This document proposes a quantitative approach to computing the image of a city. It begins by discussing how the image of a city is traditionally obtained through qualitative interviews. It then introduces concepts like legibility and imageability that contribute to a city's mental image. The core of the proposal is a multi-step process: 1) organizing city artifacts into layers, 2) ranking artifacts by size, 3) partitioning artifacts into "head" and "tail" groups using the mean size as the threshold, and 4) iteratively applying this partitioning until the distribution of large artifacts is no longer heavy-tailed. This process aims to capture the underlying "scaling pattern" common to cities where there are far more small artifacts than large ones. The

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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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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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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35. Far more short streets than large ones
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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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