Good Scientific Visualization Practices + Python
|
|
- Gerard Fisher
- 8 years ago
- Views:
Transcription
1 Good Scientific Visualization Practices + Python Kristen Thyng Python in Geosciences September 19, 2013 Kristen Thyng (Texas A&M) Visualization September 19, / 29
2 Outline Overview of Bad Plotting 1 Overview of Bad Plotting 2 Perceptually-based colormaps 3 Matplotlib Tools 4 Miscellaneous Considerations Kristen Thyng (Texas A&M) Visualization September 19, / 29
3 Overview of Bad Plotting There are lots of bad plots out there Using 3D when unnecessary Kristen Thyng (Texas A&M) Visualization September 19, / 29
4 Overview of Bad Plotting There are lots of bad plots out there What is the dominant feature of this plot? jpboyd/sciviz 1 graphbadly.pdf Kristen Thyng (Texas A&M) Visualization September 19, / 29
5 Overview of Bad Plotting There are lots of bad plots out there Inconsistent representations jpboyd/sciviz 1 graphbadly.pdf Kristen Thyng (Texas A&M) Visualization September 19, / 29
6 Overview of Bad Plotting There are lots of bad plots out there Unintuitive representation Kristen Thyng (Texas A&M) Visualization September 19, / 29
7 Overview of Bad Plotting There are lots of bad plots out there Chart junk Tufte, Edward R. (1983). The Visual Display of Quantitative Information. Cheshire, CT: Graphics Press. Kristen Thyng (Texas A&M) Visualization September 19, / 29
8 Goals in Visualization Overview of Bad Plotting Honestly present the information - no cheating! Clear presentation - nothing unnecessary Label axes, units, etc, with large lettering Intuitive, consistent markers/coloring Give context Kristen Thyng (Texas A&M) Visualization September 19, / 29
9 Better plots Overview of Bad Plotting Using 3D when unnecessary Kristen Thyng (Texas A&M) Visualization September 19, / 29
10 Better plots Overview of Bad Plotting Better presentation, though pie charts not great Kristen Thyng (Texas A&M) Visualization September 19, / 29
11 Better plots Overview of Bad Plotting What is the dominant feature of this plot? jpboyd/sciviz 1 graphbadly.pdf Kristen Thyng (Texas A&M) Visualization September 19, / 29
12 Better plots Overview of Bad Plotting Simpler is often better jpboyd/sciviz 1 graphbadly.pdf Kristen Thyng (Texas A&M) Visualization September 19, / 29
13 Better plots Overview of Bad Plotting Pick a representation and stick with it jpboyd/sciviz 1 graphbadly.pdf Kristen Thyng (Texas A&M) Visualization September 19, / 29
14 Better plots Overview of Bad Plotting Unintuitive representation Kristen Thyng (Texas A&M) Visualization September 19, / 29
15 Better plots Overview of Bad Plotting Color choices are key Kristen Thyng (Texas A&M) Visualization September 19, / 29
16 Better plots Overview of Bad Plotting Color choices are key rkriz/projects/create color table/color 07.pdf Kristen Thyng (Texas A&M) Visualization September 19, / 29
17 Better plots Overview of Bad Plotting Chart junk Tufte, Edward R. (1983). The Visual Display of Quantitative Information. Cheshire, CT: Graphics Press. Kristen Thyng (Texas A&M) Visualization September 19, / 29
18 Outline Perceptually-based colormaps 1 Overview of Bad Plotting 2 Perceptually-based colormaps 3 Matplotlib Tools 4 Miscellaneous Considerations Kristen Thyng (Texas A&M) Visualization September 19, / 29
19 Perceptually-based colormaps Data Types Categorical: Representing discrete things, not on continuum Interval or sequential: intervals of the data are equal distances Ratio or diverging: has critical value (often 0) and ratios of values are equal Kristen Thyng (Texas A&M) Visualization September 19, / 29
20 Perceptually-based colormaps Data Types Categorical: Representing discrete things, not on continuum Interval or sequential: intervals of the data are equal distances Ratio or diverging: has critical value (often 0) and ratios of values are equal Kristen Thyng (Texas A&M) Visualization September 19, / 29
21 Perceptually-based colormaps Data Types Categorical: Representing discrete things, not on continuum Interval or sequential: intervals of the data are equal distances Ratio or diverging: has critical value (often 0) and ratios of values are equal Kristen Thyng (Texas A&M) Visualization September 19, / 29
22 Perceptually-based colormaps Data Types Categorical: Representing discrete things, not on continuum Interval or sequential: intervals of the data are equal distances Ratio or diverging: has critical value (often 0) and ratios of values are equal Kristen Thyng (Texas A&M) Visualization September 19, / 29
23 Perceptually-based colormaps Data Types Categorical: Representing discrete things, not on continuum Interval or sequential: intervals of the data are equal distances Ratio or diverging: has critical value (often 0) and ratios of values are equal Kristen Thyng (Texas A&M) Visualization September 19, / 29
24 Perceptually-based colormaps Data Types Categorical: Representing discrete things, not on continuum Interval or sequential: intervals of the data are equal distances Ratio or diverging: has critical value (often 0) and ratios of values are equal Equal steps in data should correspond to equal steps in perception Kristen Thyng (Texas A&M) Visualization September 19, / 29
25 Color is 3D Perceptually-based colormaps r: saturation/color intensity, z: luminance/brightness, θ: color hue Kristen Thyng (Texas A&M) Visualization September 19, / 29
26 Color is 3D Perceptually-based colormaps r: saturation/color intensity, z: luminance/brightness, θ: color hue good for representing continuous variations in data magnitude Kristen Thyng (Texas A&M) Visualization September 19, / 29
27 Jet Luminance Perceptually-based colormaps files/28982/16/spectrum vs cubicyf.png Kristen Thyng (Texas A&M) Visualization September 19, / 29
28 Perceptually-based colormaps Hue-based Colormaps Perceptually Distort Information Kristen Thyng (Texas A&M) Visualization September 19, / 29
29 Perceptually-based colormaps Luminance and Saturation Colormaps Kristen Thyng (Texas A&M) Visualization September 19, / 29
30 Perceptually-based colormaps Improved Colormapping Kristen Thyng (Texas A&M) Visualization September 19, / 29
31 Outline Matplotlib Tools 1 Overview of Bad Plotting 2 Perceptually-based colormaps 3 Matplotlib Tools 4 Miscellaneous Considerations Kristen Thyng (Texas A&M) Visualization September 19, / 29
32 Types of Colormaps Matplotlib Tools Kristen Thyng (Texas A&M) Visualization September 19, / 29
33 Types of Colormaps Matplotlib Tools reference.html Kristen Thyng (Texas A&M) Visualization September 19, / 29
34 Types of Colormaps Matplotlib Tools reference.html Kristen Thyng (Texas A&M) Visualization September 19, / 29
35 Types of Colormaps Matplotlib Tools reference.html Kristen Thyng (Texas A&M) Visualization September 19, / 29
36 Types of Colormaps Matplotlib Tools reference.html Kristen Thyng (Texas A&M) Visualization September 19, / 29
37 Subplot Matplotlib Tools axes and figures/subplot demo.html Kristen Thyng (Texas A&M) Visualization September 19, / 29
38 Axes Grid Toolbox Matplotlib Tools Kristen Thyng (Texas A&M) Visualization September 19, / 29
39 Inset Axes Matplotlib Tools Kristen Thyng (Texas A&M) Visualization September 19, / 29
40 Overlaid Axes Matplotlib Tools Kristen Thyng (Texas A&M) Visualization September 19, / 29
41 Matplotlib Tools Overlaid Axes: Colorbar Placement Kristen Thyng (Texas A&M) Visualization September 19, / 29
42 Outline Miscellaneous Considerations 1 Overview of Bad Plotting 2 Perceptually-based colormaps 3 Matplotlib Tools 4 Miscellaneous Considerations Kristen Thyng (Texas A&M) Visualization September 19, / 29
43 Miscellaneous Considerations Use Colormaps for Different Purposes Kristen Thyng (Texas A&M) Visualization September 19, / 29
44 Cube Helix Miscellaneous Considerations Proper intensity scaling, though do consider application dag/cubehelix/ Kristen Thyng (Texas A&M) Visualization September 19, / 29
45 Give Context Miscellaneous Considerations Kristen Thyng (Texas A&M) Visualization September 19, / 29
46 Be Consistent Miscellaneous Considerations Kristen Thyng (Texas A&M) Visualization September 19, / 29
47 Miscellaneous Considerations Discrete vs. Continuous Colorbar Kristen Thyng (Texas A&M) Visualization September 19, / 29
48 Miscellaneous Considerations Diverging Colormap: Critical value treatment Kristen Thyng (Texas A&M) Visualization September 19, / 29
49 Miscellaneous Considerations Printing in Black and White from Color Change the design choices altogether, or be sure the colors will print to different grayscale values Kristen Thyng (Texas A&M) Visualization September 19, / 29
50 Color Blindness Miscellaneous Considerations Problems distinguishing green-yellow-red, and rarely blue-yellow Kristen Thyng (Texas A&M) Visualization September 19, / 29
51 Resources Miscellaneous Considerations Edward Tufte s books IBM articles: Color Brewer: Color advice for maps: IEEE Computer Society article: rkriz/projects/create color table/color 07.pdf MatLab colormap function, with lots of references: perceptually-improved-colormaps/content/pmkmp.m Cube Helix colormap: dag/cubehelix/+ Test plots for sensitivity to color blindness: Matplotlib example gallery: Useful design blog: Figures writeup: Kristen Thyng (Texas A&M) Visualization September 19, / 29
Choosing Colors for Data Visualization Maureen Stone January 17, 2006
Choosing Colors for Data Visualization Maureen Stone January 17, 2006 The problem of choosing colors for data visualization is expressed by this quote from information visualization guru Edward Tufte:
More informationExpert Color Choices for Presenting Data
Expert Color Choices for Presenting Data Maureen Stone, StoneSoup Consulting The problem of choosing colors for data visualization is expressed by this quote from information visualization guru Edward
More informationInformation visualization examples
Information visualization examples 350102: GenICT II 37 Information visualization examples 350102: GenICT II 38 Information visualization examples 350102: GenICT II 39 Information visualization examples
More informationData Visualization. Introductions
Data Visualization Introduction Process Why Not Design Principles Resources Why Tools Ben Thompson ben.thompson@kingcounty.gov WSLGAA, March 20, 2012 1 Me Data visualization Introductions 2 3 4 Data Visualization
More informationEscaping RGBland: Selecting Colors for Statistical Graphics
Escaping RGBland: Selecting Colors for Statistical Graphics Achim Zeileis, Kurt Hornik, Paul Murrell http://statmath.wu.ac.at/~zeileis/ Overview Motivation Color in statistical graphics Challenges Illustrations
More informationCommon Mistakes in Data Presentation Stephen Few September 4, 2004
Common Mistakes in Data Presentation Stephen Few September 4, 2004 I'm going to take you on a short stream-of-consciousness tour through a few of the most common and sometimes downright amusing problems
More informationVisualizing Historical Agricultural Data: The Current State of the Art Irwin Anolik (USDA National Agricultural Statistics Service)
Visualizing Historical Agricultural Data: The Current State of the Art Irwin Anolik (USDA National Agricultural Statistics Service) Abstract This paper reports on methods implemented at the National Agricultural
More informationPrinciples of Data Visualization
Principles of Data Visualization by James Bernhard Spring 2012 We begin with some basic ideas about data visualization from Edward Tufte (The Visual Display of Quantitative Information (2nd ed.)) He gives
More informationData Visualization. Scientific Principles, Design Choices and Implementation in LabKey. Cory Nathe Software Engineer, LabKey cnathe@labkey.
Data Visualization Scientific Principles, Design Choices and Implementation in LabKey Catherine Richards, PhD, MPH Staff Scientist, HICOR crichar2@fredhutch.org Cory Nathe Software Engineer, LabKey cnathe@labkey.com
More informationGRAPHING DATA FOR DECISION-MAKING
GRAPHING DATA FOR DECISION-MAKING Tibor Tóth, Ph.D. Center for Applied Demography and Survey Research (CADSR) University of Delaware Fall, 2006 TABLE OF CONTENTS Introduction... 3 Use High Information
More informationPart 2: Data Visualization How to communicate complex ideas with simple, efficient and accurate data graphics
Part 2: Data Visualization How to communicate complex ideas with simple, efficient and accurate data graphics Why visualize data? The human eye is extremely sensitive to differences in: Pattern Colors
More informationCS171 Visualization. The Visualization Alphabet: Marks and Channels. Alexander Lex alex@seas.harvard.edu. [xkcd]
CS171 Visualization Alexander Lex alex@seas.harvard.edu The Visualization Alphabet: Marks and Channels [xkcd] This Week Thursday: Task Abstraction, Validation Homework 1 due on Friday! Any more problems
More informationData Visualization Best Practice. Sophie Sparkes Data Analyst
Data Visualization Best Practice Sophie Sparkes Data Analyst http://graphics.wsj.com/infectious-diseases-and-vaccines/ http://blogs.sas.com/content/jmp/2015/03/05/graph-makeover-measles-heat-map/ http://graphics.wsj.com/infectious-diseases-and-vaccines/
More informationBest Practices for Dashboard Design with SAP BusinessObjects Design Studio
Ingo Hilgefort, SAP Mentor February 2015 Agenda Best Practices on Dashboard Design Performance BEST PRACTICES FOR DASHBOARD DESIGN WITH SAP BUSINESSOBJECTS DESIGN STUDIO DASHBOARD DESIGN What is a dashboard
More informationR Graphics Cookbook. Chang O'REILLY. Winston. Tokyo. Beijing Cambridge. Farnham Koln Sebastopol
R Graphics Cookbook Winston Chang Beijing Cambridge Farnham Koln Sebastopol O'REILLY Tokyo Table of Contents Preface ix 1. R Basics 1 1.1. Installing a Package 1 1.2. Loading a Package 2 1.3. Loading a
More informationExamples of Data Representation using Tables, Graphs and Charts
Examples of Data Representation using Tables, Graphs and Charts This document discusses how to properly display numerical data. It discusses the differences between tables and graphs and it discusses various
More informationNumbers as pictures: Examples of data visualization from the Business Employment Dynamics program. October 2009
Numbers as pictures: Examples of data visualization from the Business Employment Dynamics program. October 2009 Charles M. Carson 1 1 U.S. Bureau of Labor Statistics, Washington, DC Abstract The Bureau
More informationVisibility optimization for data visualization: A Survey of Issues and Techniques
Visibility optimization for data visualization: A Survey of Issues and Techniques Ch Harika, Dr.Supreethi K.P Student, M.Tech, Assistant Professor College of Engineering, Jawaharlal Nehru Technological
More informationData Visualization & Dashboard Design Best Practices and Tips
Data Visualization & Dashboard Design Best Practices and Tips Understanding the User is the Key to Designing User-Centric Analytical Dashboards User-centric design is Catered specifically to the needs
More informationBased on Chapter 11, Excel 2007 Dashboards & Reports (Alexander) and Create Dynamic Charts in Microsoft Office Excel 2007 and Beyond (Scheck)
Reporting Results: Part 2 Based on Chapter 11, Excel 2007 Dashboards & Reports (Alexander) and Create Dynamic Charts in Microsoft Office Excel 2007 and Beyond (Scheck) Bullet Graph (pp. 200 205, Alexander,
More informationGet The Picture: Visualizing Financial Data part 1
Get The Picture: Visualizing Financial Data part 1 by Jeremy Walton Turning numbers into pictures is usually the easiest way of finding out what they mean. We're all familiar with the display of for example
More informationQuantitative vs. Categorical Data: A Difference Worth Knowing Stephen Few April 2005
Quantitative vs. Categorical Data: A Difference Worth Knowing Stephen Few April 2005 When you create a graph, you step through a series of choices, including which type of graph you should use and several
More informationHistory and Principles of Data Visualization
History and Principles of Data Visualization (CMSC 34900-1 Topics in Scientific Computing; Autumn 2014) http://people.cs.uchicago.edu/~glk/class/histovis/ Sept 30, 2014 Gordon Kindlmann How to learn about
More informationStephen Few, Perceptual Edge Visual Business Intelligence Newsletter November/December 2008
perceptual edge Line Graphs and Irregular Intervals: An Incompatible Partnership Stephen Few, Perceptual Edge Visual Business Intelligence Newsletter November/December 28 I recently received an email from
More informationVisualization Software
Visualization Software Maneesh Agrawala CS 294-10: Visualization Fall 2007 Assignment 1b: Deconstruction & Redesign Due before class on Sep 12, 2007 1 Assignment 2: Creating Visualizations Use existing
More information(1) Organize the data
Effective Communication Through Visual Design: Tables and Charts Strategy Institute 2011 Rebecca Carr, AAU Data Exchange, rcarr2@unl.edu Mary Harrington, University of Mississippi, ccmary@olemiss.edu Creating
More informationVisualization. For Novices. ( Ted Hall ) University of Michigan 3D Lab Digital Media Commons, Library http://um3d.dc.umich.edu
Visualization For Novices ( Ted Hall ) University of Michigan 3D Lab Digital Media Commons, Library http://um3d.dc.umich.edu Data Visualization Data visualization deals with communicating information about
More informationDiagrams and Graphs of Statistical Data
Diagrams and Graphs of Statistical Data One of the most effective and interesting alternative way in which a statistical data may be presented is through diagrams and graphs. There are several ways in
More informationTechnology in Testing An Introduction to Data Visualization for Testing
Technology in Testing An Introduction to Data Visualization for Testing By: Brian D Bontempo, PhD Mountain Measurement, Inc. Brian served on the NCCA Standards Revision Steering Committee and is a Psychometric
More informationData Visualization. Steve Marschner Cornell CS 3220
Steve Marschner unless noted, images are from Tufte, The Visual Display of Quantitative Information (these slides also indebted to Pat Hanrahan s slides for CS448B at Stanford) Data A lot of 3220 is about
More informationCSU, Fresno - Institutional Research, Assessment and Planning - Dmitri Rogulkin
My presentation is about data visualization. How to use visual graphs and charts in order to explore data, discover meaning and report findings. The goal is to show that visual displays can be very effective
More informationUtilizing spatial information systems for non-spatial-data analysis
Jointly published by Akadémiai Kiadó, Budapest Scientometrics, and Kluwer Academic Publishers, Dordrecht Vol. 51, No. 3 (2001) 563 571 Utilizing spatial information systems for non-spatial-data analysis
More informationData Analysis & Visualization for Security Professionals
Data Analysis & Visualization for Security Professionals Jay Jacobs Verizon Bob Rudis Liberty Mutual Insurance Session ID: GRC- T18 Session Classification: Intermediate Key Learning Points Key Learning
More informationCollaborative Data Analysis on Wall Displays
Collaborative Data Analysis on Wall Displays Challenges for Visualization Petra Isenberg (petra.isenberg@inria.fr) Anastasia Bezerianos (anastasia.bezerianos@lri.fr) 2 [source: The Diverse and Exploding
More informationPerformance evaluation
Visualization of experimental data Jean-Marc Vincent MESCAL-INRIA Project Laboratoire d Informatique de Grenoble Universities of Grenoble, France {Jean-Marc.Vincent}@imag.fr This work was partially supported
More informationPrinciples of Data Visualization for Exploratory Data Analysis. Renee M. P. Teate. SYS 6023 Cognitive Systems Engineering April 28, 2015
Principles of Data Visualization for Exploratory Data Analysis Renee M. P. Teate SYS 6023 Cognitive Systems Engineering April 28, 2015 Introduction Exploratory Data Analysis (EDA) is the phase of analysis
More informationLecture 2: Descriptive Statistics and Exploratory Data Analysis
Lecture 2: Descriptive Statistics and Exploratory Data Analysis Further Thoughts on Experimental Design 16 Individuals (8 each from two populations) with replicates Pop 1 Pop 2 Randomly sample 4 individuals
More informationDATA VISUALIZATION 101: HOW TO DESIGN CHARTS AND GRAPHS
DATA VISUALIZATION 101: HOW TO DESIGN CHARTS AND GRAPHS + TABLE OF CONTENTS INTRO 1 FINDING THE STORY IN YOUR DATA 2 KNOW YOUR DATA 3 GUIDE TO CHART TYPES 5 Bar Chart Pie Chart Line Chart Area Chart Scatter
More informationVisualization Quick Guide
Visualization Quick Guide A best practice guide to help you find the right visualization for your data WHAT IS DOMO? Domo is a new form of business intelligence (BI) unlike anything before an executive
More informationNumber Visualization
Number Visualization Giuseppe Santucci University of Rome "La Sapienza" santucci@dis.uniroma1.it Thanks to: Ross Ihaka (very inspiring lectures) Number visualization? Obviously information visualization
More informationChapter 6: Constructing and Interpreting Graphic Displays of Behavioral Data
Chapter 6: Constructing and Interpreting Graphic Displays of Behavioral Data Chapter Focus Questions What are the benefits of graphic display and visual analysis of behavioral data? What are the fundamental
More informationVisualizing Multidimensional Data Through Time Stephen Few July 2005
Visualizing Multidimensional Data Through Time Stephen Few July 2005 This is the first of three columns that will feature the winners of DM Review's 2005 data visualization competition. I want to extend
More informationUNDERSTANDING HUMAN FACTORS OF BIG DATA VISUALIZATION
UNDERSTANDING HUMAN FACTORS OF BIG DATA VISUALIZATION Mark A. Livingston, Ph.D. Jonathan W. Decker Zhuming Ai, Ph.D. United States Naval Research Laboratory Washington, DC TTC Big Data Symposium Rosslyn,
More informationChoosing a successful structure for your visualization
IBM Software Business Analytics Visualization Choosing a successful structure for your visualization By Noah Iliinsky, IBM Visualization Expert 2 Choosing a successful structure for your visualization
More informationUnresolved issues with the course, grades, or instructor, should be taken to the point of contact.
Graphics and Data Visualization CS1501 Fall 2013 Syllabus Course Description With the advent of powerful data-mining technologies, engineers in all disciplines are increasingly expected to be conscious
More informationBest Practices in Data Visualizations. Vihao Pham 2014
Best Practices in Data Visualizations Vihao Pham 2014 Agenda Best Practices in Data Visualizations Why We Visualize Understanding Data Visualizations Enhancing Visualizations Visualization Considerations
More informationBest Practices in Data Visualizations. Vihao Pham January 29, 2014
Best Practices in Data Visualizations Vihao Pham January 29, 2014 Agenda Best Practices in Data Visualizations Why We Visualize Understanding Data Visualizations Enhancing Visualizations Visualization
More informationToday's Topics. COMP 388/441: Human-Computer Interaction. simple 2D plotting. 1D techniques. Ancient plotting techniques. Data Visualization:
COMP 388/441: Human-Computer Interaction Today's Topics Overview of visualization techniques 1D charts, 2D plots, 3D+ techniques, maps A few guidelines for scientific visualization methods, guidelines,
More informationHow To Color In A Computer Graphics Program
Color is the most relative medium in art. Josef Albers, Interaction of Color Designing Colors for Data Maureen Stone StoneSoup Consulting yellow Good painting, good coloring, is comparable to good cooking.
More informationOverview. Raster Graphics and Color. Overview. Display Hardware. Liquid Crystal Display (LCD) Cathode Ray Tube (CRT)
Raster Graphics and Color Greg Humphreys CS445: Intro Graphics University of Virginia, Fall 2004 Color models Color models Display Hardware Video display devices Cathode Ray Tube (CRT) Liquid Crystal Display
More informationWhy Taking This Course? Course Introduction, Descriptive Statistics and Data Visualization. Learning Goals. GENOME 560, Spring 2012
Why Taking This Course? Course Introduction, Descriptive Statistics and Data Visualization GENOME 560, Spring 2012 Data are interesting because they help us understand the world Genomics: Massive Amounts
More informationWhat is Visualization? Information Visualization An Overview. Information Visualization. Definitions
What is Visualization? Information Visualization An Overview Jonathan I. Maletic, Ph.D. Computer Science Kent State University Visualize/Visualization: To form a mental image or vision of [some
More informationQuantitative Displays for Combining Time-Series and Part-to-Whole Relationships
Quantitative Displays for Combining Time-Series and Part-to-Whole Relationships Stephen Few, Perceptual Edge Visual Business Intelligence Newsletter January, February, and March 211 Graphical displays
More informationDashboard Design for Rich and Rapid Monitoring
Dashboard Design for Rich and Rapid Monitoring Stephen Few Visual Business Intelligence Newsletter November 2006 This article is the fourth in a five-part series that features the winning solutions to
More informationAll Visualizations Documentation
All Visualizations Documentation All Visualizations Documentation 2 Copyright and Trademarks Licensed Materials - Property of IBM. Copyright IBM Corp. 2013 IBM, the IBM logo, and Cognos are trademarks
More informationA Short Introduction on Data Visualization. Guoning Chen
A Short Introduction on Data Visualization Guoning Chen Data is generated everywhere and everyday Age of Big Data Data in ever increasing sizes need an effective way to understand them History of Visualization
More informationThis file contains 2 years of our interlibrary loan transactions downloaded from ILLiad. 70,000+ rows, multiple fields = an ideal file for pivot
Presented at the Southeastern Library Assessment Conference, October 22, 2013 1 2 3 This file contains 2 years of our interlibrary loan transactions downloaded from ILLiad. 70,000+ rows, multiple fields
More informationTEXT-FILLED STACKED AREA GRAPHS Martin Kraus
Martin Kraus Text can add a significant amount of detail and value to an information visualization. In particular, it can integrate more of the data that a visualization is based on, and it can also integrate
More informationCreate a Data Visualization Cookbook Using Excel. Yueyue Fan, Ethan Pan, Cheryl M. Ackerman
Create a Data Visualization Cookbook Using Excel Yueyue Fan, Ethan Pan, Cheryl M. Ackerman Why learn about Data Visualization? Agenda Introduce simple steps for effective visualization Hands-on practice
More informationIris Sample Data Set. Basic Visualization Techniques: Charts, Graphs and Maps. Summary Statistics. Frequency and Mode
Iris Sample Data Set Basic Visualization Techniques: Charts, Graphs and Maps CS598 Information Visualization Spring 2010 Many of the exploratory data techniques are illustrated with the Iris Plant data
More informationVariable: characteristic that varies from one individual to another in the population
Goals: Recognize variables as: Qualitative or Quantitative Discrete Continuous Study Ch. 2.1, # 1 13 : Prof. G. Battaly, Westchester Community College, NY Study Ch. 2.1, # 1 13 Variable: characteristic
More informationSometimes We Must Raise Our Voices
perceptual edge Sometimes We Must Raise Our Voices Stephen Few, Perceptual Edge Visual Business Intelligence Newsletter January/February 9 When we create a graph, we design it to tell a story. To do this,
More informationGeneralized Automatic Color Selection for Visualization
Generalized Automatic Color Selection for Visualization Amy Ciavolino and Tim Burke Abstract Creating a perceptually distinct coloring for visualizing large data sets with one or more related properties
More informationGet to the Point HOW GOOD DATA VISUALIZATION IMPROVES BUSINESS DECISIONS
Get to the Point HOW GOOD DATA VISUALIZATION IMPROVES BUSINESS DECISIONS Whatever presents itself quickly and clearly to the mind sets it to work, to reason, and to think. - William Playfair, inventor
More informationan introduction to VISUALIZING DATA by joel laumans
an introduction to VISUALIZING DATA by joel laumans an introduction to VISUALIZING DATA iii AN INTRODUCTION TO VISUALIZING DATA by Joel Laumans Table of Contents 1 Introduction 1 Definition Purpose 2 Data
More informationGood Graphs: Graphical Perception and Data Visualization
Good Graphs: Graphical Perception and Data Visualization Nina Zumel August 28th, 2009 1 Introduction What makes a good graph? When faced with a slew of numeric data, graphical visualization can be a more
More informationData Visualization Handbook
SAP Lumira Data Visualization Handbook www.saplumira.com 1 Table of Content 3 Introduction 20 Ranking 4 Know Your Purpose 23 Part-to-Whole 5 Know Your Data 25 Distribution 9 Crafting Your Message 29 Correlation
More informationData Visualization Style Guidelines
Data Visualization Style Guidelines FOREWARD This guide includes: basic graph structure bar graphs line graphs pie charts scatter plots maps graph or chart colors choropleth colors for maps network visualization
More informationEffectively Communicating Numbers
EMBARKING ON A NEW JOURNEY Effectively Communicating Numbers Selecting the Best Means and Manner of Display by Stephen Few Principal, Perceptual Edge November 2005 SPECIAL ADDENDUM Effectively Communicating
More informationMetroBoston DataCommon Training
MetroBoston DataCommon Training Whether you are a data novice or an expert researcher, the MetroBoston DataCommon can help you get the information you need to learn more about your community, understand
More informationDesigning science graphs for data analysis and presentation
Designing science graphs for data analysis and presentation The bad, the good and the better Dave Kelly, Jaap Jasperse and Ian Westbrooke DEPARTMENT OF CONSERVATION TECHNICAL SERIES 32 Published by Science
More informationInformation Literacy Program
Information Literacy Program Excel (2013) Advanced Charts 2015 ANU Library anulib.anu.edu.au/training ilp@anu.edu.au Table of Contents Excel (2013) Advanced Charts Overview of charts... 1 Create a chart...
More informationIntroduction to MATLAB (Basics) Reference from: Azernikov Sergei mesergei@tx.technion.ac.il
Introduction to MATLAB (Basics) Reference from: Azernikov Sergei mesergei@tx.technion.ac.il MATLAB Basics Where to get help? 1) In MATLAB s prompt type: help, lookfor,helpwin, helpdesk, demos. 2) On the
More informationA Short Introduction Prepared by Mirya Holman
A Short Introduction Prepared by Mirya Holman There are three kinds of data Qualitative Quantitative Ordinal Qualitative (also called ordinal) data is distinguished by being a set of unordered categories.
More informationThe basics of storytelling through numbers
Data Visualizations 101 The basics of storytelling through numbers C olleges and universities have a lot of stories to tell to a lot of different people. Prospective students and parents want to know if
More informationProblems With Using Microsoft Excel for Statistics
Proceedings of the Annual Meeting of the American Statistical Association, August 5-9, 2001 Problems With Using Microsoft Excel for Statistics Jonathan D. Cryer (Jon-Cryer@uiowa.edu) Department of Statistics
More informationDesign and Deployment of Specialized Visualizations for Weather-Sensitive Electric Distribution Operations
Fourth Symposium on Policy and Socio-Economic Research 4.1 Design and Deployment of Specialized Visualizations for Weather-Sensitive Electric Distribution Operations Lloyd A. Treinish IBM, Yorktown Heights,
More informationMARS STUDENT IMAGING PROJECT
MARS STUDENT IMAGING PROJECT Data Analysis Practice Guide Mars Education Program Arizona State University Data Analysis Practice Guide This set of activities is designed to help you organize data you collect
More informationOBI 11g Data Visualization Best Practices
OBI 11g Data Visualization Best Practices Tim Vlamis, Vlamis Software Solutions tvlamis@vlamis.com Dan Vlamis, Vlamis Software Solutions, Inc. dvlamis@vlamis.com There is a lifetime of study in understanding
More informationPresenting Health Care Data in Visual Displays
White Paper Presenting Health Care Data in Visual Displays Employer Consulting Services Page 1 White Paper Overview The manner in which data is translated into visual images for improved interpretation
More informationTOP-DOWN DATA ANALYSIS WITH TREEMAPS
TOP-DOWN DATA ANALYSIS WITH TREEMAPS Martijn Tennekes, Edwin de Jonge Statistics Netherlands (CBS), P.0.Box 4481, 6401 CZ Heerlen, The Netherlands m.tennekes@cbs.nl, e.dejonge@cbs.nl Keywords: Abstract:
More informationCharting Your Course: Charts and Graphs for IT Projects
Charting Your Course: Charts and Graphs for IT Projects Dawn Li, Ph.D. and Gary McQuown Data and Analytic Solutions, Inc. Fairfax, VA ABSTRACT This paper describes the most common types of charts and graphs
More informationOn History of Information Visualization
On History of Information Visualization Mária Kmeťová Department of Mathematics, Constantine the Philosopher University in Nitra, Tr. A. Hlinku 1, Nitra, Slovakia mkmetova@ukf.sk Keywords: Abstract: abstract
More informationAn Open-Architecture GIS Component for Creating Multiband Traffic Density Maps
An Open-Architecture GIS Component for Creating Multiband Traffic Density Maps October 15, 2012 INFORMS Annual Meeting Phoenix, AZ David Hunt (presenting author) Francis Julian Ming Lu Marc Meketon Carl
More informationVisualizing Network Relationships
Visualizing Network Relationships Scott Murray Abstract The vast majority of network visualizations are based on simple graphs and are rendered with connecting lines that communicate only one binary value:
More informationExtensible Visualizations. Product: IBM Cognos Business Intelligence Area of Interest: Reporting
Extensible Visualizations Product: IBM Cognos Business Intelligence Area of Interest: Reporting Extensible Visualizations 2 Copyright and Trademarks Licensed Materials - Property of IBM. Copyright IBM
More informationOrford, S., Dorling, D. and Harris, R. (2003) Cartography and Visualization in Rogers, A. and Viles, H.A. (eds), The Student s Companion to
Orford, S., Dorling, D. and Harris, R. (2003) Cartography and Visualization in Rogers, A. and Viles, H.A. (eds), The Student s Companion to Geography, 2nd Edition, Part III, Chapter 27, pp 151-156, Blackwell
More informationPerception of Light and Color
Perception of Light and Color Theory and Practice Trichromacy Three cones types in retina a b G+B +R Cone sensitivity functions 100 80 60 40 20 400 500 600 700 Wavelength (nm) Short wavelength sensitive
More informationSurvey Data Analysis. Qatar University. Dr. Kenneth M.Coleman (Ken.Coleman@marketstrategies.com) - University of Michigan
The following slides are the property of their authors and are provided on this website as a public service. Please do not copy or redistribute these slides without the written permission of all of the
More informationData Visualization. BUS 230: Business and Economic Research and Communication
Data Visualization BUS 230: Business and Economic Research and Communication Data Visualization 1/ 16 Purpose of graphs and charts is to show a picture that can enhance a message, or quickly communicate
More informationBetter Information from Better Data Visualization
Better Information from Better Data Visualization Nicole Arksey, INETCO Systems Ltd. Scott Chapman, American Electric Power As performance analysis and capacity planners, we collect data to share with
More informationData Harvesting, Visualisation and Analytical Tools. John Southall, Data Librarian, LSE Library
Data Harvesting, Visualisation and Analytical Tools. John Southall, Data Librarian, LSE Library Aims Will not talk about doing any of these in detail Will not demonstrate the tools BUT it will Raise some
More informationINF2793 Research Design & Academic Writing Prof. Simone D.J. Barbosa simone@inf.puc-rio.br sala 410 RDC. presentation
On Presentations INF2793 Research Design & Academic Writing Prof. Simone D.J. Barbosa simone@inf.puc-rio.br sala 410 RDC presentation @ 2013 Simone DJ Barbosa, Departamento de Informática, PUC-Rio 1 communication
More informationMultivariate data visualization using shadow
Proceedings of the IIEEJ Ima and Visual Computing Wor Kuching, Malaysia, Novembe Multivariate data visualization using shadow Zhongxiang ZHENG Suguru SAITO Tokyo Institute of Technology ABSTRACT When visualizing
More informationA Tutorial on Color Symbolization and Data Classification for Mapping and Visualization
A Tutorial on Color Symbolization and Data Classification for Mapping and Visualization Cynthia Brewer, Penn State Geography Prepared for STIS conference sponsored by BioMedware, January 9-10, 2003, in
More informationTutorial 3: Graphics and Exploratory Data Analysis in R Jason Pienaar and Tom Miller
Tutorial 3: Graphics and Exploratory Data Analysis in R Jason Pienaar and Tom Miller Getting to know the data An important first step before performing any kind of statistical analysis is to familiarize
More informationIntroduction to Geographical Data Visualization
perceptual edge Introduction to Geographical Data Visualization Stephen Few, Perceptual Edge Visual Business Intelligence Newsletter March/April 2009 The important stories that numbers have to tell often
More informationChapter 4 Creating Charts and Graphs
Calc Guide Chapter 4 OpenOffice.org Copyright This document is Copyright 2006 by its contributors as listed in the section titled Authors. You can distribute it and/or modify it under the terms of either
More informationBernice E. Rogowitz and Holly E. Rushmeier IBM TJ Watson Research Center, P.O. Box 704, Yorktown Heights, NY USA
Are Image Quality Metrics Adequate to Evaluate the Quality of Geometric Objects? Bernice E. Rogowitz and Holly E. Rushmeier IBM TJ Watson Research Center, P.O. Box 704, Yorktown Heights, NY USA ABSTRACT
More information