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The Digital Life of Walkable Streets

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The Digital Life of Walkable Streets The Digital Life of Walkable Streets @danielequercia @rschifan @lajello @walkonomics


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We cannot afford to leave architecture to architects


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We cannot afford to leave computers to engineers


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Neil Gershenfeld Director of MIT’s Center for Bits and Atoms “Computer science is one of the worst things to happen to computers or to science because, unlike physics, it has arbitrarily segregated the notion that computing happens in an alien world.”


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U C L


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U C L daniele quercia


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U C L


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U C L


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U C L


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U C L


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offline & online


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Jane Jacob Kevin Lynch


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Quiet Happy Beauty Google for “Happy Maps”


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The shortest Path to Happiness


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Walkability


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Why Walkability? Adds 5-10 % to house prices @ the heart of the cure to the health-care crisis in US Carbon saving (light-bulbs 1 year= living in a walkable for 1 week) neighborhood in 1 week)


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Public space surrendered to cars ...


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The commuting Paradox


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“The General Theory of Walkability explains how, to be favored, a walk has to satisfy four main conditions: it must be useful, safe, comfortable, and interesting. Each of these qualities is essential and none alone is sufficient.”


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Walkonomics is a composite score of 1. Road safety (#accidents) 2. Easy to cross (street type + traffic) 3. Sidewalks (width) 4. Hilliness 5. Navigation (signs on street) 6. Safety from crime 7. Smart and Beautiful (e.g., #trees, close parks) 8. Fun and relaxing (shops, bars, restaurants)


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Hypothesis A street’s vitality is captured in the digital layer (there might be digital footprints that distinguish walkable streets from unwalkable ones)


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Method 1. Theoretically derive hypotheses concerning walkability 2. Test them 3. If supported, then “valid” scores


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Reliability Measurement error borrow measurement procedures from the literature (e.g., a buffer of 22.5 meters around each street’s polyline) Specification error (Flickr/Foursquare biases) normalization measures (e.g., z-transformations) from previous studies Sampling error minimum amount of data such that the same results on repeated trials


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Time of Day


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Jane Jacobs


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The Rockefeller Foundation gave grants for urban topics: To Kevin Lynch (MIT) for studies of urban aesthetics (Image of the City in 1960) To Jane Jacobs for studies of urban life (The Death and Life of Great American Cities in 1961)


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The Death and Life of Great American Cities the most influential book in city planning (“social capital", "mixed primary uses", "eyes on the street”) critique of the 1950s urban renewal policies (attacking Moses for “replacing well-functioning neighborhoods with Le Corbusier-inspired towers”)


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Death caused by elimination of pedestrian activity (highway construction, large-scale development projects) Life meant pedestrians at all times of the day (“sidewalk ballet”)


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nothing is safer than a city street that everybody uses “the eyes on the street”


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253 patterns of good urban design (1977)


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“At night, street crimes are most prevalent in places where there are too few pedestrians to provide natural surveillance, but enough pedestrians to make it worth a thief’s while”


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Question 1 Can safe streets be identified by night activity?


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ni (oi) is the fraction of pictures taken at night (not at night) on street i


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r(safe,night)= 0.60 safe street tend to be visited at night as well


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streets with 30+ photos = stable correlations of r > 0.6


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What about making “it worth a thief’s while”? unsafe ones are used by men only OR unsafe streets used by women


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Question 2 Can safe streets be identified by gender or age?


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Gender


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mi (fi) = fraction of male (female) users who have taken a picture on street i


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r(manhood,safety)=0.58 Safe streets tend to be visited by a predominantly male population


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Age


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r(age,safety)=0.32 unsafe streets tend to be visited by a younger population


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Crime prevention through environmental design The physical environment can be designed or manipulated to reduce fear of crime (by supporting certain activities over others)


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Questions 3 & 4 Can safe (walkable) streets be identified by the presence of specific types of places?


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R2= 74% (safety from crime) safe streets: outdoor places (mainly parks) unsafe ones: residential bits of central London that have no parks


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R2= 33% (walkability) the presence of residential areas drives most of the predictive power of the regression


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Text we gather the literature on walkability to produce a list of walkability-related keywords Line-by-line coding Collecting documents Annotating them Validating them


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Question 5 Can walkable streets be identified by walkability-related tags?


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To sum up... Picture uploads from dwellers of walkable streets differ from those of unwalkable ones, mainly in terms of upload time and tagging * * limited data vs. high penetration


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Theoretical Implication Social media = Opportunities for Theory Comforted by our validation work, urban researchers might well be enticed to use social media to answer theoretical questions that could not have been tackled before because of lack of data


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Practical Implications Room booking Urban route recommendations Real-estate


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Limitation It doesn’t work where there is little activity (yet absence/presence of venues work)


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Happy Maps The Digital Life of Walkable Streets


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Smelly Maps The Digital Life of Urban Smellscapes


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Humans Can Discriminate More than 1 Trillion Olfactory Stimuli Science, March 2014


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Yet, city planning can discriminate only a few bad odors


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nasal ranger


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smell walks Amsterdam, Pamplona, Glasgow, Edinburgh, Newport, Paris, New York.


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Good classification The first urban smell dictionary researchswinger.org/smellymaps


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Happy Maps The Digital Life of Walkable Streets


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Smelly Maps The Digital Life of Urban Smellscapes


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Chatty Maps The Digital Life of Urban Soundscapes


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The Digital Life of Walkable Streets The Digital Life of Walkable Streets @danielequercia @rschifan @lajello @walkonomics


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