WEEK 2 Flashcards

(43 cards)

1
Q

examples of digital storytelling

A

immersive media (AR, VR)
storytelling with ai
interactive and locative works
digital and film poetry
podcasts
narrative games
datasets and data visualisation
digital exhibits
preservation and curation
data journalism

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2
Q

data visualization is the process of mapping ___ to ___

A

aesthetics

variables

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3
Q

two types of data

A

quantatative
qualatative

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4
Q

quantatative

A

continuous
discrete

numerical

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4
Q

qualatative

A

categorical

nominal (colour)
ordinal (rank it)

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5
Q

aesthetics (6)

A

position
shape
size
color
line width
line type

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6
Q

mapping variables to aesthetics

A

process by which we take data (qual or quant) and map it to visual elements ie, size, shape

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7
Q

mapping variables to aesthetics typically done through?

A

scale

ex, one day of time is equal to one pixel in the visualisation

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8
Q

position

A

all 2d data vis need to be positioned in some kid of space

common x,y coordinates

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9
Q

shape

A

shape = only discrete variables so often is used to represent categories

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9
Q

size

A

often mapped to amounts or magnitude

*shape and size often map together)

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9
Q

line width

A

often used to show amount or magnitude part “in time series” data

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10
Q

line type

A

like shape, line type can only represent categorical/qualitative data

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11
Q

three categories of colour scales

A

continous
categorical/qualitative
diverging

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12
Q

continuous colours

A

used to represent numerical/countable data

Makes use of our tendency to see colours as having higher or lower values

often a single hue (from dark to light blue)

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13
Q

qualitative colours

A

to distinguish between categories, we use qualatative colours

easily distinguishable (not close together in colour) - and have no apparent order

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14
Q

divering colours

A

where the values diverge around a midpoint (ie, percent trump/clinton supporters)

scale runs from saturated to light red, to a midpoint of white white then light to saturated blue

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15
Q

HSB

A

one framework to understand how to use colour

HSB: hue, satiration, brightness

16
Q

hue

A

the colours of the spectrum

how we perceive colours hotter vs cooler

16
Q

saturation

A

richness of a particular hue

used to make colours pop to draw attention to them

17
Q

brightness

A

how bright colour is

18
Q

what to think about?

A

who is the audeince?
what effect to achieve?
- represent data values faithfully
- persuade
- spark emotion/aethetic pleasing
- other reason specific ulitatian (ie, explain covid - made to do a job)

19
Q

edward tufte

A

principle of data ink

20
Q

principle of data ink

A

core idea is that good visual communication prioritizes clarity and efficiency by minimizing unnecessary elements while maximizing the amount of information conveyed in the graphical representation

21
chartjunk
unnecessary decoration in graphs (e.g. 3D effects, backgrounds, flashy colors, clipart
22
mis-using scales (tufte)
Visual size must be proportional to data size. The space, size, or ink used to show data should match the actual value. If a bar is twice as tall, it should represent twice the amount. If you exaggerate or shrink the visual element, it misleads the viewer.
23
william playfair
father of data visualization. invented: The bar chart The line graph The pie chart First to use graphs to clearly show economic data over time. *Importance of neutrality, objectivity
24
Jon Snow, Cholera Map, 1854
Created a dot map during the 1854 cholera outbreak in London. Mapped cholera deaths and water pump locations in the Soho district One of the first examples of data visualization used for public health.
25
Worldview in Data Visualization
The way a visualization reflects cultural or political beliefs. Not just about data — also about how it’s framed and whose story is being told.
26
Emma Willard’s Historical Timeline
created a visual timeline as a river. The United States is placed in the center, flowing toward the viewer. It makes it look like all of history leads to the U.S., as if it's the main goal or most important part.
27
Data Visualization as a Tool of Oppression
In the 1930s, the U.S. government made color-coded maps of neighborhoods. Red areas were mostly where Black or immigrant people lived. These areas were labeled as “high risk” and people there were often denied loans or help to buy homes. can look neutral but still be used to discriminate — they can hide unfair decisions behind charts and colors.
28
Objectivity has become gendered…
The ‘master stereotype’: ● False binary between emotion and reason ● Gendered: the belief that women are more emotional than men ● Feminist theory: this stereotype is not just untrue but establishes/enforces hierarchies
28
Data Feminism by Catherine D’Ignazio and Lauren Klein,
- role of emotion is data vis all knowledge (including data vis) is situated, constructed by people in specific circumstances authors argue for emotion and embodiment
29
Emotion in Data Visualization
Traditional data visualizations try to look neutral and objective. But D’Ignazio and Klein argue that emotion is important—it helps people connect to data and understand real human impact. Example: Charts about injustice (like police violence or poverty) can and should evoke emotional responses.
30
Donna Haraway: Visualisation is a "Trick"
Haraway says that data visualizations often pretend to be neutral or “from nowhere.” But in reality, they reflect the choices and biases of the people who made them. So, the appearance of objectivity is misleading—it’s a "trick."
30
All Knowledge Is Situated
No one sees the world from a completely neutral or universal perspective. Knowledge (and data) is always created by someone, in a specific context, with their own background, culture, and goals. This idea is called "situated knowledge."
31
Emotion and Embodiment
D’Ignazio and Klein say that data should reflect lived experiences—our physical, emotional, and social realities. This is called embodiment—valuing personal experience as a valid source of knowledge, especially for marginalized groups.
32
Visualizations Are Persuasive (Rhetoric)
Even when visualizations look neutral, they still persuade people—they tell a story or make a case. Charts and graphs are not just facts—they are a form of argument (or rhetoric).
33
strong Objectivity (Sandra Harding)
Instead of pretending to be neutral, Harding says science should aim for "strong objectivity". That means being honest about bias, and actively including diverse perspectives—especially from people who are often left out. Neutrality can actually hide bias, making science less objective.
34
positionality (Linda Alcoff)
Everyone has a "position" based on their background—like race, gender, class, ability, etc. This affects how we see and create knowledge. Recognizing your own position helps make knowledge more honest and inclusive.
35
Elizabeth Peabody and Emotional Visualization
In the 1800s, Peabody created charts meant to spark imagination and emotion, not just show cold facts. Her work is an early example of using visualization to inspire new ways of thinking—not just to simplify data.
36
Humanistic Approach to Data
Uncertainty * Complexity * Drucker, data vs capta (taken and chosen by people). * Data is captured: it is constructed
37
Data Feminism Chapter: Key Points
All charts and graphs persuade us — they aren’t fully neutral. They create the illusion of being “objective” (the “god trick”). The authors say it’s okay to include emotion in visualizations. Emotion can help people connect with the data and understand it better.