WEEK 2

Question 1

examples of digital storytelling

Answer 1

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

Question 2

data visualization is the process of mapping ___ to ___

Answer 2

aesthetics

variables

Question 3

two types of data

Answer 3

quantatative
qualatative

Question 4

quantatative

Answer 4

continuous
discrete

numerical

Question 4

qualatative

Answer 4

categorical

nominal (colour)
ordinal (rank it)

Question 5

aesthetics (6)

Answer 5

position
shape
size
color
line width
line type

Question 6

mapping variables to aesthetics

Answer 6

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

Question 7

mapping variables to aesthetics typically done through?

Answer 7

scale

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

Question 8

position

Answer 8

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

common x,y coordinates

Question 9

shape

Answer 9

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

Question 9

size

Answer 9

often mapped to amounts or magnitude

*shape and size often map together)

Question 9

line width

Answer 9

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

Question 10

line type

Answer 10

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

Question 11

three categories of colour scales

Answer 11

continous
categorical/qualitative
diverging

Question 12

continuous colours

Answer 12

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)

Question 13

qualitative colours

Answer 13

to distinguish between categories, we use qualatative colours

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

Question 14

divering colours

Answer 14

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

Question 15

HSB

Answer 15

one framework to understand how to use colour

HSB: hue, satiration, brightness

Question 16

hue

Answer 16

the colours of the spectrum

how we perceive colours hotter vs cooler

Question 16

saturation

Answer 16

richness of a particular hue

used to make colours pop to draw attention to them

Question 17

brightness

Answer 17

how bright colour is

Question 18

what to think about?

Answer 18

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)

Question 19

edward tufte

Answer 19

principle of data ink

Question 20

principle of data ink

Answer 20

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

Question 21

chartjunk

Answer 21

unnecessary decoration in graphs (e.g. 3D effects, backgrounds, flashy colors, clipart

Question 22

mis-using scales (tufte)

Answer 22

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.

Question 23

william playfair

Answer 23

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

Question 24

Jon Snow, Cholera Map, 1854

Answer 24

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.

Question 25

Worldview in Data Visualization

Answer 25

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.

Question 26

Emma Willard’s Historical Timeline

Answer 26

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.

Question 27

Data Visualization as a Tool of Oppression

Answer 27

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.

Question 28

Objectivity has become gendered…

Answer 28

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

Question 28

Data Feminism by Catherine D’Ignazio and Lauren Klein,

Answer 28

- 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

Question 29

Emotion in Data Visualization

Answer 29

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.

Question 30

Donna Haraway: Visualisation is a "Trick"

Answer 30

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."

Question 30

All Knowledge Is Situated

Answer 30

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."

Question 31

Emotion and Embodiment

Answer 31

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.

Question 32

Visualizations Are Persuasive (Rhetoric)

Answer 32

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).

Question 33

strong Objectivity (Sandra Harding)

Answer 33

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.

Question 34

positionality (Linda Alcoff)

Answer 34

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.

Question 35

Elizabeth Peabody and Emotional Visualization

Answer 35

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.

Question 36

Humanistic Approach to Data

Answer 36

Uncertainty * Complexity * Drucker, data vs capta (taken and chosen by people). * Data is captured: it is constructed

Question 37

Data Feminism Chapter: Key Points

Answer 37

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.