Detecting and Classifying Social Events
Based on Formation Process of Human Crowds
Takuro Yonezawa, Jin Nakazawa
Graduate School of Media and Governance
Keio University
Fujisawa, Japan
Email: {takuro,jin}@ht.sfc.keio.ac.jp

Tomohiro Nagata
Research Laboratories
NTT DoCoMo, Inc.
Yokosuka, Japannagatat@nttdocomo.co.jp

Abstract—This paper proposes a method to detect anddistinguish the types of social events based on formation processof crowds. We often form crowds according to social events
such as “car accidents, baseball games or music concerts”.
Since such kinds of crowds sometimes harm our social activities(e.g., bad traffic), detection or prediction of crowds and social
events are important. This paper focuses on detecting andpredicting crowds and social events using location information,such as GPS and WiFi. The aim of this paper is two folds;(1) discussing and classifying social events based on crowds’
characterization and (2) providing design and implementationof discrimination algorithm to detect social events. Throughsimulator-based implementation and evaluation, we present theeffectiveness of our approach and discuss the pros and cons of
the system.
Keywords-Social Computing, Social Context, Mobile Sensing

I. I NTRODUCTION
People in cities gather locally and instantly, forming a
crowd, for such a special event as a music concert, caraccident, or new year’s countdown. Crowds in cities often
effect our social activities negatively in terms of social
efficiency. For example, a crowd of people waiting for a
new year’s countdown, that of curious bystanders arounda car accident, or a mass demonstration would generate a
traffic jam. Detection of such human crowds are promising
ways to realize pervasive computing environment in large
scale. A car navigation system would benefit from realtime crowd detection for automatic rerouting. A city planner
would leverage crowd information of a longer period of time
to re-design the social infrastructure in the city. Therefore,
we need a sophisticated system that is aware of crowd
characteristics, e.g., assembly and disassembly of people,
number of people, and their source events.
The purpose of our research is to detect and classify
social events which are one of the triggers to generate
crowds. There are many types of social events in urban
area such as sport events, festivals or concerts, what wecall planned social events. Additionally, there are anothertypes of social events such as curious onlookers of car
accidents or street performances, what we call incidentalsocial events. We present a social events analysis method

Hideyuki Tokuda
Graduate School of Media and Governance
Keio University
Fujisawa, Japan
hxt@ht.sfc.keio.ac.jp

based on formation process of crowds on using location
information which are generated from GPS or WiFi localization technologies[1], [4]. In this paper, we firstly classify
social events into four kinds according to two indices: time
until crowds are formed, and travel distances of people
who compose the crowds. Then, we propose social events
classification methodology and implement it on a simulator
which simply models people’s movement to compose crowds
in accordance with generation of various types of social
events.
The rest of this paper is organized as follows. In thenext section, we present motivation of the research withrelated work. Then, we discuss the characterization of social events and classified social events according to theirproperties in section 3. Based on this classification, social
events distinguishing methodology is explained in section 4
with description of its implementation and experiment ona simulator. We also discuss limitation of our methodology
in section 4. Finally, we conclude the paper, and state thefuture direction of this research.
II. C ROWDS AND S OCIAL E VENTSIn this section, we firstly show our motivation to analyze relationship between crowds and social events. Then,
we present related works for further understanding of the
motivation and context of this research.
A. Motivation
Crowds, a large number of people gathered closely together, often bring on troubles in social activities. Especially,
since the population densities of some cities in Japan arevery high, crowds often emerge and negatively affect social
infrastructures. For example, in fireworks festivals in Japan,
cell-phone services are often interrupted due to a huge number of people flocking to tiny area of the festivals. In anotherexample, during the period of FIFA World CupTM 2010,
surge of fans onto the street broke in on traffic system in
Shibuya area. Therefore, detecting or predicting crowds isimportant for people/city/company to plan or change theiractivities.

In many cases, crowds are organized by people who hassame purpose (e.g., watching baseball games or shopping
at a newly opened store). We call these factors as social
events in this paper. There are many types of social events,
whose scales and/or periods differ from each other. We also
consider that the types of social events decides crowds’ property. For example, social events such as concerts or baseballgames are strictly scheduled. In those events, crowds areperhaps gently organized before events are started, andrapidly disappear after the events are finished. Meanwhile,social events such as car accidents or fire accidents are suddenly occurring ones, hence, crowds are organized rapidly
compared to those of scheduled events.
Conversely, we consider that the types of social events
can be identified by analyzing formation process of crowds.
[2] proved that participants in social events are strongly correlated with their home location. Additionally, [2] presented
that the same type of events show similar spatial distributionof participants’ origins. Our challenge, proving relationships
between social events and formation process of crowds, wasalso strongly motivated by these facts.
B. State of the Art
Analyzing characterization of crowds has a great potential to provide many types of applications such as crowd
management, public space design or emergency evacuation.
For example, [8] proposes a “Social Coordination” system
to provide limited service resources to mass users withconsidering their mass intentions. The traditional crowds orsocial events analysis approach is achieved by aggregating
data in small scale area[3] or control point such as counting
the number of tickets sold[9]. In [9], special events areclassified in the viewpoint of the events’ periods or size,
and it analyzes the effects of crowds or events on social
economics and transportation systems.
Recent progress of mobile devices which are integrated
with various localization technologies, such as GPS, WiFi
or Bluetooth, enables researchers to analyze practical humanbehavior in larger scale fields.To understand individual human mobility pattern, the trajectory of 100,000 anonymized
mobile phones’ position were tracked and analyzed for a sixmonth period[5]. Following [5], a mount of history of eachtrajectory showed that humans follow simple reproducible
patterns, despite the diversity of their travel history.
Not only individual human behavior but also collective human behavior is also an interesting subject to beunderstood. Especially, collaboration research with mobile
telecommunication companies to access to aggregate mobile
phone data has been opened up recent methodology andexperiences of analyzing urban dynamics[6], [7], [2]. In [6],social activity pattens were observed to visualize time modesof urban space use by monitoring the intensity of people
chatting and population density based on cell-phone traffic
data. As we mentioned in previous section, [2] also presents

the relationship between social events and participants interms of spatial viewpoint. The literature also proposes
prediction methodology for the question of where people
will come from for future events, and the prediction is partly
succeeded.
Analyzing mass human behavior and bringing out relationships between the behavior and social events are one of
key challenges to expand pervasive computing applications
from small spaces such as indoors or buildings to larger
spaces such as cities, countries or global fields. To encourage
the research, modeling of crowds’ behavior in various typesof social events is an important issue.
III. S OCIAL E VENTS C LASSIFICATION
Social events can be classified according to its contents,
such as, sports, cinema, music or street performance. Obviously, by referring to place information (e.g., stadium,
museum or park) or public announcement (e.g., publicschedule on the premises’ website), the types of social events
could be identified easily. However, our question is “Is
it possible to suppose the types of social events without
knowing such contents information, but only by monitoring
crowds’ context ?”. If it is possible, not only planned social
events but also incidental social events, which have no
preliminary information, could be identified. Additionally,
the estimated types can complement to recognize correcttypes of events in cooperate with contents information.
To achieve the goal, we take a notice of an observationthat social events can be classified according to formation
process of crowds which are gathered following the occurrences of events. As we mentioned before, [2] presented
relationship between humans’ home locations and types ofsocial events. Based on and extending the perspective, we
introduce the relationship between temporal-spatial propertyof crowds and social events. We consider that audiences
around street performance or customers to newly opened
supermarket are rapidly gathered from neighbor location. On
the contrary, fans of baseball games or audiences in concerts
are supposed to gather gradually from farther locations
compared with participants in the former case. Based on
this observation, we introduce the following two indications
to distinguish social events: time before a crowd fully grows
and travel distance of people who constitute the crowds. Fig.
1 shows this classification, and its examples in accordancewith above two indications. Social events of Narrow-Rapid
type in the Fig. 1 means that participants from neighborlocation organize crowds rapidly (several hours). On thecontrary, social events of Wide-Gradual type means that
crowds, which consists of participants from far locations,continues to be formed in longer time (e.g, more than several
hours or almost a day).
In order to examine the adequacy of this observation,
analysis of collective human mobility data is indispensable.
As the first step to present this, the paper proposes a social

Figure 3. Calculating travel distance: if angle of each moving vector, θ,
continues less than 180 degrees, a participant is judged to move towards aevent place.
Figure 1.
Assumption: social events are classified according to two
indications, (1) time of crowds are being grown and (2) travel distanceof participants who join the crowds

Figure 2.

An example of a field and matrix sub-fields

events identification methodology by detecting above two
indications.
IV. M ETHODOLOGY
In this section, we present social events identification
methodology. Then, we describe prototype implementation
with simulator which expresses human mobility patterns
according to our assumption. We also present initial experiment to evaluate the methodology, and discuss limitationsof it.
A. Social Events Property Recognition
To identify the types of social events, we define propertyof social events in following expression: SocialEventProperty = { Location, Number OfTheParticipants,
FormationTime, AverageTravelDistanceOfTheParticipants }Each elements is mainly calculated by following two processes: (1) crowds detection and (2) property analysis. Let us
introduce a supposition field, which is devided into matrix
sub-fields (see Fig.2), for simple explanation. We describe
each process below in order.
1) Crowds Detection: Crowds are detected by calculating
the density of population stayed in each sub-field. Just acquiring density from one snapshot cannot judge high density
means crowds are generated or not. To recognize crowds
which is related to social events, movement informationof participants in the crowds should be obtain. Thus, weanalyze temporally transition of human location, and detect
crowds based on the density of population staying at thesame position.

2) Property Analysis: Properties of social events, whichcontains formation time and travel distance of the crowds’
participants, are derived based on detection of the crowds.
Formation time is the time since the growth of crowds started
and until it finishes. Notice that whole formation time ofcrowds can be divided into 3 categories: growing status,
stable status and disappear status. These information is also
useful for various applications, however, this paper simply
focuses on using growing period to identify social events.
This is because, we thought growing period can be adopted
to various kinds of applications which need information
about precognition of social events.
The methodology of detecting second indication, average
travel distance of participants, needs discussion, because,
it is difficult to distinguish the location where participants
started to head the target place. The simplest way is tomeasure the distance between two points of each participa nt:
from a location for certain period of time to a location where
crowd is observed. However, in this way, we cannot deals
the sudden situation (e.g., when a participant is suddenly
attracted to the nearby event and change their behavior
to get closer to the event). To deal such situations, we
propose an origin recognition method by leveraging a spatial
relationship between participants’ moving vectors and thedestination (i.e., location of the events).
Firstly, each participant’s axis is rotated by following line
connected between a participant’s current location and thedestination as right angle (see left figure in Fig.3). After theparticipant moves, the angle of moving vector on rotated axis
(θ in center figure in Fig.3 ) is evaluated; if the angle is lessthan 180 degrees, it means that the participants is moving
towards a event place. When a participant becomes a part ofa crowd, this angle judgement is executed. The calculation is
executed repetitively according to the participant’s location
history until the angle becomes greater than 180 degrees
or the participant stays in a same location for a long time
(we define these point as origin location). After finishing
the analysis, travel distance of the participant is measured
as distance between the origin location and the location of
the event.
B. Initial Evaluation
Without subscribers permission, actual use of mobility
data by a mobile phone company is prohibited by JapaneseTable I
C ALCULATED SOCIAL EVENT PROPERTIES BY FOLLOWING 4 PATTERNS .
Average Travel
Distance (pixel)
Formation Time (sec)Pattern 1

Pattern 2

Pattern 3

Pattern 4

94.52
1.89

78.26
6.53

201.738.55

223.9912.06

the result of the experiment partially showed availability ofthe proposed methodology.
Figure 4. Simulator: Robots walks randomly (left), and compose crowds
according with different formation processes (right).

law. Thus, as an initial evaluation, we checked validity ofthe proposed methodology on a simulator which simulates
imaginary human behaviors by using walking robots(seeFig.4). In the simulator, robots normally moves 1 pixel as
random walking in every per 100 milliseconds. When a pointon the simulator is clicked (which means a social event
is occurred), multiple robots creates crowd at the point by
following the type of events which we assumed in section
3. Location histories of every robots are stored, and when acrowd is recognized, social event properties are calculated.
In Fig.4, the number of blue colored number placed at upper
left corner in each sub-field shows the density of population
stayed in a certain time. Some sub-fields are filled with red:the brighter of the color shows the higher density of robots
stayed.
Table I shows analyzed values of social event properties
on the simulator of 500*500 pixels field when following 4
Patterns of robots’ behaviors are executed:
P1: 20 of 100 robots near the clicking point rapidly
gather.
P2: 20 of 100 robots near the clicking point graduallygather.
P3: 20 of 100 robots which are randomly chosen
rapidly gather.
P4: 20 of 100 robots which are randomly chosen
gradually gather.
“Rapidly” in Pattern 1 & 3 means that all selected
robots move towards clicking point immediately. Meanwhile, “gradually” in Pattern 2 & 4 means that third part ofrobots start to move immediately when an events is occurred,next third part of robots start to move 1.5 seconds later, andlast third part of robots start to move 3.0 seconds later. In
this experiment, we confirmed that different 4 Patterns canbe recognized from average travel distance and formation
time, which are calculated by our proposed methodology:
average travel distance of Pattern 3 & 4 is longer than it ofPattern 1 & 2, and formation time of Pattern 2 is longer thanit of Pattern 1 (also time of Pattern 4 is longer than it ofPattern 3). Though we must take notice that the experimentis executed under particular assumption of human behavior,

C. Discussion
Our methodology has two limitations. The density ofstaying population is expressed in static matrix sub-fields.
More flexible crowd detection such as real-time clustering(e.g.,applying k-nearest neighbor algorithm ) is necessary.
Secondly, the methodology of detecting travel distanceneeds to deal more complex human behaviors. While implemented simulator simulates simple behaviors of human,
there are more diverse behaviors in real-world. For example,
in some cases human needs to take a long way round to thedestination due to road situations. Thus, the methodology
needs to be improved in considering with more realistic
environment.
V. C ONCLUSION
Based on an observation of the relationship between
crowds and social events, we classified social events interms of crowds’ two indices; time before a crowd fully
grows and average travel distance of participants to thecrowd. Additionally, we presented social events distinguishmethodology and confirmed it’s availability by using asimulator which simply simulates imaginary human mobility
patters. Further studies considering larger real datasets ofhuman behavior and concrete fields should be performed toevaluate and improve our observation and method.
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