View
4.914
Download
0
Category
Preview:
DESCRIPTION
Case studies and relative research protocols for projects that use geolocated foursquare data for add value, identify patterns and help social cooperation
Citation preview
www.densitydesign.orgGEOLOCATED 4SQ DATA:where we are
A WEEK ON FOURSQUARE (WSJ)
URBAGRAMS
LIVEHOODS
THE EMERGENT URBAN IMAGINARIES OF GEOSOCIAL MEDIA
www.densitydesign.org
A WEEK ON FOURSQUARE (WSJ)
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 ge-olocated check-ins’ and official catego-rization of Foursquare’s venues analysis, two kind of data were compared aiming to highlights common elements and differ-ences who caracterize:- activities in both territories (New York City and San Francisco Bay area)- habits and preferences of genders
www.densitydesign.org
global (timeline)
glocal (geolocated view)
local (hot spots + focus on categories and venues)
3 READING LEVELS
GEOLOCALIZATION
CATEGORIZATION
TIMELINES
A WEEK ON FOURSQUARE (WSJ)
www.densitydesign.org
A WEEK ON FOURSQUARE (WSJ)
www.densitydesign.org
A WEEK ON FOURSQUARE (WSJ)
www.densitydesign.org
A WEEK ON FOURSQUARE (WSJ)
COLLECTION ELABORATION VISUALIZATIONwww.densitydesign.org
NYC
SF
01/21/2011 h1401/28/2011 h11per hour
start date
end date
frequency
4SQ
sourceanalysisoutputalgorythm/method
Venues pro
pert
ies name
categories
n. check-ins
lat/lon
► user gender
geolocalizationcategorizationtimenone
A WEEK ON FOURSQUARE (WSJ)
COLLECTION ELABORATION VISUALIZATIONwww.densitydesign.org
venuesanalysis
NYC
SF
4SQ
geolocalizationcategorizationtimenone
sourceanalysisoutputalgorythm/method
Venues pro
pert
ies name
categories
n. check-ins
lat/lon
► user gender
bar charts check-ins/hour
heatmaps check-ins/hour
01/21/2011 h1401/28/2011 h11per hour
start date
end date
frequency
A WEEK ON FOURSQUARE (WSJ)
COLLECTION ELABORATION VISUALIZATIONwww.densitydesign.org
venuesanalysis
NYC
SF
4SQ
sourceanalysisoutputalgorythm/method
Venues pro
pert
ies name
categories
n. check-ins
lat/lon
► user gender
ranks Most checked-in venues overall
timeline Most checked-in venue
bar charts check-ins/hour
heatmaps check-ins/hour
categories analysis
geolocalizationcategorizationtimenone
01/21/2011 h1401/28/2011 h11per hour
start date
end date
frequency
A WEEK ON FOURSQUARE (WSJ)
COLLECTION ELABORATION VISUALIZATIONwww.densitydesign.org
categories analysis
venuesanalysis
NYC/SFcomparison
NYC
SF
4SQ
sourceanalysisoutputalgorythm/method
Venues pro
pert
ies name
categories
n. check-ins
lat/lon
► user gender
ranks Most checked-in venues overall Top venues/category
timeline Most checked-in venue
bar charts check-ins/hour
heatmaps check-ins/hour
geolocalizationcategorizationtimenone
01/21/2011 h1401/28/2011 h11per hour
start date
end date
frequency
A WEEK ON FOURSQUARE (WSJ)
COLLECTION ELABORATION VISUALIZATIONwww.densitydesign.org
categories analysis
venuesanalysis
NYC/SFcomparison
gendercomparison
NYC
SF
4SQ
sourceanalysisoutputalgorythm/method
Venues pro
pert
ies name
categories
n. check-ins
lat/lon
► user gender
ranks Most checked-in venues overall Top venues/category Top venues NYC & SF Top venues NYC & SF/categ.
timeline Most checked-in venue
interactive plots/scatterplots NYC & SF check-ins/top 80 categ.
bar charts venues’ check-ins/week check-ins/category
bar charts check-ins/hour
heatmaps check-ins/hour
geolocalizationcategorizationtimenone
01/21/2011 h1401/28/2011 h11per hour
start date
end date
frequency
A WEEK ON FOURSQUARE (WSJ)
COLLECTION ELABORATION VISUALIZATIONwww.densitydesign.org
ranks Most checked-in venues overall Top venues/category Top venues NYC & SF Top venues NYC & SF/categ.
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
bar charts check-ins/hour
heatmaps check-ins/hour
others male/fem. check-ins/categ.
categories analysis
venuesanalysis
NYC/SFcomparison
gendercomparison
NYC
SF
4SQ
sourceanalysisoutputalgorythm/method
Venues pro
pert
ies name
categories
n. check-ins
lat/lon
► user gender
geolocalizationcategorizationtimenone
01/21/2011 h1401/28/2011 h11per hour
start date
end date
frequency
A WEEK ON FOURSQUARE (WSJ)
COLLECTION ELABORATION VISUALIZATIONwww.densitydesign.org
ranks Most checked-in venues overall Top venues/category Top venues NYC & SF Top venues NYC & SF/categ.
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
bar charts check-ins/hour
heatmaps check-ins/hour
others male/fem. check-ins/categ.
categories analysis
venuesanalysis
NYC/SFcomparison
gendercomparison
NYC
SF
4SQ
sourceanalysisoutputalgorythm/method
Venues properties
name
categories
n. check-ins
lat/lon
► user gender
geolocalizationcategorizationtimenone
01/21/2011 h1401/28/2011 h11per hour
start date
end date
frequency
A WEEK ON FOURSQUARE (WSJ)
www.densitydesign.org
URBAGRAMS
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, fragmenta-tion and centralisation are examined with
respect to levels of social interaction. Spa-tial clustering tendencies are analysed to determine the characteristic logics of ag-glomeration 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 con-tained the promise that “the city itself is turning into a constellation of computers” (Batty, 1997) for over a decade now.
www.densitydesign.org
New York City
Paris
London
Spatial clusterization (activity fingerprints)
Policentrucuty (functional and morphological aspects)
Fragmentation/agglomeration
Social hubs analysis
COMPARING URBAN SOCIETIES
GEOLOCALIZATION
CHARTS
CATEGORIZATION
URBAGRAMS
www.densitydesign.org
URBAGRAMS
www.densitydesign.org
URBAGRAMS
www.densitydesign.org
URBAGRAMS
COLLECTION ELABORATION VISUALIZATIONwww.densitydesign.org
NYC
PARIS
LONDON
03/200907/2010cumulative
start date
end date
frequency
4SQ
Venues properties name
categories
n. check-ins
lat/lon
sourceanalysisoutputalgorythm/method
geolocalizationcategorizationtimenone
“walkable”cells grid (400x400mt)
URBAGRAMS
COLLECTION ELABORATION VISUALIZATIONwww.densitydesign.org
NYC
PARIS
LONDON
4SQ
Venues properties name
categories
n. check-ins
lat/lon
sourceanalysisoutputalgorythm/method
geolocalizationcategorizationtimenone
categoriescomparison
areascomparison
“walkable”cells grid (400x400mt)
DBScan
03/200907/2010cumulative
start date
end date
frequency
URBAGRAMS
COLLECTION ELABORATION VISUALIZATIONwww.densitydesign.org
grid maps Activities’ “fingerprints”
geolocated maps Social activities by categories
ranks (with bar charts) Top Walkable Cells
others Venues social activity grid/category
NYC
PARIS
LONDON
4SQ
Venues properties name
categories
n. check-ins
lat/lon
sourceanalysisoutputalgorythm/method
geolocalizationcategorizationtimenone
categoriescomparison
venuescomparison
areascomparison
“walkable”cells grid (400x400mt)
DBScan
03/200907/2010cumulative
start date
end date
frequency
URBAGRAMS
COLLECTION ELABORATION VISUALIZATIONwww.densitydesign.org
grid maps Activities’ “fingerprints”
geolocated maps Social activities by categories Social activities by venue
ranks (with bar charts) Top Walkable Cells
plots/scatterplots Urban-scale Moran
others Venues social activity grid/category
NYC
PARIS
LONDON
4SQ
Venues properties name
categories
n. check-ins
lat/lon
sourceanalysisoutputalgorythm/method
geolocalizationcategorizationtimenone
citiescomparison
categoriescomparison
venuescomparison
areascomparison
“walkable”cells grid (400x400mt)
DBScan
03/200907/2010cumulative
start date
end date
frequency
URBAGRAMS
COLLECTION ELABORATION VISUALIZATIONwww.densitydesign.org
“walkable”cells grid (400x400mt)
DBScanNYC
PARIS
LONDON
4SQ
Venues properties name
categories
n. check-ins
lat/lon
sourceanalysisoutputalgorythm/method
geolocalizationcategorizationtimenone
areascomparison
citiescomparison
categoriescomparison
venuescomparison
grid maps Activities’ “fingerprints”
geolocated maps Social activities by categories Social activities by venue
cluster maps Fragmentation of social activity
ranks (with bar charts) Top Walkable Cells
plots/scatterplots Urban-scale Moran Rank-size plots for venues’ check-ins
others Venues social activity grid/category
03/200907/2010cumulative
start date
end date
frequency
URBAGRAMS
www.densitydesign.org
LIVEHOODS
Unlike the boundaries of traditional municipal organizational units such as neighborhoods, which do not always reflect the character of life in these ar-eas, the Livehoods’ clusters,are repre-sentations 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 Twit-ter. These 18 million check-ins were all collected from the Twitter public time-line, then were aligned with venue in-formation 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.
www.densitydesign.org
Creation of meaning (4SQ + Twitter)
Environment perception (real/perceived boundaries)
Habits and spatial relation of whom live the city
DATA MERGING
GEOLOCALIZATION
CLUSTERIZATION
CATEGORIZATION
RANKING
TIMELINES
LIVEHOODS
www.densitydesign.org
LIVEHOODS
www.densitydesign.org
LIVEHOODS
www.densitydesign.org
LIVEHOODS
ELABORATION VISUALIZATIONCOLLECTIONwww.densitydesign.org
sourceanalysisoutputalgorythm/method
geolocalizationcategorizationtimenone
4SQ
Twit
ter
Inte
rviews
MONTREAL
MONTREAL
PORTLAND
PORTLAND
SF BAY
SF BAY
NY CITY
NY CITY
SEATTLE
SEATTLE
VANCOUVER
VANCOUVER
PITTSBURG
PITTSBURG
PITTSBURG
check-ins
venues
► user ID
► time
► name
name
lat/lon
category
11/17/201112/17/201127 people
start date
end date
sample
2011date
2011date
spatialaffinity
personal ► age (23-62)
► education
► background
► boundariesspace
LIVEHOODS
ELABORATION VISUALIZATIONCOLLECTIONwww.densitydesign.org
geolocalizationcategorizationtimenone
4SQ
Twit
ter
Inte
rviews
MONTREAL
MONTREAL
PORTLAND
PORTLAND
SF BAY
SF BAY
NY CITY
NY CITY
SEATTLE
SEATTLE
VANCOUVER
VANCOUVER
PITTSBURG
PITTSBURG
PITTSBURG
check-ins
venues
► user ID
► time
► name
name
lat/lon
category
sourceanalysisoutputalgorythm/method
socialaffinity
spatialaffinity
personal ► age (23-62)
► education
► background
► boundariesspace
11/17/201112/17/201127 people
start date
end date
sample
2011date
2011date
LIVEHOODS
ELABORATION VISUALIZATIONCOLLECTIONwww.densitydesign.org
sourceanalysisoutputalgorythm/method
geolocalizationcategorizationtimenone
4SQ
Twit
ter
Inte
rviews
MONTREAL
MONTREAL
PORTLAND
PORTLAND
SF BAY
SF BAY
NY CITY
NY CITY
SEATTLE
SEATTLE
VANCOUVER
VANCOUVER
PITTSBURG
PITTSBURG
PITTSBURG
personal
check-ins
venues
► age (23-62)
► education
► background
► boundaries
► user ID
► time
► name
name
lat/lon
category
space
venuesactivity
socialaffinity
spatialaffinity
11/17/201112/17/201127 people
start date
end date
sample
2011date
2011date
LIVEHOODS
ELABORATION VISUALIZATIONCOLLECTIONwww.densitydesign.org
sourceanalysisoutputalgorythm/method
geolocalizationcategorizationtimenone
4SQ
Twit
ter
Inte
rviews
MONTREAL
MONTREAL
PORTLAND
PORTLAND
SF BAY
SF BAY
NY CITY
NY CITY
SEATTLE
SEATTLE
VANCOUVER
VANCOUVER
PITTSBURG
PITTSBURG
PITTSBURG
check-ins
venues
► user ID
► time
► name
name
lat/lon
category venuesactivity
socialaffinity
affinitymatrix
spatialaffinity
personal ► age (23-62)
► education
► background
► boundariesspace
11/17/201112/17/201127 people
start date
end date
sample
2011date
2011date
LIVEHOODS
ELABORATION VISUALIZATIONCOLLECTIONwww.densitydesign.org
PITTSBURG
sourceanalysisoutputalgorythm/method
geolocalizationcategorizationtimenone
4SQ
Twit
ter
Inte
rviews
MONTREAL
MONTREAL
PORTLAND
PORTLAND
SF BAY
SF BAY
NY CITY
NY CITY
SEATTLE
SEATTLE
VANCOUVER
VANCOUVER
PITTSBURG
PITTSBURG
PITTSBURG
check-ins
venues
► user ID
► time
► name
name
lat/lon
category
socialaffinity
affinitymatrix
spatialaffinity
venuesactivity
livehoods
personal ► age (23-62)
► education
► background
► boundariesspace
11/17/201112/17/201127 people
start date
end date
sample
2011date
2011date
LIVEHOODS
ELABORATION VISUALIZATIONCOLLECTIONwww.densitydesign.org
geolocated maps Cluster map of livehoods
ranks Character Related
bar charts Stats Daily pulse Hourly pulse
campussostenibile
sourceanalysisoutputalgorythm/method
geolocalizationcategorizationtimenone
4SQ
Twit
ter
Inte
rviews
MONTREAL
MONTREAL
PORTLAND
PORTLAND
SF BAY
SF BAY
NY CITY
NY CITY
SEATTLE
SEATTLE
VANCOUVER
VANCOUVER
PITTSBURG
PITTSBURG
PITTSBURG
check-ins
venues
► user ID
► time
► name
name
lat/lon
category
socialaffinity
affinitymatrix
spatialaffinity
venuesactivity
livehoods
validate
personal ► age (23-62)
► education
► background
► boundariesspace
11/17/201112/17/201127 people
start date
end date
sample
2011date
2011date
LIVEHOODS
ELABORATION VISUALIZATIONCOLLECTIONwww.densitydesign.org
geolocated maps Cluster map of livehoods
ranks Character Related
bar charts Stats Daily pulse Hourly pulse
campussostenibile
sourceanalysisoutputalgorythm/method
geolocalizationcategorizationtimenone
4SQ
Twit
ter
Inte
rviews
MONTREAL
MONTREAL
PORTLAND
PORTLAND
SF BAY
SF BAY
NY CITY
NY CITY
SEATTLE
SEATTLE
VANCOUVER
VANCOUVER
PITTSBURG
PITTSBURG
PITTSBURG
check-ins
venues
► user ID
► time
► name
name
lat/lon
category
socialaffinity
affinitymatrix
spatialaffinity
venuesactivity
livehoods
validate
personal ► age (23-62)
► education
► background
► boundariesspace
11/17/201112/17/201127 people
start date
end date
sample
2011date
2011date
LIVEHOODS
www.densitydesign.org
THE EMERGENT URBAN IMAGINARIES OF GEOSOCIAL MEDIA
Geosocial databases inhabit the virtu-al 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 his-torical context to a place, but it also gives access to the contemporary pulse of the ways that geosocial users perceive, ex-perience and interact in places.These data can be assembled to speak to the imaginaries of sub-city scale com-munities blendind urban place-frames and the geoweb to show how we per-
ceive and understand urban imaginar-ies as well as how the geoweb is an ever-more integral element of daily life.Imaginaries are not simply passive repre-sentations of sociocultural reality, but are instead active elements in the structuring of individual social, cultural and spatial practice. The imaginaries would be socio-spatial meaning that data generated by individuals about space via Foursquare would tend to broadcast personal per-ceptions about how spaces are used and/or experienced.
www.densitydesign.org
Mapping the research area (census + checkinmania.com)
Tips text analysis (classification code)
DATA MERGING
QUALITATIVE ANALYSIS
GEOLOCALIZATION
CODIFICATION
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) 0.125 0.042 0.167Tacoma 2 (152 total tips, 33 per sq. mile) 0.171 0.125 0.099Tacoma 3 (29 total tips, 5 per sq. mile) 0.172 0.035 0.138Seattle 1 (782 total tips, 74 per sq. mile) 0.263 0.115 0.067Seattle 2 (51 total tips, 44 per sq. mile) 0.314 0.373 0.039Seattle 3 (84 total tips, 33 per sq. mile) 0.202 0.214 0.060Seattle 4 (21 total tips, 19 per sq. mile) 0.333 0.000 0.190Mean (30 per sq. mile) 0.245 0.130 0.080Standard deviation 0.073 0.120 0.050
Socio-economically advantaged areas (1,358 total tips) Code1 Code2 Code3
Seattle A (48 total tips, 31per sq.mile) 0.292 0.354 0.021Seattle B (214 total tips, 54 per sq. mile) 0.266 0.117 0.042Seattle C (588 total tips, 127 per sq. mile) 0.226 0.119 0.039Seattle D (242 total tips, 36 per sq. mile) 0.401 0.136 0.062Seattle E (80 total tips, 94 per sq. mile) 0.263 0.100 0.088Seattle F (186 total tips, 26 per sq. mile) 0.307 0.140 0.059Mean 0.280 0.132 0.049Standard deviation 0.055 0.187 0.021Composite mean (45 per sq. mile) 0.264 0.131 0.061Composite standard deviation 0.073 0.107 0.050
THE EMERGENT URBAN IMAGINARIES OF GEOSOCIAL MEDIA
www.densitydesign.org
THE EMERGENT URBAN IMAGINARIES OF GEOSOCIAL MEDIA
Socio-economically distressed areas (1,143 total tips) Code1 Code2 Code3
Tacoma 1 (24 total tips, 5per sq.mile) 0.125 0.042 0.167Tacoma 2 (152 total tips, 33 per sq. mile) 0.171 0.125 0.099Tacoma 3 (29 total tips, 5 per sq. mile) 0.172 0.035 0.138Seattle 1 (782 total tips, 74 per sq. mile) 0.263 0.115 0.067Seattle 2 (51 total tips, 44 per sq. mile) 0.314 0.373 0.039Seattle 3 (84 total tips, 33 per sq. mile) 0.202 0.214 0.060Seattle 4 (21 total tips, 19 per sq. mile) 0.333 0.000 0.190Mean (30 per sq. mile) 0.245 0.130 0.080Standard deviation 0.073 0.120 0.050
Socio-economically advantaged areas (1,358 total tips) Code1 Code2 Code3
Seattle A (48 total tips, 31per sq.mile) 0.292 0.354 0.021Seattle B (214 total tips, 54 per sq. mile) 0.266 0.117 0.042Seattle C (588 total tips, 127 per sq. mile) 0.226 0.119 0.039Seattle D (242 total tips, 36 per sq. mile) 0.401 0.136 0.062Seattle E (80 total tips, 94 per sq. mile) 0.263 0.100 0.088Seattle F (186 total tips, 26 per sq. mile) 0.307 0.140 0.059Mean 0.280 0.132 0.049Standard deviation 0.055 0.187 0.021Composite mean (45 per sq. mile) 0.264 0.131 0.061Composite standard deviation 0.073 0.107 0.050
www.densitydesign.org
THE EMERGENT URBAN IMAGINARIES OF GEOSOCIAL MEDIA
Socio-economically distressed areas (1,143 total tips) Code1 Code2 Code3
Tacoma 1 (24 total tips, 5per sq.mile) 0.125 0.042 0.167Tacoma 2 (152 total tips, 33 per sq. mile) 0.171 0.125 0.099Tacoma 3 (29 total tips, 5 per sq. mile) 0.172 0.035 0.138Seattle 1 (782 total tips, 74 per sq. mile) 0.263 0.115 0.067Seattle 2 (51 total tips, 44 per sq. mile) 0.314 0.373 0.039Seattle 3 (84 total tips, 33 per sq. mile) 0.202 0.214 0.060Seattle 4 (21 total tips, 19 per sq. mile) 0.333 0.000 0.190Mean (30 per sq. mile) 0.245 0.130 0.080Standard deviation 0.073 0.120 0.050
Socio-economically advantaged areas (1,358 total tips) Code1 Code2 Code3
Seattle A (48 total tips, 31per sq.mile) 0.292 0.354 0.021Seattle B (214 total tips, 54 per sq. mile) 0.266 0.117 0.042Seattle C (588 total tips, 127 per sq. mile) 0.226 0.119 0.039Seattle D (242 total tips, 36 per sq. mile) 0.401 0.136 0.062Seattle E (80 total tips, 94 per sq. mile) 0.263 0.100 0.088Seattle F (186 total tips, 26 per sq. mile) 0.307 0.140 0.059Mean 0.280 0.132 0.049Standard deviation 0.055 0.187 0.021Composite mean (45 per sq. mile) 0.264 0.131 0.061Composite standard deviation 0.073 0.107 0.050
www.densitydesign.org
THE EMERGENT URBAN IMAGINARIES OF GEOSOCIAL MEDIA
Socio-economically distressed areas (1,143 total tips) Code1 Code2 Code3
Tacoma 1 (24 total tips, 5per sq.mile) 0.125 0.042 0.167Tacoma 2 (152 total tips, 33 per sq. mile) 0.171 0.125 0.099Tacoma 3 (29 total tips, 5 per sq. mile) 0.172 0.035 0.138Seattle 1 (782 total tips, 74 per sq. mile) 0.263 0.115 0.067Seattle 2 (51 total tips, 44 per sq. mile) 0.314 0.373 0.039Seattle 3 (84 total tips, 33 per sq. mile) 0.202 0.214 0.060Seattle 4 (21 total tips, 19 per sq. mile) 0.333 0.000 0.190Mean (30 per sq. mile) 0.245 0.130 0.080Standard deviation 0.073 0.120 0.050
Socio-economically advantaged areas (1,358 total tips) Code1 Code2 Code3
Seattle A (48 total tips, 31per sq.mile) 0.292 0.354 0.021Seattle B (214 total tips, 54 per sq. mile) 0.266 0.117 0.042Seattle C (588 total tips, 127 per sq. mile) 0.226 0.119 0.039Seattle D (242 total tips, 36 per sq. mile) 0.401 0.136 0.062Seattle E (80 total tips, 94 per sq. mile) 0.263 0.100 0.088Seattle F (186 total tips, 26 per sq. mile) 0.307 0.140 0.059Mean 0.280 0.132 0.049Standard deviation 0.055 0.187 0.021Composite mean (45 per sq. mile) 0.264 0.131 0.061Composite standard deviation 0.073 0.107 0.050
www.densitydesign.org
THE EMERGENT URBAN IMAGINARIES OF GEOSOCIAL MEDIA
Socio-economically distressed areas (1,143 total tips) Code1 Code2 Code3
Tacoma 1 (24 total tips, 5per sq.mile) 0.125 0.042 0.167Tacoma 2 (152 total tips, 33 per sq. mile) 0.171 0.125 0.099Tacoma 3 (29 total tips, 5 per sq. mile) 0.172 0.035 0.138Seattle 1 (782 total tips, 74 per sq. mile) 0.263 0.115 0.067Seattle 2 (51 total tips, 44 per sq. mile) 0.314 0.373 0.039Seattle 3 (84 total tips, 33 per sq. mile) 0.202 0.214 0.060Seattle 4 (21 total tips, 19 per sq. mile) 0.333 0.000 0.190Mean (30 per sq. mile) 0.245 0.130 0.080Standard deviation 0.073 0.120 0.050
Socio-economically advantaged areas (1,358 total tips) Code1 Code2 Code3
Seattle A (48 total tips, 31per sq.mile) 0.292 0.354 0.021Seattle B (214 total tips, 54 per sq. mile) 0.266 0.117 0.042Seattle C (588 total tips, 127 per sq. mile) 0.226 0.119 0.039Seattle D (242 total tips, 36 per sq. mile) 0.401 0.136 0.062Seattle E (80 total tips, 94 per sq. mile) 0.263 0.100 0.088Seattle F (186 total tips, 26 per sq. mile) 0.307 0.140 0.059Mean 0.280 0.132 0.049Standard deviation 0.055 0.187 0.021Composite mean (45 per sq. mile) 0.264 0.131 0.061Composite standard deviation 0.073 0.107 0.050
ELABORATION VISUALIZATIONCOLLECTIONwww.densitydesign.org
geolocated maps Land use in S&W Seattle Population density S&W Seattle
sourceanalysisoutputalgorythm/method
geolocalizationcategorizationtimenone
chec
kinm
ania
.com
(4S
Q)ce
nsus
TACOMA
TACOMA
SEATTLE
SEATTLE
venues
people
tips
land
name
lat/lon
user ID
text string
► poverty
► education
► income
► land use
► density
areaselection
2011date
2011date
THE EMERGENT URBAN IMAGINARIES OF GEOSOCIAL MEDIA
ELABORATION VISUALIZATIONCOLLECTIONwww.densitydesign.org
geolocated maps Land use in S&W Seattle Population density S&W Seattle Cluster analysis Output S&W Seattle
sourceanalysisoutputalgorythm/method
geolocalizationcategorizationtimenone
venues
people
tips
land
name
lat/lon
user ID
text string
► poverty
► education
► income
► land use
► density
textanalysis
areaselection
chec
kinm
ania
.com
(4S
Q)ce
nsus
TACOMA
TACOMA
SEATTLE
SEATTLE
2011date
2011date
THE EMERGENT URBAN IMAGINARIES OF GEOSOCIAL MEDIA
ELABORATION VISUALIZATIONCOLLECTIONwww.densitydesign.org
geolocated maps Land use in S&W Seattle Population density S&W Seattle Cluster analysis Output S&W Seattle
sourceanalysisoutputalgorythm/method
geolocalizationcategorizationtimenone
venues
people
tips
land
name
lat/lon
user ID
text string
► poverty
► education
► income
► land use
► density
textanalysis
areaselection
code 1:social
engagment
code 2:attachment to place
code 3:fear andavoidance
areadefinition
chec
kinm
ania
.com
(4S
Q)ce
nsus
TACOMA
TACOMA
SEATTLE
SEATTLE
2011date
2011date
THE EMERGENT URBAN IMAGINARIES OF GEOSOCIAL MEDIA
ELABORATION VISUALIZATIONCOLLECTIONwww.densitydesign.org
geolocated maps Land use in S&W Seattle Population density S&W Seattle Cluster analysis Output S&W Seattle Study areas (block group cluster)
sourceanalysisoutputalgorythm/method
geolocalizationcategorizationtimenone
venues
people
tips
land
name
lat/lon
user ID
text string
► poverty
► education
► income
► land use
► density
textanalysis
areaselection
code 1:social
engagment
code 2:attachment to place
code 3:fear andavoidance
areadefinition
tip analysis
chec
kinm
ania
.com
(4S
Q)ce
nsus
TACOMA
TACOMA
SEATTLE
SEATTLE
2011date
2011date
THE EMERGENT URBAN IMAGINARIES OF GEOSOCIAL MEDIA
ELABORATION VISUALIZATIONCOLLECTIONwww.densitydesign.org
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
sourceanalysisoutputalgorythm/method
geolocalizationcategorizationtimenone
venues
people
tips
land
name
lat/lon
user ID
text string
► poverty
► education
► income
► land use
► density
textanalysis
areaselection
code 1:social
engagment
code 2:attachment to place
code 3:fear andavoidance
areadefinition
tip analysis
chec
kinm
ania
.com
(4S
Q)ce
nsus
TACOMA
TACOMA
SEATTLE
SEATTLE
2011date
2011date
THE EMERGENT URBAN IMAGINARIES OF GEOSOCIAL MEDIA
ELABORATION VISUALIZATIONCOLLECTIONwww.densitydesign.org
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
sourceanalysisoutputalgorythm/method
geolocalizationcategorizationtimenone
venues
people
tips
land
name
lat/lon
user ID
text string
► poverty
► education
► income
► land use
► density
textanalysis
code 1:social
engagment
code 2:attachment to place
code 3:fear andavoidance
tip analysis
areaselection
areadefinition
chec
kinm
ania
.com
(4S
Q)ce
nsus
TACOMA
TACOMA
SEATTLE
SEATTLE
2011date
2011date
THE EMERGENT URBAN IMAGINARIES OF GEOSOCIAL MEDIA
www.densitydesign.orgREPRESENTATIVENESS:meaning of data
FAR FROM THE EYES, CLOSE ON THE WEB
ESTIMATING PEOPLE PERCEPTION OF INTIMACY
www.densitydesign.org
ESTIMATING PEOPLE PERCEPTION OF INTIMACY
How does the intimacy relate to privacy? The main scope of the research is to esti-mate people intimacy to detect when in-formation 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 re-quest 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 re-searchers derived the users’ level of inti-macy in particular places and intervals of time. The research uses an “Intimacy Estimation Algorithm” that compute data from the devices.
www.densitydesign.org
For the “observers” category:
BLUETOOTH --> number of devices around the user can reveal the number of people opbserving him;
RING STATUS --> representing the willing-ness of the user to share the events of the device with other;
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;
OUTGOING SMS --> if a user is exchang-ing 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;
For the “safe-place” category:
CHARGING STATUS --> if the phone is charging it can indicate that the user is currently in a trusted place;
RING STATUS --> is related to “how much” the user wants to be disturbed by exter-nal events;
INDOOR/OUTDOOR --> there is a high probability that if the user is outdoor, he may not be in a safe place.
ESTIMATING PEOPLE PERCEPTION OF INTIMACY
ELABORATIONCOLLECTION SCOPEwww.densitydesign.org
sourceanalysisoutputalgorythm/method
mobi
le d
ata
(MDC
) FEATURES
observers
bluetooth
ring status
outgoing call
outgoing SMS
safe-places charging status
ring status
indoor/Outdoor
2010date
ESTIMATING PEOPLE PERCEPTION OF INTIMACY
ELABORATIONCOLLECTION SCOPEwww.densitydesign.org
sourceanalysisoutputalgorythm/method
FEATURES
observers
bluetooth
ring status
outgoing call
outgoing SMS
safe-places charging status
ring status
indoor/Outdoor
mobi
le d
ata
(MDC
)
IntimacyEstimationAlgorithm
2010date
ESTIMATING PEOPLE PERCEPTION OF INTIMACY
ELABORATION SCOPECOLLECTIONwww.densitydesign.org
FEATURES
observers
bluetooth
ring status
outgoing call
outgoing SMS
safe-places charging status
ring status
indoor/Outdoor
sourceanalysisoutputalgorythm/method
mobi
le d
ata
(MDC
)
IntimacyEstimationAlgorithm
observersand
safe places
demographic analisys
intimacylevel
2010date
ESTIMATING PEOPLE PERCEPTION OF INTIMACY
ELABORATION SCOPECOLLECTIONwww.densitydesign.org
FEATURES
observers
bluetooth
ring status
outgoing call
outgoing SMS
safe-places charging status
ring status
indoor/Outdoor
sourceanalysisoutputalgorythm/method
mobi
le d
ata
(MDC
)
IntimacyEstimationAlgorithm
observersand
safe places
developapplication
demographic analisys
intimacylevel
2010date
ESTIMATING PEOPLE PERCEPTION OF INTIMACY
ELABORATION SCOPECOLLECTIONwww.densitydesign.org
FEATURES
observers
bluetooth
ring status
outgoing call
outgoing SMS
safe-places charging status
ring status
indoor/Outdoor
sourceanalysisoutputalgorythm/method
mobi
le d
ata
(MDC
)
IntimacyEstimationAlgorithm
observersand
safe places
developapplication
demographic analisys
intimacylevel
2010date
ESTIMATING PEOPLE PERCEPTION OF INTIMACY
www.densitydesign.org
FAR FROM THE EYES, CLOSE ON THE WEB
Analising a large dataset of anonymised snapshot of Tuenti’s friendship con-nections, that includes about 9.8 million registered users, more than 580 million friendship links, about 500 million inter-actions during a 3 month period and the user’s the self-reported city residence, this research aims to study how social inter-actions is related to users’ geograph-ic locations. While spatial prooximity greatly affects how users establish their connections on online platforms, the re-searchers 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 architec-tures that optimise distributed storage of data related to online social plat-forms based on users’ geographic loca-tions. Similarly, it is important for system that exploit geographic locality of interest to serve content items requested through online social network services. The find-ings also likely to help other domains such as link predition, tie strengh inter-ference and user profiling: the observed spatiual patterns can me also included in security mechanism to detect malicious and spam accounts.
www.densitydesign.org
Geographi properties:
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 us-ers are at a distance of 10km or less, while only 10% of all distances between users are below 100 km.
Interaction analysis:
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; an-other process impacts the level of inter-action among connected users and ap-pears unrelated to spatial proximity.
FAR FROM THE EYES, CLOSE ON THE WEB
ELABORATION SCOPECOLLECTIONwww.densitydesign.org
tuen
ti FRIENDSHIP
user’s connections friendship links
wall comments
sourceanalysisoutputalgorythm/method
2010date
FAR FROM THE EYES, CLOSE ON THE WEB
ELABORATION SCOPECOLLECTIONwww.densitydesign.org
tuen
ti FRIENDSHIP
user’s connections friendship links
wall commentsgeographic properties
sourceanalysisoutputalgorythm/method
2010date
FAR FROM THE EYES, CLOSE ON THE WEB
ELABORATION SCOPECOLLECTIONwww.densitydesign.org
tuen
ti FRIENDSHIP
user’s connections friendship links
wall commentsgeographic properties
interaction analysis
sourceanalysisoutputalgorythm/method
2010date
FAR FROM THE EYES, CLOSE ON THE WEB
ELABORATION SCOPECOLLECTIONwww.densitydesign.org
tuen
ti FRIENDSHIP
user’s connections friendship links
wall commentsgeographic properties
interaction analysis
geo-related data storage
architectures
sourceanalysisoutputalgorythm/method
2010date
FAR FROM THE EYES, CLOSE ON THE WEB
ELABORATION SCOPECOLLECTIONwww.densitydesign.org
tuen
ti FRIENDSHIP
user’s connections friendship links
wall commentsgeographic properties
interaction analysis
link prediction
geo-related data storage
architectures
sourceanalysisoutputalgorythm/method
2010date
FAR FROM THE EYES, CLOSE ON THE WEB
ELABORATION SCOPECOLLECTIONwww.densitydesign.org
tuen
ti FRIENDSHIP
user’s connections friendship links
wall commentsgeographic properties
interaction analysis
link prediction
tie strenghinterference
geo-related data storage
architectures
sourceanalysisoutputalgorythm/method
2010date
FAR FROM THE EYES, CLOSE ON THE WEB
ELABORATION SCOPECOLLECTIONwww.densitydesign.org
tuen
ti FRIENDSHIP
user’s connections friendship links
wall commentsgeographic properties
interaction analysis
link prediction
tie strenghinterference
user profiling
geo-related data storage
architectures
sourceanalysisoutputalgorythm/method
2010date
FAR FROM THE EYES, CLOSE ON THE WEB
ELABORATION SCOPECOLLECTIONwww.densitydesign.org
tuen
ti FRIENDSHIP
user’s connections friendship links
wall commentsgeographic properties
interaction analysis
link prediction
tie strenghinterference
user profiling
securitymechanisms
geo-related data storage
architectures
sourceanalysisoutputalgorythm/method
2010date
FAR FROM THE EYES, CLOSE ON THE WEB
ELABORATION SCOPECOLLECTIONwww.densitydesign.org
tuen
ti FRIENDSHIP
user’s connections friendship links
wall commentsgeographic properties
interaction analysis
link prediction
tie strenghinterference
user profiling
securitymechanisms
geo-related data storage
architectures
sourceanalysisoutputalgorythm/method
2010date
FAR FROM THE EYES, CLOSE ON THE WEB
www.densitydesign.org
A WEEK ON FOURSQUARE (WSJ)Where the Young and Tech-Savvy GoA. Sun - J. Valentino-DeVries - Z. SewardMay 19, 2011 [http://graphicsweb.wsj.com/documents/FOUR-SQUAREWEEK1104/]
THE EMERGENT URBAN IMAGINARIES OF GEOSOCIAL MEDIAThe emergent urban imaginaries of geosocial mediaM. James KelleySpringer Science+Business Media B.V.2011[http://www.springerlink.com/content/u56612253r57257h/fulltext.pdf]
LIVEHOODSThe Livehoods Project: Utilizing Social Me-dia to Understand the Dynamics of a CityJ. Cranshaw - R. Schwartz - J. I. Hong - N. SadehSchool of Computer Science, Carnegie Mellon University, Pittsburgh2011[http://livehoods.org/maps/nyc#][http://livehoods.org/research]
PERCEPTION OF INTIMACYEstimating People Perception of Inti-macy in Daily Life from Context Data Collected with Their Mobile PhoneM. Gustarini - K. Wac2010[research.nokia.com]
URBAGRAMSSensing the urban: using location-based social network data in urban analysisA. Bawa-Cavia2011[http://urbagram.net/media/SensingTheUrban-WP.pdf]
ArchipelagoA. Bawa-Cavia2010[http://www.urbagram.net/archipelago/]
FAR FROM THE EYES,CLOSE ON THE WEBFar from the eyes, close on the Web: impact of geographic distance on online social interactionsA. Kaltenbrunner - S. Scellato - Y. Volkovich - D. Laniado - D. Currie - E. J. Jutemar - C. MascoloIn ACM SIGCOMM Workshop on Online Social Networks (WOSN 2012) - Helsinki, FinlandAugust 2012[http://www.cl.cam.ac.uk/~cm542/papers/wo-sn12-kaltenbrunner.pdf]
BIBLIOGRAPHY
Recommended