Data Visualization: Exploring and Explaining with Data

by Jeffrey D. Camm, James J. Cochran, Michael J. Fry, Jeffrey W. Ohlmann

Overview

DATA VISUALIZATION: Exploring and Explaining with Data is designed to introduce best practices in data visualization to undergraduate and graduate students. This is one of the first books on data visualization designed for college courses. The book contains material on effective design, choice of chart type, effective use of color, how to both explore data visually, and how to explain concepts and results visually in a compelling way with data. The book explains both the "why" of data visualization and the "how." That is, the book provides lucid explanations of the guiding principles of data visualization through the use of interesting examples.

Product Details

ISBN-13:	9780357631348
Publisher:	Cengage Learning
Publication date:	05/18/2021
Series:	MindTap Course List
Pages:	448
Product dimensions:	8.40(w) x 10.70(h) x 0.80(d)

About the Author

Jeffrey D. Camm is the Inmar Presidential Chair of Analytics and Senior Associate Dean for Faculty in the School of Business at Wake Forest University. Born in Cincinnati, Ohio, he holds a B.S. from Xavier University (Ohio) and a Ph.D. from Clemson University. Prior to joining the faculty at Wake Forest, Dr. Camm served on the faculty of the University of Cincinnati. He has also been a visiting scholar at Stanford University and a visiting professor of business administration at the Tuck School of Business at Dartmouth College. Dr. Camm has published more than 45 papers in the general area of optimization applied to problems in operations management and marketing. He has published his research in Science, Management Science, Operations Research, The INFORMS Journal on Applied Analytics and other professional journals. Dr. Camm was named the Dornoff Fellow of Teaching Excellence at the University of Cincinnati and he was the recipient of the 2006 INFORMS Prize for the Teaching of Operations Research Practice. A firm believer in practicing what he preaches, he has served as a consultant to numerous companies and government agencies. Dr. Camm served as editor-in-chief of INFORMS Journal on Applied Analytics and is an INFORMS fellow.

James J. Cochran is Professor of Applied Statistics, the Mike and Cathy Mouron Research Chair and Associate Dean for Faculty and Research at the University of Alabama. Born in Dayton, Ohio, he earned his B.S., M.S. and M.B.A. degrees from Wright State University and his Ph.D. from the University of Cincinnati. Dr. Cochran has served at The University of Alabama since 2014 and has been a visiting scholar at Stanford University, Universidad de Talca, the University of South Africa and Pole Universitaire Leonard de Vinci. Dr. Cochran has published more than 50 papers in the development and application of operations research and statistical methods. He has published his research in Management Science, The American Statistician, Communications in Statistics-Theory and Methods, Annals of Operations Research, European Journal of Operational Research, Journal of Combinatorial Optimization, INFORMS Journal on Applied Analytics, BMJ Global Health and Statistics and Probability Letters. He was the 2008 recipient of the INFORMS Prize for the Teaching of Operations Research Practice and the 2010 recipient of the Mu Sigma Rho Statistical Education Award. He received the Founders Award in 2014 and the Karl E. Peace Award in 2015 from the American Statistical Association. In 2017 he received the American Statistical Association’s Waller Distinguished Teaching Career Award and in 2018 he received the INFORMS President’s Award. Dr. Cochran is an elected member of the International Statistics Institute, a fellow of the American Statistical Association and a fellow of INFORMS. A strong advocate for effective statistics and operations research education as a means of improving the quality of applications to real problems, Dr. Cochran has organized and chaired teaching workshops throughout the world.

Michael J. Fry is Professor of Operations, Business Analytics and Information Systems, Lindner Research Fellow and Managing Director of the Center for Business Analytics in the Carl H. Lindner College of Business at the University of Cincinnati. Born in Killeen, Texas, he earned a B.S. from Texas A&M University and his M.S.E. and Ph.D. from the University of Michigan. He has been at the University of Cincinnati since 2002, where he was previously department head. He has also been a visiting professor at Cornell University and the University of British Columbia. Dr. Fry has published more than 25 research papers in journals such as Operations Research, M&SOM, Transportation Science, Naval Research Logistics, IISE Transactions, Critical Care Medicine and INFORMS Journal on Applied Analytics. His research interests are in applying quantitative management methods to the areas of supply chain analytics, sports analytics and public-policy operations. He has worked with many organizations for his research, including Dell, Inc., Starbucks Coffee Company, Great American Insurance Group, the Cincinnati Fire Department, the State of Ohio Election Commission, the Cincinnati Bengals and the Cincinnati Zoo and Botanical Garden. Dr. Fry was named a finalist for the Daniel H. Wagner Prize for Excellence in Operations Research Practice, and he has been recognized for both his research and teaching excellence at the University of Cincinnati.

Jeffrey W. Ohlmann is Associate Professor of Business Analytics and Huneke Research Fellow in the Tippie College of Business at the University of Iowa. Born in Valentine, Nebraska, he earned a B.S. from the University of Nebraska and his M.S. and Ph.D. from the University of Michigan. He has been at the University of Iowa since 2003. Dr. Ohlmann’s research on the modeling and solution of decision-making problems has produced more than two dozen research papers in journals such as Operations Research, Mathematics of Operations Research, INFORMS Journal on Computing, Transportation Science, the European Journal of Operational Research and INFORMS Journal on Applied Analytics (formerly Interfaces). He has collaborated with companies such as Transfreight, LeanCor, Cargill, the Hamilton County Board of Elections as well as three National Football League franchises. Because of the relevance of his work to industry, he was bestowed the George B. Dantzig Dissertation Award and was recognized as a finalist for the Daniel H. Wagner Prize for Excellence in Operations Research Practice.

Table of Contents

About the Authors xi

Preface xiii

Chapter 1 Introduction 2

1.1 Analytics 3

1.2 Why Visualize Data? 4

Data Visualization for Exploration 4

Data Visualization for Explanation 7

1.3 Types of Data 8

Quantitative and Categorical Data 8

Cross-Sectional and Time Series Data 9

Big Data 10

1.4 Data Visualization in Practice 11

Accounting 11

Finance 12

Human Resource Management 13

Marketing 14

Operations 14

Engineering 16

Sciences 16

Sports 17

Summary 18

Glossary 19

Problems 20

Chapter 2 Selecting a Chart Type 26

2.1 Defining the Goal of Your Data Visualization 28

Selecting an Appropriate Chart 28

2.2 Creating and Editing Charts in Excel 29

Creating a Chart in Excel 30

Editing a Chart in Excel 30

2.3 Scatter Charts and Bubble Charts 32

Scatter Charts 32

Bubble Charts 33

2.4 Line Charts, Column Charts, and Bar Charts 35

Line Charts 35

Column Charts 39

Bar Charts 41

2.5 Maps 42

Geographic Maps 42

Heat Maps 44

Treemaps 45

2.6 When to Use Tables 47

Tables versus Charts 47

2.7 Other Specialized Charts 49

Waterfall Charts 49

Stock Charts 51

Funnel Charts 52

2.8 A Summary Guide to Chart Selection 54

Guidelines for Selecting a Chart 54

Some Charts to Avoid 55

Excel's Recommended Charts Tool 57

Summary 59

Glossary 60

Problems 61

Chapter 3 Data Visualization and Design 76

3.1 Preattentive Attributes 78

Color 81

Form 81

Length and Width 84

Spatial Positioning 87

Movement 87

3.2 Gestalt Principles 88

Similarity 88

Proximity 88

Enclosure 89

Connection 89

3.3 Data-Ink Ratio 91

3.4 Other Data Visualization Design Issues 98

Minimizing Eye Travel 98

Choosing a Font for Text 100

3.5 Common Mistakes in Data Visualization Design 102

Wrong Type of Visualization 102

Trying to Display Too Much Information 104

Using Excel Default Settings for Charts 106

Too Many Attributes 108

Unnecessary Use of 3D 109

Summary 111

Glossary 111

Problems 112

Chapter 4 Purposeful Use of Color 128

4.1 Color and Perception 130

Attributes of Color: Hue, Saturation, and Luminance 130

Color Psychology and Color Symbolism 132

Perceived Color 132

4.2 Color Schemes and Types of Data 135

Categorical Color Schemes 135

Sequential Color Schemes 137

Diverging Color Schemes 139

4.3 Custom Color Using the HSL Color System 141

4.4 Common Mistakes in the Use of Color in Data Visualization 146

Unnecessary Color 146

Excessive Color 148

Insufficient Contrast 151

Inconsistency Across Related Charts 153

Neglecting Colorblindness 153

Not Considering the Mode of Delivery 156

Summary 156

Glossary 157

Problems 157

Chapter 5 Visualizing Variability 174

5.1 Creating Distributions from Data 176

Frequency Distributions for Categorical Data 176

Relative Frequency and Percent Frequency 179

Visualizing Distributions of Quantitative Data 181

5.2 Statistical Analysis of Distributions of Quantitative Variables 193

Measures of Location 193

Measures of Variability 194

Box and Whisker Charts 197

5.3 Uncertainty in Sample Statistics 200

Displaying a Confidence Interval on a Mean 201

Displaying a Confidence Interval on a Proportion 203

5.4 Uncertainty in Predictive Models 205

Illustrating Prediction Intervals for a Simple Linear Regression Model 205

Illustrating Prediction Intervals for a Time Series Model 208

Summary 211

Glossary 211

Problems 213

Chapter 6 Exploring Data Visually 226

6.1 Introduction to Exploratory Data Analysis 228

Espléndido Jugo y Batido, Inc. Example 229

Organizing Data to Facilitate Exploration 230

6.2 Analyzing Variables One at a Time 234

Exploring a Categorical Variable 234

Exploring a Quantitative Variable 237

6.3 Relationships between Variables 242

Crosstabulation 242

Association between Two Quantitative Variables 247

6.4 Analysis of Missing Data 256

Types of Missing Data 256

Exploring Patterns Associated with Missing Data 258

6.5 Visualizing Time-Series Data 260

Viewing Data at Different Temporal Frequencies 260

Highlighting Patterns in Time Series Data 262

Rearranging Data for Visualization 266

6.6 Visualizing Geospatial Data 269

Choropleth Maps 269

Cartograms 272

Summary 273

Glossary 274

Problems 275

Chapter 7 Explaining Visually to Influence with Data 284

7.1 Know Your Audience 287

Audience Member Needs 287

Audience Member Analytical Comfort Levels 289

7.2 Know Your Message 292

What Helps the Decision Maker? 293

Empathizing with Data 294

7.3 Storytelling with Charts 300

Choosing the Correct Chart to Tell Your Story 300

Using Preattentive Attributes to Tell Your Story 304

7.4 Bringing It All Together: Storytelling and Presentation Design 306

Aristotle's Rhetorical Triangle 307

Freytag's Pyramid 308

Storyboarding 311

Summary 313

Glossary 313

Problems 314

Chapter 8 Data Dashboards 322

8.1 What Is a Data Dashboard? 324

Principles of Effective Data Dashboards 325

Applications of Data Dashboards 325

8.2 Data Dashboards Taxonomies 327

Data Updates 327

User Interaction 327

Organizational Function 328

8.3 Data Dashboard Design 328

Understanding the Purpose of the Data Dashboard 329

Considering the Needs of the Data Dashboard's Users 329

Data Dashboard Engineering 330

8.4 Using Excel Tools to Build a Data Dashboard 331

Espléndido Jugo y Batido, Inc. 331

Using PivotTables, PivotCharts, and Slicers to Build a Data Dashboard 332

Linking Slicers to Multiple PivotTables 343

Protecting a Data Dashboard 346

Final Review of a Data Dashboard 347

8.5 Common Mistakes in Data Dashboard Design 348

Summary 349

Glossary 349

Problems 350

Chapter 9 Telling the Truth with Data Visualization 360

9.1 Missing Data and Data Errors 363

Identifying Missing Data 363

Identifying Data Errors 366

9.2 Biased Data 369

Selection Bias 369

Survivor Bias 372

9.3 Adjusting for Inflation 374

9.4 Deceptive Design 377

Design of Chart Axes 377

Dual-Axis Charts 381

Data Selection and Temporal Frequency 382

Issues Related to Geographic Maps 386

Summary 388

Glossary 389

Problems 389

References 397

Index 399