geolocated foursquare data: where we are

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


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


global (timeline)

glocal (geolocated view)

local (hot spots + focus on categories and venues)

3 READING LEVELS

GEOLOCALIZATION

CATEGORIZATION

TIMELINES


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



venuesanalysis

NYC

SF

4SQ



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



venuesanalysis

NYC

SF

4SQ


Venues pro

pert

ies name

categories

n. check-ins

lat/lon

► user gender

ranks Most checked-in venues overall

timeline Most checked-in venue



categories analysis


01/21/2011 h1401/28/2011 h11per hour

start date

end date

frequency



categories analysis

venuesanalysis

NYC/SFcomparison

NYC

SF

4SQ


Venues pro

pert

ies name

categories

n. check-ins

lat/lon

► user gender

ranks Most checked-in venues overall Top venues/category





01/21/2011 h1401/28/2011 h11per hour

start date

end date

frequency



categories analysis

venuesanalysis

NYC/SFcomparison

gendercomparison

NYC

SF

4SQ


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.


interactive plots/scatterplots NYC & SF check-ins/top 80 categ.

bar charts venues’ check-ins/week check-ins/category




01/21/2011 h1401/28/2011 h11per hour

start date

end date

frequency





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



others male/fem. check-ins/categ.

categories analysis

venuesanalysis

NYC/SFcomparison

gendercomparison

NYC

SF

4SQ


Venues pro

pert

ies name

categories

n. check-ins

lat/lon

► user gender


01/21/2011 h1401/28/2011 h11per hour

start date

end date

frequency





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



others male/fem. check-ins/categ.

categories analysis

venuesanalysis

NYC/SFcomparison

gendercomparison

NYC

SF

4SQ


Venues properties

name

categories

n. check-ins

lat/lon

► user gender


01/21/2011 h1401/28/2011 h11per hour

start date

end date

frequency



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.


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


URBAGRAMS


NYC

PARIS

LONDON

03/200907/2010cumulative

start date

end date

frequency

4SQ

Venues properties name

categories

n. check-ins

lat/lon



“walkable”cells grid (400x400mt)

URBAGRAMS


NYC

PARIS

LONDON

4SQ


categories

n. check-ins

lat/lon



categoriescomparison

areascomparison


DBScan


start date

end date

frequency

URBAGRAMS


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


categories

n. check-ins

lat/lon




venuescomparison

areascomparison


DBScan


start date

end date

frequency

URBAGRAMS



geolocated maps Social activities by categories Social activities by venue


plots/scatterplots Urban-scale Moran


NYC

PARIS

LONDON

4SQ


categories

n. check-ins

lat/lon



citiescomparison


venuescomparison

areascomparison


DBScan


start date

end date

frequency

URBAGRAMS



DBScanNYC

PARIS

LONDON

4SQ


categories

n. check-ins

lat/lon



areascomparison

citiescomparison


venuescomparison


geolocated maps Social activities by categories Social activities by venue

cluster maps Fragmentation of social activity


plots/scatterplots Urban-scale Moran Rank-size plots for venues’ check-ins



start date

end date

frequency

URBAGRAMS


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.


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


LIVEHOODS

ELABORATION VISUALIZATIONCOLLECTIONwww.densitydesign.org



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



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

spatialaffinity


► education

► background

► boundariesspace

11/17/201112/17/201127 people

start date

end date

sample

2011date

2011date

LIVEHOODS




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




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


► education

► background

► boundariesspace

11/17/201112/17/201127 people

start date

end date

sample

2011date

2011date

LIVEHOODS


PITTSBURG



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


► education

► background

► boundariesspace

11/17/201112/17/201127 people

start date

end date

sample

2011date

2011date

LIVEHOODS


geolocated maps Cluster map of livehoods

ranks Character Related

bar charts Stats Daily pulse Hourly pulse

campussostenibile



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


► education

► background

► boundariesspace

11/17/201112/17/201127 people

start date

end date

sample

2011date

2011date

LIVEHOODS



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.


Mapping the research area (census + checkinmania.com)

Tips text analysis (classification code)

DATA MERGING

QUALITATIVE ANALYSIS

GEOLOCALIZATION

CODIFICATION



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



geolocated maps Land use in S&W Seattle Population density S&W Seattle



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



geolocated maps Land use in S&W Seattle Population density S&W Seattle Cluster analysis Output S&W Seattle



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



geolocated maps Land use in S&W Seattle Population density S&W Seattle Cluster analysis Output S&W Seattle



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



geolocated maps Land use in S&W Seattle Population density S&W Seattle Cluster analysis Output S&W Seattle Study areas (block group cluster)



venues

people

tips

land

name

lat/lon

user ID

text string

► poverty

► education

► income

► land use

► density

textanalysis

areaselection

code 1:social

engagment



areadefinition

tip analysis

chec

kinm

ania

.com

(4S

Q)ce

nsus

TACOMA

TACOMA

SEATTLE

SEATTLE

2011date

2011date




tabs Content analysis of check-in tips



venues

people

tips

land

name

lat/lon

user ID

text string

► poverty

► education

► income

► land use

► density

textanalysis

areaselection

code 1:social

engagment



areadefinition

tip analysis

chec

kinm

ania

.com

(4S

Q)ce

nsus

TACOMA

TACOMA

SEATTLE

SEATTLE

2011date

2011date




tabs Content analysis of check-in tips



venues

people

tips

land

name

lat/lon

user ID

text string

► poverty

► education

► income

► land use

► density

textanalysis

code 1:social

engagment



tip analysis

areaselection

areadefinition

chec

kinm

ania

.com

(4S

Q)ce

nsus

TACOMA

TACOMA

SEATTLE

SEATTLE

2011date

2011date


www.densitydesign.orgREPRESENTATIVENESS:meaning of data

FAR FROM THE EYES, CLOSE ON THE WEB

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.


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.


ELABORATIONCOLLECTION SCOPEwww.densitydesign.org


mobi

le d

ata

(MDC

) FEATURES

observers

bluetooth

ring status

outgoing call

outgoing SMS

safe-places charging status

ring status

indoor/Outdoor

2010date


ELABORATIONCOLLECTION SCOPEwww.densitydesign.org


FEATURES

observers

bluetooth

ring status

outgoing call

outgoing SMS


ring status

indoor/Outdoor

mobi

le d

ata

(MDC

)

IntimacyEstimationAlgorithm

2010date


ELABORATION SCOPECOLLECTIONwww.densitydesign.org

FEATURES

observers

bluetooth

ring status

outgoing call

outgoing SMS


ring status

indoor/Outdoor


mobi

le d

ata

(MDC

)


observersand

safe places

demographic analisys

intimacylevel

2010date



FEATURES

observers

bluetooth

ring status

outgoing call

outgoing SMS


ring status

indoor/Outdoor


mobi

le d

ata

(MDC

)


observersand

safe places

developapplication

demographic analisys

intimacylevel

2010date




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.


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.



tuen

ti FRIENDSHIP

user’s connections friendship links

wall comments


2010date



tuen

ti FRIENDSHIP


wall commentsgeographic properties


2010date



tuen

ti FRIENDSHIP



interaction analysis


2010date



tuen

ti FRIENDSHIP




geo-related data storage

architectures


2010date



tuen

ti FRIENDSHIP




link prediction


architectures


2010date



tuen

ti FRIENDSHIP




link prediction

tie strenghinterference


architectures


2010date



tuen

ti FRIENDSHIP




link prediction


user profiling


architectures


2010date



tuen

ti FRIENDSHIP




link prediction


user profiling

securitymechanisms


architectures


2010date



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

geolocated foursquare data: where we are

Design