2023 27th International Conference Information Visualisation (IV)Visualizing Congestion at Mass-Gathering Eventswith Proximity-Based Networks
Sayaka Morikoshi

Masaki Onishi

Takayuki Itoh

Ochanomizu University
Tokyo, Japan
g1820540@is.ocha.ac.jp

AIST
Tokyo, Japan
onishi-masaki@aist.go.jp

Ochanomizu University
Tokyo, Japan
itot@is.ocha.ac.jp

a result, numerous studies for analyzing human trafﬁc havebeen presented.
There have been several methods for visualizing congestion.
Alia et al. [1] represented the crowded areas with heatmaps.
Wang et al. [19] visualize an undirected graph with walkways
as edges and intersections as nodes. Each node is assigned aresistance that indicates how much energy a pedestrian mightneed to navigate through that node. However, conventional
methods do not identify the walking paths of the persons
involved in the congestion. Regarding infectious diseases, aparticular warning situation is the proximity of the pedestriangroups and stagnated ﬂow. On the other hand, there are few
methods for the visualization of the people ﬂow based on theproximity.
Based on these backgrounds, we speciﬁcally focus on the“proximity status” of individuals in our analysis. In addition,
we develop a method that visualizes the proximity and thewalking paths of proximate persons to observe characteristics
with a high risk of infection. This method constructs a single
visualization space by linking the following three components.
(Figure 1)

Abstract—Infectious diseases are typically transmitted throughclose contact with infected persons. The effective management ofovercrowding is a crucial issue for events with a large numberof attendees. Since the COVID-19 outbreak, analyzing people
ﬂow to recognize pedestrian behavior and walking patterns havebeen attracted studies. Visualizing crowded high-risk situations
for infection at large gatherings is a complex task. It requires anapproach that can effectively represent both spatial and temporalfeatures while ensuring that the visibility of walking paths is not
signiﬁcantly compromised. To address these issues, we propose
a novel approach for visualizing proximity as a network that
represents the distance relationship between pedestrians. We
developed the visualization system linking three components:
Proximity Network, the walking paths of selected pedestrians
from the network, and the temporal statistics of pedestrian trafﬁc.
Users of this system can freely select a group of pedestrians from
Proximity Network and observe the paths of the selected pedestrians. This procedure enables better visibility of walking paths
and an understanding of their spatio-temporal characteristics
because only a smaller number of paths are drawn. This paper
presents our case study of the proposed method for visualizing
pedestrian proximity using real-world people ﬂow data collected
at an event site.
Index Terms—Visualization, People ﬂow, COVID-19.

I. I NTRODUCTION

Proximity Network: A network generated by representing pedestrians as nodes and connecting pedestrians who
have proximity by edges (Figure 1(a)).
• Proximity Path: Walking paths of pedestrians speciﬁed
in Proximity Network (Users can click on nodes to selectdrawing targets) (Figure 1(b)).
• Pedestrian Statistics: Bar chart representing the numberof pedestrians per second (Figure 1(c)).
•

People have been expected to avoid the “three Cs” (closed
spaces, crowded places, and close-contact settings) in responseto the outbreak of COVID-19, and to keep any necessary
outings or travel short to reduce physical contact with others.
These requests are based on the idea that infectious diseases
spread through contact with infected persons. For that reason,
mass-gathering events such as sports events or music concerts
held under such circumstances, reducing overcrowding has
been an important issue. Therefore, the analysis of pedestrian
movement has been an actively discussed topic.
There have been various methods for measuring the people
ﬂow including capturing video images via cameras and obtaining data from GPS. We can obtain various insights regarding
human behavior patterns and walking conditions by analyzing
the people ﬂow data. Such insights can be applied in various
ﬁelds, including tourism, urban planning, disaster prevention,marketing, and others. For example, we can use it to improve
the layout of merchandise in shopping malls [17] or to identify
problems with evacuation routes during disaster drills [10].
Thus, the analysis of people ﬂow supports our daily lives. As

2375-0138/23/$31.00 ©2023 IEEE
DOI 10.1109/IV60283.2023.00038

We can get an overview of how proximity among individuals is taking place by observing Proximity Network.
Furthermore, we can discover the clusters of pedestrians’
proximity in the network and the patterns of their walking
paths.
The remainder of this paper is organized as follows. Section
2 introduces related work on the visualization of pedestrianpaths and the analysis of the people ﬂow during an outbreak.
Section 3 presents the detail of the proposed technique. Section
4 introduces case studies using the people ﬂow before andafter the spread of infection to observe differences. Section 5summarizes this paper.

172

Fig. 1: Visualization system of pedestrian walking behavior(a)Proximity Network. (b)Proximity Path. (c)Pedestrian Statistics.
proposed a method that simultaneously displays major routes
classiﬁed by spectral clustering and temporal changes in ﬂow
rate per clustered routes using the ThemeRiver technique. As
an experimental approach to investigate this issue, Wielebski etal. [20] conducted a comparative experiment of features fromvisualizations of the same walking path information using six
different methods.
Visualization of moving objects has a serious problem ofcomprehensibility due to the visual cluttering as the number ofmoving objects increases. Several methods have been reported
that apply clustering, sampling, or character-coding to a setof paths to draw characteristic paths. Based on this idea,
Andrienko et al. [2] proposed an interactive clustering method
for trajectories and visualized popular walking patterns on
maps. Yabushita et al. [22] proposed a method for summarizing and visualizing people ﬂow by approximating walking
paths on a two-dimensional grid and drawing bundles ofsimilar walking paths with a large number of people walking.
Guo et al. [8] classiﬁed walkers’ trajectories according totheir speed and direction, and also developed a system tovisualize important trajectories using meaningful colors basedon HSV model. Miyagi et al. [12] presented a visualization
technique that ﬁrstly compresses the people ﬂow datasets bya character-coding method and then applies natural language
processing methods to extract movement patterns and classify

II. R ELATED W ORK
A. Visualization of walking paths
There are numerous methods to visualize pedestrian walking
behavior. However, visualization techniques specialized to ahuge amount of data are required because the people ﬂow
information often contains large-scale spatial-temporal data.
One of the typical ideas for visualizing such data is applying
spatial-temporal 3D visualization. McArdle et al. [11] proposed a visualization method that represents spatial-temporal
information by drawing a walking path with a space-time cube
(STC). Andrienko et al. [3] proposed a “trajectory wall” as anextension of space-time cubes. This approach needs to tackle
visual cluttering as the number of pedestrians increases andthe need for manipulation to observe features.
Another idea is linking multiple visualization components torepresent such complex data. It is difﬁcult to visualize all features in a single static visualization component, so it is better tocorrelate features that can be represented on multiple visualization components together. As a linked visualization approach,
Guo et al. [7] developed a composite visualization tool toanalyze patterns of various moving vehicles, by adopting not
only direct drawing of trajectories on maps, but also other visualization methods including piled polyline charts, scatterplots,
and parallel coordinates plots. Additionally, Fukute et al. [6]173

walking paths. Tsuchida et al. [18] developed a visualization
system that ﬁrstly represents the distribution of walking speed
and then provides an interaction mechanism to specify userinterested subregions and display paths only through thespeciﬁed subregions.
Though there have been a lot of visualization techniques
for the paths of pedestrians, there have been a small numberof methods focusing on the proximity of the pedestrians. As
an example, Gupta et al. [9] visualized relationships among asmall number of pedestrians and places where multiple persons
stayed at the same time. Differently from such studies, ourmethod aims to visualize only the paths of pedestrians related
to infection caused by the proximity and to visualize thefeatures of both time and space by linking three visualization
methods.

protection is a concern if the people ﬂow data is measured bya camera and human faces are therefore taken. On the other
hand, LiDAR only stores the distance and angle to the target
object, thus people ﬂow data can be measured without storing
personally identiﬁable information. Figure 2 shows an example
of measurements by LiDAR.

B. People ﬂow and COVID-19
With the outbreak of the COVID-19 infection, there havebeen various studies related to the analysis of the people
ﬂow, including the effects of restrictions on movement due tomeasures such as emergency declarations on the people ﬂow[16] [4] and comparisons of human behavior patterns before
and after the spread of infection [5] [21].
Physical distancing is one of the most effective infectious
disease control measures. Rezaei and Azarmi [14] developeda system to detect violating physical distancing in real-timefrom image data acquired by surveillance cameras. Moritz
et al. [13] measured the number of contacts during concerts
held under three different scenarios: (1) No restrictions (thepre-pandemic setting), (2) moderate restrictions (checkerboard
pattern seating, twice as many entrances as in (2)), and (3)strong restrictions (pairwise seating with 1.5 meters interspace
to the next pair, four times as many entrances as in (1)), toinvestigate the risk of infection during large events. The results
showed that scenarios (2) and (3) resulted in a strong reduction
in contacts.
The infection spreads through contact with infected individuals. Therefore, it is effective to avoid high density situations
to reduce the opportunities for people to come into contact
with each other and limit the spread of infectious diseases [15].
Thus, it is important to observe the proximity of pedestrians;
however, there have been a small number of visualization
methods for the people ﬂow based on proximity. Our method
differs from conventional methods in that it emphasizes theprocess of narrowing down the walking paths with a high riskof infection through visualizing proximity status.

Fig. 2: A screen of LiDAR when measuring the people ﬂow.
We record time, pedestrian ID and coordinates through themeasurement. The paths of pedestrians can be reproduced byconcatenating the coordinates of pedestrians with the same ID
in chronological order.
B. Extraction of proximity information
The method detects pairs of pedestrians that have been inclose proximity to each other for a certain period of time,
based on the coordinates of pedestrians that appear at thesame time. In this study, “proximity” is deﬁned as the statein which two pedestrians have been within a certain distancefor a certain period of time. We deﬁned two meters as thethreshold distance based on the deﬁnition of physical distancein Japan. In addition, we deﬁned 60 seconds as the thresholdperiod of time because there has been an illustrative case of
infection occurring through one to two minutes of contact withan infected person [23].
C. Visualization
The proposed visualization system consists of three linked
visualization components: Proximity Network, Proximity Path,and Pedestrian Statistics (Figure 1). This subsection describes
a detailed implementation of each visualization component.
1) Proximity Network: This component visualizes the proximity status as a network consisting of nodes corresponding
to pedestrians and edges connecting pedestrians that are inproximity as shown in Figure 1(a). Only pedestrians that meetthe proximity condition appear as nodes. Edges become thicker
as the contact duration increases, and the colors of nodes
depict the time when the pedestrian is walking.
2) Proximity Path: By specifying a set of pedestrians fromthe network described in Section III-C1, the walking paths ofthe speciﬁed pedestrians are drawn as shown in Figure 1(b).
One path corresponds to one pedestrian. Here, the display
space of this component has the same aspect ratio as theIII. P ROCESSING F LOW OF THE P RESENTED
V ISUALIZATION S YSTEM
This section presents the processing ﬂow of the proposedsystem consisting of the following three steps: people ﬂow
measurement, extraction of proximity, and visualization.
A. People ﬂow measurement
We measure the people ﬂow using LiDAR, a remote sensing
technology that uses laser light to measure distances. Privacy

174

space where the people ﬂow data was measured. Users canobserve the paths of speciﬁed pedestrians in detail since asmaller number of the paths are drawn while avoiding visual
cluttering. Furthermore, the colors of the walking paths depict
when a pedestrian walked there during the total walking timeof the speciﬁed pedestrians. This allows users to understandsituations where the walking paths are close at a certain period.
3) Pedestrian Statistics: The number of pedestrians observed at each time point is displayed as a bar chart asshown in Figure 1(c). The vertical line shows the number ofpedestrians and the horizontal one shows the time. Pedestrian
Statistics uses the same color-coding as in Figure 1(a) based on
the time period. Meanwhile, the time during which speciﬁed
pedestrians were walking is displayed in gray as shown in
Figure 1(c). This allows users to understand the positionof the time when speciﬁed pedestrians were walking in theentire dataset. Moreover, users can understand the relationshipbetween the occurrence of proximity and the number ofpedestrians.

Fig. 3: Different phases of the game in ﬁve colors.

On the day the measurements were taken, a soccer gamewas being played at the stadium, so the color of the nodes
in Proximity Network (Figure 1(a)) and Pedestrian Statistics(Figure 1(c)) was divided into ﬁve colors based on the phaseof the soccer game (Figure 3). Difference colors represent thephase of the match in Figure 3.
1) Proximity occurrence.: Figure 4 shows an example ofProximity Network, while Figure 5 shows an example ofPedestrian Statistics from the measurement data of (a) to (f).
As a result of Proximity Network, the nodes of Before the
Match (Red nodes) appeared more than in other phases. On
the other hand, the nodes of After the Match (Blue nodes) appeared relatively rare, even though the number of pedestrians isabout the same as Before the Match. This appearance indicatesthat spectators went home promptly after the match both “Before the pandemic” and “With the pandemic”. Furthermore,
there are orange, yellow, and green nodes corresponding to
the proximity that appeared during the match “Before thepandemic”, but not “With the pandemic”. This appearance
indicates a decrease in the number of spectators leaving theirseats during the game. We assume three possible reasons forthis change in the spectators. Firstly, it could be due to theprohibition of eating food in the venue during the measurement
period of “With the pandemic”. The spectators can usually
buy meals at food stands in the concourses of the stadiums
we have measured. However, the food stands were closed for
infection control. Secondly, it could be just due to the attitudeof the spectators. During the period of “With the pandemic”,
a cap was imposed on the maximum number of spectators inthe stadium. Therefore, the spectators in the venue might beenthusiastic soccer fans, indicating that they might focus on
the game during the match.
2) The size of proximity group: We have drawn the walking
paths of the proximity groups that Figure 4(b) contains. First,
we draw a small group of 2 to 3 persons, as shown in Figure4(b)(1).
The result shown in Figure 6(a) indicates that their walking
paths are similar in time and route, which suggests that thesmall group of pedestrians might be acquaintances.
On the other hand, in Figure 6(b), the paths of Figure4(b)(2) exhibit few commonalities, while a high frequency of

IV. C ASE STUDYThis section presents an example analysis of the people ﬂow
measured before and after the spread of COVID-19 using the
proposed method. The goal of this case study is to detectchanges in the movements of persons before and after the
spread of infectious diseases.
A. Dataset
We measured the people ﬂow in the stadium, assuming asporting event as a mass-gathering event. It is estimated thatthe concourse is particularly crowded because the spectators
can move around freely, so we installed the measurement
instrument in a part of the concourse. As a result, we prepared
three datasets for [Before the pandemic] from 2019, and threedatasets for [With the pandemic] from 2020. All measurements
were carried out continuously for six hours, from before thestart of the game until its completion.
Table I shows the approximate number of pedestrians included in each dataset that compares before and after theoutbreak of the COVID-19 infection. The datasets are sorted
by date of data measurement. Remark that it is possible tocount the same pedestrian multiple times as different ones
since the LiDAR cannot assign the same ID to a pedestrian
again when it goes outside the measurement range and thencomes back.
TABLE I: Approximation of pedestrian trafﬁc.
Before the pandemic
With the pandemic

(a)
27000
(d)
3000

(b)
70000
(e)
4000

(c)
44000
(f)
4000

B. ResultThis subsection introduces visualization results of the datameasured at the stadiums, as shown in Table I.

175

(a)

(b)

(c)

(d)

(e)

(f)

Fig. 4: Visualization results of Proximity Network.
Future issues of this study are the following.

proximity occurs within a certain range. Thus, their proximity
is probably unintended. Figure 4(b) contains a gigantic proximity community of more than 100 pedestrians. This groupis formed by the overlapping unconscious contacts of small
groups. This means that pedestrians associated with such large
groups possibly have unknowingly come into contact with
infected. Therefore, we need to be on the lookout for theoccurrence of such groups in terms of infectious diseases. On
the other hand, the sizes of the proximity groups were notvery large after the spread of COVID-19. This result suggeststhat in addition to the small number of pedestrians, spectators
had the awareness to keep their distance from others.

Applying people ﬂow data measured under other conditions.
• Improvement of network layout and drawing methods.
• Automatic recommendation of features to be observed• Validate infection control measures based on the ﬁndingsof the proposed method.
•

We applied the data measured before and after the outbreakas a visualization example in this study. It would be possible
to compare changes in walking routes due to conditions suchas weather or days of the week, by applying the people ﬂow
data measured under other conditions. It can also be applied
to data measured at other locations to see how the structureof the building affects the people ﬂow. In addition, we would
like to improve the layout algorithm of Proximity Network
because the current implementation does not represent spatial
information. When dealing with larger data sets, there is anincreasing need to extract spatio-temporal information from
Proximity Network to gain a comprehensive understanding of
the proximity situation at a glance.
Furthermore, automatic recommendation system of features
to be observed can be a signiﬁcant help to users whenusing this visualization system. Our future work includes
the development of an algorithm that detects anomalies andindicates when and where they are occurring for the above

V. C ONCLUSION
This paper proposed a system to visualize the characteristicsof the people ﬂow with a high risk of infection by focusing
on the proximity state. This method tackles the visibility issue
by extracting pedestrians in close proximity from large-scale
people ﬂow data and by drawing only the walking paths ofuser-speciﬁed pedestrians. This feature facilitates the discovery
of walking path characteristics related to infection.
In Section IV, we presented the visualization results ofmeasured data before and after the outbreak using the proposed
method. The results show that the occurrence of proximity wassigniﬁcantly reduced and the sizes of the proximity groups got
smaller after the outbreak.

176

R EFERENCES[1] A. Alia, M. Maree, M. Chraibi, On the exploitation of GPS-based datafor real-time visualisation of pedestrian dynamics in open environments,
Behaviour & Information Technology (2021).
[2] G. Andrienko, N. Andrienko, S. Rinzivillo, M. Nanni, D. Pedreschi, F.
Giannotti, Interactive visual clustering of large collections of trajectories,
2009 IEEE Symposium on Visual Analytics Science and Technology, 310 (2009).
[3] G. Andrienko, N. Andrienko, H. Schumann, C. Tominski, Visualizationof Trajectory Attributes in Space-Time Cube and Trajectory Wall,
Cartography from Pole to Pole - Lecture Notes in Geoinformation and
Cartography, 157-163 (2014).
[4] M. Arimura, T. V. Ha, K. Okumura, T. Asada, Changes in urban mobility
in Sapporo city, Japan due to the Covid-19 emergency declarations,
Transportation research interdisciplinary perspectives, Vol. 7, 100212(2020).
[5] R. Askarizad, H. Jinliao, S. Jafari, The inﬂuence of COVID-19 on thesocietal mobility of urban spaces, Cities, Vol. 119, 103388 (2021).
[6] A. Fukute, T. Itoh, M. Onishi, A Linked Visualization of Trajectoryand Flow Quantity to Support Analysis of People Flow, 2013 17th
International Conference on Information Visualisation, 561-567 (2013).
[7] H. Guo, Z. Wang, B. Yu, H. Zhao, X. Yuan, TripVista: Triple Perspective
Visual Trajectory Analytics and its application on microscopic trafﬁcdata at a road intersection, IEEE Paciﬁc Visualization Symposium, 163170 (2011).
[8] Y. Guo, Q. Xu, X. Li, X. Luo, M. Sbert, A New Scheme for Trajectory
Visualization, 18th International Conference on Information Visualisation (IV2014), 40-45 (2014).
[9] S. Gupta, M. Dumas, M. J. McGufﬁn, T. Kapler, MovementSlicer: Better
Gantt charts for visualizing behaviors and meetings in movement data,
IEEE Paciﬁc Visualization Symposium, 168-175 (2016).
[10] M. Imanishi, T. Sano, Route Choice and Flow Rate in Theatre Evacuation Drills: Analysis of Walking Trajectory Data-Set. Fire Technol 55,
569–593 (2019).
[11] G. McArdle, U. Demšar, S. van der Spek, S. McLoone, Classifying
pedestrian movement behaviour from GPS trajectories using visualization and clustering, Annals of GIS, Vol. 20, 85–98 (2014).
[12] Y. Miyagi, M. Onishi, C. Watanabe, T. Itoh, M. Takatsuka, Classiﬁcation
and Visualization for Symbulic People Flow Data, Journal of Visual
Languages and Computing, Vol. 43, 91–102 (2017).
[13] S. Moritz, C. Gottschick, J. Horn, M. Popp, S. Langer, B. Klee, R. Mikolajczyk, The risk of indoor sports and culture events for the transmissionof COVID-19, Nature communications, Vol 12, 5096 (2021).
[14] M. Rezaei, M. Azarmi, Deepsocial: Social distancing monitoring andinfection risk assessment in covid-19 pandemic, Applied Sciences, Vol.
10, No. 21, 2020.
[15] J. Rocklöv, H. Sjödin, High population densities catalyse the spread of
COVID-19, Journal of travel medicine, Vol. 27, No. 3 (2020).
[16] J. Shen, Measuring the impact of mitigation measures on infection riskof covid-19 in Hong Kong since February 2020, Cities, Vol. 114, 103192(2021).
[17] K. Teknomo, G. P. Gerilla, Pedestrian static trajectory analysis of a
hypermarket, Proceedings of the Eastern Asia Society for Transportation
Studies, Vol. 7, 220-220 (2009).
[18] N. Tsuchida, Y. Miyagi, M. Onishi, T. Itoh, Spatial and Temporal
Visualization of Pedestrians Based on Walking States, 24th International
Conference on Information Visualisation (IV2020), 448–453 (2020).
[19] J. Wang, Z. Wood, M. Worboys, Conﬂict in pedestrian networks,
Geospatial Data in a Changing World: Selected papers of the 19th
AGILE Conference on Geographic Information Science. Springer International Publishing, 261–278 (2016).
[20] Ł. Wielebski, B. Medyńska-Gulij, Ł. Halik, F. Dickmann, Time, spatial,
and descriptive features of pedestrian tracks on set of visualizations,
ISPRS International Journal of Geo-Information, Vol. 9, No. 6, 1–20
(2020).
[21] R. Xin, T. Ai, L. Ding, R. Zhu, L. Meng, Impact of the COVID-19
pandemic on urban human mobility-A multiscale geospatial network
analysis using New York bike-sharing data, Cities, 126, 103677 (2022).
[22] H. Yabushita, T. Itoh, Summarization and visualization of pedestrian
tracking data, 2011 15th International Conference on Information Visualisation, 537–542 (2011).
[23] Japan Society of Disaster Nursing, Q&A about COVID-19 No.30-3[published in Japanese], https://www.jsdn.gr.jp/CMS/wp-content/
uploads/db97d373100e283f5bb7d9710b206e65.pdf, 11–11 (2021).

(a)

(b)

(c)

(d)

(e)

(f)

Fig. 5: Visualization results of Pedestrian Statistics.

(a) Figure 5(b)(1)(b) Figure 5(b)(2)(c) Color map of trajectory

Fig. 6: Visualization results of Pedestrian Path.
purpose.
Finally, we can devise infectious disease control measures
based on the information obtained from the visualization system. The proposed system can be applied to the people ﬂows
obtained from crowd simulation. Therefore, we can comparechanges in pedestrian movement between the measured data
and the simulated data with the infection control measuresdeveloped.

177