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e Digital Life of Walkable Streets
The Digital Life of Walkable Streets
@danielequercia @rschifan @lajello @walkonomics
We cannot afford to leave architecture to architects
We cannot afford to leave computers to engineers
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.”
The Digital Life of Walkable Streets
The Digital Life of Walkable Streets
daniele quercia
The Digital Life of Walkable Streets
The Digital Life of Walkable Streets
The Digital Life of Walkable Streets
The Digital Life of Walkable Streets
The Digital Life of Walkable Streets
The Digital Life of Walkable Streets
offline & online
The Digital Life of Walkable Streets
The Digital Life of Walkable Streets
Jane Jacob
Kevin Lynch
Shortest
Quiet
Happy
Beauty
Google for “Happy Maps”
The shortest Path to Happiness
Walkability
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)
Public space surrendered to cars ...
The commuting Paradox
“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.”
The Digital Life of Walkable Streets
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)
The Digital Life of Walkable Streets
The Digital Life of Walkable Streets
Hypothesis
A street’s vitality is captured in the digital layer
(there might be digital footprints that distinguish walkable
streets from unwalkable ones)
Method
1. Theoretically derive hypotheses concerning walkability
2. Test them
3. If supported, then “valid” scores
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
Time of Day
Jane Jacobs
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)
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”)
Death caused by elimination of pedestrian activity
(highway construction, large-scale development projects)
Life meant pedestrians at all times of the day
(“sidewalk ballet”)
nothing is safer than a city
street that everybody uses
“the eyes on the street”
253 patterns of good urban design (1977)
“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”
Question 1
Can safe streets be identified by night activity?
n
i
(o
i
) is the fraction of pictures taken at night (not at night) on street i
r(safe,night)= 0.60
safe street tend to be visited at night as well
●●●
●
1
2
3
4
5
Day Anytime Night
photo@night
safety
●
●
●
●
●
●
● ●
● ●
● ● ●
●
●
●
●
●
●
●
● ●
●
●
● ●
●
● ●
● ● ● ●
●
●
●
● ●
●
●
●
●
0.0
0.2
0.4
0.6
0 10 20 30 40
number of Flickr photos per street
r(safety,photo@night)
streets with 30+ photos = stable correlations of r > 0.6
What about making “it worth a thief’s while”?
unsafe ones are used by men only
OR unsafe streets used by women
Question 2
Can safe streets be identified by gender or age?
0
100
200
300
0.0 2.5 5.0 7.5
log(male)
0
100
200
300
400
500
0.0 2.5 5.0 7.5
log(female)
Gender
m
i
(f
i
) = fraction of male (female) users who have taken a picture on street i
r(manhood,safety)=0.58
Safe streets tend to be visited by a predominantly male population
●●
1
2
3
4
5
Q1 Q2 Q3 IQR
manhood
safety
●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
● ●
●
●
●
●
●
●
● ● ●
● ● ●
●
●
● ●
●
●
●
●
●
● ● ●
● ●
●
●
●
●
●
●
●
●
●
●
●
● ●
●
0.0
0.2
0.4
0.6
200 400 600
number of Flickr users per street
r(safety,manhood)
0
200
400
600
800
30 40 50 60
Average Age
Age
r(age,safety)=0.32
unsafe streets tend to be visited by a younger population
●
●
●
●
●
● ●
●
● ●
● ●
● ● ●
●
●
● ●
●
0.0
0.1
0.2
0.3
0 50 100 150 200
number of Flickr users per street
r(safety,age)
Crime prevention through environmental design
The physical environment can be designed or
manipulated to reduce fear of crime (by
supporting certain activities over others)
Questions 3 & 4
Can safe (walkable) streets be identified by the
presence of specific types of places?
To test the extent to which safety is associated with the pres-
enceof specific places, webuild alinear model that predicts safety
scores from the presence of first-level Foursquare categories. That
is, astreet’spredicted safety scoreiscomputed from thefraction of
places on it that fall into thedifferent categories:
saf etyi = ↵+β1 ar ts+β2 college+β3 f ood+β4 nightlif e+
β5 outdoor s + β6 residential + β7 shopping, +β8 tr avel + e.
It turnsout that theregression showsanadjusted R2
of 74%, sug-
gesting that safety can be accurately predicted only from the pres-
6
R2
= 74% (safety from crime)
safe streets: outdoor places (mainly parks)
unsafe ones: residential bits of central London that have no parks
R2
= 33% (walkability)
the presence of residential areas drives most
of the predictive power of the regression
Text
we gather the literature on walkability to
produce a list of walkability-related keywords
Line-by-line coding
1.Collecting documents
2.Annotating them
3.Validating them
Question 5
Can walkable streets be identified by walkability-related tags?
0.49
0.78−
0.89
0.5−
0.0
0.5
car (ci) walkability (wi) z w− alkability
tag category
r(walkability,tagpresence)
●
●
0.2
0.4
0.6
0.8
1.0
<748 <1750 <2000 <2250 <2500
number of Flickr tags per street
r(walkability,z−walkability)
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
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
Practical Implications
Room booking
Urban route recommendations
Real-estate
Limitation
It doesn’t work where there is little activity
(yet absence/presence of venues work)
Happy Maps
The Digital Life of Walkable Streets
Smelly Maps
The Digital Life of Urban Smellscapes
Humans Can Discriminate More than 1 Trillion Olfactory Stimuli
Science, March 2014
Yet, city planning can discriminate only a few bad odors
nasal ranger
smell walks
Amsterdam,
Pamplona, Glasgow,
Edinburgh, Newport,
Paris, New York.
Good classification
The first urban smell dictionary
researchswinger.org/smellymaps
Happy Maps
The Digital Life of Walkable Streets
Smelly Maps
The Digital Life of Urban Smellscapes
Chatty Maps
The Digital Life of Urban Soundscapes
The Digital Life of Walkable Streets
The Digital Life of Walkable Streets
The Digital Life of Walkable Streets
e Digital Life of Walkable Streets
The Digital Life of Walkable Streets
@danielequercia @rschifan @lajello @walkonomics

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The Digital Life of Walkable Streets

Editor's Notes

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