Bin Jiang on "Computing the image of the city"

1. Computing the Image of the
City
Bin Jiang
University of Gävle, Sweden
http://fromto.hig.se/~bjg/

2. Outline of the talk
 The image of the city
 How the city looks like?
 Gaussian way of thinking
 Scaling way of thinking
 Head/tail division rule
 Head/tail breaks
 How to compute the image of the city?
 Conclusion
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3. The image of the city
Portugali 1996
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4. Five city elements
© Lynch (1960)
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5. More attention on large and complex objects
© Yarbus (1967)
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6. © Jiang and Liu (2012) 6

7. Two key concepts of the image of the city
 Legibility refers to a particular (visual) quality or
(apparent) clarity that makes the city’s layout or
structure recognizable, identifiable, and eventually
imageable in the human minds.
 Imageability is a quality of a city artifact that gives on
an observer a strong vivid image.
 Gibson’s affordance: A city or city artifacts due to
their distinguished properties (geometric, visual,
topological or semantic) affords remembering to
shape a mental map in the human minds.
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8. How to obtain the image of the city?
 It is obtained through interviewing city
residents
 by map drawing,
 comparing with photographs, and
 walking in the physical spaces in the city.
 Qualitative approach in essence.
 Herewith I propose a quantitative approach:
computing the image of the city
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9. Which pattern looks like the city?
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10. Scaling of geographic space (a hidden order)
Jiang B., Liu X. and Jia T. (2011), Scaling of geographic space as a universal rule for map
generalization, Preprint: http://arxiv.org/abs/1102.1561.
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11. © Fischer (2010) 11

12. © Watz (2008)
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13. 13

14. Jackson Pollock (1912–1956)
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15. Fractal flames (http://electricsheep.org/)
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16. To create beauty (http://electricsheep.org/)
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17. 17

18. Why atoms are so small?
 In his 1945 book what is
life? Schrödinger asked the
above question.
 The answer is that the high
level of organization
necessary for life is only
possible in a macroscope
system; otherwise the
order would be destroyed
by microscope fluctuations. The fine structure creates
 Atoms > molecules > cells soul in terms of
>tissues > organs > body Christopher Alexander
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19. Geometric order vs structural order
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20. 20

21. 21

22. 22

23. 23

24. Hidden order: Watts Towers
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25. Hidden order: Watts Towers (detailed looks)
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26. 26

27. 27

28. A power law and its cousins
y  x
ln y   ln x
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29. Head/tail division rule
 Given a variable x, if its values follow a heavy tailed
distribution, then the mean of x can divide all the
values into two parts: those above the mean in the
head and those below the mean in the tail (Jiang
and Liu 2012).
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30. Head/tail movement
 AT&T  Skype
 Britinica  Wikipedia
 National mapping agency  OpenStreetMap
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31. Head/tail breaks
 Iteratively apply the head/tail division rule to
dataset with a heavy tailed distribution, untill the
data in head is no longer heavy tailed
distributed, or specifically, the number in the
head is no longer a minority (e.g., < 40%).
 Both the number of classes and class intervals
are automatically or naturally determined.
 For example, four classes: [min, m1), [m1, m2),
[m2, m3), [m3, max].
 Head/tail breaks is more natural than natural
breaks.
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32. Why more natural than natural breaks?
 Reflects human binary thinking.
 Captures the scaling pattern of the data
 Both the number of classes and class intervals
are automatically or naturally determined.
 Reflects figure/ground perception.
 Essence of nature is ”far more small things than
large ones”.
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33. Computing the image of the city
 Step 1: organize all city artifacts layer by layer
 Step 2: all the city artifacts must be organized in
terms of city artifacts rather than geometric
primitives such as points, lines and polygons
 Step 3: rank the city artifacts of the same type from
the largest to the smallest
 Step 4: partition all the artifacts into two categories:
those below the mean (in the tail) and those above
the mean (in the head)
 Step 5: continue step 4 until the artifacts in the head
are non longer heavy tailed
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34. 34

35. Far more short streets than large ones
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36. 36

37. 37

38. Conclusion
 The image of the city is computable.
 This is based on the assumption that the city
holds the living structure or scaling pattern –
far more small things than large ones.
 The image of the city arise out of the
underlying scaling.
 Legibility and imageability are measurable.
 My proposal relies on increasing availablity of
geographic information on cities (e.g., GPS
trajectories etc.).
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39. Thank you very much!!!
 Questions and comments?
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