We look at presenting more than one scale of info at a time.

1. 2013
Game Design 2
Lecture 9: Micro / Macro Readings
http://gcugd2.com
david.farrell@gcu.ac.uk

2. Micro / Macro Readings
‘A method for presenting large quantities of data at high
densities in a way that a broad overview of the data is
given and yet an immense amount of detail is provided.’
Ruddle 2002

3. Definition
• Visualising data at two levels in one image
• Micro Data (low level detail)
• Macro Data (high level detail)
• User / Viewer can get a rough idea at a
glance but also see detailed information

5. Layering & Separation?
• Layering & Separation == multiple types of
information + how to separate
• Micro & Macro == multiple scales of
information

6. Spatial
• Maps can show geographical breakdown of
a location as well as local detail
• Geometry of land mass as well as regional
breakdown.

7. §

9. 4th Dimension
• More subtle macro readings can look into
time.

11. • The circular layout of the centre of Senlis
shows its history as a Gallo-Roman
fortification.

14. • Glasgow’s industrial history, built around
the River Clyde is apparent by the density
on its shore.
• Stirling borders a river but the lack of focus
shows its different history.

15. Symbolic Use
• Micro / Macro design
is not always geographic.
• This poster shows that
from the work of many
hands, one great plan
will be fulfilled.

16. Character Design
• In games, it is often possible to read many
scales of information from looking at a
character:
• character class, team, attack, defence,
health etc.

18. Combining M/M & L&S
• The London Air Quality Network website
has to provide a very dense set of data in
an intuitive interface.
• They layer user interface elements over a
rich map which shows different types of
data as well as different scales of data.
• http://bit.ly/londonair

21. ddddddddddfsdfadf
• dd

22. Meaning through Scale
• The Vietnam Veterans Memorial achieves its
visual and emotional strength through
micro / macro design.
(Tufte, p43)

24. • 58,000 dead soldiers
• scale can be seen at a
distance
• Individual names up close
• Ordered by date of
death

25. Relative Data
• http://bit.ly/billiondollargram
• Shows spend / cost in billions
from US budget and events
• Can see immediately highest
spent areas
• Can also see actual numbers

28. Stem and Leaf
• Like a bar chart / histogram but uses the
space that would normally be used by solid
blocks of colour.

29. 218 Mountains
•d

30. • Glance tells distribution
• Analysis provides more
• Scale given
• Necessary to round

31. 292 Trains

32. 292 Trains (badly)

33. • 777 more characters
• hard to see how frequently trains leave at a
given hour
• rush hour?
• 11pm trains?

34. Stem & Leaf Improve?
• How can you modify the stem & leaf plot
to show more information (such as
platform number)?

35. Back to Back S&L

36. Missile or Toothbrush?
• 7000 objects > 10 cm in diameter in space
• rocket engines, bin bags, frozen sewage,
shrapnel from tests, 1 wrench and 1
toothbrush
• Only 5% are functional satellites
• Necessary to track for safety of launches

39. • Note the ring on the second image.
• this is the geostationary orbit used by
satellites
• The scale of the problem can be seen, not
only in overall but also in terms of orbit
height and relative density of areas.

40. Why Micro & Macro?
• We thrive in information rich contexts
• Visually rich displays are not only
appropriate to convey information but are
often the optimal way to do so.

41. • If information is spread over multiple
screens, the user needs to keep that
information in memory
• If information is condensed into one
screen / graphic, it only requires
understanding.

42. • Micro / Macro designs enforce local and
global comparisons but do so without the
need to context switch.
• Power is given to the user to decide what
level of detail is required.

43. Downsides of M/M
• creating good Micro / Macro design is hard.
• it is easier to have one display for each
scale of data.
• it may be necessary to gather or process
more data (e.g. stem plot vs bar chart)
• it may be difficult to blend the scales
together.

44. Too complicated?
• Don’t forget that the data is never the
problem.
‘Clutter and confusion are failures of
design, not attributes of information‘
(Tufte)