Visual communication of qualitative and quantitative data (v. 2021 ITA)

data & content design
Frieda Brioschi - frieda.brioschi@gmail.com
Emma Tracanella - emma.tracanella@gmail.com
VISUAL COMMUNICATION OF
QUANTITATIVE AND QUALITATIVE DATA
LESSON 7 - 2020

IF YOU TORTURE THE DATA LONG
ENOUGH, IT WILL CONFESS
R.H. Coase, British economist
LESSON 8
2

DATA
QUANTITATIVE AND
QUALITATIVE

LESSON 7
QUALITATIVE VS QUANTITATIVE DATA
Quantitative data can be counted, measured, and expressed using numbers.
Qualitative data is descriptive and conceptual.
Qualitative data can be categorized based on traits and characteristics.
4

LESSON 7
QUALITATIVE DATA
Qualitative data is non-statistical and is typically unstructured or semi-
structured in nature.
This data is usually categorized based on properties, attributes, labels, and other
identiﬁers.
Qualitative data can be used to ask the question “why?” It is investigative and is
often open-ended until further research is conducted.
Generating this data from qualitative research is used for theorizations,
interpretations, developing hypotheses, and initial understandings.
5

LESSON 7
6

LESSON 7
QUANTITATIVE DATA
Quantitative data is statistical and is typically structured in nature – meaning it is
more rigid and deﬁned. This type of data is measured using numbers and
values, which makes it a more suitable candidate for data analysis.
Quantitative data is concise and close-ended.
It can be used to ask the questions “how much?” or “how many?”, followed by
conclusive information.
7

LESSON 7
8

LESSON 7
DISCRETE AND CONTINUOUS DATA
Quantitative data can actually be broken into further sub-categories:
▸ Discrete data: cannot be broken down into smaller parts. This type of data
consists of integers (positive and negative numbers e.g. -100, 10, 100...) and
is finite (meaning it reaches a limit).
▸ Continuous data: can be infinitely broken down into smaller parts or data
that continuously fluctuates.
9

VISUALIZATION
QUANTITATIVE DATA

LESSON 7
DATA VISUALISATION VOCABULARY
Data visualisation is anything that converts data sources into a visual
representation (like charts, graphs, maps, sometimes even just tables).
▸ Scientific visualization: the visualization of scientific data that have close
ties to real-world objects with spatial properties. Different scientific fields
often have very specific conventions for doing their own types of
visualizations.
▸ Information visualization: covering most statistical charts and graphs but
also other visual/spatial metaphors that can be used to represent data sets
that don't have inherent spatial components.
11

LESSON 7
DATA VISUALIZATION OR INFOGRAPHIC?
Data visualization is, at its core, the visual representation of any numerical
information. It can be limited to a single graph or chart.
Infographics, meanwhile, are a collection of interrelated information — not all of it
numbers-driven. Infographics may incorporate data visualizations, but they might
also incorporate other types of information visualization, such as illustrations and
icons. A quality infographic should always use as little text as possible to get its
message across.
In fact, an infographic may potentially contain no data visualization at all.
▸ https://killervisualstrategies.com/data-visualizations
12

LESSON 7
EDWARD TUFTE’S RECIPE
Excellence in statistical graphics consists of complex ideas communicated with clarity,
precision and efﬁciency. Graphical displays should:
▸ show the data
▸ induce the viewer to think about the substance rather than about methodology,
graphic design, the technology of graphic production or something else
▸ avoid distorting what the data has to say
▸ present many numbers in a small space
▸ make large data sets coherent
13

LESSON 7
EDWARD TUFTE’S RECIPE
▸ encourage the eye to compare different pieces of data
▸ reveal the data at several levels of detail, from a broad overview to the ﬁne
structure
▸ serve a reasonably clear purpose: description, exploration, tabulation or decoration
▸ be closely integrated with the statistical and verbal descriptions of a data set.
Graphics reveal data. Indeed graphics can be more precise and revealing than
conventional statistical computations.
https://archive.org/details/visualdisplayofq00tuft
14

LESSON 7
15

LESSON 8
16

LESSON 8
17

TACTICS
VISUAL COMMUNICATION
Photo by ev on Unsplash

LESSON 7
DO USE THE FULL AXIS
19
▸ https://guides.library.duke.edu/c.php?g=289796&p=1934004

LESSON 7
AVOID DISTORTION
For bar charts, the numerical axis (often the y axis) must start at
zero. Our eyes are very sensitive to the area of bars, and we draw
inaccurate conclusions when those bars are truncated.
20

LESSON 7
WIDE RANGES
If you have one or two very tall bars, you might consider using multiple
charts to show both the full scale and a "zoomed in" view.
21

LESSON 7
CONSISTENT INTERVALS
Using the full axis also means that you should not skip values when
you have numerical data.
22

LESSON 7
DO SIMPLIFY LESS
IMPORTANT INFORMATION
23

LESSON 7
ELIMINATE OR RESERVE
Chart elements like gridlines, axis labels, colors, etc.
can all be simpliﬁed to highlight what is most
important/relevant/interesting. You may be able to
eliminate gridlines or reserve colors for isolating
individual data series and not for differentiating
between all of the series being presented.
24

LESSON 7
ELIMINATE OR RESERVE
Although it is possible to tell hundred stories using a single line
chart, it makes a lot of sense to keep the focus on just one story.
Therefore you should highlight just one or two important lines in the
chart, but keep the others as context in the background.
25
▸ https://www.vis4.net/blog/2012/06/doing-the-line-charts-right/

LESSON 7
DO BE CREATIVE WITH YOUR
LEGENDS AND LABELS
26

LESSON 7
ROTATE BARS & PUT VALUE LABELS ON BARS
27

LESSON 7
DON'T USE 3D OR BLOW
APART EFFECTS
28

LESSON 7
AGAINST 3D
Studies show that 3D eﬀects reduce comprehension. Blow apart
effects likewise make it hard to compare elements and judge areas.
29

LESSON 7
DON'T USE MORE THAN
SIX COLORS
30

LESSON 7
DIFFERENCES BETWEEN COLORS
Using color categories that are relatively universal makes it easier to
see differences between colors.
The more colors you need (that is, the more categories you try to
visualize at once), the harder it is to do this.
31

LESSON 7
COLOR AND ORDER
We often think that the order of colors in our "rainbow" is easy for
everyone to understand, but this order is not universal and will make
charts and maps harder to read.
32

LESSON 7
NO RAINBOWS
If you want color to show a numerical value, use a range that goes
from white to a highly saturated color in one of the universal color
categories.
33

LESSON 7
COLOR BLINDNESS
34

LESSON 7
DON'T CHANGE STYLE
MIDSTREAM
35

LESSON 7
EASY COMPARISON
Our visual system can detect anomalies in patterns. Try keeping the
form of a chart consistent across a series so differences from one
chart to another will pop out.
Use the same colors, axes, labels, etc. across multiple charts.
36

LESSON 7
DON'T PUSH
“VISUAL MATH"
37

LESSON 7
EASY COMPARISON
If the chart makes it hard to
understand an important
relationship between variables, do
the extra calculation and visualize
that as well.
38

LESSON 7
DON'T OVERLOAD THE
CHART
39

LESSON 7
READING EVERY ELEMENT ONE BY ONE
40

LESSON 7
DEFINITION
42
A data visualization is a visual representation of data created to amplify the
cognitive processing and the social application of the data represented (Borgo/
Cairo 2013).
The main division line on the content side is whether the data that are visualized
are numerical o non-numerical.
Non-numerical data might refer to collections of documents, network relations,
topographical structures, etc.

LESSON 7
MAIN SOURCES OF NON-NUMERICAL DATA COLLECTION
43

LESSON 8
44

LESSON 7
GOAL
The most common sources of qualitative data include interviews, observations,
and documents, none of which can be “crunched” easily by statistical software.
The goal of qualitative data analysis is to uncover emerging themes, patterns,
concepts, insights, and understandings. Qualitative studies often use an analytic
framework — a network of linked concepts and classiﬁcations — to understand an
underlying process; that is, a sequence of events or constructs and how they
relate.
▸ https://www.sagepub.com/sites/default/ﬁles/upm-binaries/43144_12.pdf
45

LESSON 7
QUALITATIVE DATA ANALYSIS
Data analysis in qualitative research focuses on qualities more than quantities.
The statistical focus on the p value in quantitative research is replaced in
qualitative research with pattern seeking and the extraction of meaning from rich,
complex sources of linguistic (narrative) or visual (image) data.
Much effort is directed toward the creation of categories. Words, symbols,
metaphors, vignettes, and an entire array of creative linguistic tools or visual
displays may be used instead of the “number crunching” employed in qualitative
data analysis.
46

LESSON 7
CREATIVE THINKING
The types of thinking and skills needed for qualitative data analysis are different
from those needed for quantitative data analysis. Creativity, divergent thinking,
keen perception of patterns among ambiguity, and strong writing skills are
helpful for qualitative data analysis.
Qualitative analysis is less dependent on computing software. Whereas statistical
analysis often centers on the p value, qualitative data analysis involves more time-
consuming extraction of meaning from multiple sources of complex data.
47

LESSON 7
THE QUALITATIVE METAPHORS
Qualitative data analysts face the task of recording data via a variety of methods
(interviews, observation, ﬁeld notes, etc.), coding and categorizing (using a
variety of clustering and classiﬁcation schemes), attaching concepts to the
categories, linking and combining (integrating) abstract concepts, creating theory
from emerging themes, and writing an understanding.
Metaphors are useful as interpretive tools in this process, serving a heuristic
(guiding) role or explaining the elements of a theory.
48

LESSON 7
KALEIDOSCOPE
One useful metaphor is a kaleidoscope for the
purpose of describing qualitative data analysis.
Grouping similar data bits together, then
comparing bits within a pile. Differentiation
creates subpiles, which eventually become
connected by a pattern they share. This process
requires continual “back and forth” reﬁnement
until a grand concept emerges.
▸ https://www.sagepub.com/sites/default/ﬁles/upm-binaries/
43144_12.pdf
49

LESSON 7
JIGSAW PUZZLE
Assembling data into an explanation is akin to reassembling puzzle
pieces. One strategy is grouping all pieces that look alike, sky for
example, and placing these pieces near the top. Other sketchy-looking
objects may be grouped together using any dimension (e.g., color)
whose properties make conceptual sense.
Puzzle pieces will have to be rearranged many times before the
reassembled pieces emerge into a coherent pattern. If successful, a
whole structure will eventually be built, held tight by the interconnected
pieces.
50

LESSON 7
SYMPHONY
Qualitative data analysis is best understand as a symphony based on three elegant
but simple notes: noticing, collecting, and thinking. Clearly not linear, the process is
described as iterative (a repeating cycle), recursive (returning to a previous point),
and “holographic” (each “note” contains a whole) with “swirls and eddies.”
When one notices, one records information and codes it using an organizing
framework. When one collects, one shifts and sorts information. When one thinks,
one ﬁnds patterns, makes sense of them, and makes discoveries (including
“wholes” and “holes”).
51

LESSON 7
DATA VISUALIZATION AS A TOOL
Data visualization can be a powerful tool in qualitative reporting. While we
certainly can’t completely escape text-centric pages in our qualitative reports,
graphics add visual interest and help break up the monotony of pages (or slides)
of text.
Graphics help support qualitative ﬁndings and enable us to communicate in
more interesting ways beyond words on paper (or a screen). Effective data
visualization can also help readers understand concepts more quickly and easily
and make information more memorable.
52
https://www.qrca.org/blogpost/1488356/323845/Data-Visualization-3-Ways-to-Make-Your-Qualitative-Reports-Pop

LESSON 7
DATA VISUALIZATION IN JOURNALISM
Newspapers and other media outlets have jumped on board the data
visualization.
Publications like The Washington Post, The New York Times and the Los
Angeles Times employ full-time data journalists to augment their reporting.
These folks take an enormous trove of data on a particular topic and expertly
slice, dice and manipulate the information into interactive graphics that
communicate big ideas in an accessible and elegant way.
53

LESSON 7
THE CONFIRMED U.S. MEASLES CASES BY COUNTY IN 2019
54
https://www.nytimes.com/interactive/2019/health/measles-outbreak.html

LESSON 7
THE EARLIER START OF SPRING IN SOME PARTS OF THE U.S.
55
https://www.washingtonpost.com/graphics/2018/national/early-spring/

LESSON 7
THE EARLIER START OF SPRING IN SOME PARTS OF THE U.S.
56
https://www.washingtonpost.com/graphics/2018/national/early-spring/

VISUALIZATION
QUALITATIVE DATA

LESSON 7
MIND MAP
A mind map is a hierarchical diagram used to visually organize information,
showing relationships among pieces of the whole.
It is often created around a single concept, drawn as an image in the center of a
blank page, to which associated representations of ideas such as images, words
and parts of words are added. Major ideas are connected directly to the central
concept, and other ideas branch out from those major ideas.
▸ https://en.wikipedia.org/wiki/Mind_map
58

LESSON 7
BUZAN’S GUIDELINES
1. Start in the center with an image of the topic, using at least 3 colors.
2. Use images, symbols, codes, and dimensions throughout your mind map.
3. Select key words and print using upper or lower case letters.
4. Each word/image is best alone and sitting on its own line.
5. The lines should be connected, starting from the central image. The lines
become thinner as they radiate out from the center.
59

LESSON 7
BUZAN’S GUIDELINES
6. Make the lines the same length as the word/image they support.
7. Use multiple colors throughout the mind map, for visual stimulation and also for
encoding or grouping.
8. Develop your own personal style of mind mapping.
9. Use emphasis and show associations in your mind map.
10.Keep the mind map clear by using radial hierarchy or outlines to embrace your
branches.
▸ https://en.wikipedia.org/wiki/Mind_map
60

LESSON 8
61https://www.mindmeister.com/blog/why-mind-mapping/

LESSON 7
62

LESSON 7
FLOW DIAGRAM
A ﬂow diagram is a diagram that visually
displays interrelated information such as
events, steps in a process, functions, etc., in
an organized fashion, such as sequentially or
chronologically.
▸ https://books.google.com/books?
id=qusmDAAAQBAJ&printsec=frontcover#v=onepage&q=%2
2ﬂow%20diagram%22&f=false
63

LESSON 8
64

LESSON 7
65https://kallwejt.com/ﬁlter/Baltimore/Baltimore-Waste-1

LESSON 7
CUSTOMER JOURNEY MAPS
66
Customer journey maps are
another way to employ data
visualization in qualitative
reports.
Is a way of walking through a
process or service, from the
perspective of someone who is
interacting with it.

LESSON 7
67
https://neiltamplin.me/an-example-customer-journey-map-for-a-housing-association-22b3719dcc10

LESSON 7
68
https://www.brightvessel.com/customer-journey-map-2018/

LESSON 7
WORD CLOUDS
The most obvious strategy for visualizing text-based data: the word
cloud, also known as a tag cloud.
Frequent words or phrases are shown in larger, bolder font.
Less-frequent words or phrases are shown in a smaller font.
Word clouds are okay for visualizing one-word descriptions, but not
for visualizing all your qualitative data.
69

LESSON 7
ONE WORD FOR TEACHER
70
People described
their favorite teacher
using only one word
and the adjectives
were visualized in a
word cloud shaped
like an apple.

LESSON 7
BEFORE AFTER COMPARISON
71
Word clouds are also
great for before/after
comparisons, like
these tweets
describing breakups.
https://www.vice.com/en_us/article/ezvaba/what-our-breakups-look-like-on-twitter

LESSON 7
ONE WORD FOR OBAMA
72
People described
Barack Obama using
only one word and
the adjectives were
visualized in a bubble
cloud (and then color-
coded by the
sentiment or tone of
that adjective).

LESSON 7
COLOR-CODED PHRASES
73
The New York Times’
election coverage in
2016 compared and
contrasted speeches
from Donald Trump
and Hillary Clinton.
First, the New York
Times team presented
miniature thumbnail
images of each
nominee’s convention
speech.
https://www.nytimes.com/interactive/2016/07/29/us/elections/trump-clinton-pence-kaine-speeches.html

LESSON 7
COLOR-CODED PHRASES
74
Directly underneath the
thumbnails, the New
York Times team pulled
out a few sample
quotes so that readers
can get a sense of what
was said.

LESSON 7
MAPS
Maps are artifacts that help us make decisions, in so much as they visually
organize data and information on a space; their aim is to make what they see
comprehensible and usable, to bring it to our knowledge.
The language of maps, in particular, is a circular course that starts from
humanity’s need to explore its surroundings by sharing information, and ending
with the need to plan and shape the reality in which it is immersed. Observation,
abstraction and landing are processes that take place in the ends of those who
perform them.
▸ https://it.moleskine.com/mind-maps-and-infographics/p0198
75

A MAP IS NOT THE TERRITORY IT REPRESENTS, BUT
IF CORRECT, IT HAS SIMILAR STRUCTURE TO THE
TERRITORY, WHICH ACCOUNTS FOR ITS
USEFULNESS
Alfred Korzybski
LESSON 7
76

LESSON 7
TUBE MAP
The ﬁrst diagrammatic map of London's rapid transit network was designed by Harry Beck in
1931. Beck was a London Underground employee who realised that because the railway ran
mostly underground, the physical locations of the stations were largely irrelevant to the
traveller wanting to know how to get from one station to another — only the topology of the
route mattered.
To this end, Beck devised a simpliﬁed map, consisting of stations, straight line segments
connecting them, and the River Thames; lines ran only vertically, horizontally, or on 45-degree
diagonals. To make the map clearer and to emphasise connections, Beck differentiated
between ordinary stations (marked with tick marks) and interchange stations (marked with
diamonds).
▸ https://en.wikipedia.org/wiki/Tube_map
77

LESSON 8
78

LESSON 7
79

LESSON 8
80

LESSON 7
81
Maps, by Mizielinskas Mizielinski

LESSON 7
82

LESSON 7
83
CC-BY-NC xkcd,
https://xkcd.com/256/

LESSON 7
84
CC-BY-NC xkcd,

LESSON 7
85
CC-BY-NC xkcd,

LESSON 7
BUBBLE GRAPH
86
During the 2012 London
Olympics, The New York
Times kept a running medal
count by country and visualized
the data in a simple table

LESSON 7
BUBBLE GRAPH
87
The Times formatted the same information into a bubble graph. This approach
does a much better job conveying magnitude.
https://www.nytimes.com/interactive/projects/london2012/results

LESSON 7
BUBBLE GRAPH
88

LESSON 7
89

LESSON 7
▸ https://stephanieevergreen.com/qualitative-chart-chooser/
90

LESSON 7
▸ https://stephanieevergreen.com/qualitative-chart-chooser/
91

EXAMPLES
SOME
PHOTO BY JAREDD CRAIG ON UNSPLASH

LESSON 7
93
https://www.washingtonpost.com/graphics/entertainment/game-of-thrones/

LESSON 7
94
https://www.washingtonpost.com/graphics/2018/entertainment/avengers-inﬁnity-war-mcu-timeline/

CONVERTERS
DOG’S AGE
PHOTO BY JAREDD CRAIG ON UNSPLASH

LESSON 7
96
https://www.akc.org/expert-advice/
health/how-to-calculate-dog-years-
to-human-years/

LESSON 7
97
https://www.washingtonpost.com/business/2019/11/28/scientists-have-come-up-with-better-way-convert-your-dogs-age-human-years/

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