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We introduce Instagram as a data source for use by scholars in urban studies and neighboring disciplines and propose ways to operationalize key concepts in the study of cities. These data can help shed light on segregation, the formation of subcultures, strategies of distinction, and status hierarchies in the city. Drawing on two datasets of geotagged Instagram posts from Amsterdam and Copenhagen collected over a twelve-week period, we present a proof of concept for how to explore and visualize sociospatial patterns and divisions in these two cities. We take advantage of both the social and the geographic aspects of the data, using network analysis to identify distinct groups of users and metrics of unevenness and diversity to identify socio-spatial divisions. We also discuss some of the limitations of these data and methods and suggest ways in which they can complement established quantitative and qualitative approaches in urban scholarship.
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RESEARCH ARTICLE
How to Study the City on Instagram
John D. Boy
1
*, Justus Uitermark
1,2
1Sociology Department, University of Amsterdam, Amsterdam, The Netherlands, 2Sociology Department,
Erasmus University, Rotterdam, The Netherlands
*jboy@uva.nl
Abstract
We introduce Instagram as a data source for use by scholars in urban studies and neighbor-
ing disciplines and propose ways to operationalize key concepts in the study of cities.
These data can help shed light on segregation, the formation of subcultures, strategies of
distinction, and status hierarchies in the city. Drawing on two datasets of geotagged Insta-
gram posts from Amsterdam and Copenhagen collected over a twelve-week period, we
present a proof of concept for how to explore and visualize sociospatial patterns and divi-
sions in these two cities. We take advantage of both the social and the geographic aspects
of the data, using network analysis to identify distinct groups of users and metrics of
unevenness and diversity to identify socio-spatial divisions. We also discuss some of the
limitations of these data and methods and suggest ways in which they can complement
established quantitative and qualitative approaches in urban scholarship.
Introduction
Since its launch in 2010, Instagram has quickly become one of the most widely used social net-
working platforms in the world [1]. In early 2015, it was reported that over 200 million users
around the world use the service to share 70 million pictures per day [2]. As a visual-locative
social medium, Instagram can be regarded as a participatory sensing system [3,4]. Its users pro-
duce data as they navigate their everyday lives, smartphones in hand. These data are centralized
in the Instagram platform. As such, it lends itself as an unparalleled data source for social
researchers. In this paper, we introduce methods that can be used by scholars to make sense of
contemporary urban dynamics. In particular, our interest is to explore and visualize sociospa-
tial patterns and divisions within the city. We apply these methods to two cities, Amsterdam
and Copenhagen. This application is a proof of concept to demonstrate the possibilitiesas
well as the limitationsof these data and methods to inform the work of urban scholars.
Related Work
Our approach for studying social divisions draws on two literatures: social science literature on
cities, and computer science literature on social media. Social scientists have long been inter-
ested in social divisions within the city. Building on the foundational work on urban social
ecology by the Chicago School [5], they have mapped the uneven distribution of population
groups and identified determinants and outcomes of economic and ethnic segregation [68].
PLOS ONE | DOI:10.1371/journal.pone.0158161 June 23, 2016 1/16
a11111
OPEN ACCESS
Citation: Boy JD, Uitermark J (2016) How to Study
the City on Instagram. PLoS ONE 11(6): e0158161.
doi:10.1371/journal.pone.0158161
Editor: Tobias Preis, University of Warwick, UNITED
KINGDOM
Received: January 28, 2016
Accepted: June 10, 2016
Published: June 23, 2016
Copyright: © 2016 Boy, Uitermark. This is an open
access article distributed under the terms of the
Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any
medium, provided the original author and source are
credited.
Data Availability Statement: Data have been
collected through the public Instagram API (https://
www.instagram.com/developer) and are securely
stored in an internal database. Access to the data is
restricted to avoid personal identification of users and
to comply with Instagram Terms of Use (https://www.
instagram.com/about/legal/terms/) and API Terms
(https://www.instagram.com/about/legal/terms/api/).
Network data are available in anonymized form from
a public repository at doi:10.5281/zenodo.45272.
Other Instagram data is publicly available through the
API.
Funding: This work was funded in part by the
Research Council of Norway (no. 231344) and the
The vast majority of studies have researched residential segregation through official registry
data. While such studies provide a range of insights, it is widely acknowledged that these stud-
ies do not cover some essential dimensions of differentiation within the city, as they account
more for where people reside than for how they move through urban space in their daily lives.
The urban landscape is marked by numerous processes of enclave formation that do not
revolve around places of residence, i.e. neighborhoods, but around places of work, leisure, or
retail [913]. Researchers have relied on ethnography or survey research to grasp these subtle
and dynamic patterns of segregation [14,15]. While these methods provide distinct advantages,
they are arguably less suited to capture relations and flows outside the bounds of the research
site [1618].
User-generated data from location-based social networking platforms hold the promise of
filling in the gaps in the picture presented by current research on urban dynamics [19]. As the
product of a participatory sensing system, these data add to the methodological toolkit of
scholars of the city. These data can be used to identify groups on the basis of observed behavior
rather than using a predefined classificatory scheme, allowing for a more fine-grained and up-
to-date breakdown of urban populations into subgroups.
However, researchers seeking to put these opportunities to use should be aware that social
media data do not simply reflect the activities of urban dwellers. This is especially true for the
Instagram data we utilize in this paper. Instagram users selectively represent their lifeworlds by
showcasing images they feel are suited for circulation. This also means that they represent the
city and their place within it in a curated manner. Users typically do not report on their visits
to the supermarket or their commute to work. Instead they share images as part of strategies of
distinction: they picture themselves with friends, in nice outfits, in places that are special to
them [20]. In a word, they use Instagram to mark their place in the social structure and within
the city. By associating with each other (by following, liking, or commenting) and tagging the
same places, users form communities at the interface of online and offline spaces. By mapping
these processes of association and place demarcation, we can investigate how communities
emerge at this interface and create sociocultural domains. Such processes could before only be
grasped through surveys or ethnographies of concrete settings, but now we can use social
media data to investigate on a large scale and in detail how city dwellers associate with one
another and form communities.
These data can also give insight into the places or sets of places in which different groups in
the city spend their waking hours and into the role these places perform in the formation of
groups and subcultures. In this context, Lofland understands a citys public realm to be made
up of places in which city dwellers encounter strangers [21]. These places are quintessential
sites of urban life because they require people to interact with others with which they have no
intimate bonds [22]. As such, they also serve as sites to cultivate cosmopolitan habits [23].
Urban dwellers can also transform nominally public places into a group-specific domain. Such
parochial places serve to solidify group identities and reaffirm boundaries [21]. Thus, identify-
ing which places in a city are cosmopolitan and which are parochial is important for an under-
standing of patterns of encounter and enclavement in the city. In addition, divisions between
groups can also occur in time rather than in space, for instance when places and areas become
exclusive sites in a citys nightlife [2427].
While several scholars working at the juncture between geography and computer science
have begun using social media data [2831], within the last half decade computer scientists
have conducted most of the work taking advantage of location-based social networks [32].
Before the recent rise of Instagram, the focus was on geotagged tweets and data sourced from
Foursquare, the check-in service. Cranshaw et al.s Livehoods Project presents a method to
study urban dynamics and structure through social media data using machine-learning
How to Study the City on Instagram
PLOS ONE | DOI:10.1371/journal.pone.0158161 June 23, 2016 2/16
Netherlands Organization for Scientific Research (no.
451-12-035).
Competing Interests: The authors have declared
that no competing interests exist.
techniques [33]. Their methodology aggregates individual data points (Foursquare checkins)
using spatial clustering techniques to identify areas that emerge from the actions of city dwell-
ers. Frias-Martinez et al. use geotagged tweets to study land use and sites of interest in New
York City. On the basis of spatio-temporal patterns in the data, they distinguish areas in the
city used primarily for leisure, business, or residential purposes [34]. Silva et al. use Foursquare
data to create heatmaps to visualize urban dynamics on the basis of individual trajectories. The
resulting visualization shows the overall likelihood of a citys inhabitants of transitioning
between different types of spaces, such as public transit hubs and places of work [35,36].
More recently Silva et al. have moved from using Foursquare data to using Instagram data
as the basis for a participatory sensing system.(In [37] Silva et al. compare the two data
sources.) In using Instagram data to study urban dynamics, they find spatio-temporal patterns
to be correlated with routine activities of city dwellers. As such, it can serve to identify places or
sets of places of cultural activity [38]. A series of interdisciplinary collaborations between com-
puter scientists and art historians have also sought to make sense of Instagram in the context of
the city. By analyzing large datasets of geotagged posts collected in cities throughout the world,
these contributions seek to visualize differences in rhythms and content between cities [3941].
Other contributions by researchers around Lev Manovich include http://selfiecity.net and
http://on-broadway.nyc.
In sum, while scholars have undertaken promising forays, research so far has been limited.
Especially when compared to Twitter, research on Instagram is in its infancy. There are impor-
tant theoretical reasons for filling this lacuna, as Instagram data enable researchers to shed new
light on processes that have long occupied scholars of cities, including the formation of subcul-
tures, segregation, and the cosmopolitan or parochial nature of places within the city. Our con-
tribution is to develop a number of methods to illuminate these processes and to provide a
proof of concept for how these methods can be put to work.
Data
We utilize both network and spatial data sourced from Instagram. Network data allow us to
identify groups, while spatial data allows us to map the places that Instagram users picture.
Taken together, we can use this data to identify socio-spatial divisions by investigating the pres-
ence or absence of social groups in places throughout the city.
We collect both kinds of data from Instagram using the platforms application program-
ming interface (API). For this purpose we built and used kijkeens [42], a tool that polls the
Instagram APIs location endpoint at regular intervals to gather all geotagged posts from an
urban area, stores post metadata in a database, and, after a specified delay, gathers network
data (likesand comments) for each of the posts. We created two dataset of posts published in
Amsterdam and Copenhagen over a twelve-week period. In Amsterdam we collected 953,403
posts between 19 April and 12 July 2015, while in Copenhagen we collected 890,621 posts
between 25 May and 17 August 2015. Because we are interested in everyday patterns of urban
dwelling, we only considered posts by users who had posted in the city over a time spanning
four weeks or longer to eliminate likely tourists. This cut the number of posts down to 442,246
and 507,445 posts, respectively.
We stored a variety of metadata about each of these posts. Most importantly for our pur-
poses here are the data about social activity (likesand comments) and tagged locations. We
stored the social activity on each post about 24 hours after the initial publication of the post.
Since most of the activity on a post occurs within the first few hours of its life, this accounts for
the bulk of the interactions garnered by the geotagged posts in our dataset. In Amsterdam,
there were over 16 million interactions, of which 1.1 million were between local users in our
How to Study the City on Instagram
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dataset. In Copenhagen, the number of interactions was over 21 million, of which 1.8 million
were between users in our dataset. It bears keeping in mind that the API returns a maximum of
140 likes per post, so we cannot capture all interactions for very popular users whose number
of likes regularly exceeds this number. As a result of this restriction of the data, our analysis
may underestimate the centrality of certain users in the overall network.
Methods
Our analysis combines several methods. We use network analysis to identify groups and met-
rics of unevenness and diversity to identify socio-spatial divisions.
Identifying Groups Using Network Analysis
How can we identify groups of city dwellers? Where previous scholars had to collect data for
their analyses through painstaking community studies [43,44], we are able to use the network
data captured on Instagram. Instagram users give recognition to others on the platform by lik-
ing and commenting on their posts. For the purposes of our network analysis, we understand
reciprocated recognition (mutual liking and/or commenting) to constitute a social tie between
two users. Research on social media use suggests that this provides a surer indicator of a social
tie between users than mere followership [45]. We construct an undirected, unweighted net-
work graph on the basis of our interaction data. Table 1 provides some metrics on the two city
networks.
We identify subgroups among Instagram users by applying a technique called community
detection. The method we use, called the Louvain method of modularity optimization [46],
progressively groups connected nodes in a network together until it reaches an optimal level of
clustering. We use the igraph package [47] and the implementation of the Louvain algorithm
by Traag [48]. We perform community detection on the largest connected component of each
graph, which accounts for most nodes with reciprocated ties in both networks (93.9 percent for
Amsterdam and 96.6 percent for Copenhagen). We consider only clusters of at least five hun-
dred users. We chose this cutoff to keep the number of clusters manageable after determining
that the clusters above this cutoff contain most nodes. Those interested in studying specific
subcultures may want to include even some of the smaller, more marginal clusters, but that is
not necessary for our purposes.
Next, we characterize these groups and find what, aside from the overall network structure,
makes them distinct. We tried out a variety of methods. At first, we sought to characterize clus-
ters in an automated manner by using user profile data. Instagram users have the possibility of
filling in a 150-character biographyfield. Our attempts to use text analysis techniques such
as tf-idf [49] to characterize clusters on the basis of this textual data failed to yield reliable or
valid results. Instead, we opted to rely on a combination of network analysis and manual classi-
fication to characterize groups. Despite some shortcomings, this seems the most suitable
approach given the data we have.
Table 1. Networks of Mutual Ties in Amsterdam and Copenhagen.
City Total Nodes Nodes With
Reciprocated
Ties
Nodes in Largest
Component
Edges Median Number
of Ties
Modularity Clusters
Amsterdam 30,964 20,135 18,916 56,126 3 0.635 12
Copenhagen 41,232 32,339 31,227 98,978 4 0.618 16
doi:10.1371/journal.pone.0158161.t001
How to Study the City on Instagram
PLOS ONE | DOI:10.1371/journal.pone.0158161 June 23, 2016 4/16
In a first step, we analyze the structure of subgraphs. We are particularly interested in the
density of ties and in whether certain nodes stand out as hubs. If we find that subgraphs are
tightly knit and organized around hubs, then we have a rationale for characterizing groups in
terms of their most central users, which we can regard as group focal points [50]. To determine
tie density of subgroups, we calculate the local average clustering coefficient and compare it to
the clustering coefficient of a random ErdősRényi graph with an equal number of nodes and
edges [51]. We report a logged ratio of clustering coefficients (log Csubgraph
Crandom ) to compare observed
tie density to that of a random graph. If the clustering coefcient is signicantly higher than in
the random graph, we can conclude that tie density is high. To determine the extent to which
subgraphs are organized around hubs, we inspect the centrality distribution of each subgraph
and report goodness-of-t measures (Kolmogorov-Smirnov distance) for three heavy-tailed
distributions: power law, lognormal, and stretched exponential distributions [52]. We use Page
Rank as a measure of network centrality [53].
As we will see, the subgraphs have a skewed, heavy-tailed centrality distribution (indicative
of hubs) and high tie density (as compared to random graphs). We exploit these network fea-
tures and characterize groups according to their focal points. We did so manually. The authors
each looked at the data to inductively arrive at a characterization for each cluster and then
compared results to fine-tune the results of this inductive process. Future research may want to
develop a coding scheme for these purposes, but that is not something we could draw on here.
In characterizing these central accounts, we first consider the user profile, and then we analyze
the content of their pictures and the tagged locations. Often users list their profession or affilia-
tion in their biography which we then only have to verify by studying their images, but other
times we have to determine their social and cultural background through close study of the
content of their images. Our analysis focused on the ten most central accounts. While a more
exhaustive manual analysis undoubtedly would have revealed further nuances, we found that
examining the ten most central accounts provided us with a good impression of the cluster in
the sense that examining additional accounts did not lead us to fundamentally change our
characterization.
Mapping Social Divisions and Interactions
How can we measure the level and nature of segregation and interaction between groups?
Sociological studies of urban segregation have employed a number of different metrics. The
index of dissimilarity (DIS) was long considered the gold standard of residential segregation
measures [54], and it is particularly suited to capture the evenness of populations in an urban
area [55]. The segregation of a minority group M across k different areal units i relative to a
majority population W is measured as follows:
DIS ¼
1
2X
k
i¼1
mi
M
wi
W
Here m
i
and w
i
refer to the subpopulations of the minority and minority populations found
in each areal unit. This index has a variety of characteristics to recommend it. Above all, it is
easy to interpret. The value of DIS corresponds to the proportion of the minority group that
would have to relocate to achieve a fully even distribution. We use the dissimilarity index to
measure group segregation, since we are interested in how evenly groups are present in places
around the city. In calculating this measure of evenness, we take each cluster as a minority
groupand compare it to the other clusters combined forming the majority group.
How to Study the City on Instagram
PLOS ONE | DOI:10.1371/journal.pone.0158161 June 23, 2016 5/16
We are also interested in exchanges between groups. Two groups can be related to one
another by being in frequent interaction with one another, for instance by liking and comment-
ing on each others posts. The strength of this relation is indicated by the weight of the edge
connecting the two groups in the cluster graph. The edge weight is calculated by summing up
reciprocated ties between members of both groups. We normalize edge weights by dividing an
edges weight by the combined number of nodes in the two clusters it connects.
Locating Cosmopolitan and Parochial Places
Which places facilitate encounters between members of different groups, and which are
exclusive to members of the same group? We rank places from most parochial to most cosmo-
politan by employing a diversity measure known as the divergence index. It compares the
expected distribution of groups in a place given what we know about the overall distribution
of these groups in the city as a whole to the observed distribution within that place. Instagram
users can tag their posts with predefined locations, but they can also define their own place
names (or at least they could during our period of data collection). We manually verified and,
where necessary, merged place names. We only consider places tagged in at least 25 posts by
at least 15 different users. If each group was represented in the proportion in which it is pres-
ent in the city, we would have a situation of full diversity. The value of the measure would be
0. The further the expected and observed distributions diverge in a particular place, the less
diverse that place can be said to be. The divergence index (DIV) is used in both the social sci-
ences and the life sciences to measure population diversity [56,57]. The divergence index in
areal unit i is defined as follows:
DIVi¼X
s
m¼1
pimlog
pim
pm
In this equation, π
m
corresponds to the overall relative occurrence of cluster m, and pim
refers to the relative occurrence of cluster m in areal unit i. For ease of interpretability, we
report standardized divergence indices, which we calculate by dividing DIV
i
by max(DIV)
[56]. A value of 1 thus indicates maximum divergence (i.e., lowest diversity).
Unlike other diversity measures based on concepts from information theory, DIV is not
impacted by the number of subgroups being considered [56]. This is important because net-
work structures differ between cities, so community detection can yield a different numbers of
subgroups. If we want diversity measures to be comparable, they must not be influenced by
how many communities there are.
Results
Groups
Community detection allowed us to identify twelve clusters of 500 or more users in Amsterdam
(Tables 2and 3) and sixteen clusters above the cutoff in Copenhagen (Tables 4and 5). The
users in these clusters account for more than two thirds of all users with reciprocated ties in the
case of Amsterdam and more than three quarters of users in the case of Copenhagen.
All cluster subgraphs have clustering coefficients that are significantly higher than in a cor-
responding randomly generated graph. For all clusters, the difference exceeds an order of mag-
nitude. This points to the high density of ties within the clusters. Furthermore, their centrality
distributions hew closely to a heavy-tailed distribution (see also S1 Fig), which speaks to the
existence of hubs within each cluster. This provides us with a rationale to characterize groups
in terms of their most central users.
How to Study the City on Instagram
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In the case of Amsterdam, we are able to characterize eleven out of twelve clusters using
manual classification of central users. The groupness of the clusters generally is rooted in
shared professions, interests, lifestyles, and hangouts. Some clusters, such as AMS5 (consisting
mostly of high school students), have no strong tie to particular places and no common ways
of identifying. They are, however, at a similar stage in their lives. We might say that the young
people in this cluster have not (yet) developed a distinctive style or autonomous identity.
Finally, AMS6 a cluster with an unusually high proportion of private accountshas no basis
for groupness that we can discern.
In Copenhagen, a greater number of clusters is characterized by shared stage of life rather
than shared professions or hangouts. Part of the reason is that more teenagers are visible on
Instagram in Copenhagen than in Amsterdam, indicating either a higher level of adoption of
the social network among Danish youth or a lower aversion to setting accounts to public and
using geotags. Nonetheless, several clusters are clearly defined by shared characteristics, inter-
ests or professions.
Comparing Amsterdam to Copenhagen, we can see some commonalities and differences.
Both cities contain a sizeable cluster of image makers whose main occupation on the platform,
Table 2. Clusters detected in Amsterdam.
Cluster Posts Users Posts per User (Mean) Followers (Median) Private Accounts (%)
AMS1 2,287 30,565 13.4 462 15.6
AMS2 2,115 49,424 23.4 525 4.2
AMS3 1,908 49,858 26.1 319 3.0
AMS4 1,846 34,888 18.9 456 5.0
AMS5 1,245 16,635 13.4 365 18.4
AMS6 768 10,557 13.7 366 19.2
AMS7 872 12,151 13.9 431 12.8
AMS8 694 8,034 11.6 309 8.8
AMS9 693 11,831 17.1 295 3.7
AMS10 604 10,061 16.7 259 5.8
AMS11 595 9,753 16.4 284 6.1
AMS12 523 8,323 15.9 251 6.3
doi:10.1371/journal.pone.0158161.t002
Table 3. Additional data on Amsterdam clusters.
Cluster Label DIS Power Law Fit Lognormal Fit Stretched
Exponential Fit
Clustering Ratio
AMS1 Urban Street Culture 0.464 0.069 0.021 0.020 1.69
AMS2 Lifestyle Vanguard 0.332 0.041 0.029 0.038 1.74
AMS3 City Imagers 0.368 0.033 0.012 0.025 1.77
AMS4 Cultural Entrepreneurs 0.297 0.057 0.020 0.026 1.68
AMS5 High School Students 0.434 0.075 0.023 0.023 1.68
AMS6 unclear 0.409 0.050 0.036 0.048 1.57
AMS7 Party Buffs 0.465 0.053 0.018 0.026 1.55
AMS8 Teenage Bon Vivants 0.306 0.054 0.035 0.048 1.52
AMS9 Visual Professionals 0.336 0.062 0.032 0.038 1.64
AMS10 Cultural Explorers 0.405 0.034 0.035 0.047 1.47
AMS11 Health & Lifestyle Devotees 0.349 0.051 0.052 0.070 1.44
AMS12 Coffee Acionados 0.356 0.058 0.050 0.070 1.44
doi:10.1371/journal.pone.0158161.t003
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whether vocationally or avocationally, is to picture the city in which they are based (AMS3 and
CPH5). Places tagged by users in these clusters include well-known parks, buildings and struc-
tures. In both cities, clusters vary in size, in degrees of activity, and in popularity. The most
active cluster in Amsterdam (AMS3) has an average of 26.1 geotagged posts per user during
the twelve-week period under investigation and the least active (AMS8) has 11.6 posts per user.
In Copenhagen, the most active cluster (CPH4) has an average of 22.2 geotagged posts per
user, while the least active (CPH9) has only 7.2. Finally, the number of followers also varies
between clusters. On average, follower numbers in Amsterdam are higher than in Copenhagen,
Table 4. Clusters detected in Copenhagen.
Cluster Users Posts Posts per User (Mean) Followers (Median) Private Accounts (%)
CPH1 4,445 73,803 16.6 303 3.7
CPH2 3,605 32,514 9.0 325 7.2
CPH3 2,733 23,727 8.7 410.5 10.4
CPH4 2,766 61,462 22.2 321.5 2.5
CPH5 2,421 41,982 17.3 188 3.6
CPH6 2,339 23,662 10.1 246.5 6.4
CPH7 2,268 35,012 15.4 221 4.4
CPH8 818 12,053 14.7 251 8.6
CPH9 798 5,778 7.2 315 6.7
CPH10 774 9,197 11.9 197 4.8
CPH11 727 9,829 13.5 195 4.6
CPH12 654 8,483 13.0 221 7.0
CPH13 634 7,187 11.3 262 5.9
CPH14 622 7,030 11.3 183.5 4.8
CPH15 552 6,370 11.5 189.5 5.0
CPH16 513 7,135 13.9 171 4.3
doi:10.1371/journal.pone.0158161.t004
Table 5. Additional data on Copenhagen clusters.
Cluster Label DIS Power Law Fit Lognormal Fit Stretched
Exponential Fit
Clustering Ratio
CPH1 Designers 0.335 0.051 0.014 0.019 1.942
CPH2 College Students 0.300 0.050 0.033 0.046 2.066
CPH3 Gymnasium Students 0.379 0.052 0.031 0.043 1.998
CPH4 Design & Family 0.315 0.035 0.016 0.028 1.852
CPH5 Photographers 0.305 0.027 0.017 0.034 1.844
CPH6 College Students 0.267 0.046 0.023 0.032 2.056
CPH7 Fitness & Nutrition 0.366 0.034 0.013 0.033 1.925
CPH8 Teenagers 0.504 0.066 0.042 0.050 1.793
CPH9 Gymnasium Students 0.364 0.056 0.024 0.027 1.673
CPH10 unclear 0.314 0.047 0.042 0.047 1.735
CPH11 Political Activists 0.326 0.040 0.022 0.038 1.692
CPH12 Urban Street Culture 0.359 0.056 0.052 0.064 1.598
CPH13 High-Brow Culture 0.337 0.053 0.036 0.048 1.622
CPH14 unclear 0.376 0.072 0.068 0.085 1.645
CPH15 Fashion & Marketing 0.353 0.075 0.038 0.051 1.485
CPH16 Yoga & Family 0.391 0.043 0.038 0.053 1.464
doi:10.1371/journal.pone.0158161.t005
How to Study the City on Instagram
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and the spread of follower numbers is also greater. The Lifestyle Vanguard in Amsterdam
(AMS2) has the highest average number of followers, and in contrast, the most popular Copen-
hagen cluster (CPH3) consists of high school students.
Social Divisions and Interactions
As indicated by the dissimilarity index (DIS) reported in Tables 2b and 3b, the presence of clus-
ters in places around the city is uneven. To achieve an even groupwise distribution of posts, on
average about one in three posts would have to be posted from elsewhere. The uneven presence
of clusters throughout the city does not mean that they are completely isolated, however. Figs 1
and 2show levels of interaction between clusters in the two cities.
In Amsterdam, clusters AMS2 and AMS4 have 0.45 mutual ties per user, the strongest tie
between two clusters. Given the similarity in lifestyle orientations between the two clusters
one consisting of the Lifestyle Vanguard, the other consisting of Cultural Entrepreneursthis
link is not too surprising. The next strongest tie is between clusters AMS4 and AMS7, the
Fig 1. Levels of interaction between clusters in Amsterdam. Cells show the average number of mutual ties per user between members of two clusters.
Darker shaded cells indicate a greater number of mutual ties. Blank cells indicate an absence of interaction.
doi:10.1371/journal.pone.0158161.g001
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Cultural Entrepreneurs and the Party Buffs, another pair of clusters whose lifestyles, while not
overlapping, appear to have an affinity. Clusters AMS1 and AMS3 have strong ties to AMS7
and AMS2, respectively. These five interconnected clusters are among the most popular, and
they are each defined by shared lifestyles and professions rather than a common stage of life.
In Copenhagen, clusters CPH1 and CPH4 have a strongest tie, with 0.37 mutual relations
per user. Both of these clusters are firmly grounded in design professions and affinities, though
CPH4 has a higher degree of young parents whose family life appears on Instagram alongside
their interest in design. Clusters CPH2 and CPH3, large clusters consisting of men and women
in their teens and twenties attending secondary and postsecondary education institutions, also
have a strong tie.
Cosmopolitan and Parochial Places
Users tagged 367 places in Amsterdam and 680 places in Copenhagen. The following visualiza-
tions show the places in order of diversity, focusing only on the thirty most tagged locations for
Fig 2. Levels of interaction between clusters in Copenhagen. Cells show the average number of mutual ties per user between membersof two clusters.
Darker shaded cells indicate a greater number of mutual ties. Blank cells indicate an absence of interaction.
doi:10.1371/journal.pone.0158161.g002
How to Study the City on Instagram
PLOS ONE | DOI:10.1371/journal.pone.0158161 June 23, 2016 10 / 16
ease of presentation (Fig 3 shows the distribution of the divergence indices for all tagged places
in both cities). This way of presenting our data has the benefit of making patterns of group co-
occurrence in places easily apparent. The most diverse places are tagged by members of all clus-
ters, suggesting they are places of encounter between a wide array of different groups (i.e., cos-
mopolitan places). The least diverse are the exclusive domain of just one or two clusters (i.e.,
parochial places). Those with middling levels of diversity are overwhelmingly tagged by mem-
bers of the same three to four groups. They are neither exclusive parochial domains, nor are
they places of broad encounter.
The Rollende Keukens (rolling kitchens) open air food cart festival in Amsterdam has
near-proportional representation from all twelve clusters of Instagram users (see Fig 4). Like
some other placeswe find in our data, the festival is actually a temporary happening, which
in this case took place in the Westerpark, a public park in the west of Amsterdam. The location
of the event might explain its broad appeal, as the list of the most cosmopolitan places includes
the Vondelpark, Westerpark, Westergasfabriek (a cultural center located in the Westerpark),
and the Museum Squareall public places. The parks and squares which Amsterdam Insta-
gram users fondly picture and associate themselves with frequently become sites of encounter
between different groups.
Fig 3. Distribution of divergence indices in Amsterdam and Copenhagen. The divergence index is inversely proportional to the
diversity of a place. We calculate the index for each place in the two cities in our study. The graph shows the distribution of standardized
scores.
doi:10.1371/journal.pone.0158161.g003
How to Study the City on Instagram
PLOS ONE | DOI:10.1371/journal.pone.0158161 June 23, 2016 11 / 16
These public places are followed by popular cafes and other hangouts, an independent con-
cert venue, and two of the citys famous art museums. Several of these places have the strong
presence of AMS3, the cluster of City Imagers who picture famous sights. Clusters AMS2 and
AMS4 are also a constant presence, while clusters AMS5 and AMS6 are absent from several of
them. As we move closer to the parochial end of the spectrum, there is a marked increase in
music festivals, clubs, and large concert venues. Several of these places have a strong presence
of members of clusters AMS1, AMS4, and AMS7. The most parochial places, which include
restaurants, clubs, and a gym frequented by Health and Lifestyle Devotees (AMS11), are the
preferred hangout of members of just a single cluster. The type of places featured among the
most parochial suggest that the city is particularly segregated at nighttime, since they include
several nightlife locations. Some clusters are completely absent from the most parochial places,
including the Visual Professionals (AMS9), Cultural Explorers (AMS10), and the Coffee Afi-
cionados (AMS12).
Like in the case of Amsterdam, Copenhagens public parks and places are frequently sites of
encounter (see Fig 5). Among the most cosmopolitan places are the Fælledparken (Commons
Park) and the Tivoli, an amusement park. Users also frequently tag the neighborhoods of Ves-
terbro, Nørrebro, and the redeveloped harbor area Islands Brygge. These are not so much
places as they are areas, so we cannot conclude that they are places of encounter. They are,
however, areas that Instagram users across the board associate themselves with by tagging
them in their posts.
Fig 4. Visualization of place diversity in Amsterdam. The horizontal bar graph on the right shows the value of the divergence measure (DIV), with higher
values indicating lower diversity. The heatmap in the middle indicates the relative presence or different clusters. Darker shaded cells shaded indicate
greater presence of a cluster; white cells indicate complete absence.
doi:10.1371/journal.pone.0158161.g004
How to Study the City on Instagram
PLOS ONE | DOI:10.1371/journal.pone.0158161 June 23, 2016 12 / 16
The places with middling levels of diversity include hangouts popular among the younger
users in clusters CPH2 and CPH3 (high school and college students), including a pedestrian
shopping area and two bars in the downtown area. The most parochial places, finally, are the
near-exclusive domain of a single cluster. The Trailerpark Festival, a music festival held in
August, was tagged almost exclusively by users in cluster CPH1. Original Coffee, a coffee shop
with locations in several neighborhoods, is the near-exclusive domain of CPH3 and two other
clusters of teenage users, CPH8 and CPH9. Finally, Northmodern, a trade show dedicated to
Danish design products, is overwhelmingly tagged by users in the Design & Family cluster
(CPH4).
Discussion
Urban researchers interested in social divisions traditionally have had to choose between two
options that each have considerable tradeoffs. On the one hand, they could study social-spatial
divisions quantitatively with the tradeoff that they had to ask questions that can be answered
through data drawn from official records. On the other hand, they could study more complex
and dynamic processes through which subcultures create and claim spaces, but then they had
to resort to time and labor-intensive methods that can only be applied in a limited number of
places or on limited samples. By using data drawn from social media, researchers of cities can
begin to investigate at a very large scale and in minute detail how urban dwellers form groups
Fig 5. Visualization of place diversity in Copenhagen. The horizontal bar graph on the right shows the value of the divergence measure (DIV), with
higher values indicating lower diversity. The heatmap in the middle indicates the relative presence or different clusters. Darker shaded cells shaded indicate
greater presence of a cluster; white cells indicate complete absence.
doi:10.1371/journal.pone.0158161.g005
How to Study the City on Instagram
PLOS ONE | DOI:10.1371/journal.pone.0158161 June 23, 2016 13 / 16
within and through urban space. Instagram, in particular, offers extraordinary opportunities to
users to showcase where they are and whom they associate with. Through its API and some of
the methods we and others have developed, the medium also offers extraordinary opportunities
for researchers interested in investigating segregation, the formation of subcultures, strategies
of distinction, and status hierarchies.
Notwithstanding these opportunities, we should mention some caveats [5861]. The lives of
Instagram users are not contained within the platform, so our access to their lives is very much
incomplete. The representations on Instagram, moreover, are highly selective. It would be mis-
taken to consider Instagram as somehow representative of the sum total of city dwellersuses
of space. We should only look at Instagram if we are interested in what we can find there: the
pictures and connections that selectively represent selected parts of the city from a selective
group of urban dwellers. Our purpose was mainly to provide a proof of conceptby develop-
ing a range of methods and demonstrating how Instagram data can shed new light on classic
issues in the study of the city. While some of our findings would most likely prove robust (e.g.,
central public parks are widely popular and very cosmopolitan), other findings are more provi-
sional. To benefit from the opportunities the data offer, it is necessary to carefully specify ques-
tions and complement Instagram with other sources of data.
Supporting Information
S1 Fig. Page Rank distributions of cluster subgraphs.
(EPS)
Author Contributions
Analyzed the data: JDB. Wrote the paper: JDB JU.
References
1. Duggan M. Mobile messaging and social media 2015. Washington, D.C.: Pew Research Center;
2015. Available: http://www.pewinternet.org/2015/08/19/mobile-messaging-and-social-media-2015/
2. McCracken H. Instagrams all-new search & explore features will change how you use Instagram. Fast
Company; 2015. Available: http://www.fastcompany.com/3047726/tech-forecast/with-new-search-
explore-features-instagram-is-changing-how-youll-use-instagram
3. Burke J, Estrin D, Hansen M, Parker A, Ramanathan N, Reddy S, et al. Participatory sensing. Proceed-
ings of the world sensor web workshop. Boulder, Colo.: Association for Computing Machinery;
2006. pp. 15.
4. Lane ND, Eisenman SB, Musolesi M, Miluzzo E, Campbell AT. Urban sensing systems: Opportunistic
or participatory? Proceedings of the 9th workshop on mobile computing systems and applications. New
York: Association for Computing Machinery; 2008. 10.1145/1411759.1411763
5. Park RE. The city: Suggestions for the investigation of human behavior in the city environment. Ameri-
can Journal of Sociology. 1915; 20: 577612.
6. Massey DS, Denton NA. American apartheid: Segregation and the making of the underclass. Cam-
bridge, Mass.: Harvard University Press; 1998.
7. OConnor A, Tilly C, Bobo LD, editors. Urban inequality: Evidence from four cities. New York: Russell
Sage Foundation; 2003.
8. Charles CZ. The dynamics of racial residential segregation. Annual Review of Sociology. 2003; 29:
167207. doi: 10.1146/annurev.soc.29.010202.100002
9. Bellah RN, Madsen R, Sullivan WM, Swidler A, Tipton SM. Habits of the heart: Individualism and com-
mitment in American life. Berkeley, Calif.: University of California Press; 1985.
10. Maffesoli M. The time of the tribes: The decline of individualism in mass society. London: Sage; 1996.
11. Hannerz U. Exploring the city. New York: Columbia University Press; 1980.
12. Robson G, Butler T. Coming to terms with London: Middle class communities in a global city. Interna-
tional Journal of Urban and Regional Research. 2001; 25: 7086. doi: 10.1111/1468-2427.00298
How to Study the City on Instagram
PLOS ONE | DOI:10.1371/journal.pone.0158161 June 23, 2016 14 / 16
13. Silver D, Clark TN, Yanez CJN. Scenes: Social context in an age of contingency. Social Forces. 2010;
88: 22932324. doi: 10.1353/sof.2010.0041
14. Molotch H. Managed integration: Dilemmas of doing good in the city. Berkeley, Calif.: University of Cal-
ifornia Press; 1972.
15. Blokland TV, van Eijk G. Do people who like diversity practice diversity in neighbourhood life? Neigh-
bourhood use and the social networks of diversity seekersin a mixed neighbourhood in the Nether-
lands. Journal of Ethnic and Migration Studies. 2010; 36: 313332. doi: 10.1080/13691830903387436
16. Desmond M. Relational ethnography. Theory and Society. 2014; 43: 547579. doi: 10.1007/s11186-
014-9232-5
17. Burawoy M. The extended case method: Four countries, four decades, four great transformations, and
one theoretical tradition. Berkeley, Calif.: University of California Press; 2009.
18. Batty M. The new science of cities. Cambridge, Mass.: MIT Press; 2003.
19. Goodchild MF. Citizens as voluntary sensors: Spatial data infrastructure in the world of web 2.0. Inter-
national Journal of Spatial Data Infrastructures. 2007; 2: 2432. Available: http://ijsdir.jrc.ec.europa.eu/
index.php/ijsdir/article/view/28
20. Bourdieu P. Photography: A middle-brow art. Palo Alto, Calif.: Stanford University Press; 1996.
21. Lofland LH. The public realm: Exploring the citys quintessential social territory. New York: Aldine De
Gruyter; 1998.
22. Sennett R. The fall of public man. Cambridge: Cambridge University Press; 1977.
23. Anderson E. The cosmopolitan canopy: Race and civility in everyday life. New York: W.W. Norton;
2012.
24. Talbot D. Regulating the night: Race, culture and exclusion in the making of the night-time economy.
Aldershot: Ashgate; 2007.
25. May RAB. Urban nightlife: Entertaining race, class, and culture in public space. New Brunswick, N.J.:
Rutgers University Press; 2014.
26. van Liempt I, van Aalst I, Schwanen T. Introduction: Geographies of the urban night. Urban Studies.
2015; 52: 407421. doi: 10.1177/0042098014552933
27. Hunter MA. The nightly round: Space, social capital, and urban black nightlife. City & Community. 2010;
9: 165186. doi: 10.1111/j.1540-6040.2010.01320.x
28. Hudson-Smith A, Crooks A, Gibin M, Milton R, Batty M. Neogeography and web 2.0: Concepts, tools
and applications. Journal of Location Based Services. 2009; 3: 118145. doi: 10.1080/
17489720902950366
29. Sui D, Goodchild M. The convergence of GIS and social media: Challenges for GIScience. International
Journal of Geographical Information Science. 2011; 25: 17371748. doi: 10.1080/13658816.2011.
604636
30. Kelley MJ. The emergent urban imaginaries of geosocial media. GeoJournal. 2013; 78: 181203. doi:
10.1007/s10708-011-9439-1
31. Shelton T, Poorthuis A, Graham M, Zook M. Mapping the data shadows of hurricane sandy: Uncovering
the sociospatial dimensions of big data. Geoforum. 2014; 52: 167179. doi: 10.1016/j.geoforum.
2014.01.006
32. Zheng Y. Tutorial on location-based social networks. WWW 2012. Association for Computing Machin-
ery; 2012. Available: http://research.microsoft.com/apps/pubs/default.aspx?id=163521
33. Cranshaw J, Schwartz R, Hong JI, Sadeh N. The Livehoods project: Utilizing social media to under-
stand the dynamics of a city. Sixth international AAAI conference on weblogs and social media. Dublin;
2012.
34. Frias-Martinez V, Soto V, Hohwald H, Frias-Martinez E. Characterizing urban landscapes using geolo-
cated tweets. Privacy, security, risk and trust. IEEE Computer Society; 2012. pp. 239248. 10.1109/
SocialCom-PASSAT.2012.19
35. Silva TH, Vaz de Melo POS, Almeida JM, Salles J, Loureiro AAF. Visualizing the invisible image of cit-
ies. 2012 IEEE international conference on green computing and communications. 2012. pp. 382389.
10.1109/GreenCom.2012.62
36. Silva TH, Vaz de Melo POS, Almeida JM, Salles J, Loureiro AAF. Challenges and opportunities on the
large scale study of city dynamics using participatory sensing. 2013 IEEE symposium on computers
and communications. 2013. pp. 000528000534. 10.1109/ISCC.2013.6755000
37. Silva TH, Vaz de Melo POS, Almeida JM, Salles J, Loureiro AAF. A comparison of Foursquare and
Instagram to the study of city dynamics and urban social behavior. Proceedings of the 2nd ACM
SIGKDD international workshop on urban computing. New York: Association for Computing Machinery;
2013. pp. 18. 10.1145/2505821.2505836
How to Study the City on Instagram
PLOS ONE | DOI:10.1371/journal.pone.0158161 June 23, 2016 15 / 16
38. Silva TH, Vaz de Melo POS, Almeida JM, Salles J, Loureiro AAF. A picture of Instagram is worth more
than a thousand words: Workload characterization and application. Proceedings of the IEEE interna-
tional conference on distributed computing in sensor systems. Cambridge, Mass.: IEEE Computer
Society; 2013. pp. 123132. 10.1109/DCOSS.2013.59
39. Hochman N, Schwartz R. Visualizing Instagram: Tracing cultural visual rhythms. Sixth international
AAAI conference on weblogs and social media. Association for the Advancement of Artificial Intelli-
gence; 2012.
40. Schwartz R, Hochman N. The social media life of public spaces: Reading places through the lens of
geo-tagged data. Wilkena R, Goggin G, editors. Locative media. New York: Routledge; 2014;
41. Hochman N, Manovich L. Zooming into an Instagram city: Reading the local through social media. First
Monday. 2013; 18. doi: 10.5210/fm.v18i7.4711
42. Boy JD. Kijkeens: A tool for researchers. 2015. 10.5281/zenodo.34500
43. Mitchell JC, editor. Social networks in urban situations. Manchester: Manchester University Press;
1969.
44. Fischer CS. To dwell among friends: Personal networks in town and city. Chicago: Chicago University
Press; 1982.
45. Boyd D. Its complicated: The social lives of networked teens. New Haven, Conn.: Yale University
Press; 2014.
46. Blondel VD, Guillaume J-L, Lambiotte R, Lefebvre E. Fast unfolding of communities in large networks.
Journal of Statistical Mechanics: Theory and Experiment. 2008; 2008: P10008.
47. Csardi G, Nepusz T. The igraph software package for complex network research. InterJournal. 2006;
Complex Systems: 1695. Available: http://igraph.org
48. Traag VA. Louvain-igraph: Implementation of the Louvain algorithm for various methods for use with
igraph in Python. 2015. 10.5281/zenodo.35117
49. Salton G, Buckley C. Term-weighting approaches in automatic text retrieval. Information Processing &
Management. 1988; 24: 513523. doi: 10.1016/0306-4573(88)90021-0
50. Uitermark J, Traag VA, Bruggeman J. Dissecting discursive contention: A relational analysis of the
Dutch debate on minority integration, 19902006. Social Networks. Forthcoming 2016. Available:
https://www.academia.edu/25777048
51. Watts DJ, Strogatz SH. Collective dynamics of small-worldnetworks. Nature. 1998; 393: 440442.
doi: 10.1038/30918 PMID: 9623998
52. Alstott J, Bullmore E, Plenz D. Powerlaw: A Python package for analysis of heavy-tailed distributions.
PLoS ONE. 2014; 9: 111. doi: 10.1371/journal.pone.0085777
53. Brin S, Page L. The anatomy of a large-scale hypertextual web search engine. Computer Networks and
ISDN Systems. 1998; 30: 107117.
54. Duncan OD, Duncan B. A methodological analysis of segregation indices. American Sociological
Review. 1955; 20: 210217.
55. Massey DS, Denton NA. The dimensions of residential segregation. Social Forces. 1988; 67: 281315.
56. Roberto E. Measuring inequality and segregation [Internet]. 2015. Available: http://arxiv.org/abs/1508.
01167
57. Abou-Moustafa KT. Divergence measures as diversity indices [Internet]. 2014. Available: http://arxiv.
org/abs/1408.2863
58. Boyd D, Crawford K. Critical questions for big data: Provocations for a cultural, technological, and
scholarly phenomenon. Information, Communication & Society. 2012; 15: 662679. doi: 10.1080/
1369118x.2012.678878
59. Manovich L. Trending: The promises and challenges of big social data. In: Gold MK, editor. Debates in
the digital humanities. Minneapolis, Minn.: University of Minnesota Press; 2012. pp. 460475.
60. Tufekci Z. Big questions for social media big data: Representativeness, validity, and other methodologi-
cal pitfalls. Eighth international conference on weblogs and social media. Ann Arbor, Mich.: AAAI;
2014. Available: http://www.aaai.org/ocs/index.php/ICWSM/ICWSM14/paper/view/8062
61. Kitchin R. The real-time city? Big data and smart urbanism. GeoJournal. 2014; 79: 114. doi: 10.1007/
s10708-013-9516-8
How to Study the City on Instagram
PLOS ONE | DOI:10.1371/journal.pone.0158161 June 23, 2016 16 / 16

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