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Designing
Data
Visualizations
to Strengthen
Health
Systems Amanda Makulec Visual Analytics Advisor, JSI
Jeff Knezovich Director, Quaternary Consulting
Health Systems Research Symposium
15 November 2016 | Vancouver, BC, Canada
Amanda Makulec, MPH Visual Analytics Advisor
John Snow Inc.
Jeff Knezovich Director
Quaternary Consulting
conceptual data driven
declarative
exploratory
idea illustration everyday data viz
idea generation visual discovery
Matrix credit: Harvard Business Review’s Good Charts
Data Journalism
Learn more on how to craft effective data journalism at http://datajournalismhandbook.org/
Where did viz start?
Key terms and
definitions
What is data visualisation?
• A way of visually conveying information – often
quantitative in nature – in an accurate, compelling
format.
• Usually makes relationships more apparent (e.g. by
clustering, color coding and by putting items in scale).
• Can be static or interactive.
Data visualisation, or
information visualisation?
1001000 1100101
1101100 1101100
1101111
Data visualisation, or
information visualisation?
72 101 108
108 111
Data visualisation, or
information visualisation?
Brief history
Are data visualisations new?
• William Playfair
– 1786: line
graph and bar
chart of
economic data
– 1801: pie
chart and
circle graph
• Florence Nightingale
– 1858 polar area diagram
Are data visualisations new?
Are data visualisations new?
• John Snow
– 1854: Mapping
deaths from a
Cholera
outbreak in
central
London
Who’s your audience?
Each persona represents a significant
portion of people in the real world
and enables the designer to focus on a
manageable and memorable cast of
characters, instead of focusing on
thousands of individuals.”
persona, n.
“A persona is depicted as a specific
person but is not a real individual;
rather, it is synthesized from observations
of many people.
From: https://www.smashingmagazine.com/2014/08/a-closer-look-at-personas-part-1/
Interests?
Motivations?
Action?
steps to develop simple
data viz audience
personas
1. Pick your
stakeholder groups
2. Identify key
personas within a
stakeholder group
sample template
3. Map personas by
dimension.
Factual, analytical Feeling, intuitive Personal gain System gains
External recognition Internal reward Simplicity Complexity
Complacent Driver of change Debilitated by chaos Thrives on chaos
Team oriented Individual/loaner Problem solver Defeatist
Black and white Compromise Receptive Rigid
Team leader Team member Carrot eater Stick driven
Short term focus Long term vision Self accountable Cheater
Internal motive External motive Technical Political
Needs clarification Self-motivated Empowerer Underminer
Works better in group Works better alone Data driven Story motivated
Values independence Values collaboration Head Heart
Personas on Continuums
Much like the Myers-Briggs personality scale, personas can be ranked along continuums of
characteristics that may impact their use of data for decisionmaking. Some examples of different
“poles” identified in workshops are below.
Ministry of Health
Rachel The Technocrat
Pascal The Politician
for example
segmented to
Wants stories
Wants numbers
Motivated by passion
Motivated by money
Act based on feelings
Act based on data
Resilient problem solver
Frustrated bureaucrat
Champion for your issue
Oppose your issue
mapping by
dimension
Wants stories
Wants numbers
Motivated by passion
Motivated by money
Act based on feelings
Act based on data
Resilient problem solver
Frustrated bureaucrat
Champion for your issue
No knowledge of your issue
mapping by
dimension
Rachel
the Technocrat
Pascal
the Politician
find your persona
Not just
job titles
stakeholder groups
organization names
Focus on the human side of
your data viz audience.
Let’s look at some
visualizations.
WTF is wrong with
the following
visualisations?
WTF is wrong with:
Image from Patients Association: http://www.patients-association.org.uk/reports/waiting-times-report-feeling-wait/
WTF is wrong with:
Image from WTFviz: http://visual.ly/buzzing-trends-real-estate-market-bangalore
WTF is wrong with:
Image from WTFviz: http://visual.ly/beyond-facebook-marketing-new-generation
WTF is wrong with:
Image from WTFviz: http://viz.wtf/image/110276700184
WTF is wrong with:
Image from WTFviz: https://twitter.com/BofA_News/status/558242696415166464
On Think Tanks Data
Visualisation Competition
Objectives:
Inspire Strengthen
capacity
Encourage
Designing your
visualizations
“The two optic nerves [in the
eyes] are sending what we
now know are 20 megabits a
second of information back to
the brain.”
- Edward Tufte
System 1 vs System 2 thinking
17 x 24 = ?
Example from Graham Odds
Conscious v sub-conscious bandwidth
0 2 4 6 8 10
Taste
Auditory
Olfactory
Tactile
Visual
Sub-conscious (millions of bits per second)
0 10 20 30 40
Conscious (bits per second)
Adapted from: Tor Norretranders' The User Illusion
make data sticky
Gestalt design: taking
advantage of sub-concsious
processing
Gestalt design principles:
Implications for data visualisation
Adapted from Alberto Cairo
Gestalt design principles:
Semiotics and iconography
☺
Who makes a good
data visualisation?
Who makes a good
visualisation?
Communication
Research
Design
Technology
From: https://onthinktanks.org/articles/visualising-data-both-a-science-and-an-art/
• Data literacy – merging and tidying datasets
• Statistical competencies – mean v median,
ordinal versus scalar
• Research methods - sampling
• Research context
Research
• For data collection – e.g. web scrapers
• For data storage – e.g. database and SQL
• For data manipulation – e.g. SPSS, R
• For data visualisation – e.g. coding, like jQuery,
HTML5
Technology
• Appropriate visualisation types
• Chart fundamentals
• Colour and form
Design
• Understanding of users’ needs and
perceptions
• Finding and refining messages
• Telling stories
Communication
What makes a good
data visualisation?
What makes a good visualisation?
Interesting
Function
Form
Integrity
(McCandless, 2012)
Function
Source: Gregor Aisch - http://slidesha.re/1jTg5Eq
Source: Adapted from from The Wall Street Journal guide to information graphics
Form
Integrity
What do you think are key
elements of effective
data visualisation?
preattentive attributes
strategic chart selection
“ t h i n k i n g w i t h
y o u r l i z a r d
b r a i n ”
preattentive attributes
Paired Column Column
Bar Paired Bar Stacked Bar
Stacked Column
Slope
compare categories
Histogram Box and Whiskers Confidence Interval
distribution
Scatterplot Bubble
relationship
Line
Stacked Area Spark Lines
Dot Plot
time series
0 20 40 60 80
why dots?
0
20
40
60
80
Facility1
Facility2
Facility3
Facility4
Facility5
Facility6
Facility7
Facility8
Year 1
Year 5
0 20 40 60 80
Facility 1
Facility 2
Facility 3
Facility 4
Facility 5
Facility 6
Facility 7
Facility 8
Pie Donut
part-to-whole
a cautionary tale
Image credit from: https://www.biostat.wisc.edu/~kbroman/topten_worstgraphs/bell_fig3.jpg
Image Source:
http://www.nytimes.com/imagepages/2008/
03/16/magazine/16wwln-
lede.graphic.ready.html
Tree Map
0% 20% 40% 60% 80% 100%
Icon Matrix
part-to-whole better
839
294
145
50
Small Multiples Chart
Horizontal Stacked Bar
icon matrix
pick your icon
for social share &
infographics
View & order a copy at http://policyviz.com/graphic-continuum/
chart type quick reference
Common Pitfalls
Things to avoid when
designing your
visualizations.
Many courtesy of Gregor Aisch
(http://slidesha.re/1jTg5Eq)
Using 3d
25%
50%
25% 33%
33%
33%
vs
Going colour crazy
0
1
2
3
4
5
6
7
8
9
10
Category 1 Category 2 Category 3 Category 40
1
2
3
4
5
6
7
8
9
10
Category 1 Category 2 Category 3 Category 4
vs
Quick colour aside
Analogous (similar colours)
monochromatic
complementary
From: Data visualisation: a practical guide to producing effective visualisations for research communication http://resyst.lshtm.ac.uk/resources/data-visualisation-practical-guide-producing-effective-visualisations-research
Not remembering the objective
Team 1
Team 2
Person A
Person B
Person C
Person D
Person E
Person F
Person G
Person H
vs
Not thinking about order
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
A B C D0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
D A C B
vs
Not scaling to zero
11.5
12
12.5
13
13.5
14
14.5
15
Category1
Category2
Category3
Category4
0
2
4
6
8
10
12
14
16
Category1
Category2
Category3
Category4
vs
Not scaling to zero (exceptions)
vs
Not labelling directly
Series 1
Series 2
Series 3
0
1
2
3
4
5
6
1990 1995 2000 2005
0
1
2
3
4
5
6
1990 1995 2000 2005
Series 1 Series 2 Series 3
vs
Non-descriptive titles
Non-descriptive titles
Youth Unemployment Rates in Europe
Non-descriptive titles
Youth Unemployment on Historical High
Non-descriptive titles
Youth Unemployment Divides Europe
Non-descriptive titles
Youth Unemployment Divides Europe
Seasonally adjusted unemployment rates of under 25s
Seven deadly sins
of
data visualisation
https://onthinktanks.org/articles/on-datavis-judging-jeff-knezovichs-advice/
From: https://onthinktanks.org/articles/on-datavis-judging-jeff-knezovichs-advice/
Not telling
a story
Misrespresentation
Shifting
scales
(without mentioning it)
Over-
reliance on
text
Ambiguity
Over-
complication
Forgetting
the point
Break time! Download me:
tinyurl.com/DataVizatHSR
Designing in
Excel
Think like a
designer
Simple LESS IS MORE
white space is nice
three things t o i m p r o v e e v e r y c h a r t
1: declutter
“Erase non-data - ink , wi th in rea son .”
8
14.9
11.7
38.9
19.2
16.6
20.4
26.2
0
5
10
15
20
25
30
35
40
45
Facility 1 Facility 2 Facility 3 Facility 4 Facility 5 Facility 6 Facility 7 Facility 8
Year 1
Year 1
0
10
20
30
40
50
Facility 1 Facility 2 Facility 3 Facility 4 Facility 5 Facility 6 Facility 7 Facility 8
2: color sparingly
0
10
20
30
40
50
Facility 1 Facility 2 Facility 3 Facility 4 Facility 5 Facility 6 Facility 7 Facility 8
0
10
20
30
40
50
Facility 1 Facility 2 Facility 3 Facility 4 Facility 5 Facility 6 Facility 7 Facility 8
0
1
2
3
4
5
6
Category 1 Category 2 Category 3 Category 4
Series 1 Series 2 Series 3
Super special brand-compliant graph!
Inspired by Cole Naussbaumer at Stanford’s Data on Purpose, February 2016
4.3
2.5
3.5
4.5
Category 1 Category 2 Category 3 Category 4
Series 1 Series 2 Series 3
Easier to see a key data series graph!
Inspired by Cole Naussbaumer at Stanford’s Data on Purpose, February 2016
be mindful
custom styles
3: purposeful title
0
10
20
30
40
50
Facility 1 Facility 2 Facility 3 Facility 4 Facility 5 Facility 6 Facility 7 Facility 8
Quality of Care Score by Facility Ghana, 2015
0
10
20
30
40
50
Facility 1 Facility 2 Facility 3 Facility 4 Facility 5 Facility 6 Facility 7 Facility 8
Facility 4 showed the highest quality of care. Despite scoring highest, its overall score was below 50%, indicating there is
work to be done to improve quality of care.
38.9
Facility 1 Facility 2 Facility 3 Facility 4 Facility 5 Facility 6 Facility 7 Facility 8
Facility 4 showed the highest quality of care. Despite scoring highest, its overall score was below 50%, indicating there is
work to be done to improve quality of care.
chart templates
favorite things
Graphic Continuum
Dot plots
Icon matrix
Canva
Piktochart
Noun Project
Visage Data Viz 101
Pexels
Slidedocs
Google Sheets
High Charts
Insp
ira
tio
n
Skill building
data viz recs
duarte.com // storycorps.org // skillshare.com
storytelling recs
