DOCSLIB.ORG

Communicating Through Infographics: Visualizing Scientific and Engineering Information

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Communicating Through Infographics: Visualizing Scientific and Engineering Information Communicating through infographics: visualizing scientific and engineering information Christa Kelleher Department of Earth and Ocean Sciences Nicholas School of the Environment Duke University hp://xkcd.com/688/ 1 Model diagnostics, catchment modeling, and visualization of large environmental datasets [Kelleher et al., 2012; Kelleher et al., 2013; Sawicz et al., 2013; Kelleher et al., in prep] 2 The importance of How much data is created every minute? visualization As of 2012 …. • 100,000 tweets • 2,000,000 google search queries • 47,000 app downloads • 571 new websites • 217 new mobile web users 3 Source: [Visual News, hp://www.visualnews.com/2012/06/19/how-much-data-created-every-minute/?view=infographic] The importance of How much data is created every minute? visualization 4 Source: [Mashable, hp://mashable.com/2014/04/23/data-online-every-minute/?] Resources Data analysis: Matlab R, Python Spaal analysis: ArcGIS R Mapping packages, qGIS, GRASS GIS, SAGA Fine tuning: Illustrator Inkscape, CorelDRAW 5 HOW DO WE CREATE EFFECTIVE VISUALIZATIONS? 6 Rule 1 Choose an effective plot type The type of plot you use should compliment the type of data you have (and the message you are conveying) [Source: Matlab, http://www.mathworks.com/help/matlab/creating_plots/figures-plots-and-graphs.html] 7 Rule 1 Choose an effective plot type Plots use attributes to ‘encode’ information… …BUT our ability to quantitatively perceive these attributes differs! 8 [Kelleher and Wagner (2011); Few (2009)] Posion Common Non-aligned Length Details Direcon Angle Our accuracy to rank quantitative perceptual tasks varies across Area different encoding objects and graphs More accurate Volume Patterns Shading Color saturaon 9 [Mackinlay (1986), ACM Transacons on Graphics] Rule 1 Choose an effective plot type 4 DETAILS 2 Line graph 0 -2 J F M A M J J A S O N D Annual VS PATTERNS Heat map (Pattern plot) 10 [Kelleher and Wagner (2011); Few (2009)] Rule 1 Choose an effective plot type Line plots highlight details in the timeseries – high values, low values, and value comparisons Comparing Trends in Air Temperature for 8 Sites 4 2 0 -2 J F M A M J J A S O N D Annual 11 [Kelleher and Wagner (2011); Few (2009)] Rule 1 Choose an effective plot type Heat maps (pattern plots) highlight patterns in the data, and allow easy comparison (2) (4) (6) (8) 14 Jan °C 10yrs-1 Feb 12 Mar > -1.5 Apr 10 -0.5 May -0.25 Jun 8 Jul 0.25 Aug 6 0.5 Sep 1.5 Oct 4 2.5 Nov < 3.5 Dec 2 Annual σ 0 0 2 4 6 8 (1) (3) (5) (7) 12 [Kelleher and Wagner (2011); Few (2009)] Rule 1 Choose an effective plot type Aggregating data, troops versus cost, 1980 - 2012 Overall trends …. Data source: Time Magazine Creator: Jorge Camoes Chart: http://www.excelcharts.com/blog/redraw-troops-vs-cost-time-magazine/ 13 … vs details Data source: Time Magazine Creator: Jorge Camoes Chart: http://www.excelcharts.com/blog/redraw-troops-vs-cost-time-magazine/ 14 Rule 1 Choose an effective plot type Select attributes which can be easily perceived depending on your graph’s message 15 [Robbins (2006), accessed at: h"p://www.perceptualedge.com/arQcles/b-eye/dot_plots.pdf] Rule 1 Choose an effective plot type Pie charts are usually considered ineffective 25% 25% ?? This one is up to you! [Few (2007), accessed at: h"p://www.perceptualedge.com/arQcles/visual_business_intelligence/ save_the_pies_for_dessert.pdf] 16 Rule 1 Choose an effective plot type Tables are a good alternative Companies Percentage Company B 40% Company C 25% Company D 17% Company A 10% Company E 7% Company F 1% Total 100% [Few (2007), accessed at: h"p://www.perceptualedge.com/arQcles/visual_business_intelligence/ save_the_pies_for_dessert.pdf] 17 Rule 2 Remove chart junk (Tufte, 1983) [S. Khosla, Global Post, hp://www.globalpost.com/dispatch/news/regions/americas/brazil/140702/graphic-brazil-red-yellow-cards18 ] Rule 2 Remove chart junk (Tufte, 1983) Chartjunk represents any ink on a given graph which displays redundant or non-data information Reduction in chartjunk 30 30 30 20 20 20 10 10 10 [Tue (1983); Bray, Data-Ink Rao Example 1, accessed at: h"p://www.tbray.org/ongoing/data-ink/di1] 19 Rule 2 Remove chart junk (Tufte, 1983) This includes 3D graphics [Kelleher and Wagner (2011), Environmental Modelling and Soware] 20 Display the same number of dimensions as the Rule 3 dataset With a single encoding attribute, we can easily identify the object that doesn’t follow the pattern Enclosure OrientationOrientation Enclosure ColourColor [Spence (2007), Informa+on Visualiza+on] 21 Display the same number of dimensions as the Rule 3 dataset FIND THE RED SQUARE! When we mix too many attributes, we make it harder to detect patterns [Spence (2007), Informa+on Visualiza+on] 22 Display the same number of dimensions as the Rule 3 dataset [DarkHorse Analytics, [The Economist, http://www.economist.com/blogs/ graphicdetail/2014/07/daily-chart-3] http://darkhorseanalytics.com/blog/data-looks-better-naked/] 23 Consider the use of color to fit your chart and the Rule 4 type of data you’re displaying Sequential Diverging Categorical Example: air Example: anomalies Example: colors, political temperature, traffic parties, car types/ counts manufacturors [ColorBrewer2, online at h"p://colorbrewer2.org/] 24 Consider the use of color to fit your chart and the Rule 4 type of data you’re displaying Colorbrewer2 (colorbrewer2.org/), developed by Cynthia Brewer (Penn State) [ColorBrewer2, online at h"p://colorbrewer2.org/] 25 Rule 5 Maintain axes when comparing subplots 10 10 9 9.5 8 7 9 6 (IMDB) Rating 8.5 5 4 8 10 20 30 40 50 60 70 80 90 10 20 30 40 50 60 Episode # Episode # Data source: IMDB Location: http://graphtv.kevinformatics.com/ 26 Rule 5 Maintain axes when comparing subplots 10 10 8 8 6 6 4 4 IMDB Rating IMDB 2 2 0 0 10 20 30 40 50 60 70 80 90 10 20 30 40 50 60 Episode # Episode # Data source: IMDB Location: http://graphtv.kevinformatics.com/ 27 Rule 5 Maintain axes when comparing subplots Period 1 Precipitation Period 2 50 Period 1 50 Period 2 50 50 7 1948 - 1957 7 Visualization programs usually 7 7 6 45 45 6 45 6 45require that you manually set 6 6 5 the axes and ranges between 5 40 5 40 5 40 5 40 4 plots 4 4 4 35 4 35 35 35 33 3 3 3 30 30 30 2 30 2 2 2 2 25 25 1 25-130 -120 -110 -100 -90 -80 -70 25-130 -120 -110 -100 -90 -80 -70 1 -130 -120 -110 -100 -90 -80 -70 -130 -120 -110 -100 -90 -80 -70 Period 1 Period 2 Period 1 Period 2 50 50 50 50 7 7 7 7 Period 31958 - 1967 7 Period 4 50 Period 3 50 Period 4 45 5045 6 50 45 6 45 6 6 7 6 7 7 7 45 5 45 40 5 40 5 5 6 40 5 4540 6 45 6 6 4 4 40 454 40 5 35 4 35 5 35 4035 5 40 3 3 3 4 3 34 35 4 35 4 30 30 30 3530 2 35 2 232 3 2 3 3 30 1 30 25 3025 121 30 2 25-130 -120 -110 -100 -90 -80 -70 25-130 -120 -110 -100 -90 -80 -70 2 2 -130 -120 -110 -100 -90 -80 -70 -130 -120 -110 -100 -90 -80 -70 [C. Kelleher, K. Sawicz] 28 25 1 25 25-130 -120 -110 -100 -90 -80 -70 1 25-130 -120 -110 -100 -90 -80 -70 -130 -120 -110 -100 -90 -80 -70 -130 -120 -110 -100 -90 -80 -70 Period 3 Period 4 50 Period 3 50 Period 4 50 50 7 7 7 7 45 45 45 6 45 6 6 6 40 5 40 5 40 5 40 5 4 4 35 4 35 4 35 35 3 3 3 3 30 30 30 2 30 2 2 2 25 1 25 25-130 -120 -110 -100 -90 -80 -70 1 25-130 -120 -110 -100 -90 -80 -70 -130 -120 -110 -100 -90 -80 -70 -130 -120 -110 -100 -90 -80 -70 Rules for specific plot types Magnitude Change Correlation Distribution 29 Rules for effective use Reference to zero Use rotated bar charts if there are more than 8-10 categories Include a legend Be aware of scaling issues (important if you’re showing more than one data series) [Few, 2009; Gary Klass, Illinois State, h"p://lilt.ilstu.edu/gmklass/pos138/datadisplay/secons/goodcharts.htm] 30 Reference the y-axis to zero on bar charts 2000 2000 1900 1500 1800 1000 1700 1600 500 1500 0 A B C A B C This accentuates the perceived differences between graphed entities [Kelleher and Wagener (2011), Environmental Modelling and Soware; Wainer (1984) The Am sta+s+cian] 31 Reference the y-axis to zero on bar charts [Yau (2012), FlowingData.com, accessed at: h"p://flowingdata.com/2012/08/06/fox-news-conQnues-charQng-excellence/] 32 Rotate your chart if displaying more than 8-10 categories [Gary Klass, Illinois State, h"p://lilt.ilstu.edu/gmklass/pos138/datadisplay/secons/goodcharts.htm] 33 Rotate your chart if displaying more than 8-10 categories Relative Poverty Rates in Wealthy Nations, 2000 Finland Elderly Norway Children Sweden Switzerland* Belgium Austria France* Germany Luxembourg Netherlands* Canada U.K.* Australia* Spain Italy Ireland U.S. 0 10 20 30 40 [Gary Klass, Illinois State, h"p:// % living in families below 50% of median family income lilt.ilstu.edu/gmklass/pos138/ * most recent year datadisplay/secons/goodcharts.htm] source: Luxembourg Income Study 34 Stacked bar charts make sense when comparing the series closest to the x-axis [Gary Klass, Illinois State, h"p://lilt.ilstu.edu/gmklass/pos138/datadisplay/secons/goodcharts.htm] 35 Bar charts and scaling issues … especially important when displaying two data series with very different magnitudes [Gary Klass, Illinois State, h"p://lilt.ilstu.edu/gmklass/pos138/datadisplay/secons/goodcharts.htm] 36 Bar charts and scaling issues Highlights the fact that labor participation is large across all years Misses that the unemployment rate increases by a factor of 30% [Gary Klass, Illinois State, h"p://lilt.ilstu.edu/gmklass/pos138/datadisplay/secons/goodcharts.htm] 37 Bar charts and scaling issues Displaying the y-axis on a log scale can help to highlight the rate of change Labor force participation and Percentage change in labor force unemployment, 1970 - 2000 participation and unemployment between decades 50 40 30 20 10 0 −10 1970 - 1980 1980 - 1990 1990 - 2000 [Gary Klass, Illinois State, h"p://lilt.ilstu.edu/gmklass/pos138/datadisplay/secons/goodcharts.htm] 38 Example – the paintings of Bob Ross An analysis of 381 Bob Ross episodes ‘The top-line results are to be expected — wouldn’t you know, he did paint a bunch of mountains, trees and lakes! — but then I put some numbers to Ross’s classic figures of speech.
Load more

Recommended publications
  • The Savvy Survey #16: Data Analysis and Survey Results1 Milton G
    AEC409 The Savvy Survey #16: Data Analysis and Survey Results1 Milton G. Newberry, III, Jessica L. O’Leary, and Glenn D. Israel2 Introduction In order to evaluate the effectiveness of a program, an agent or specialist may opt to survey the knowledge or Continuing the Savvy Survey Series, this fact sheet is one of attitudes of the clients attending the program. To capture three focused on working with survey data. In its most raw what participants know or feel at the end of a program, he form, the data collected from surveys do not tell much of a or she could implement a posttest questionnaire. However, story except who completed the survey, partially completed if one is curious about how much information their clients it, or did not respond at all. To truly interpret the data, it learned or how their attitudes changed after participation must be analyzed. Where does one begin the data analysis in a program, then either a pretest/posttest questionnaire process? What computer program(s) can be used to analyze or a pretest/follow-up questionnaire would need to be data? How should the data be analyzed? This publication implemented. It is important to consider realistic expecta- serves to answer these questions. tions when evaluating a program. Participants should not be expected to make a drastic change in behavior within the Extension faculty members often use surveys to explore duration of a program. Therefore, an agent should consider relevant situations when conducting needs and asset implementing a posttest/follow up questionnaire in several assessments, when planning for programs, or when waves in a specific timeframe after the program (e.g., six assessing customer satisfaction.
    [Show full text]
  • Realistic Modeling and Rendering of Plant Ecosystems
    Realistic modeling and rendering of plant ecosystems Oliver Deussen1 Pat Hanrahan2 Bernd Lintermann3 RadomÂõr MechÏ 4 Matt Pharr2 Przemyslaw Prusinkiewicz4 1 Otto-von-Guericke University of Magdeburg 2 Stanford University 3 The ZKM Center for Art and Media Karlsruhe 4 The University of Calgary Abstract grasslands, human-made environments, for instance parks and gar- dens, and intermediate environments, such as lands recolonized by Modeling and rendering of natural scenes with thousands of plants vegetation after forest ®res or logging. Models of these ecosystems poses a number of problems. The terrain must be modeled and plants have a wide range of existing and potential applications, including must be distributed throughout it in a realistic manner, re¯ecting the computer-assisted landscape and garden design, prediction and vi- interactions of plants with each other and with their environment. sualization of the effects of logging on the landscape, visualization Geometric models of individual plants, consistent with their po- of models of ecosystems for research and educational purposes, sitions within the ecosystem, must be synthesized to populate the and synthesis of scenes for computer animations, drive and ¯ight scene. The scene, which may consist of billions of primitives, must simulators, games, and computer art. be rendered ef®ciently while incorporating the subtleties of lighting Beautiful images of forests and meadows were created as early in a natural environment. as 1985 by Reeves and Blau [50] and featured in the computer We have developed a system built around a pipeline of tools that animation The Adventures of Andre and Wally B. [34]. Reeves and address these tasks.
    [Show full text]
  • Evolution of the Infographic
    EVOLUTION OF THE INFOGRAPHIC: Then, now, and future-now. EVOLUTION People have been using images and data to tell stories for ages—long before the days of the Internet, smartphones, and Excel. In fact, the history of infographics pre-dates the web by more than 30,000 years with the earliest forms of these visuals being cave paintings that helped early humans find food, resources, and shelter. But as technology has advanced, so has our ability to tell meaningful stories. Here’s a look into the evolution of modern infographics—where they’ve been, how they’ve evolved, and where they’re headed. Then: Printed, static infographics The 20th Century introduced the infographic—a staple for how we communicate, visualize, and share information today. Early on, these print graphics married illustration and data to communicate information in a revolutionary way. ADVANTAGE Design elements enable people to quickly absorb information previously confined to long paragraphs of text. LIMITATION Static infographics didn’t allow for deeper dives into the data to explore granularities. Hoping to drill down for more detail or context? Tough luck—what you see is what you get. Source: http://www.wired.co.uk/news/archive/2012-01/16/painting- by-numbers-at-london-transport-museum INFOGRAPHICS THROUGH THE AGES DOMO 03 Now: Web-based, interactive infographics While the first wave of modern infographics made complex data more consumable, web-based, interactive infographics made data more explorable. These are everywhere today. ADVANTAGE Everyone looking to make data an asset, from executives to graphic designers, are now building interactive data stories that deliver additional context and value.
    [Show full text]
  • Visualization in Multiobjective Optimization
    Final version Visualization in Multiobjective Optimization Bogdan Filipič Tea Tušar Tutorial slides are available at CEC Tutorial, Donostia - San Sebastián, June 5, 2017 http://dis.ijs.si/tea/research.htm Computational Intelligence Group Department of Intelligent Systems Jožef Stefan Institute Ljubljana, Slovenia 2 Contents Introduction A taxonomy of visualization methods Visualizing single approximation sets Introduction Visualizing repeated approximation sets Summary References 3 Introduction Introduction Multiobjective optimization problem Visualization in multiobjective optimization Minimize Useful for different purposes [14] f: X ! F • Analysis of solutions and solution sets f:(x ;:::; x ) 7! (f (x ;:::; x );:::; f (x ;:::; x )) 1 n 1 1 n m 1 n • Decision support in interactive optimization • Analysis of algorithm performance • X is an n-dimensional decision space ⊆ Rm ≥ • F is an m-dimensional objective space (m 2) Visualizing solution sets in the decision space • Problem-specific ! Conflicting objectives a set of optimal solutions • If X ⊆ Rm, any method for visualizing multidimensional • Pareto set in the decision space solutions can be used • Pareto front in the objective space • Not the focus of this tutorial 4 5 Introduction Introduction Visualization can be hard even in 2-D Stochastic optimization algorithms Visualizing solution sets in the objective space • Single run ! single approximation set • Interested in sets of mutually nondominated solutions called ! approximation sets • Multiple runs multiple approximation sets • Different
    [Show full text]
  • Using Survey Data Author: Jen Buckley and Sarah King-Hele Updated: August 2015 Version: 1
    ukdataservice.ac.uk Using survey data Author: Jen Buckley and Sarah King-Hele Updated: August 2015 Version: 1 Acknowledgement/Citation These pages are based on the following workbook, funded by the Economic and Social Research Council (ESRC). Williamson, Lee, Mark Brown, Jo Wathan, Vanessa Higgins (2013) Secondary Analysis for Social Scientists; Analysing the fear of crime using the British Crime Survey. Updated version by Sarah King-Hele. Centre for Census and Survey Research We are happy for our materials to be used and copied but request that users should: • link to our original materials instead of re-mounting our materials on your website • cite this as an original source as follows: Buckley, Jen and Sarah King-Hele (2015). Using survey data. UK Data Service, University of Essex and University of Manchester. UK Data Service – Using survey data Contents 1. Introduction 3 2. Before you start 4 2.1. Research topic and questions 4 2.2. Survey data and secondary analysis 5 2.3. Concepts and measurement 6 2.4. Change over time 8 2.5. Worksheets 9 3. Find data 10 3.1. Survey microdata 10 3.2. UK Data Service 12 3.3. Other ways to find data 14 3.4. Evaluating data 15 3.5. Tables and reports 17 3.6. Worksheets 18 4. Get started with survey data 19 4.1. Registration and access conditions 19 4.2. Download 20 4.3. Statistics packages 21 4.4. Survey weights 22 4.5. Worksheets 24 5. Data analysis 25 5.1. Types of variables 25 5.2. Variable distributions 27 5.3.
    [Show full text]
  • Inviwo — a Visualization System with Usage Abstraction Levels
    IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS, VOL X, NO. Y, MAY 2019 1 Inviwo — A Visualization System with Usage Abstraction Levels Daniel Jonsson,¨ Peter Steneteg, Erik Sunden,´ Rickard Englund, Sathish Kottravel, Martin Falk, Member, IEEE, Anders Ynnerman, Ingrid Hotz, and Timo Ropinski Member, IEEE, Abstract—The complexity of today’s visualization applications demands specific visualization systems tailored for the development of these applications. Frequently, such systems utilize levels of abstraction to improve the application development process, for instance by providing a data flow network editor. Unfortunately, these abstractions result in several issues, which need to be circumvented through an abstraction-centered system design. Often, a high level of abstraction hides low level details, which makes it difficult to directly access the underlying computing platform, which would be important to achieve an optimal performance. Therefore, we propose a layer structure developed for modern and sustainable visualization systems allowing developers to interact with all contained abstraction levels. We refer to this interaction capabilities as usage abstraction levels, since we target application developers with various levels of experience. We formulate the requirements for such a system, derive the desired architecture, and present how the concepts have been exemplary realized within the Inviwo visualization system. Furthermore, we address several specific challenges that arise during the realization of such a layered architecture, such as communication between different computing platforms, performance centered encapsulation, as well as layer-independent development by supporting cross layer documentation and debugging capabilities. Index Terms—Visualization systems, data visualization, visual analytics, data analysis, computer graphics, image processing. F 1 INTRODUCTION The field of visualization is maturing, and a shift can be employing different layers of abstraction.
    [Show full text]
  • Monte Carlo Likelihood Inference for Missing Data Models
    MONTE CARLO LIKELIHOOD INFERENCE FOR MISSING DATA MODELS By Yun Ju Sung and Charles J. Geyer University of Washington and University of Minnesota Abbreviated title: Monte Carlo Likelihood Asymptotics We describe a Monte Carlo method to approximate the maximum likeli- hood estimate (MLE), when there are missing data and the observed data likelihood is not available in closed form. This method uses simulated missing data that are independent and identically distributed and independent of the observed data. Our Monte Carlo approximation to the MLE is a consistent and asymptotically normal estimate of the minimizer ¤ of the Kullback- Leibler information, as both a Monte Carlo sample size and an observed data sample size go to in¯nity simultaneously. Plug-in estimates of the asymptotic variance are provided for constructing con¯dence regions for ¤. We give Logit-Normal generalized linear mixed model examples, calculated using an R package that we wrote. AMS 2000 subject classi¯cations. Primary 62F12; secondary 65C05. Key words and phrases. Asymptotic theory, Monte Carlo, maximum likelihood, generalized linear mixed model, empirical process, model misspeci¯cation 1 1. Introduction. Missing data (Little and Rubin, 2002) either arise naturally|data that might have been observed are missing|or are intentionally chosen|a model includes random variables that are not observable (called latent variables or random e®ects). A mixture of normals or a generalized linear mixed model (GLMM) is an example of the latter. In either case, a model is speci¯ed for the complete data (x; y), where x is missing and y is observed, by their joint den- sity f(x; y), also called the complete data likelihood (when considered as a function of ).
    [Show full text]
  • Blueprints Free
    FREE BLUEPRINTS PDF Barbara Delinsky | 512 pages | 25 Feb 2016 | Little, Brown Book Group | 9780349405049 | English | London, United Kingdom Blueprints | Changing Lives and Shaping Futures in Southwest Pennsylvania and West Virginia How does Blueprints complicated structure with so many parts, materials and workers come together? The answer is in the history of blueprints. These documents are truly the Blueprints of any construction project but they have been around for some time now. So, where did blueprints originate from and where are they evolving today? Before blueprints evolved into their modern form, look and purpose, drawings from the medieval times appear to be their earliest formations. The Plan of St. Gall, is one of the oldest known surviving architectural plans. Some historians consider Blueprints 9th century drawing as the very beginning of the history of Blueprints. Mysteriously, the monastery depicted in the drawing was never actually built. So, a group in Germany is using this drawing, along with period tools and techniques, to learn Blueprints about architectural history. You can view a detailed Blueprints and models based on the plan here. The documents that emerged from the Blueprints era look more like modern blueprints than Blueprints ones from the Blueprints Period. In fact, Blueprints and engineer Filippo Brunelleschi Blueprints the camera obscura to copy architectural details from the classical ruins that inspired his work. Today, Brunelleschi is considered to be the father the modern history of blueprints. The architects of the Blueprints period brought architectural drawing Blueprints we know it into existence, precisely and accurately reproducing the detail Blueprints a structure via the tools of scale and perspective.
    [Show full text]
  • Interactive Statistical Graphics/ When Charts Come to Life
    Titel Event, Date Author Affiliation Interactive Statistical Graphics When Charts come to Life [email protected] www.theusRus.de Telefónica Germany Interactive Statistical Graphics – When Charts come to Life PSI Graphics One Day Meeting Martin Theus 2 www.theusRus.de What I do not talk about … Interactive Statistical Graphics – When Charts come to Life PSI Graphics One Day Meeting Martin Theus 3 www.theusRus.de … still not what I mean. Interactive Statistical Graphics – When Charts come to Life PSI Graphics One Day Meeting Martin Theus 4 www.theusRus.de Interactive Graphics ≠ Dynamic Graphics • Interactive Graphics … uses various interactions with the plots to change selections and parameters quickly. Interactive Statistical Graphics – When Charts come to Life PSI Graphics One Day Meeting Martin Theus 4 www.theusRus.de Interactive Graphics ≠ Dynamic Graphics • Interactive Graphics … uses various interactions with the plots to change selections and parameters quickly. • Dynamic Graphics … uses animated / rotating plots to visualize high dimensional (continuous) data. Interactive Statistical Graphics – When Charts come to Life PSI Graphics One Day Meeting Martin Theus 4 www.theusRus.de Interactive Graphics ≠ Dynamic Graphics • Interactive Graphics … uses various interactions with the plots to change selections and parameters quickly. • Dynamic Graphics … uses animated / rotating plots to visualize high dimensional (continuous) data. 1973 PRIM-9 Tukey et al. Interactive Statistical Graphics – When Charts come to Life PSI Graphics One Day Meeting Martin Theus 4 www.theusRus.de Interactive Graphics ≠ Dynamic Graphics • Interactive Graphics … uses various interactions with the plots to change selections and parameters quickly. • Dynamic Graphics … uses animated / rotating plots to visualize high dimensional (continuous) data.
    [Show full text]
  • A Very Simple Approach for 3-D to 2-D Mapping
    Image Processing & Communications, vol. 11, no. 2, pp. 75-82 75 A VERY SIMPLE APPROACH FOR 3-D TO 2-D MAPPING SANDIPAN DEY (1), AJITH ABRAHAM (2),SUGATA SANYAL (3) (1) Anshin Soft ware Pvt. Ltd. INFINITY, Tower - II, 10th Floor, Plot No.- 43. Block - GP, Salt Lake Electronics Complex, Sector - V, Kolkata - 700091 email: [email protected] (2) IITA Professorship Program, School of Computer Science, Yonsei University, 134 Shinchon-dong, Sudaemoon-ku, Seoul 120-749, Republic of Korea email: [email protected] (3) School of Technology & Computer Science Tata Institute of Fundamental Research Homi Bhabha Road, Mumbai - 400005, INDIA email: [email protected] Abstract. libraries with any kind of system is often a tough trial. This article presents a very simple method of Many times we need to plot 3-D functions e.g., in mapping from 3-D to 2-D, that is free from any com- many scientific experiments. To plot this 3-D func- plex pre-operation, also it will work with any graph- tions on 2-D screen it requires some kind of map- ics system where we have some primitive 2-D graph- ping. Though OpenGL, DirectX etc 3-D rendering ics function. Also we discuss the inverse transform libraries have made this job very simple, still these and how to do basic computer graphics transforma- libraries come with many complex pre-operations tions using our coordinate mapping system. that are simply not intended, also to integrate these 76 S. Dey, A. Abraham, S. Sanyal 1 Introduction 2 Proposed approach We have a pictorial representation (Fig.1) of our 3-D to 2-D mapping system: We have a function f : R2 → R, and our intention is to draw the function in 2-D plane.
    [Show full text]
  • The New Plot to Hijack GIS and Mapping
    The New Plot to Hijack GIS and Mapping A bill recently introduced in the U.S. Senate could effectively exclude everyone but licensed architects, engineers, and surveyors from federal government contracts for GIS and mapping services of all kinds – not just those services traditionally provided by surveyors. The Geospatial Data Act (GDA) of 2017 (S.1253) would set up a system of exclusionary procurement that would prevent most companies and organizations in the dynamic and rapidly growing GIS and mapping sector from receiving federal contracts for a very-wide range of activities, including GPS field data collection, GIS, internet mapping, geospatial analysis, location based services, remote sensing, academic research involving maps, and digital or manual map making or cartography of almost any type. Not only would this bill limit competition, innovation and free-market approaches for a crucial high-growth information technology (IT) sector of the U.S. economy, it also would cripple the current vibrant GIS industry and damage U.S. geographic information science, research capacity, and competitiveness. The proposed bill would also shackle government agencies, all of which depend upon the productivity, talent, scientific and technical skills, and the creativity and innovation that characterize the vast majority of the existing GIS and mapping workforce. The GDA bill focuses on a 1972 federal procurement law called the Brooks Act that reasonably limits federal contracts for specific, traditional architectural and engineering services to licensed A&E firms. We have no problem with that. However, if S.1253 were enacted, the purpose of the Brooks Act would be radically altered and its scope dramatically expanded by also including all mapping and GIS services as “A&E services” which would henceforth would be required to be procured under the exclusionary Brooks Act (accessible only to A&E firms) to the great detriment of the huge existing GIS IT sector and all other related companies and organizations which have long been engaged in cutting-edge GIS and mapping.
    [Show full text]
  • Defining Visual Rhetorics §
    DEFINING VISUAL RHETORICS § DEFINING VISUAL RHETORICS § Edited by Charles A. Hill Marguerite Helmers University of Wisconsin Oshkosh LAWRENCE ERLBAUM ASSOCIATES, PUBLISHERS 2004 Mahwah, New Jersey London This edition published in the Taylor & Francis e-Library, 2008. “To purchase your own copy of this or any of Taylor & Francis or Routledge’s collection of thousands of eBooks please go to www.eBookstore.tandf.co.uk.” Copyright © 2004 by Lawrence Erlbaum Associates, Inc. All rights reserved. No part of this book may be reproduced in any form, by photostat, microform, retrieval system, or any other means, without prior written permission of the publisher. Lawrence Erlbaum Associates, Inc., Publishers 10 Industrial Avenue Mahwah, New Jersey 07430 Cover photograph by Richard LeFande; design by Anna Hill Library of Congress Cataloging-in-Publication Data Definingvisual rhetorics / edited by Charles A. Hill, Marguerite Helmers. p. cm. Includes bibliographical references and index. ISBN 0-8058-4402-3 (cloth : alk. paper) ISBN 0-8058-4403-1 (pbk. : alk. paper) 1. Visual communication. 2. Rhetoric. I. Hill, Charles A. II. Helmers, Marguerite H., 1961– . P93.5.D44 2003 302.23—dc21 2003049448 CIP ISBN 1-4106-0997-9 Master e-book ISBN To Anna, who inspires me every day. —C. A. H. To Emily and Caitlin, whose artistic perspective inspires and instructs. —M. H. H. Contents Preface ix Introduction 1 Marguerite Helmers and Charles A. Hill 1 The Psychology of Rhetorical Images 25 Charles A. Hill 2 The Rhetoric of Visual Arguments 41 J. Anthony Blair 3 Framing the Fine Arts Through Rhetoric 63 Marguerite Helmers 4 Visual Rhetoric in Pens of Steel and Inks of Silk: 87 Challenging the Great Visual/Verbal Divide Maureen Daly Goggin 5 Defining Film Rhetoric: The Case of Hitchcock’s Vertigo 111 David Blakesley 6 Political Candidates’ Convention Films:Finding the Perfect 135 Image—An Overview of Political Image Making J.
    [Show full text]