Computing the Image of theCity Bin Jiang University of Gävle, Sweden http://fromto.hig.se/~bjg/
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 2
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. 7
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 8
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 18
A power law and its cousins y x ln y ln x 28
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). 29
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. 31
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”. 32
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 33
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.). 38
Thank you very much!!! Questions and comments? 39
A particular slide catching your eye?
Clipping is a handy way to collect important slides you want to go back to later.