What is data visualization & how can you use it in your daily - PowerPoint PPT Presentation

What is data visualization & how can you use it in your daily work? Anamaria (Ana) Crisan PhD Student University of British Columbia Evening Rounds November, 15 th , 2016

WHO AM I? + Data Visualization Skills! Computer Science Skills (work experience) EXPER EX ERIEN ENCE Ge GenomeDX BC BCCDC Un Undergrad Masters Mas PhD Ph (Clinical) (Public Health) CompSci Bioinformatics @amcrisan http://cs.ubc.ca/~acrisan

WHAT DO I RESEARCH? Treatment Outcomes Person Genomic Place Time Patient Data Geography / Contact Network Location time Medical Health Officers TB Clinicians TB Nurses Researchers Community Leaders

WHAT DO I RESEARCH?

WHAT YOU CAN TAKE AWAY FROM THIS TALK Data Visualization is not an art or graphic design project Deciding upon the most appropriate data visualization can be a research problem Design & Evaluation Think about ”why, what, and how” framework Think broadly, progressively find the right data visualization Example : Visualization and Election Example: Communicating Patient Risk

IF VISUALIZATIONS WERE CARS A BEAUTIFULLY IMPRACTICAL OPTION Ferrari visualizations look super cool and take a lot of time, effort, and resources to produce, but they’re not necessarily practical for most applications. Worthwhile creating sometimes, but think it through.

IF VISUALIZATIONS WERE CARS A LESS BEAUTIFUL PRACTICAL OPTION Toyota Visualizations are well engineered and fit a variety of needs making it a more practical choice. Also, less expensive (time, effort, money) than a Ferrari. Lacks the “wow” factor of a Ferrari, but can hold its own.

IF VISUALIZATIONS WERE CARS POSSIBLY DANGEROUS Pinto visualizations are tempting because they inexpensive (really low time, energy, money), but they are questionably engineered. Aspects of the visualization are not properly tested with stakeholders, and it can explode if tapped the wrong way.

DATA VISUALIZATION IS NOT AN ART PROJECT

EXERCISE: VISUALIZING TWO QUANTITIES • Before we talk “big data” let’s talk “artisanal small batch data” • With the paper in front of you, sketch out as many examples as you can to visualize the following to numbers: 75 37 example:

EXERCISE: VISUALIZING TWO QUANTITIES Why do this exercise? http://www.scribblelive.com/blog/2012/07/27/45-ways-to-communicate-two-quantities/

EXAMPLE : CHANGING ARTERY VISUALIZATIONS MOTIVATION: Improve accuracy to identify blockages in heart arteries Borkin (2011). “Evaluation of Artery Visualizations for Heart Disease Diagnosis”

EXAMPLE : CHANGING ARTERY VISUALIZATIONS MOTIVATION: Improve accuracy to identify blockages in heart arteries Borkin (2011). “Evaluation of Artery Visualizations for Heart Disease Diagnosis”

EXAMPLE : CHANGING ARTERY VISUALIZATIONS RESULTS: Revised visualizations had higher accuracy EXISTING STANDARD REVISED VISUALIZATION Accuracy : 39% Accuracy: 91% Borkin (2011). “Evaluation of Artery Visualizations for Heart Disease Diagnosis”

DATA VISUALIZATION IN THREE QUESTIONS Why? (Motivation) Why do you need to visualize data? What? (Data) What kind of data is being visualized? How? (Visual and Interaction Design) How is data being visualized?

A QUICK NOTE ON “WHAT” Don’t just visualize the raw data! Munzner (2014) “Visualization Analysis and Design” 16

DATA VISUALIZATION IN THREE QUESTIONS Design Evaluation Does the visualization solve a Why? relevant problem? Are you using the right data, or What? deriving the right data? Are the visual and interactive How? design choices appropriate? 17

HOW TO DESIGN & EVALUATE DATA VIZ A visualization can be decomposed into four layers Visual + Interaction Technique Motivation Data Design Choices Why? What? How? Munzner (2014) “Visualization Analysis and Design” 18

HOW TO DESIGN & EVALUATE DATA VIZ A visualization can be decomposed into four layers DESIGN Visual + Interaction Technique Motivation Data Design Choices Why? What? How? Design Process: start with the “why” (domain problem) work your way in to “how” Munzner (2014) “Visualization Analysis and Design” 19

HOW TO DESIGN & EVALUATE DATA VIZ A visualization can be decomposed into four layers EVALUATION Visual + Interaction Technique Motivation Data Design Choices Why? What? How? Evaluation Process: start with the “how” and assess if it’s the right choice for the “why” and “what” Munzner (2014) “Visualization Analysis and Design” 20

HOW TO DESIGN & EVALUATE DATA VIZ A visualization can be decomposed into four layers Visual + Interaction Technique Motivation Data Design Choices Why? What? How? We’ll talk a little bit about the “how” today Munzner (2014) “Visualization Analysis and Design” 21

BREAKING DOWN “HOW” Building up a visualization from geometric points Munzner (2014) “Visualization Analysis and Design” 22

BREAKING DOWN “HOW” Some visualizations are more effective than others Least Errors Most errors Cleveland and McGill 1984; Heer and Bostock 2010

EXAMPLE: BREAKING DOWN A VISUALIZATION Vertical Position Vertical Position Vertical Position Vertical Position Horizontal Position Horizontal Position Horizontal Position Colour Colour Size Munzner (2014) “Visualization Analysis and Design”

EXAMPLE: BREAKING DOWN A VISUALIZATION Colour = Continent Transparency = Similarity Position: LE Size = Population Position: HE Five dimensions are plotted in 2D (4 continuous dimensions & 1 categorical dimension) 25

EXAMPLE: BREAKING DOWN A VISUALIZATION *Note* not the same data

Find more terrible visualizations here!

SOFTWARE TOOLS FOR DATA VISUALIZATION Matthew Brehmer’s totally subjective ranking of vis design tools

BUT ALSO PEN & PAPER! Dear Data Project (Lupi & Posavec)

VISUALIZING AN ELECTION

US ELECTIONS DATA VISUALIZATIONS Point of example is not to discuss: • • Correctness / relevancy of polling or forecasting • Politics of results Very interesting data visualizations emerged • from US election cycle (before & after) Forecasting relied on reporting probabilities; also • commonly reported in medicine 31

PROBABILITY INCONSISTENTLY INTERPRETTED 32 http://bit.ly/1FxtT2z

US ELECTIONS DATA VISUALIZATIONS WHY Show forecasted voter intentions WHAT % chance of “winning” state; geography HOW Choropleth map 33

US ELECTIONS DATA VISUALIZATIONS WHY Show forecasted voter intentions WHAT % chance of “winning” state; geography; # of EC votes HOW Cartogram 34

US ELECTIONS DATA VISUALIZATIONS WHY Show forecasted voter intentions WHAT % chance of “winning” state; # of EC votes HOW Snakey Diagram 35

US ELECTIONS DATA VISUALIZATIONS WHY Support for each party by region WHAT Margin of win; total # votes cast; geography HOW Choropleth map 36

US ELECTIONS DATA VISUALIZATIONS WHY Changes in votes cast between 2016 & 2012 WHAT Changing support; margin (points) of lead by party HOW Choropleth map 37

US ELECTIONS DATA VISUALIZATIONS WHY Changes in votes cast between 2016 & 2012 WHAT Changing support; margin (points) of lead by party HOW Choropleth map 38

US ELECTIONS DATA VISUALIZATIONS WHY Changes in votes cast between 2016 & 2012 WHAT changing support; margin (percentage) of lead by party; # EC votes HOW Stacked bar chart 39

US ELECTIONS DATA VISUALIZATIONS • All visualizations have trying to solve very similar problem § Show how people may vote & how this effects elections • Very different types of data shown in each visualization § Visualization only as good as underlying data § VERY important to understand data sources • Different use of visual metaphors, some simple, some complex 40

COMMUNICATING PATIENT RISK

HOW DO WE COMMUNICATE RISK? XKCD Comic #881 42

EVIDENCE FROM RISK COMMUNICATION (difficult to understand) (easier to understand) < < Probability Frequency Visualization 60% 6 in 10 • Numeracy : the ability to reason with numbers Individuals with low numeracy have a difficulty interpreting § numbers and probabilities • Visualizations can help people with low numeracy make sense of data But - limited guidance toward vis design • Different visualizations are not equally effective § 43 Whiting (2015) “How well do health professionals interpret diagnostic information? A systematic review”

EXAMPLE : SHARED DECISION MAKING STUDY DESIGN RESULTS Visualization improved comprehension of both doctors and patients Quasi-randomized trial with four conditions Outcome : correctly calculating the risk (essentially a math test) Visualization improved concordance between doctors and patients Visual Aid R R Probability N A D N No Visual Aid D Patients O + M Doctors Visual Aid R I Frequency N Z D No Visual Aid E Garcia-Retamero et . al (2013) “Visual representation of statistical information improves diagnostic inferences in doctors and their patients”

EXAMPLE : BREAST CANCER TX CHOICE Baseline Visualization Alternative 1 Alternative 2 45

EXAMPLE : WWW. VIZHEALTH.ORG