; such curves are usually called isocontours (or isolines). 2 Isocontours are typically considered in the context of functions f : R R, e. -
Dynamic Visualizations of Language Change Motion Charts on the Basis of Bivariate and Multivariate Data from Diachronic Corpora*
Dynamic visualizations of language change Motion charts on the basis of bivariate and multivariate data from diachronic corpora* Martin Hilpert Freiburg Institute for Advanced Studies This paper uses diachronic corpus data to visualize language change in a dynam- ic fashion. Bivariate and multivariate data sets form the input for so-called mo- tion charts, i.e. series of diachronically ordered scatterplots that can be viewed in sequence. Based on data from COHA (Davies 2010), two case studies illustrate recent changes in American English. The first study visualizes change in a diachronic analysis of ambicategorical nouns and verbs such as hope or drink; the second study shows structural change in the behavior of complement-taking predicates such as expect or remember. Whereas motion charts are typically used to represent bivariate data sets, it is argued here that they are also useful for the analysis of multivariate data over time. The present paper submits multivariate diachronic data to a multi-dimensional scaling analysis. Viewing the resulting data points in separate time slices offers a holistic and intuitive representation of complex linguistic change. Keywords: motion charts, diachronic corpora, COHA, multi-dimensional scaling, bivariate data, multivariate data 1. Introduction This paper presents a way to visualize processes of language change in a dynamic fashion. For this, it uses so-called motion charts (Gesmann & de Castillo 2011), which are series of diachronically ordered scatterplots. Viewing these plots se- quentially on a computer screen creates the impression of continuous motion and allows the viewer to process complex information in an intuitive way. On printed pages, the sequential graphs can be read in the way one would read a comic strip: the frame of reference is held constant, but items within that frame change from one picture to the next.