How to Study the City on Instagram

Article (PDF Available)inPLoS ONE 11(6):e0158161 · June 2016with 452 Reads
DOI: 10.1371/journal.pone.0158161
Abstract
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.
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
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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
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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
How to Study the City on Instagram
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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.
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    O artigo teve como objetivo mapear os conteúdos em redes sociais sobre a nutrição infantil e BLW, visando compreender como se organizam enquanto comunidades de aprendizagem. A metodologia utilizada foi do tipo explicativa de abordagem mista. Na coleta de dados, foram analisadas 6 comunidades do Facebook e 4 perfis do Instagram com representatividade na temática nutrição infantil. Resultados: O quantitativo de postagens por mídia nas 10 comunidades ocorreu da seguinte forma: Texto 6.543 postagens (63%); Imagem 3.250 postagens (31%); Vídeo 647 postagens (6%). O Instagram apresentou mais postagens de imagens do que textos; ao contrário das comunidades de aprendizagem do Facebook que produzem maior quantidade de conhecimento por meio de textos narrativos. Notou-se que os perfis do Instagram possuem número maior de seguidores em relação aos grupos do Facebook, porém a quantidade de postagens é menor, o que condiz com o estilo de utilização dessas redes sociais. O Facebook, por formar grupos, apresenta maior interatividade com os usuários, ao contrário do Instagram que apresenta uma rede social mais passiva e com a maior parte dos usuários observando e curtindo postagens. As comunidades de aprendizagem em redes sociais demonstraram que são espaços efetivos para apoio e construção coletiva de saberes com grande potencial de expansão nas áreas de educação e de saúde. Essa aprendizagem se dá, principalmente. por meio de trocas colaborativas e construção interativa de conhecimento.AbstractThis article aimed to map the contents on communities in social networks about the children’s nutrition and BLW, aiming to understand how are organized while communities of learning. The methodology used was of the explanatory type and mixed approach. The data collection of the research was carried out online and 6 communities of Facebook and 4 Instagram profiles were selected with great representativeness in the theme of children’s nutrition and BLW. Results: The number of postings per media in the 10 communities occurred as follows: Text 6.543 posts (63%); Image 3,250 posts (31%); Video 647 posts (6%). Instagram presented more pictures posts than texts unlike Facebook's learning communities, that produce more knowledge through narrative texts. It was noted that Instagram profiles have more followers in relation to Facebook groups, but the number of posts is lower, which is consistent with the style of use of these social networks. The Facebook for to form groups has more interactivity with users, opposite of Instagram that presents a more passive social network and with the majority of users observing and enjoying postings. In relation to the objective of the research that was to map the content present in social networks on child nutrition and BLW. The learning communities in social networks have proved to be effective spaces for support and collective construction of knowledge with great potential for expansion in the areas of education and health.Keywords: Social networks. Children’s nutrition. Virtual learning communities.ResumenEl artículo tuvo como objetivo mapear los contenidos en redes sociales sobre la nutrición infantil y BLW, buscando comprender cómo se organizan como comunidades de aprendizaje. La metodología utilizada fue del tipo explicativo de abordaje mixto. En la recolección de datos se analizaron 6 comunidades de Facebook y 4 perfiles del Instagram con representatividad en la temática nutrición infantil. Resultados: El cuantitativo de las entradas por medios en las 10 comunidades se produjo de la siguiente manera: Texto 6.543 postes (63%); Figura 3.250 entradas (31%); Vídeo 647 entradas (6%). El Instagram presentó más mensajes de imágenes que textos a diferencia de las comunidades de aprendizaje de Facebook, que producen mayor cantidad de conocimiento a través de textos narrativos. Se ha notado que los perfiles del Instagram tienen un número mayor de seguidores en relación a los grupos de Facebook, pero la cantidad de entradas es menor, lo que concuerda con el estilo de utilización de esas redes sociales. Facebook por formar grupos presenta mayor interactividad con los usuarios, a diferencia de Instagram que presenta una red social más pasiva y con la mayoría de los usuarios observando y disfrutando de los posts. Las comunidades de aprendizaje en redes sociales demostraron ser espacios efectivos para apoyo y construcción colectiva de saberes con gran potencial de expansión en el área de educación y de salud. Este aprendizaje se da principalmente a través de intercambios colaborativos y construcción interactiva de conocimiento.Palabras clave: Redes sociales. Nutrición infantil. Comunidades virtuales de aprendizaje.
  • Article
    Developments in ICT and the massive growth in social media usage have increased the availability of data on travel behaviour. This brings an array of new possibilities to improve destination management through Data-driven decisions. This data, however, needs to be analysed and interpreted in order to be beneficial for destination management. Different kinds of methodologies and data have already been applied to analyse spatial behaviour of tourists between and within destinations. The novelty of our paper in this sense that we apply a relational approach by conducting a network analysis methodology on a readily available big data source: user generated content (UGC) from TripAdvisor. The collected data from the city of Antwerp, Belgium shows how locals, Belgians, Europeans and non-Europeans have distinct review patterns, but also shows recurring behavioural patterns. By comparing the relational constellation of the review network to the spatial distribution of central and peripheral attractions, hotels and restaurants, we discuss the added value of social network analysis on UGC for translating (big) data into applicable information and knowledge. The results show a dominant position of a limited number of clustered attractions in the historic city centre, and shows how geographical proximity and relational proximity are interrelated for international reviewers but less for domestic reviewers. This finding is translated into a set of recommendations for policy makers and destination managers trying to accomplish a better distribution of tourists over the entire destination.
  • Article
    Over the past decade, there has been an unprecedented boom in the application of big data derived from location‐aware technology in GIScience. However, reflections on data quality lead to concerns about inaccurate or even erroneous spatial analysis results. This article contributes a meta‐analysis of big data‐enabled social sensing in spatial analysis. Big data‐enabled research has substantially enhanced our understanding of human population distribution and dynamics, land use, travel behavior, movement patterns, and spatial social networks from city to country to global levels. However, the effectiveness of big data‐enabled social sensing can be undermined by five critical issues related to research design: data volume, spatial coverage, locational information quality, and representativeness across socioeconomic and demographic groups. We suggest a framework for addressing the inherent bias in spatial big data at multiple stages of the research lifecycle and applying them to appropriate topics and scales of applications.
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    Recent developments in the field of machine learning offer new ways of modelling complex socio-spatial processes, allowing us to make predictions about how and where they might manifest in the future. Drawing on earlier empirical and theoretical attempts to understand gentrification and urban change, this paper shows it is possible to analyse existing patterns and processes of neighbourhood change to identify areas likely to experience change in the future. This is evidenced through an analysis of socio-economic transition in London neighbourhoods (based on 2001 and 2011 Census variables) which is used to predict those areas most likely to demonstrate ‘uplift’ or ‘decline’ by 2021. The paper concludes with a discussion of the implications of such modelling for the understanding of gentrification processes, noting that if qualitative work on gentrification and neighbourhood change is to offer more than a rigorous post-mortem then intensive, qualitative case studies must be confronted with – and complemented by – predictions stemming from other, more extensive approaches. As a demonstration of the capabilities of machine learning, this paper underlines the continuing value of quantitative approaches in understanding complex urban processes such as gentrification. Please cite published article in all cases; however, a pre-peer review copy is available in our IR: https://kclpure.kcl.ac.uk/portal/files/97604699/Author_s_final_version.pdf
  • Book
    Despite today's booming economy, secure work and upward mobility remain out of reach for many central-city residents. Urban Inequality presents an authoritative new look at the racial and economic divisions that continue to beset our nation's cities. Drawing upon a landmark survey of employers and households in four U.S. metropolises, Atlanta, Boston, Detroit, and Los Angeles, the study links both sides of the labor market, inquiring into the job requirements and hiring procedures of employers, as well as the skills, housing situation, and job search strategies of workers. Using this wealth of evidence, the authors discuss the merits of rival explanations of urban inequality. Do racial minorities lack the skills and education demanded by employers in today's global economy? Have the jobs best matched to the skills of inner-city workers moved to outlying suburbs? Or is inequality the result of racial discrimination in hiring, pay, and housing? Each of these explanations may provide part of the story, and the authors shed new light on the links between labor market disadvantage, residential segregation, and exclusionary racial attitudes. In each of the four cities, old industries have declined and new commercial centers have sprung up outside the traditional city limits, while new immigrant groups have entered all levels of the labor market. Despite these transformations, longstanding hostilities and lines of segregation between racial and ethnic communities are still apparent in each city. This book reveals how the disadvantaged position of many minority workers is compounded by racial antipathies and stereotypes that count against them in their search for housing and jobs. Until now, there has been little agreement on the sources of urban disadvantage and no convincing way of adjudicating between rival theories. Urban Inequality aims to advance our understanding of the causes of urban inequality as a first step toward ensuring that the nation's cities can prosper in the future without leaving their minority residents further behind.
  • Code
    Kijkeens is a tool for researchers who want to collect data from Twitter and/or Instagram and save it for further analysis. It is designed to run as a background job on a server, continuously polling the respective platform's API for new posts of interest. Posts are either stored in a database or handed off to a queue for delayed processing. The latest version of the tool can be found in this repository: https://bitbucket.org/jboy1/kijkeens.
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    Full-text available
    This paper elaborates a relational approach to examine discursive contention. We develop a network method to identify groups forming through contentious interactions as well as relational measures of polarization, leadership, solidarity and various aspects of discursive power. The paper analyzes how an assimilationist movement confronted its adversaries in the Dutch debate on minority integration. Over different periods in the debate, we find a recurrent pattern: a small yet cohesive group of challengers with strong discursive leaders forces their framing of integration issues upon other participants. We suggest that the pattern found in our study may exemplify a more universal network pattern behind discursive contention.
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    The term Big Data is applied to data sets whose size is beyond the ability of commonly used software tools to capture, manage, and process the data within a tolerable elapsed time. Big data sizes are a constantly moving target, currently ranging from a few dozen terabytes to many petabytes of data in a single data set. This chapter addresses some of the theoretical and practical issues raised by the possibility of using massive amounts of social and cultural data in the humanities and social sciences. These observations are based on the author’s own experience working since 2007 with large cultural data sets at the Software Studies Initiative at the University of California, San Diego. The issues discussed include the differences between ‘deep data’ about a few people and ‘surface data’ about many people; getting access to transactional data; and the new “data analysis divide” between data experts and researchers without training in computer science.
  • Article
    In this remarkable collection of essays, Michael Burawoy develops the extended case method by connecting his own experiences among workers of the world to the great transformations of the twentieth century-the rise and fall of the Soviet Union and its satellites, the reconstruction of U.S. capitalism, and the African transition to post-colonialism in Zambia. Burawoy's odyssey began in 1968 in the Zambian copper mines and proceeded to Chicago's South Side, where he worked as a machine operator and enjoyed a unique perspective on the stability of advanced capitalism. In the 1980s, this perspective was deepened by contrast with his work in diverse Hungarian factories. Surprised by the collapse of socialism in Hungary in 1989, he journeyed in 1991 to the Soviet Union, which by the end of the year had unexpectedly dissolved. He then spent the next decade studying how the working class survived the catastrophic collapse of the Soviet economy. These essays, presented with a perspective that has benefited from time and rich experience, offer ethnographers a theory and a method for developing novel understandings of epochal change.
  • Article
    The era of Big Data has begun. Computer scientists, physicists, economists, mathematicians, political scientists, bio-informaticists, sociologists, and other scholars are clamouring for access to the massive quantities of information produced by and about people, things, and their interactions. Significant questions emerge. Will large-scale search data help us create better tools, services, and public goods? Or will it usher in a new wave of privacy incursions and invasive marketing? Will data analytics help us understand online communities and political movements? Or will it be used to track protesters and suppress speech? Will it transform how we study human communication and culture, or narrow the palette of research options and alter what 'research' means? Given the rise of Big Data as a socio-technical phenomenon, we argue that it is necessary to critically interrogate its assumptions and biases. In this article, we offer six provocations to spark conversations about the issues of Big Data: a cultural, technological, and scholarly phenomenon that rests on the interplay of technology, analysis, and mythology that provokes extensive utopian and dystopian rhetoric.
  • Article
    The Earth’s surface is currently occupied by more than six billion humans. Each human being begins acquiring geographic knowledge at an early age, and by adulthood has constructed elaborate mental understanding of the areas where he or she lives and works, as well as of areas that may have been visited or learned about. Such knowledge includes placenames, topographic features, and transport networks – indeed many of the themes that are so difficult to acquire by automated means. The knowledge will have been acquired through up to five functioning senses, augmented by books, magazines, television, and the Internet. Indeed, one might think of humanity as a large collection of intelligent,mobile sensors, equipped with abilities to interpret and integrate that range from the rudimentary in the case of young children to the highly developed skills of field scientists. These abilities can be augmented with devices that collect other geographic information, from cellphones enabled with GPS, vehicles that track position, digital cameras, or sensors that monitor atmospheric pollution and are carried on the body. Specialists may be trained to observe particular types of geographic information, as for example when surveyors collect information on position, maintenance workers for a utility company collect information on the condition of distributed assets, or soldiers in the field collect information on artillery damage or the enemy’s current positions. In summary, the six billion humans constantly moving about the planet collectively possess an incredibly rich store of knowledge about the surface of the Earth and its properties.