Research���� Paper

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Vol.11,DBSJ Journal,
Journal,Vol.
11,No.2
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October2012
2012

Looking into Socio-cognitive
Relations between Urban Areas
based on Crowd Movements
Monitoring with Twitter
Shoko WAKAMIYAj
Kazutoshi SUMIYAk

Ryong LEEi

Due to the proliferation of location-based information
services, there is abundant urban information whichmakes us difficult to catch up with the characteristics anddynamics of our living space. However, nowadays, crowd
lifelogs shared over social network sites are attracting agreat deal of attention as a novel source to search for local
information from the massive voices and lifelogs of crowds.
In this regard, we can further look into urban images
representing how we recognize a city in mind through thedirect massive crowd experiences. In this work, we
explore crowd-experienced local information overlocation-based social network sites to derive much better
understandable and useful urban images. In detail, we
propose a method to generate a socio-cognitive map wherecharacteristic urban clusters are projected based on
cognitive distance between urban areas. Specifically, inorder to measure cognitive distances between urban
clusters and examine their influential strengths, weobserve crowd·s movements over Twitter. Finally, we show
an experimental result of generating a socio-cognitive
map illustrating crowd-sourced cognitive relations
between urban clusters in Kinki area, Japan.

1. Introduction

Recently, due to the widespread of location-based
information services, the explosive growth of local
information like maps, geographical statistics and local
web pages virtually enables us to search for whatever wewant for our daily decision makings. However, it isincreasingly becoming difficult more and more to explore
useful information from the overflowing local information.
In order to support user·s urban lives with local
information, we need to extract the essence of the complex
and dynamic nature of urban area. In many respects, weneed much intuitive and succinct maps which are
well-arranged to describe the overall characteristics andjStudent Member. Graduate School of Human Scienceand Environment, University of Hyogo
ne11n002@stshse.u-hyogo.ac.jpiRegular Member. Graduate School of Human Scienceand Environment, University of Hyogo, NICT
lee.ryong@gmail.com
kRegular Member. Graduate School of Human Scienceand Environment, University of Hyogo
sumiya@shse.u-hyogo.ac.jp

dynamics. Therefore, it is critical to generate a kind of
cognitive map enabling us to easily take the image of ourliving space.
In this work, we challenge to explore crowd-experienced
local information over social network sites such as
Twitter1 and Foursquare2 to extract urban images basedon crowd experiences in the real space. Indeed, so far, inorder to grasp how people live in an urban space, person
trip (PT) survey3 has often been conducted. Especially, inJapan, this kind of survey has been made every 10 years
or more under the jurisdiction of the Ministry of Land,
Infrastructure, Transport and Tourism. Although we canobtain a snapshot of people·s daily trip in an urban areaby this kind of survey, it would not be enough to examine
dynamic social nature of urban space because practically
impossible to consecutively capture our daily lives.
In this paper, for the purpose of exploring crowd·scommon cognition to an urban area, we generate asocio-cognitive map using crowd·s movements observed
over Twitter. Especially, as for crowd·s common cognition,
we focus on cognitive distances between urban areas. On
the basis of crowd·s movements with spatio-temporallyindexed tweets, we measure cognitive distances between
urban clusters and reallocating the urban clusters basedon the relative closeness by means of Multi-dimensional
Scaling (MDS). Furthermore, we compute their influential
strengths for emphasizing the urban clusters eventually
by drawing a cognitive map with Weighted Voronoi
Diagram. In particular, we summarize our contributionsof this work as follows: exploiting crowd-experienced local information frommassive crowd lifelogs over location-based social
network sites, presenting cognitive distances between urban clusters
for deriving common urban cognition, and representing crowd-experienced urban image as asocio-cognitive map.
The remainder of this paper is organized as follows.
Section 2 addresses our research model for generating asocio-cognitive map with social network sites anddescribes related work. Section 3 details a method forsocio-cognitive map generation exploiting crowd·s lifelogs.
Section 4 shows an experimental result with geo-tagged
tweets over Twitter. Section 5 describes the conclusionand future work.

2. Exploiting
Information

Crowd-experienced

Local

In this section, we introduce our approach to explore
crowd·s urban cognition by exploiting crowd-experienced
local information publicly shared over social network sites.
Then, we briefly describe some related work in terms of
crowd-experienced local information.

2.1 Exploring Crowd·s Cognition for Urban Area

In this work, we attempt to extract crowd·s common

Twitter: http://twitter.com/
Foursquare: https://foursquare.com/
3 Person Trip Survey (PT) Japan, C.C.:
http://www.mlit.go.jp/crd/tosiko/pt/map_e.html
1
2

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Research���� Paper

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Vol.11,DBSJ Journal,
Journal,Vol.
11,No.2
No.2
October
October2012
2012

Fig. 2. Crowd·s movements in an urban area
observable via Twitter

Fig. 1. Research model: Distilling crowd·s cognition tourban areas through location-based social network sites
images of an urban area with crowd-sourced lifelogs oversocial network sites and eventually represent them as asocio-cognitive map. For this purpose, we first propose amodel for generating a socio-cognitive map withlocation-based social networks sites as shown in Fig. 1. As
illustrated in Fig. 1 (a), nowadays, thanks to theproliferation of social network sites and the widespread
smartphone technology, crowd-soured lifelogs reflecting
their activities, minds, etc. in urban areas are available inan unprecedented large scale. Next, we extract crowd·s
movements as shown in Fig. 2 (b) from the collected
crowd·s urban lifelogs as depicted in Fig. 2 (a) and explore
distance-based relations between urban areas based on
the crowd·s movements as illustrated in Fig. 1 (b). Finally,in order to represent images of urban areas hidden incrowd·s minds, we generate a socio-cognitive map wherelandmark clusters are re-mapped based on the observed
relations as shown in Fig. 1 (c).

2.2 Related WorkOne of noteworthy characteristics of crowd lifelogs as
local information is the capability to monitor most
up-to-date social situation by massive crowd·s experiencesin the real world. Some research work by Sakaki et al [1]and Lee et al. [2] focused on detecting geo-social events or
phenomena from the crowd·s messages on Twitter.
Furthermore, the other critical characteristic is that

crowd lifelogs are regarded as the accumulation of crowd·s
experiences and knowledge in urban space. Thus,
exploring crowd-sourced experiences effectively is one of
key topics of recent research studies of location-based
services and social network sites. McArdle et al. [3]investigated the usefulness of digital footprints ofindividual movement for calibrating human mobility
models within an urban traffic micro-simulation
framework. Hsieh et al. [4] presented a method torecommend a time-sensitive trip route by squeezing a lotof knowledge from check-in data over location-based
services. In our previous work [5], we proposed a method
to characterize urban area by exploring latent patterns ofcrowd behavior using Twitter.
This paper is significantly different from these studies
because we aim to measure urban relations focusing on
crowd·s movements between urban clusters and generate
a socio-cognitive map based on the relations.

3. Integrating Crowd-experienced Local
Information with a Map

In this section, we describe a detailed procedure to
generate a socio-cognitive map with crowd·s movements
through Twitter.

3.1 Extracting Crowd·s Movements from Social
Network Sites

In order to extract crowd-experienced local informationfrom crowd lifelogs over social network sites, we utilize
Twitter with which we collect geo-tagged tweets as shownin Fig. 3 (1). However, it takes a considerable amount oftime and efforts to acquire a significant number ofgeo-tagged tweets due to practical limitation of an open
API4 provided by Twitter. Therefore, in order to efficiently
Twitter Open API: http://apiwiki.twitter.com/TwitterSearch-API -Method%3A-search42― 20 ―

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Fig. 4. Urban clusters based on geographic distribution ofgeo-tagged tweets

Fig. 3. Process of socio-cognitive map generation
monitor crowd behavior for a specific region of any size,
we utilized a geographic tweets gathering system which
was developed in our previous work [2].
Next, we are interested in crowd·s movements which canbe extracted with primary metadata of geo-tagged tweet;
user ID, timestamp, and location information alreadyidentified as coordinates by means of geo-coding which
can translate place names into the corresponding exact
locations with Google Map·s geo-coding service 5 .
Furthermore, in order to effectively search moving
segments, we arrange the tweets based on user ID andsort each user·s tweets in the order of the timestamp.
Here, if the successive geo-tagged tweets were written at
different locational points, we consider this means a user·s
moving segment depicted by a blue line in Fig. 2 (b).
Consequently, we could observe crowd·s movements using
their lifelogs on Twitter.

3.2 Looking for Crowded Urban Clusters

In general, a map consists of some important landmarks.
Similarly, we should find representative geographicalobjects or areas. In this paper, we define a populated
space as an urban cluster and locate them based on thegeographical density of geo-tagged tweets as shown in Fig.
3 (2). However, because there are too many tweet
occurrence locations at which one or more persons were
published geo-tagged tweets, it would require unbearable
Google Geocoding API: http://code.google.com/intl/ja/
apis/maps/documentation/geocoding/

5

computational efforts to cluster and finally find out urban
clusters. Hence, we need to reduce the data size in muchsmaller and computable size with less loss of essential
quality of the original distribution. For this, we adopted
NNClean (Nearest Neighbor Clutter Removal) algorithm[6] which can filter high-frequency locational points. Then,in order to locate urban clusters with the ideally reduced
dataset, we applied DBSCAN (Density-Based Spatial
Clustering of Applications with Noise) algorithm [7]. Here,
we defined the original variant as a minimum number ofpoints MinPts within a specified radius radius. In Fig. 4,
we show urban clusters located by empirically setting
MinPts and radius to 6 tweets and 650m, respectively.
Eventually, we found 44 urban clusters (ܿଵ ~ܿସସ ) which are
represented by convex-hull based boundary polygons [8].
In this method, we are easily and successfully able toachieve simple findings of significant urban clusters only
according to geo-tagged tweets.

3.3 Examining Cognitive Distances between
Urban Clusters

Then, we need to define and measure cognitive distances
between urban clusters as shown in Fig. 3 (3). We assume
that if the distance between urban clusters is somewhat
long in the real space and lots of crowds are moving in andout between these clusters, the crowds may cognitively
construct a map in mind, where these clusters become
closer than the actual physical distance. Therefore, we
consider that urban clusters which are tightly associatedwith each other in terms of crowd experiences should be
projected close on a socio-cognitive map. On the basis of
this assumption, we made a formula to calculate cognitive
distances between two urban clusters as follows:‫ݐݏ݅ܦ݃݋ܥ‬൫ܿ௜ ǡ ܿ௝ ൯ ൌ ‫ݓ‬ଵ ή ‫ݐݏ݅ܦܿݑܧ‬൫ܿ௜ ǡ ܿ௝ ൯ ൅ ‫ݓ‬ଶ ή ‫ݐݏ݅ܦ݌ݔܧ‬൫ܿ௜ ǡ ܿ௝ ൯(1)ሺ‫ݓ‬ଵ ൅ ‫ݓ‬ଶ ൌ ͳǤͲǡ ‫ݓ‬ଵ ǡ ‫ݓ‬ଶ  ൒ Ͳሻ
‫ݐݏ݅ܦ݌ݔܧ‬൫ܿ௜ ǡ ܿ௝ ൯ ൌଵ͓ெ௢௩ௌ௘௚൫௖೔ ǡ௖ೕ ൯ାଵ(2)where the function C ‫ݐݏ݅ܦ݃݋‬൫ܿ௜ ǡ ܿ௝ ൯ calculates cognitive
distances between two distinct urban clusters ܿ௜ and ܿ௝ .
Specifically, cognitive distance is calculated by physicaldistance and experiential distance between the urban
clusters. The Euclidean distance calculates physicaldistance between the urban clusters by the function

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Fig. 7. Geographic influential strengths of urban clusters

Projecting Urban
3.4
Cognitive Distances

Clusters

based

on

Next, we need to project urban clusters based on thecalculated cognitive distances as shown in Fig. 3 (4). In
order to represent intuitive distance between urban
clusters, we need to appropriately allocate the clusters ona cognitive map.
In this paper, we decided to apply Multi-Dimensional
Scaling (MDS) [9] which can allocate given dataset of
multi-dimensional space by considering similarities (ordissimilarities) in the dataset. Therefore, it can allocate
data in the neighborhood if the similarity between thedata is high, vice versa. Specifically, this algorithm starts
with a matrix of similarities between multiple objects,
then assigns a location to each item in N-dimensional
space, where N is specified a priori as shown in Fig. 6. In
the experiment described in Section 4, we mapped urban
clusters in two-dimensional space by applying MDS.
We also labeled names of generated clusters by a Reverse
Geocoding service 6 which translates a given location
coordinate into a textual place name. For each cluster, we
obtain its representative place name by using this service.

Fig. 5. Generating a cognitive distance matrix

3.5 Emphasizing Urban Clusters based on
Influential Strengths

Fig. 6. Projecting urban clusters based on cognitive
distances by Multi-Dimensional Scaling (MDS)‫ݐݏ݅ܦܿݑܧ‬൫ܿ௜ ǡ ܿ௝ ൯Ǥ The experiential distance is calculated by
the function ‫ݐݏ݅ܦ݌ݔܧ‬൫ܿ௜ ǡ ܿ௝ ൯ which is based on the total
number of crowd moving segments between the urban
clusters and calculate by the function ͓‫݃݁ܵݒ݋ܯ‬൫ܿ௜ ǡ ܿ௝ ൯.
‫ ݐݏ݅ܦܿݑܧ‬and ‫ ݐݏ݅ܦ݌ݔܧ‬are normalized and weighted by
two values ‫ݓ‬ଵ ‫ݓ‬ଶ, respectively. We calculate cognitive
distances between all pairs of urban clusters located in
the urban area. As a result, we construct matrixes interms of physical distance, experiential distance andcognitive distance, respectively as illustrated in Fig. 5.
These weighting values can be adjusted by user·s
purposes for generating a cognitive map. For example, if auser wants to generate a cognitive map by focusing on thecrowd·s movements, he/she can set a high weight to ‫ݓ‬ଶ ǤIn the experiment in Section 4, we will show a cognitive
map generated in extreme cases weighting the maximum
value on physical distance and experiential distancerespectively.

We could generate a tentative socio-cognitive map by theabove steps. However, its representation would be far
from a map apparently, because it just represents
point-based distribution of urban clusters. Therefore, inorder to show a socio-cognitive map in an intuitive way,
we decided to emphasize featured urban clusters on thesocio-cognitive map by focusing on crowd·s movements.
Thus, we measure an influential strength in an urban
area by examining crowd·s movements involving theurban cluster as illustrated in Fig. 3 (5). We define an
influential strength involving an urban cluster ܿ௜ by theformula as follows.
‫݁ܿ݊݁ݑ݈݂݊ܫ‬ሺܿ௜ ሻ ൌ ͓‫݃݁ܵݒ݋ܯ‬ሺܿ௜ ሻ
Fig. 7 shows two urban clusters in Osaka and in Kyoto in
our finding. Interestingly, effective ranges of a cluster
located in Osaka in Fig. 7 (a) covers the most major urban
clusters in our experimental area of Kinki area, Japan.
Then, we emphasize urban clusters based on the
Google Reverse Geocoding API:
https://developers.google.com/maps/documentation/geocod
ing/#ReverseGeocoding

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influential strengths of urban clusters as shown in Fig. 3(6). For intuitive representation as a map, if an urban
cluster has strong influential strength in an urban area,
the cluster should be represented largely. For this, weapplied a space partitioning method, Weighted Voronoi
Diagram [10] to the above result. Here, Voronoi Diagram
is a well-known geometric representation for partitioninga space drawing a line between two points keeping withthe same distance. However, this simple method cannot
show influential strengths of urban clusters. Thus, we
decided to utilize Weighted Voronoi Diagram which canhave a weight extending its size. In our method, weapplied an influential strength as a weighting value oneach partitioned area by Voronoi Diagram.

4. Experiment

We describe the experiment for extracting crowd·s
cognition for an urban area and visualizing it as asocio-cognitive map as described in Section 3. We present
our dataset collected from Twitter in Section 4.1 and show
socio-cognitive maps generated with the dataset in
Section 4.2.

4.1 Dataset

In this experiment, we were able to collect 157,097
geo-tagged tweets by 25,674 distinct users in a weekday(April 23rd, 2012) in a Kinki area including Kobe, Osaka,
Kyoto and Nara in Japan (longitude range = [134.122433,
136.337186], latitude range = [33.810804, 36.785050])
from Twitter using the geographical tweet gatheringsystem developed in our previous work [2]. By using thesegeo-tagged tweets distributed as shown in Fig. 2 (a), we
locate urban clusters as shown in Fig. 4 and extract
crowd·s moving segments as illustrated in Fig. 2 (b).

4.2 Looking into Crowd-experienced Urban
Relations

In order to generate a socio-cognitive map, we examined
crowd-experienced proximity of urban clusters by
calculating cognitive distances between urban clusters.
Especially, in order to observe how geographicalproximities between urban clusters are sophisticated bycrowd·s cognition, we defined cognitive distances byadjusting weighting values on physical distance andexperiential distance. As a result, Fig. 8 show two
different socio-cognitive maps which projected urban
clusters based on cognitive distances by means of MDS. In
these maps, texts represent labels of urban clusters.
Specifically, Fig. 8 (a) present a socio-cognitive map which
was generated by projecting urban clusters based on
cognitive distance with the maximum weighting value (=1.0) on physical distance. Specifically, on the figure, thedistance between KobeChuoWard ( ଻ ) and OsakaChuo
Ward ( ଶ଴ ) is nearly similar with the distance between
KobeChuoWard ( ଻ ) and KyotoNakagyoWard ( ଵସ ). Not
surprisingly, these relative distances are the same withgeographical relationship. In contrast, in case of Fig. 8 (b),the socio-cognitive map was generated based on cognitive
distance with the maximum weighting value (= 1.0) on
experiential distance. As a result, OsakaChuoWard ( ଶ଴ ) is
projected far from KobeChuoWard ( ଻ ) than Kyoto
NakagyoWard ( ଵସ ). Thus, we could successfully extract

Fig. 8. Socio-cognitive maps based on cognitive distance
crowd·s cognition for the urban area which is often
different from conventional local information.
However, the visual representation of MDS-basedsocio-cognitive map is hard to be regarded as a map.
Therefore, we decided to emphasize urban clusters bypartitioning the generated map for emphasizing eachcluster·s influence more effectively and intuitively.
Specifically, Fig. 9 shows a socio-cognitive map visualized
top-10 urban clusters by allocating regions to themaccording to their influential strengths. Interestingly,
from this map, we can know which urban clusters are
significant in crowd·s cognition. For example, Kobe
ChuoWard ( ଻ ) is closer to KyotoNakagyoWard ( ଵସ ) thanOsakaChuoWard ( ଶ଴ ) which is different from the real
space. In addition, we can grasp which urban cluster plays

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Fig.9. Weighted Voronoi-based socio-cognitive map
the role of hub in this urban area. For example, regions ofclusters in OsakaChuoWard ( ଶ଴ ) covered a large space on
this map. In fact, some other clusters located in Osakasuch as OsakaHigashiyodogawaWard ( ଶଷ ), Osaka
SumiyoshiWard ( ଷସ ), OsakaYodogawaWard ( ଷଷ ), ToyonakaKitasakuraduka ( ଶସ ) are projected close to urban
clusters located in other prefectures like Nara
Hashimotocho ( ସ ) or KyotoMinamiWard ( ଵ଺ ). Voronoibased visualization of socio- cognitive map would be
helpful to grasp significant clusters and theirrelationships intuitively.

5. Conclusion and Future WorkIn this paper, we propose a method to draw asocio-cognitive map for exploring crowd·s cognition for an
urban area by exploiting crowd-experienced local
information over Twitter. In the experiment, we
successfully showed a definite distinction between
geographical relationships in the real space and crowd·s
cognition for the urban area.
In future work, we will conduct on comprehensive
socio-cognitive map generation by further observing
various crowd·s experiences utilizing crowd lifelogs oversocial network sites. In addition, we will study various
useful metrics of distance for supporting user·s decision
makings such as distance based on crowd·s traveling time.

[Acknowledgement]This research was supported in part by the 7th Microsoft
Research IJARC Core Project and Grant-in-Aid for JSPS
Fellows 24.9154 from the Ministry of Education, Culture,
Sports, Science, and Technology of Japan.

[References�

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microblogs. World Wide Web Special Issue on Mobile
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City-scale traffic simulation from digital footprints, In
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check-in data to recommend time-sensitive routes, In
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Urban Computing, pp. 55-62 (2012)[5] Lee, R., Wakamiya, S., Sumiya, K.: Urban area
characterization based on crowd behavioral lifelogs over
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(2012)[6] Byers, S., Raftery, A.: Nearest-neighbour clutter
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Shoko WAKAMIYA

She is a student of Graduate School of Human Scienceand Environment, University of Hyogo. Her researchinterests
includesocio-geographic
mininganduser-generated contents. She is a student member ofInformation Processing Society of Japan and the
Database Society of Japan.

Ryong LEE

He is a researcher in NICT and a visiting researcher of
School of Human Science and Environment, University of
Hyogo. He received a Ph.D. and a M.S. from Graduate
School of Social Informatics, Kyoto University in 2003.
His research interests include geographic information
systems, social network analysis and web information
retrieval systems.

Kazutoshi SUMIYA

He is a Professor at School of Human Science andEnvironment, University of Hyogo. He received the Ph.D.
degree in engineering in 1998 from the Kobe University
Graduate School of Science and Technology. His researchinterests include web information systems, spatiotemporal database, broadcast computing, mobile
multimedia, and multimedia databases.

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