Ga data visworkshop19aug2014hwv0.3

INTRODUCTION TO DATA VISUALIZATION

August 19, 2014 General Assembly

Hunter Whitney

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INTRODUCTION

HUNTER WHITNEY2

‣ Senior UX Designer, specializing in health, medicine, and the life sciences !

‣ Consultant on UXD and data visualization projects ranging from scientific research interfaces to consumer mobile health apps. !

‣ Author of “Data Insights” published by Morgan Kaufmann/Elsevier Nov. 2012 !‣ Contributing Editor UX Magazine ongoing series about UXD and data visualization !

‣ @hunterwhitney

INTRODUCTION

HELLO!!‣ Who are you? !

‣ What do you do? !

‣ What’s your learning goal for today? !

‣ Is there a topic you’d like to visualize in the exercise today?

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OBJECTIVES AND AGENDA

Sections:

1) What is Data Visualization?

2) Data Visualization Purposes

3) Data Visualization Processes and Practices

4) Case Studies

5) Class Exercise

6) Resources and Conclusions

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CLASS EXERCISE PRELIMINARIES

DISCUSSION

Toward the end of class, we’re going to split up into groups and create data visualization concept designs. As we go through each section, think about applying the ideas we cover to a project you might choose. !Topic suggestion for the final exercise - create a visualization that shows how a series of events unfolds over time. Be creative. It doesn’t have to be just a timeline on an x-axis. This can be applied to many areas including - business (e.g., patterns of timing from VC funding to IPO), sports (e.g., changes ball possession during a game), medicine (e.g., the spread of an epidemic)

START THINKING…5

KEY QUESTIONS TO ADDRESS IN YOUR PROJECTS

‣What is the purpose/value of the visualization? ‣Who are the intended users? ‣How was the data selected and acquired? ‣What design elements were used and why?

CLASS EXERCISE PRELIMINARIES 6

‣ We’re only scratching the surface of every topic presented here. !

‣ The main goal is for you to look at data visualization with a holistic perspective. !

‣ Whatever your levels of skill and experience are, you have something to offer.

KEEP IN MIND… 7

INTRODUCTION TO DATA VISUALIZATION

SECTION 1: WHAT IS DATA VISUALIZATION?

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Applying selected graphical approaches to represent data can make it much easier and faster to find patterns and gain meaningful insights. !It’s about the effective visual encoding of data.

http://www.gapminder.org/

SECTION 1: WHAT IS DATA VISUALIZATION? 9

http://www.gapminder.org/

720349656089226535931140790070322302076958689027429003358787115045223998424533087922668417382319480046553364246202505406711172160430997890121737608183566145635519888049583302306957749597705315240714467203496560892265359311407900703223020769586890274290033587871150452239984245330879226684173823194800465533642462025054067111721604309978901217376081835661456355

For example, take a wild guess at the number of 7s in this set-


720349656089226535931140790070322302076958689027429003358787115045223998424533087922668417382319480046553364246202505406711172160430997890121737608183566145635519888049583302306957749597705315240714467203496560892265359311407900703223020769586890274290033587871150452239984245330879226684173823194800465533642462025054067111721604309978901217376081835661456355

It’s the same set of numbers, but a different visual encoding. Easier this time!

Now, try guessing again-


Spreadsheet Takes a lot of effort to process

Data Visualization It’s much easier to grasp

VISUALIZATION HELPS US IMMEDIATELY SEE PATTERNSSECTION 1: WHAT IS DATA VISUALIZATION? 12

A substantial portion of our brains is devoted to visual processing

Source:http://www.flickr.com/photos/orangeacid/234358923/Creative Commons Attribution License

Source:http://en.wikipedia.org/wiki/File:Brodmann_areas_17_18_19.pngGNU Free Documentation License

WE ARE WIRED FOR VISUALIZATION

10 Million Bits Per Second

Source:Current Biology (July 2006) by Judith McLean and Michael A. Freed

SECTION 1: WHAT IS DATA VISUALIZATION? HUMAN BRAIN 13

http://www.flickr.com/photos/orangeacid/234358923/


http://commons.wikimedia.org/wiki/GNU_Free_Documentation_License

http://www.slideshare.net/slideshow/view?login=johnwhalen&title=cognitive-science-of-design-in-10-minutes-or-less

SECTION 1: WHAT IS DATA VISUALIZATION? BRAIN SYSTEMS 14


TAPPING IN TO OUR PERCEPTUAL POWERSThe pop-out effects are due to your brain’s “pre-attentive” processing

SECTION 1: WHAT IS DATA VISUALIZATION? PRE-ATTENTIVE PROCESSING 15

What is easier to distinguish here - color or shape differences?

Some attributes pop out more than others.

16SECTION 1: WHAT IS DATA VISUALIZATION? PRE-ATTENTIVE PROCESSING


SECTION 1: WHAT IS DATA VISUALIZATION? PRE-ATTENTIVE PROCESSING

SHAPE

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SECTION 3: DATA VISUALIZATION PROCESS AND PRACTICES

Adapted from Stephen Few.

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‣ Length Triesman & Gormican [1988] ‣ Width Julesz [1985] ‣ Size Triesman & Gelade [1980] ‣ Curvature Triesman & Gormican [1988] ‣ Number Julesz [1985]; Trick & Pylyshyn [1994] ‣ Terminators Julesz & Bergen [1983] ‣ Intersection Julesz & Bergen [1983] ‣ Closure Enns [1986]; Triesman & Souther [1985] ‣ Color (hue) Nagy & Sanchez [1990, 1992]; D'Zmura [1991]Kawai et al. ‣ Intensity Beck et al. [1983]; Triesman & Gormican [1988] ‣ Flicker Julesz [1971] ‣ Direction of motion Nakayama & Silverman [1986]; Driver & McLeod [1992] ‣ Binocular luster Wolfe & Franzel [1988] ‣ Stereoscopic depth Nakayama & Silverman [1986] ‣ 3-D depth cues Enns [1990] ‣ Lighting direction Enns [1990]

SECTION 1: WHAT IS DATA VISUALIZATION? PRE-ATTENTIVE PROCESSING RESEARCH 19

GRAPHICAL BUILDING BLOCKS AND STRUCTURES The components of visualizations fit into a larger context of goals, users, and the media in which they are presented. !

SECTION 1: WHAT IS DATA VISUALIZATION? BUILDING OUT 20

Idea: forms or patterns transcend the stimuli used to create them. Why do patterns emerge? Under what circumstances? !Principles of Pattern Recognition: “gestalt” German for “pattern” or “form, configuration” Original proposed mechanisms turned out to be wrong Rules themselves are still useful

GESTALT PRINCIPLES

http://sixrevisions.com/web_design/gestalt-principles-applied-in-design/http://graphicdesign.spokanefalls.edu/tutorials/process/gestaltprinciples/gestaltprinc.htm

SECTION 1: WHAT IS DATA VISUALIZATION? GESTALT 21

http://graphicdesign.spokanefalls.edu/tutorials/process/gestaltprinciples/gestaltprinc.htm

What do you see here?

http://sixrevisions.com/web_design/gestalt-principles-applied-in-design/

SECTION 1: WHAT IS DATA VISUALIZATION? GESTALT 22

USER CONTROL: HIGH

STATIC

EXPLAINEXPLORE

(e.g., data-intensive research applications)

(e.g., print infographic advocacy )

(e.g., interactive infographic journalism)

(e.g., data-rich visualizations with limited interactivity)

DYNAMIC

USER CONTROL: LOW

SECTION 1: WHAT IS DATA VISUALIZATION? INTERFACE INFLECTION 23

SECTION 2: DATA VISUALIZATION PURPOSES

INTRODUCTION TO DATA VISUALIZATION 24


EXPLAINANALYZEEXPLORE TRACK ALERT

Source: Data Insights: New Ways to Visualize and Make Sense of Data, by Hunter Whitney, Morgan Kaufmann; 2012.

Data visualizations can be applied to a number of different purposes that sometimes overlap and sometimes don’t. Here are a few examples- !

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Overview first, zoom and filter, then details-on-demand.

“Overview first, zoom and filter, then details-on-demand.”

SHNEIDERMAN’S MANTRA FOR VISUAL EXPLORATION-SECTION 2: DATA VISUALIZATION PURPOSES 26

http://visualization.geblogs.com/wp-content/viz_includes/reports/#y=69&s=5&c=4&w=3&i=-1

EXPLORECross-platform project explores 120 years’ worth of GE annual reports, spanning 1892-2011. The visualization shows how certain keywords were used over time.

SECTION 2: DATA VISUALIZATION PURPOSES 27

http://visualization.geblogs.com/wp-content/viz_includes/reports/#y=69&s=5&c=4&w=3&i=-1


TRACKThis visualization tracks near real-time wind data around the US using data from the National Digital Forecast Database. The prominence of the white lines indicates the strength of velocity of the winds.

ANALYZE

Source:http://en.wikipedia.org/wiki/1854_Broad_Street_cholera_outbreak

John Snow’s Map of the 1854 Broad Street Cholera Outbreak.


http://en.wikipedia.org/wiki/1854_Broad_Street_cholera_outbreak

ANALYZEChris McKinley, a UCLA grad student in mathematics, “hacked” OKCupid data and used statical clustering approaches to optimize his own search for love. !Do you see any potential concerns with this kind of visualization? …and the underlying assumptions?


http://www.wired.com/wiredscience/2014/01/how-to-hack-okcupid/

http://www.wired.com/wiredscience/2014/01/how-to-hack-okcupid/

ANALYZELooking for clues to symptoms in the brain of a solider with traumatic brain injury.

http://vimeo.com/album/2489932/video/72228226h"p://www.defense-‐update.com/analysis/analysis_270507_blast.htm


http://vimeo.com/album/2489932/video/72228226

ALERTAkamai Real Time Web Monitor. Visualizations to help identify global regions with the greatest cyber attack traffic, cities with the slowest Web connections (latency), and geographic areas with the most Web traffic (traffic density).

Source:Akamai Real Time Web Monitor http://www.akamai.com/html/technology/dataviz1.html



EXPLAINCharles Joseph Minard’s 1861 Map of Napoleon’s March

Source:http://en.wikipedia.org/wiki/File:Prianishnikov_1812.jpg

Source:http://en.wikipedia.org/wiki/Charles_Joseph_Minard


http://en.wikipedia.org/wiki/File:Prianishnikov_1812.jpg

http://en.wikipedia.org/wiki/Charles_Joseph_Minard

EXPLAINNY Times 512 Paths to the White House !

Source:http://elections.nytimes.com/2012/results/president/scenarios


http://mattmckeon.com/facebook-privacy/


CLEAN DATA AND CLEAR QUERIES

“Find patients that did not have a Stroke while taking Drug A and Drug B.”

Visualization can make errors incorrect assumptions easier to notice.

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INSERT CHAPTER TITLE

CLEAN DATA AND CLEAR QUERIES

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What%the%user%thinks:"""

User’s%Ques0on:""“Find"pa*ents"for"whom"Drug"A"was"started,"who"then"had"a"Stroke,"and"who"then"stopped"taking"Drug"A.”""""

What%the%system%is%doing:"""

Time%

Drug%A%Start%Drug%A%End%

Time%

Drug%A%Start%Drug%A%End%

Unique%ID%

!‣ Marketing/Sales - (e.g., better

understanding the behavior of customers, social networks) !‣ Health - (e.g.,developing new medical

treatments) !

‣ Security - (e.g., detecting cyber attacks) !‣ Etc… !‣ The right combinations will vary from

domain to domain

UNLOCKING THE VALUE OF DATASECTION 2: DATA VISUALIZATION PURPOSES 37

http://www.turbosquid.com/3d-models/3d-max-bank-vault/596172

http://www.turbosquid.com/3d-models/3d-max-bank-vault/596172

CLASS EXERCISE (CONTINUED)

DISCUSSION KEY QUESTIONS TO ADDRESS

‣ What are the main functions (e.g., exploratory, tracking, explanatory, etc.?)

‣ What kinds of design elements might you want to use?

‣ What level of interactivity might be good to include?

For whichever subject area you choose, think about the basic design elements and functions that might work best. These questions will come into sharper focus as you learn more about the goals of the users.

CONSIDERATIONS FOR YOUR CLASS PROJECT38

Keep in mind - the value of data depends on what you do with it !!


Source: Reprinted in Data Insights: New Ways to Visualize and Make Sense of Data, by Hunter Whitney, Morgan Kaufmann; 2012.


VISUALIZATION IS ONLY THE TIP OF THE ICEBERGData visualization is only a part of a much larger process that includes identifying the purpose of the visualization, the kinds of people who will use it, the types of data that can be collected and analyzed, and good design choices. !

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VISUALIZATION IS PART OF AN ITERATIVE PROCESS

SECTION 3: DATA VISUALIZATION PROCESS AND PRACTICES 42


ROLE

• RESEARCHER !!!!• PUBLIC

PRIOR KNOWLEDGE

• NONE !!!!• SUBJECT EXPERT

USE FREQUENCY

• ONCE A DECADE !!!!• EVERY HOUR

USERSUSER QUESTION 1 - WHO VIEWS THE DATA?


PURPOSE

HYPOTHESIS?

• WHAT ARE WETRYING TO LEARN OR SHOW?

• HOW DO WE KNOWIF WE ACHIEVED IT?

GOAL?

• WHAT ARE THEBOUNDARIES?

PARAMETERS?


DATA QUESTION 1 - WHO OWNS IT?

PRIMARY

• YOU COLLECT IT • YOU OWN IT • NOBODY ELSE HAS IT

• OTHERS COLLECT IT • OTHERS OWN IT • OTHERS HAVE IT

SECONDARY

DATASECTION 3: DATA VISUALIZATION PROCESS AND PRACTICES 45

DATA QUESTION 2 - DOES IT CHANGE?

DYNAMIC

• CHANGES OFTEN • COLLECTED OFTEN • TIME WINDOW

MATTERS

• DOES NOT CHANGE • COLLECT IT ONCE • TIME WINDOW

MATTERS

STATIC

DATASECTION 3: DATA VISUALIZATION PROCESS AND PRACTICES 46

STATISTICAL SUMMARIZATION AND ANALYSISVisualizations can clarify or obscure the statical summarization of data.

http://blog.visual.ly/using-visual-reasoning-to-understand-numbers/


http://blog.visual.ly/using-visual-reasoning-to-understand-numbers/

THE MARRIAGE OF DESIGN AND DATA DATA CAN BE BROKEN INTO TWO MAJOR CLASSES: DISCRETE AND CONTINUOUS



THE MARRIAGE OF DESIGN AND DATA SECTION 3: DATA VISUALIZATION PROCESS AND PRACTICES 49


Nominal Scale: This is simply putting items together without ordering or ranking them (e.g., an apple, an orange, and a tomato).

Ordinal Scale: Elements of the data describe properties of objects or events that are ordered by some characteristic.

THE MARRIAGE OF DESIGN AND MEASUREMENTS SECTION 3: DATA VISUALIZATION PROCESS AND PRACTICES 50


Interval Scale: These are data that are measured on some kind of scale, often temporal (e.g., the days of the week, hours of the day).

THE MARRIAGE OF DESIGN AND MEASUREMENTS

Ratio Scale: An ordered series of numbers assigned to items (objects, events, etc.) that allow for estimating and comparing different measures in terms of multiples, such as “half as many” or “four times as heavy.”



WHAT ARE YOU TRYING TO DO WITH THE DATA?


Source: Reprinted in Data Insights: New Ways to Visualize and Make Sense of Data, by Hunter Whitney, Morgan Kaufmann; 2012.

http://phys.org/news/2013-10-visualization.html

THERE ARE ENDLESS FORMS OF VISUALIZATIONSECTION 3: DATA VISUALIZATION PROCESS AND PRACTICES 53

CHART EFFECTIVENESS

Source: Enrico Bertini, Assistant Professor at NYU-Poly (@filwd)


Think about good design practices: selective labeling !


Schwabish, Jonathan A. 2014. "An Economist's Guide to Visualizing Data." Journal of Economic Perspectives, 28(1): 209-34. DOI: 10.1257/jep.28.1.209

Which one is bigger?

A B

A

B


Think about good design practices: proximity !

Think about good design practices: multiples !


Schwabish, Jonathan A. 2014. "An Economist's Guide to Visualizing Data." Journal of Economic Perspectives, 28(1): 209-34. DOI: 10.1257/jep.28.1.209

‣ How do you design the “perfect” visualization? !

‣ There’s no perfect visualization: the design space is just too big! !

‣ But it’s up to you to design the one that fits...


Consider the implication of display and interaction styles for visualizations


A FEW DATA VISUALIZATION TOOLS:


SECTION 4: EXAMPLES AND CASE STUDIES !

SECTION 4: INTRODUCTION TO DATA VISUALIZATION 61

EXAMPLES AND CASE STUDIES: GAPMINDER WORLD

Let’s evaluate this well-known example of data visualization:

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http://www.gapminder.org

http://www.gapminder.org

PROCESS CASE STUDIES: VISUALIZING NIKEFUEL BAND DATA

http://fathom.info/latest

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What works and what doesn’t?


NikeFuel Band data visualization.

CASE STUDIES: NIKEFUEL BAND DATA 64

CASE STUDIES: VISUALIZING NIKEFUEL BAND DATA

“The Nike+ branding guide contains a gradient shift from red to yellow to green as users approach their daily goals.”

Instead of adjusting the gradient from left to right, we rotated it 90 degrees to highlight the best moments—or peaks—of the day in green. This idea eventually made its way to the vertical color shift used in the final poster.

EVOLVING A DESIGN:


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CASE STUDIES: VISUALIZING NIKEFUEL BAND DATA 66


“Adjusting the beginning and the end of the day from 3am to 3am helped center the activity on the horizontal axis. I wanted to get away from the gradient lines, but the graph started to feel like a hairball.”

“Filling each day’s plot with a transparent value more effectively highlighted the times of the day with consistently high activity.”


CASE STUDIES: VISUALIZING NIKEFUEL BAND DATA

“This was my first choice for color, but the blues became muddy when printed.”


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CRITIQUE: TWITTER REVERB 68

CASE STUDIES: INVESTIGATIONThink of an investigation in which the answer may lie hidden within stacks of evidence, time is limited, and there are serious consequences for any errors.

Source: http://www.theguardian.com/tv-and-radio/tvandradioblog+medical-drama

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http://www.theguardian.com/tv-and-radio/tvandradioblog+medical-drama

Users often have to work with evidence that is: !‣ Fragmentary, mixed, and incomplete ‣ Delivered in an asynchronous, non-sequential manner

CASE STUDIES: INVESTIGATION 70

CASE STUDIES: INVESTIGATING INTERNATIONAL BANK FRAUD 71

CASE STUDIES: TRACKING TAINTED FOOD IN THE SUPPLY 72

Consider Crime Data in rural Africa… !Collected using manual, paper-based processes. Inconsistent formats, poor storage, difficult to analyze, store, manage, visualize, or share. Spatial data consists of ‘in Monrovia’ or ‘near the school’

73CASE STUDIES: INVESTIGATION CRIME REPORTING IN RURAL AFRICA

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“Applied field ethnography”, data, and map visualizations

CASE STUDIES: INVESTIGATION CRIME REPORTING IN RURAL AFRICA

Paper to Cloud

CASE STUDIES: INVESTIGATION CRIME REPORTING IN RURAL AFRICA 72

And Back to Paper

CASE STUDIES: INVESTIGATION CRIME REPORTING IN RURAL AFRICA 73

SECTION 5: CLASS EXERCISE


DATA VISUALIZATION EXERCISE

‣ Get into groups or more, and discuss the ideas and examples you have in mind.

‣ Then...

• Select the purpose, audience, and data you want to use for a visualization

• Design the visualization on the provided poster paper

• Be ready to share your results and describe your thought process

EXERCISE FINALE: THINK TIME78

INSERT CHAPTER TITLE

Streamgraph Space Time Cube Gantt Chart

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RESOURCES AND CONCLUSIONS


DATA VISUALIZATION RESOURCESRESOURCES

!!!‣ Flowing Data (http://flowingdata.com/ !‣ Fast Company. Co.design. (http://www.fastcodesign.com/) !

‣ UX Magazine (http://uxmag.com/) !

‣ The Human-Computer Interaction Lab (http://www.cs.umd.edu/hcil/) !

‣ A Periodic Table of Visualization Methods (www.visual-literacy.org/periodic_table/periodic_table.html) !

Sites:

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http://flowingdata.com

http://flowingdata.com/

http://www.fastcodesign.com/

http://www.fastcodesign.com/

http://uxmag.com/

http://uxmag.com/

http://www.cs.umd.edu/hcil/

http://www.visual-literacy.org/periodic_table/periodic_table.html

http://www.visual-literacy.org/periodic_table/periodic_table.html

DATA VISUALIZATION BOOKS:

‣ Bertin, J. (2011). Semiology of graphics: Diagrams, networks, maps. (Berg, W. J., Trans.) Redlands, CA: Esri Press. (Original work published 1965)

‣ Card, S. K., Mackinlay, J. D., & Shneiderman, B. (Eds.). (1999). Readings in information visualization: Using vision to think. San Francisco, CA: Morgan Kaufmann Publishers.

‣ Few, S. C. (2009). Now you see it: Simple visualization techniques for quantitative analysis. Oakland, CA: Analytics Press.

‣ Few, S. C. (2004). Show me the numbers: Designing tables and graphs to enlighten. Oakland, CA: Analytics Press.

‣ Fry, B. (2008). Visualizing data. Sebastopol, CA: O’Reilly Media, Inc.

‣ Segaran, T., & Hammerbacher, J. (Eds.) (2009). Beautiful data: The stories behind elegant data solutions. Sebastopol, CA: O’Reilly Media, Inc.

‣ Tufte, E.R. (1997). Visual explanations: Images and quantities, evidence and narrative. Cheshire, CT: Graphics Press, LLC.

‣ Ware, C. (2008). Visual thinking for design. Burlington, MA: Morgan Kaufmann Publishers.

‣ Whitney, H. (2012) Data Insights New Ways to Visualize and Make Sense of Data Morgan Kaufmann/Elsevier 2012.

‣ Wilkinson, L. (2005). The grammar of graphics. Chicago, IL: Springer.

‣ Yau, N. (2011). Visualize this: The flowing data guide to design, visualization, and statistics. Indianapolis, IN: Wiley Publishing, Inc.

RESOURCES 82

PERSPECTIVE: BIOTECHNOLOGY EXECUTIVEDATA VISUALIZATION PROCESS AND PRACTICES 83

!‣ “We usually have an underlying narrative or hypothesis that is driving the

analysis, but even with that you have to be ready for a surprise. Be willing to go where the data leads you, provided you have good data from multiple sources.” !

‣ “We try to have teams involved in the data collection and analysis process ‘from soup to nuts’. If people join only at the end of the process, you could be setting yourself up for failure.” !

‣ “If you rely on just one data set, you can be totally misled.”

CONCLUDING THOUGHTS•Data visualization involves learning about the rules and the process

•Start with the problem, not with the data or the visualization

•Think big: find the data you need

•Visualize your data in multiple ways

•Know your audience and their goals

RESOURCES AND CONCLUSIONS 84

QUESTIONS?CONCLUSIONS - CLASS CLOSING / Q&A

CONTACT: HUNTER WHITNEY [email protected] @HUNTERWHITNEY

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mailto:[email protected]?subject=

EXTRASINTRODUCTION TO DATA VISUALIZATION 86

DISCUSSION KEY CHALLENGE / QUESTION

GUN HOMICIDES IN AMERICA IN 2010EXTRAS 87

DISCUSSION KEY CHALLENGE / QUESTION

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GUN HOMICIDES IN AMERICA IN 2010EXTRAS

WEBSITES

Jerome Cukier http://www.jeromecukier.net/projects/crime/murders.html

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GUN HOMICIDES IN AMERICA IN 2010

Periscopic.com http://guns.periscopic.com/?year=2010

EXTRAS

http://www.jeromecukier.net/projects/crime/murders.html

http://www.jeromecukier.net/projects/crime/murders.html

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http://gis.cdc.gov/grasp/fluview/fluportaldashboard.html

EXTRAS

http://gis.cdc.gov/grasp/fluview/fluportaldashboard.html

Ga data visworkshop19aug2014hwv0.3

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