Case studies and relative research protocols for projects that use geolocated foursquare data for add value, identify patterns and help social cooperation

1. GEOLOCATED 4SQ DATA:
where we are
www.densitydesign.org
A WEEK ON FOURSQUARE (WSJ)
URBAGRAMS
LIVEHOODS
THE EMERGENT URBAN IMAGINARIES OF GEOSOCIAL MEDIA

2. A WEEK ON FOURSQUARE (WSJ)
www.densitydesign.org
To learn about where people go and what
they do on Foursquare, Digits collected
every check-in on the service for a week
earlier this year (starting at noon Eastern
on Friday, Jan. 21 until noon on Friday Jan.
28), via the Foursquare “firehose” aiming
to see where people checked in around
New York City and San Francisco over
the course of the week.
New York City and San Francisco were
among the first cities where people start-
ed using Foursquare, and the company’s
founders say it’s because the service spread
first among their own friends. Through geolocated check-ins’ and official categorization of Foursquare’s venues analysis,
two kind of data were compared aiming to
highlights common elements and differences who caracterize:
- activities in both territories (New York
City and San Francisco Bay area)
- habits and preferences of genders

3. A WEEK ON FOURSQUARE (WSJ)
www.densitydesign.org
3 READING LEVELS
global (timeline)
glocal (geolocated view)
local (hot spots + focus on categories and venues)
GEOLOCALIZATION
CATEGORIZATION
TIMELINES

4. A WEEK ON FOURSQUARE (WSJ)
www.densitydesign.org

5. A WEEK ON FOURSQUARE (WSJ)
www.densitydesign.org

6. A WEEK ON FOURSQUARE (WSJ)
www.densitydesign.org

7. A WEEK ON FOURSQUARE (WSJ)
ELABORATION
VISUALIZATION
www.densitydesign.org
SF
NYC
Venues properties
4SQ
COLLECTION
name
categories
n. check-ins
lat/lon
► user
start date
end date
frequency
gender
01/21/2011
01/28/2011
per hour
h14
h11
source
analysis
output
algorythm/method
geolocalization
categorization
time
none

8. A WEEK ON FOURSQUARE (WSJ)
COLLECTION
ELABORATION
VISUALIZATION
www.densitydesign.org
SF
NYC
Venues properties
4SQ
venues
analysis
name
categories
n. check-ins
lat/lon
► user
start date
end date
frequency
bar charts
check-ins/hour
gender
01/21/2011
01/28/2011
per hour
heatmaps
check-ins/hour
h14
h11
source
analysis
output
algorythm/method
geolocalization
categorization
time
none

9. A WEEK ON FOURSQUARE (WSJ)
COLLECTION
ELABORATION
venues
analysis
www.densitydesign.org
VISUALIZATION
ranks
Most checked-in venues overall
SF
NYC
Venues properties
4SQ
timeline
Most checked-in venue
name
categories
analysis
categories
n. check-ins
lat/lon
► user
start date
end date
frequency
bar charts
check-ins/hour
gender
01/21/2011
01/28/2011
per hour
heatmaps
check-ins/hour
h14
h11
source
analysis
output
algorythm/method
geolocalization
categorization
time
none

10. A WEEK ON FOURSQUARE (WSJ)
COLLECTION
ELABORATION
ranks
Most checked-in venues overall
Top venues/category
NYC
Venues properties
4SQ
venues
analysis
NYC/SF
comparison
SF
name
www.densitydesign.org
VISUALIZATION
timeline
Most checked-in venue
categories
analysis
categories
n. check-ins
lat/lon
► user
start date
end date
frequency
bar charts
check-ins/hour
gender
01/21/2011
01/28/2011
per hour
heatmaps
check-ins/hour
h14
h11
source
analysis
output
algorythm/method
geolocalization
categorization
time
none

11. A WEEK ON FOURSQUARE (WSJ)
COLLECTION
ELABORATION
ranks
Most checked-in venues overall
Top venues/category
Top venues NYC & SF
Top venues NYC & SF/categ.
NYC
Venues properties
4SQ
venues
analysis
NYC/SF
comparison
SF
name
categories
analysis
categories
lat/lon
start date
end date
frequency
gender
comparison
gender
01/21/2011
01/28/2011
per hour
timeline
Most checked-in venue
interactive plots/scatterplots
NYC & SF check-ins/top 80 categ.
bar charts
venues’ check-ins/week
check-ins/category
n. check-ins
► user
www.densitydesign.org
VISUALIZATION
bar charts
check-ins/hour
heatmaps
check-ins/hour
h14
h11
source
analysis
output
algorythm/method
geolocalization
categorization
time
none

12. A WEEK ON FOURSQUARE (WSJ)
COLLECTION
ELABORATION
ranks
Most checked-in venues overall
Top venues/category
Top venues NYC & SF
Top venues NYC & SF/categ.
NYC
Venues properties
4SQ
venues
analysis
NYC/SF
comparison
SF
name
categories
analysis
categories
lat/lon
start date
end date
frequency
gender
comparison
gender
01/21/2011
01/28/2011
per hour
timeline
Most checked-in venue
interactive plots/scatterplots
NYC & SF check-ins/top 80 categ.
Male/fem. check-ins/top 80 categ.
Male/fem. check-ins/Popul. venues
bar charts
venues’ check-ins/week
check-ins/category
check-ins worldwide
n. check-ins
► user
www.densitydesign.org
VISUALIZATION
bar charts
check-ins/hour
heatmaps
check-ins/hour
h14
h11
others
male/fem. check-ins/categ.
source
analysis
output
algorythm/method
geolocalization
categorization
time
none

13. A WEEK ON FOURSQUARE (WSJ)
COLLECTION
ELABORATION
ranks
Most checked-in venues overall
Top venues/category
Top venues NYC & SF
Top venues NYC & SF/categ.
NYC/SF
comparison
NYC
name
Venues properties
4SQ
venues
analysis
SF
categories
analysis
categories
lat/lon
start date
end date
frequency
gender
comparison
gender
01/21/2011
01/28/2011
per hour
timeline
Most checked-in venue
interactive plots/scatterplots
NYC & SF check-ins/top 80 categ.
Male/fem. check-ins/top 80 categ.
Male/fem. check-ins/Popul. venues
bar charts
venues’ check-ins/week
check-ins/category
check-ins worldwide
n. check-ins
► user
www.densitydesign.org
VISUALIZATION
bar charts
check-ins/hour
heatmaps
check-ins/hour
h14
h11
others
male/fem. check-ins/categ.
source
analysis
output
algorythm/method
geolocalization
categorization
time
none

14. URBAGRAMS
www.densitydesign.org
A spatial analysis of the aggregate activity
generated by such networks can show us
how social activity in a city is distributed,
revealing fine-grained spatial patterns
evident in the social life of cities.
Large-scale data from one such network
is analysed across three cities in order to
produce an inter-urban analysis. Hubs are
identified from activity distributions, and
measures of polycentricity, fragmentation and centralisation are examined with
respect to levels of social interaction. Spatial clustering tendencies are analysed to
determine the characteristic logics of agglomeration in urban social activity.
These comparative measures are used to
discuss the spatial structure of the three
cities in question. ‘Networked urbanism’
(Graham and Marvin, 2001a) has contained the promise that “the city itself is
turning into a constellation of computers”
(Batty, 1997) for over a decade now.

15. URBAGRAMS
www.densitydesign.org
COMPARING URBAN SOCIETIES
New York City
Paris
London
GEOLOCALIZATION
Spatial clusterization (activity fingerprints)
Policentrucuty (functional and morphological aspects)
Fragmentation/agglomeration
CHARTS
Social hubs analysis
CATEGORIZATION

16. URBAGRAMS
www.densitydesign.org

17. URBAGRAMS
www.densitydesign.org

18. URBAGRAMS
www.densitydesign.org

19. URBAGRAMS
COLLECTION
ELABORATION
4SQ
Venues properties
PARIS
www.densitydesign.org
“walkable”
cells grid
(400x400mt)
LONDON
NYC
VISUALIZATION
name
categories
n. check-ins
lat/lon
start date
end date
frequency
03/2009
07/2010
cumulative
source
analysis
output
algorythm/method
geolocalization
categorization
time
none

20. URBAGRAMS
COLLECTION
ELABORATION
“walkable”
cells grid
(400x400mt)
PARIS
Venues properties
4SQ
LONDON
NYC
VISUALIZATION
www.densitydesign.org
areas
comparison
DBScan
name
categories
n. check-ins
lat/lon
start date
end date
frequency
03/2009
07/2010
cumulative
categories
comparison
source
analysis
output
algorythm/method
geolocalization
categorization
time
none

21. URBAGRAMS
COLLECTION
ELABORATION
“walkable”
cells grid
(400x400mt)
NYC
PARIS
Venues properties
4SQ
LONDON
areas
comparison
grid maps
Activities’ “fingerprints”
DBScan
name
www.densitydesign.org
VISUALIZATION
geolocated maps
Social activities by categories
categories
n. check-ins
ranks (with bar charts)
Top Walkable Cells
lat/lon
start date
end date
frequency
03/2009
07/2010
cumulative
categories
comparison
others
Venues social activity grid/category
venues
comparison
source
analysis
output
algorythm/method
geolocalization
categorization
time
none

22. URBAGRAMS
COLLECTION
ELABORATION
“walkable”
cells grid
(400x400mt)
NYC
PARIS
Venues properties
4SQ
LONDON
areas
comparison
grid maps
Activities’ “fingerprints”
DBScan
name
categories
www.densitydesign.org
VISUALIZATION
cities
comparison
geolocated maps
Social activities by categories
Social activities by venue
n. check-ins
ranks (with bar charts)
Top Walkable Cells
lat/lon
start date
end date
frequency
03/2009
07/2010
cumulative
categories
comparison
plots/scatterplots
Urban-scale Moran
others
Venues social activity grid/category
venues
comparison
source
analysis
output
algorythm/method
geolocalization
categorization
time
none

23. URBAGRAMS
COLLECTION
ELABORATION
“walkable”
cells grid
(400x400mt)
NYC
PARIS
Venues properties
4SQ
LONDON
areas
comparison
grid maps
Activities’ “fingerprints”
DBScan
name
categories
www.densitydesign.org
VISUALIZATION
cities
comparison
geolocated maps
Social activities by categories
Social activities by venue
n. check-ins
cluster maps
Fragmentation of social activity
lat/lon
ranks (with bar charts)
Top Walkable Cells
start date
end date
frequency
03/2009
07/2010
cumulative
categories
comparison
plots/scatterplots
Urban-scale Moran
Rank-size plots for venues’ check-ins
others
Venues social activity grid/category
venues
comparison
source
analysis
output
algorythm/method
geolocalization
categorization
time
none

24. LIVEHOODS
www.densitydesign.org
Unlike the boundaries of traditional
municipal organizational units such as
neighborhoods, which do not always
reflect the character of life in these areas, the Livehoods’ clusters,are representations of the dynamic areas that
comprise the city. The data comes from
two sources. Approximately 11 million
foursquare check-ins from the dataset
of Chen et al. (2011) were combined with
a dataset of 7 million checkins that were
downloaded between June and Decem-
ber of 2011. Foursquare check-ins are
by default not publicly visible, however
users may elect to share their check-ins
publicly on social networks such as Twitter. These 18 million check-ins were all
collected from the Twitter public timeline, then were aligned with venue information from the foursquare API. One
of the main contributions is the design
of an affinity matrix between check-in
venues that effectively blends spatial
affinity and social affinity.

25. LIVEHOODS
www.densitydesign.org
DATA MERGING
Creation of meaning (4SQ + Twitter)
GEOLOCALIZATION
Environment perception (real/perceived boundaries)
Habits and spatial relation of whom live the city
CLUSTERIZATION
CATEGORIZATION
RANKING
TIMELINES

26. LIVEHOODS
www.densitydesign.org

27. LIVEHOODS
www.densitydesign.org

28. LIVEHOODS
www.densitydesign.org

29. LIVEHOODS
ELABORATION
COLLECTION
VISUALIZATION
www.densitydesign.org
PITTSBURG
MONTREAL
PORTLAND
spatial
affinity
name
venues
4SQ
NY CITY
SEATTLE
lat/lon
category
SF BAY
date
2011
VANCOUVER
PITTSBURG
NY CITY
PORTLAND
SEATTLE
check-ins
Twitter
MONTREAL
SF BAY
► user
ID
► time
► name
date
2011
personal
PITTSBURG
space
Interviews
VANCOUVER
► age
(23-62)
► education
► background
► boundaries
start date
end date
sample
11/17/2011
12/17/2011
27 people
source
analysis
output
algorythm/method
geolocalization
categorization
time
none

30. LIVEHOODS
ELABORATION
COLLECTION
VISUALIZATION
www.densitydesign.org
PITTSBURG
MONTREAL
PORTLAND
spatial
affinity
name
venues
4SQ
NY CITY
SEATTLE
lat/lon
category
SF BAY
date
2011
VANCOUVER
social
affinity
PITTSBURG
NY CITY
PORTLAND
SEATTLE
check-ins
Twitter
MONTREAL
SF BAY
► user
ID
► time
► name
date
2011
personal
PITTSBURG
space
Interviews
VANCOUVER
► age
(23-62)
► education
► background
► boundaries
start date
end date
sample
11/17/2011
12/17/2011
27 people
source
analysis
output
algorythm/method
geolocalization
categorization
time
none

31. LIVEHOODS
ELABORATION
COLLECTION
VISUALIZATION
www.densitydesign.org
PITTSBURG
MONTREAL
PORTLAND
spatial
affinity
name
venues
4SQ
NY CITY
SEATTLE
lat/lon
venues
activity
category
SF BAY
date
2011
VANCOUVER
social
affinity
PITTSBURG
NY CITY
PORTLAND
SEATTLE
check-ins
Twitter
MONTREAL
SF BAY
► user
ID
► time
► name
date
2011
personal
PITTSBURG
space
Interviews
VANCOUVER
► age
(23-62)
► education
► background
► boundaries
start date
end date
sample
11/17/2011
12/17/2011
27 people
source
analysis
output
algorythm/method
geolocalization
categorization
time
none

32. LIVEHOODS
ELABORATION
COLLECTION
VISUALIZATION
www.densitydesign.org
PITTSBURG
MONTREAL
PORTLAND
spatial
affinity
name
venues
4SQ
NY CITY
SEATTLE
lat/lon
SF BAY
date
2011
VANCOUVER
social
affinity
PITTSBURG
PORTLAND
SEATTLE
check-ins
Twitter
MONTREAL
NY CITY
venues
activity
category
SF BAY
► user
ID
affinity
matrix
► time
► name
date
2011
personal
PITTSBURG
space
Interviews
VANCOUVER
► age
(23-62)
► education
► background
► boundaries
start date
end date
sample
11/17/2011
12/17/2011
27 people
source
analysis
output
algorythm/method
geolocalization
categorization
time
none

33. LIVEHOODS
ELABORATION
COLLECTION
VISUALIZATION
www.densitydesign.org
PITTSBURG
MONTREAL
PORTLAND
spatial
affinity
name
venues
4SQ
NY CITY
SEATTLE
lat/lon
SF BAY
date
2011
VANCOUVER
social
affinity
PITTSBURG
PORTLAND
SEATTLE
check-ins
Twitter
MONTREAL
NY CITY
venues
activity
category
SF BAY
► user
ID
affinity
matrix
► time
► name
date
livehoods
2011
personal
PITTSBURG
space
Interviews
VANCOUVER
► age
(23-62)
► education
► background
► boundaries
start date
end date
sample
11/17/2011
12/17/2011
27 people
source
analysis
output
algorythm/method
geolocalization
categorization
time
none

34. LIVEHOODS
ELABORATION
COLLECTION
www.densitydesign.org
VISUALIZATION
PITTSBURG
MONTREAL
PORTLAND
spatial
affinity
name
venues
4SQ
NY CITY
SEATTLE
lat/lon
SF BAY
date
2011
VANCOUVER
social
affinity
PITTSBURG
PORTLAND
SEATTLE
check-ins
Twitter
MONTREAL
NY CITY
SF BAY
► user
ID
► name
date
ranks
Character
Related
affinity
matrix
bar charts
Stats
Daily pulse
Hourly pulse
livehoods
2011
personal
validate
space
Interviews
geolocated maps
Cluster map of livehoods
► time
VANCOUVER
PITTSBURG
venues
activity
category
► age
(23-62)
campus
sostenibile
► education
► background
► boundaries
start date
end date
sample
11/17/2011
12/17/2011
27 people
source
analysis
output
algorythm/method
geolocalization
categorization
time
none

35. LIVEHOODS
ELABORATION
COLLECTION
www.densitydesign.org
VISUALIZATION
PITTSBURG
MONTREAL
PORTLAND
spatial
affinity
name
venues
4SQ
NY CITY
SEATTLE
lat/lon
SF BAY
date
2011
VANCOUVER
social
affinity
PITTSBURG
PORTLAND
SEATTLE
check-ins
Twitter
MONTREAL
NY CITY
SF BAY
► user
ID
► name
date
ranks
Character
Related
affinity
matrix
bar charts
Stats
Daily pulse
Hourly pulse
livehoods
2011
personal
validate
space
Interviews
geolocated maps
Cluster map of livehoods
► time
VANCOUVER
PITTSBURG
venues
activity
category
► age
(23-62)
campus
sostenibile
► education
► background
► boundaries
start date
end date
sample
11/17/2011
12/17/2011
27 people
source
analysis
output
algorythm/method
geolocalization
categorization
time
none

36. THE EMERGENT URBAN IMAGINARIES OF GEOSOCIAL MEDIA
www.densitydesign.org
Geosocial databases inhabit the virtual space in which geosocial media are
produced, and the information that they
contain is both archival and generative
meaning that not only does it provide historical context to a place, but it also gives
access to the contemporary pulse of the
ways that geosocial users perceive, experience and interact in places.
These data can be assembled to speak to
the imaginaries of sub-city scale communities blendind urban place-frames
and the geoweb to show how we per-
ceive and understand urban imaginaries as well as how the geoweb is an evermore integral element of daily life.
Imaginaries are not simply passive representations of sociocultural reality, but are
instead active elements in the structuring
of individual social, cultural and spatial
practice. The imaginaries would be sociospatial meaning that data generated by
individuals about space via Foursquare
would tend to broadcast personal perceptions about how spaces are used
and/or experienced.

37. THE EMERGENT URBAN IMAGINARIES OF GEOSOCIAL MEDIA
www.densitydesign.org
DATA MERGING
Mapping the research area (census + checkinmania.com)
QUALITATIVE ANALYSIS
Tips text analysis (classification code)
GEOLOCALIZATION
CODIFICATION

38. THE EMERGENT URBAN IMAGINARIES OF GEOSOCIAL MEDIA
www.densitydesign.org

39. THE EMERGENT URBAN IMAGINARIES OF GEOSOCIAL MEDIA
www.densitydesign.org

40. THE EMERGENT URBAN IMAGINARIES OF GEOSOCIAL MEDIA
www.densitydesign.org

41. THE EMERGENT URBAN IMAGINARIES OF GEOSOCIAL MEDIA
www.densitydesign.org

42. THE EMERGENT URBAN IMAGINARIES OF GEOSOCIAL MEDIA
www.densitydesign.org
Socio-economically distressed areas (1,143 total tips)
Code1
Code2
Code3
Tacoma 1 (24 total tips, 5per sq.mile)
Tacoma 2 (152 total tips, 33 per sq. mile)
Tacoma 3 (29 total tips, 5 per sq. mile)
Seattle 1 (782 total tips, 74 per sq. mile)
Seattle 2 (51 total tips, 44 per sq. mile)
Seattle 3 (84 total tips, 33 per sq. mile)
Seattle 4 (21 total tips, 19 per sq. mile)
Mean (30 per sq. mile)
Standard deviation
0.125
0.171
0.172
0.263
0.314
0.202
0.333
0.245
0.073
0.042
0.125
0.035
0.115
0.373
0.214
0.000
0.130
0.120
0.167
0.099
0.138
0.067
0.039
0.060
0.190
0.080
0.050
Socio-economically advantaged areas (1,358 total tips)
Code1
Code2
Code3
Seattle A (48 total tips, 31per sq.mile)
Seattle B (214 total tips, 54 per sq. mile)
Seattle C (588 total tips, 127 per sq. mile)
Seattle D (242 total tips, 36 per sq. mile)
Seattle E (80 total tips, 94 per sq. mile)
Seattle F (186 total tips, 26 per sq. mile)
Mean
Standard deviation
Composite mean (45 per sq. mile)
Composite standard deviation
0.292
0.266
0.226
0.401
0.263
0.307
0.280
0.055
0.264
0.073
0.354
0.117
0.119
0.136
0.100
0.140
0.132
0.187
0.131
0.107
0.021
0.042
0.039
0.062
0.088
0.059
0.049
0.021
0.061
0.050

43. THE EMERGENT URBAN IMAGINARIES OF GEOSOCIAL MEDIA
www.densitydesign.org
VISUALIZATION
SEATTLE
tips
TACOMA
venues
checkinmania.com (4SQ)
ELABORATION
SEATTLE
census
COLLECTION
name
lat/lon
user ID
text string
date
2011
people
► poverty
► education
area
selection
► income
land
TACOMA
geolocated maps
Land use in S&W Seattle
Population density S&W Seattle
► land
use
► density
date
source
analysis
output
algorythm/method
2011
geolocalization
categorization
time
none

44. THE EMERGENT URBAN IMAGINARIES OF GEOSOCIAL MEDIA
COLLECTION
ELABORATION
VISUALIZATION
www.densitydesign.org
SEATTLE
tips
TACOMA
venues
checkinmania.com (4SQ)
SEATTLE
census
text
analysis
name
lat/lon
user ID
text string
date
2011
people
► poverty
► education
area
selection
► income
land
TACOMA
geolocated maps
Land use in S&W Seattle
Population density S&W Seattle
Cluster analysis Output S&W Seattle
► land
use
► density
date
source
analysis
output
algorythm/method
2011
geolocalization
categorization
time
none

45. THE EMERGENT URBAN IMAGINARIES OF GEOSOCIAL MEDIA
COLLECTION
ELABORATION
tips
venues
SEATTLE
TACOMA
www.densitydesign.org
code 1:
social
engagment
code 2:
attachment
to place
code 3:
fear and
avoidance
name
lat/lon
user ID
geolocated maps
Land use in S&W Seattle
Population density S&W Seattle
Cluster analysis Output S&W Seattle
text string
date
2011
area
definition
SEATTLE
► poverty
people
TACOMA
► education
area
selection
► income
land
census
checkinmania.com (4SQ)
text
analysis
VISUALIZATION
► land
use
► density
date
source
analysis
output
algorythm/method
2011
geolocalization
categorization
time
none

46. THE EMERGENT URBAN IMAGINARIES OF GEOSOCIAL MEDIA
COLLECTION
ELABORATION
tips
venues
SEATTLE
TACOMA
www.densitydesign.org
code 1:
social
engagment
code 2:
attachment
to place
code 3:
fear and
avoidance
name
lat/lon
tip
analysis
user ID
text string
date
2011
geolocated maps
Land use in S&W Seattle
Population density S&W Seattle
Cluster analysis Output S&W Seattle
Study areas (block group cluster)
area
definition
SEATTLE
► poverty
people
TACOMA
► education
area
selection
► income
land
census
checkinmania.com (4SQ)
text
analysis
VISUALIZATION
► land
use
► density
date
source
analysis
output
algorythm/method
2011
geolocalization
categorization
time
none

47. THE EMERGENT URBAN IMAGINARIES OF GEOSOCIAL MEDIA
COLLECTION
ELABORATION
tips
venues
SEATTLE
TACOMA
www.densitydesign.org
code 1:
social
engagment
code 2:
attachment
to place
code 3:
fear and
avoidance
name
lat/lon
tip
analysis
user ID
text string
date
2011
area
definition
geolocated maps
Land use in S&W Seattle
Population density S&W Seattle
Cluster analysis Output S&W Seattle
Study areas (block group cluster)
tabs
Content analysis of check-in tips
SEATTLE
► poverty
people
TACOMA
► education
area
selection
► income
land
census
checkinmania.com (4SQ)
text
analysis
VISUALIZATION
► land
use
► density
date
source
analysis
output
algorythm/method
2011
geolocalization
categorization
time
none

48. THE EMERGENT URBAN IMAGINARIES OF GEOSOCIAL MEDIA
COLLECTION
ELABORATION
tips
venues
SEATTLE
TACOMA
www.densitydesign.org
code 1:
social
engagment
code 2:
attachment
to place
code 3:
fear and
avoidance
name
lat/lon
tip
analysis
user ID
text string
date
2011
area
definition
geolocated maps
Land use in S&W Seattle
Population density S&W Seattle
Cluster analysis Output S&W Seattle
Study areas (block group cluster)
tabs
Content analysis of check-in tips
SEATTLE
► poverty
people
TACOMA
► education
area
selection
► income
land
census
checkinmania.com (4SQ)
text
analysis
VISUALIZATION
► land
use
► density
date
source
analysis
output
algorythm/method
2011
geolocalization
categorization
time
none

49. REPRESENTATIVENESS:
meaning of data
www.densitydesign.org
ESTIMATING PEOPLE PERCEPTION OF INTIMACY
FAR FROM THE EYES, CLOSE ON THE WEB

50. ESTIMATING PEOPLE PERCEPTION OF INTIMACY
www.densitydesign.org
How does the intimacy relate to privacy?
The main scope of the research is to estimate people intimacy to detect when information about the users’ context can be
collected and shared in order to develop
applications that automatically control
how events and notifications to the users
(receiving a message, a call, an email, a request of approval etc.) are handled by his/
her smartphone or other devices in the
environment, for example assuming that
when the user is intimate the alerts shall
be less intrusive. Analizing raw data of
“Mobile Data Challenge“ collected from 38
selected participants using smartphones
in their daily life and use and elaborating
informations using an algorithm, the researchers derived the users’ level of intimacy in particular places and intervals
of time. The research uses an “Intimacy
Estimation Algorithm” that compute data
from the devices.

51. ESTIMATING PEOPLE PERCEPTION OF INTIMACY
www.densitydesign.org
For the “observers” category:
For the “safe-place” category:
BLUETOOTH --> number of devices
around the user can reveal the number of
people opbserving him;
CHARGING STATUS --> if the phone is
charging it can indicate that the user is
currently in a trusted place;
RING STATUS --> representing the willingness of the user to share the events of the
device with other;
RING STATUS --> is related to “how much”
the user wants to be disturbed by external events;
OUTGOING CALLS --> the duration of a
call made by the user and the relation with
the called person can give a hint about
how the user feels about speaking on
the phone at that moment;
INDOOR/OUTDOOR --> there is a high
probability that if the user is outdoor, he
may not be in a safe place.
OUTGOING SMS --> if a user is exchanging many SMS with a family member or a
friend it may indicate that is in company of
people that are not supposed to know
the content of the conversation;

52. ESTIMATING PEOPLE PERCEPTION OF INTIMACY
ELABORATION
FEATURES
SCOPE
www.densitydesign.org
observers
bluetooth
ring status
outgoing call
outgoing SMS
safe-places
mobile data (MDC)
COLLECTION
charging status
ring status
indoor/Outdoor
date
2010
source
analysis
output
algorythm/method

53. ESTIMATING PEOPLE PERCEPTION OF INTIMACY
ELABORATION
FEATURES
SCOPE
www.densitydesign.org
observers
bluetooth
ring status
outgoing call
outgoing SMS
safe-places
mobile data (MDC)
COLLECTION
Intimacy
Estimation
Algorithm
charging status
ring status
indoor/Outdoor
date
2010
source
analysis
output
algorythm/method

54. ESTIMATING PEOPLE PERCEPTION OF INTIMACY
ELABORATION
FEATURES
observers
bluetooth
outgoing call
SCOPE
www.densitydesign.org
observers
and
safe places
ring status
outgoing SMS
safe-places
mobile data (MDC)
COLLECTION
Intimacy
Estimation
Algorithm
intimacy
level
charging status
ring status
demographic
analisys
indoor/Outdoor
date
2010
source
analysis
output
algorythm/method

55. ESTIMATING PEOPLE PERCEPTION OF INTIMACY
ELABORATION
FEATURES
observers
bluetooth
outgoing call
SCOPE
www.densitydesign.org
observers
and
safe places
ring status
outgoing SMS
safe-places
mobile data (MDC)
COLLECTION
Intimacy
Estimation
Algorithm
intimacy
level
develop
application
charging status
ring status
demographic
analisys
indoor/Outdoor
date
2010
source
analysis
output
algorythm/method

56. ESTIMATING PEOPLE PERCEPTION OF INTIMACY
ELABORATION
FEATURES
observers
bluetooth
outgoing call
SCOPE
www.densitydesign.org
observers
and
safe places
ring status
outgoing SMS
safe-places
mobile data (MDC)
COLLECTION
Intimacy
Estimation
Algorithm
intimacy
level
develop
application
charging status
ring status
demographic
analisys
indoor/Outdoor
date
2010
source
analysis
output
algorythm/method

57. FAR FROM THE EYES, CLOSE ON THE WEB
www.densitydesign.org
Analising a large dataset of anonymised
snapshot of Tuenti’s friendship connections, that includes about 9.8 million
registered users, more than 580 million
friendship links, about 500 million interactions during a 3 month period and the
user’s the self-reported city residence, this
research aims to study how social interactions is related to users’ geographic locations. While spatial prooximity
greatly affects how users establish their
connections on online platforms, the researchers found that social interactions
are only weakly affected by distance: this
suggest that once social connection are
established other factors may influence
how users send messages to their friends.
On the other hand, more active users tend
to preferentially interact over short-range
connections.
This observation is crucial for architectures that optimise distributed storage
of data related to online social platforms based on users’ geographic locations. Similarly, it is important for system
that exploit geographic locality of interest
to serve content items requested through
online social network services. The findings also likely to help other domains
such as link predition, tie strengh interference and user profiling: the observed
spatiual patterns can me also included in
security mechanism to detect malicious
and spam accounts.

58. FAR FROM THE EYES, CLOSE ON THE WEB
www.densitydesign.org
Geographi properties:
Interaction analysis:
Analizing the spatial properties of the
Tuenti social network, may be assumed
that users tend to preferentially connect
to closer users (as found in many other
online social network).
About 60% of social links between users are at a distance of 10km or less,
while only 10% of all distances between
users are below 100 km.
There a re two process taking place. One
process, strongly affected by geographic
distance, influences how users connect
to each other, i.e. their frienship links; another process impacts the level of interaction among connected users and appears unrelated to spatial proximity.

59. FAR FROM THE EYES, CLOSE ON THE WEB
FRIENDSHIP
ELABORATION
user’s connections
tuenti
COLLECTION
SCOPE
www.densitydesign.org
friendship links
wall comments
date
2010
source
analysis
output
algorythm/method

60. FAR FROM THE EYES, CLOSE ON THE WEB
FRIENDSHIP
ELABORATION
user’s connections
tuenti
COLLECTION
SCOPE
www.densitydesign.org
friendship links
wall comments
geographic
properties
date
2010
source
analysis
output
algorythm/method

61. FAR FROM THE EYES, CLOSE ON THE WEB
FRIENDSHIP
ELABORATION
user’s connections
tuenti
COLLECTION
SCOPE
www.densitydesign.org
friendship links
wall comments
geographic
properties
date
2010
interaction
analysis
source
analysis
output
algorythm/method

62. FAR FROM THE EYES, CLOSE ON THE WEB
COLLECTION
ELABORATION
www.densitydesign.org
SCOPE
FRIENDSHIP
user’s connections
tuenti
geo-related data
storage
architectures
friendship links
wall comments
geographic
properties
date
2010
interaction
analysis
source
analysis
output
algorythm/method

63. FAR FROM THE EYES, CLOSE ON THE WEB
COLLECTION
ELABORATION
www.densitydesign.org
SCOPE
FRIENDSHIP
user’s connections
tuenti
geo-related data
storage
architectures
friendship links
link prediction
wall comments
geographic
properties
date
2010
interaction
analysis
source
analysis
output
algorythm/method

64. FAR FROM THE EYES, CLOSE ON THE WEB
COLLECTION
ELABORATION
www.densitydesign.org
SCOPE
FRIENDSHIP
user’s connections
tuenti
geo-related data
storage
architectures
friendship links
link prediction
wall comments
geographic
properties
date
2010
interaction
analysis
tie strengh
interference
source
analysis
output
algorythm/method

65. FAR FROM THE EYES, CLOSE ON THE WEB
COLLECTION
ELABORATION
www.densitydesign.org
SCOPE
FRIENDSHIP
user’s connections
tuenti
geo-related data
storage
architectures
friendship links
link prediction
wall comments
geographic
properties
date
2010
interaction
analysis
tie strengh
interference
user profiling
source
analysis
output
algorythm/method

66. FAR FROM THE EYES, CLOSE ON THE WEB
COLLECTION
ELABORATION
www.densitydesign.org
SCOPE
FRIENDSHIP
user’s connections
tuenti
geo-related data
storage
architectures
friendship links
link prediction
wall comments
geographic
properties
date
2010
interaction
analysis
tie strengh
interference
user profiling
security
mechanisms
source
analysis
output
algorythm/method

67. FAR FROM THE EYES, CLOSE ON THE WEB
COLLECTION
ELABORATION
www.densitydesign.org
SCOPE
FRIENDSHIP
user’s connections
tuenti
geo-related data
storage
architectures
friendship links
link prediction
wall comments
geographic
properties
date
2010
interaction
analysis
tie strengh
interference
user profiling
security
mechanisms
source
analysis
output
algorythm/method

68. BIBLIOGRAPHY
www.densitydesign.org
A WEEK ON FOURSQUARE (WSJ)
Where the Young and Tech-Savvy Go
A. Sun - J. Valentino-DeVries - Z. Seward
May 19, 2011
[http://graphicsweb.wsj.com/documents/FOURSQUAREWEEK1104/]
URBAGRAMS
Sensing the urban: using locationbased social network data in urban
analysis
A. Bawa-Cavia
2011
[http://urbagram.net/media/SensingTheUrbanWP.pdf]
Archipelago
A. Bawa-Cavia
2010
[http://www.urbagram.net/archipelago/]
LIVEHOODS
The Livehoods Project: Utilizing Social Media to Understand the Dynamics of a City
J. Cranshaw - R. Schwartz - J. I. Hong N. Sadeh
School of Computer Science, Carnegie
Mellon University, Pittsburgh
2011
[http://livehoods.org/maps/nyc#]
[http://livehoods.org/research]
THE EMERGENT URBAN IMAGINARIES
OF GEOSOCIAL MEDIA
The emergent urban imaginaries of
geosocial media
M. James Kelley
Springer Science+Business Media B.V.
2011
[http://www.springerlink.com/content/
u56612253r57257h/fulltext.pdf]
PERCEPTION OF INTIMACY
Estimating People Perception of Intimacy in Daily Life from Context Data
Collected with Their Mobile Phone
M. Gustarini - K. Wac
2010
[research.nokia.com]
FAR FROM THE EYES,
CLOSE ON THE WEB
Far from the eyes, close on the Web:
impact of geographic distance on online
social interactions
A. Kaltenbrunner - S. Scellato - Y. Volkovich - D.
Laniado - D. Currie - E. J. Jutemar - C. Mascolo
In ACM SIGCOMM Workshop on Online Social
Networks (WOSN 2012) - Helsinki, Finland
August 2012
[http://www.cl.cam.ac.uk/~cm542/papers/wosn12-kaltenbrunner.pdf]