data communication Flashcards

1
Q

data visualisation

A

process of transforming complex data into visual formats to communicate insights clearly and effectively

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2
Q

ethical data visualisation

A

practice of presenting data in a fair, accurate, and responsible way, ensuring transparency and avoiding misleading or biased representations

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3
Q

principles of ethical data visualisation

A
  • accuracy
  • clarity
  • transparency
  • fairness
  • context
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4
Q

accuracy

A

data represented truthfully without manipulation

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5
Q

clarity

A

data presented in way easy to understand and interpret correctly

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6
Q

transparency

A

open about data source, methods used, limitations

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7
Q

fairness

A

avoids bias or misleading techniques and shows relevant data

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8
Q

context

A

provides necessary background info to allow accurate interpretation

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9
Q

data communication

A

occurs when data visualisations are used along non-visual communication elements to convey a message

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10
Q

effective data communucation

A
  • clear;
  • easy to understand
  • impactful
  • tells a story
  • accurate;
  • visually engaging
  • context
  • display
  • de-clutter
  • attention-seeking
  • think like a designer
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11
Q

graphical displays should…

A
  • show data
  • focus on substance
  • be accurate
  • be efficient
  • be coherent
  • allow for comparison
  • be detailed
  • serve a purpose
  • integrate stats and descriptions
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12
Q

categories of graphics

A
  • comparison
  • time series
  • ranking
  • correlation
  • geographical
  • other
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13
Q

visual perception

A
  • order
  • hierarchy
  • clarity
  • relationship
  • conventional
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14
Q

idea illustration

A

conceptual and declarative

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15
Q

idea generation

A

conceptual and exploratory

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16
Q

every data visualisation

A

data-driven and declarative

17
Q

visual discovery

A

data-driven and exploratory

18
Q

ethical data visualisation/design thinking

A

human-centred approach focusing on understanding user needs, defining problems, ideating solutions, prototyping, and testing

19
Q

common ethical issues

A
  • misleading scales/axes
  • cherry-picking data
  • distorting visual elements
  • data omissions and incomplete context
20
Q

best ethical practises

A
  • choose appropriate visualisations
  • maintain consistent scales
  • provide clear labels and legends
  • avoid visual distortion