ANTENNA: A Tool for Visual Analysis of Urban Mobility based on Cell

Phone Data

Pedro Silva

, Catarina Mac¸

, Jo

ao Correia

, Penousal Machado

and Evgheni Polisciuc

CISUC, Department of Informatics Engineering, University of Coimbra, Coimbra, Portugal

Keywords:

Urban Mobility, Flow Visualization, Visual Analytics, Spatio-temporal Data, Information Visualization.

Abstract:

Nowadays, the collection of data from ubiquitous urban sensors, such as smartphones, can be used to analyse,

understand, and proﬁle urban mobility. This analysis requires dynamic, autonomous, and effective ways to

parse, reduce and retrieve mobility patterns from large heterogeneous datasets. In this design study, we present

ANTENNA, a visual analysis tool that allows the identiﬁcation and analysis of urban mobility patterns based

on mobile cell phone data. In particular, we present a visualization that is prepared for multiple scenarios of

analysis, providing speciﬁc visualization approaches for different sets of tasks. We developed diverse visu-

alization models to characterise inter- and intra-urban mobility. To validate ANTENNA, we conducted user

tests with experts of different domains. The results suggest the appropriateness and usefulness of ANTENNA

for each of the presented usage scenarios.

1 INTRODUCTION

Proﬁling of urban movements has traditionally relied

on the knowledge of land use patterns, but, while land

use and transportation infrastructures tend to remain

the same for a long time, movement patterns, on the

other hand, often change. Transport planning input

data mostly comes from expensive traditional survey

methods, time-consuming and often result in a lim-

ited view of what is happening. In contrast, pervasive

computing devices and call detail records (e.g., tele-

phone calls, text messages, internet access) provide

unprecedented digital footprints, telling where and

when people are, often in real-time. Large volumes

of human mobility data are generated every second

through ubiquitous systems, such as mobile technolo-

gies and wireless networks (Krings et al., 2009). All

these mobility data, together with modern techniques

for geoprocessing, data fusion and visualization of-

fer new possibilities for deriving activity patterns and

enabling dynamic mobility proﬁling (Lin and Hsu,

2014) for predicting future movements or destina-

tions (Calabrese et al., 2013), trafﬁc forecast (Krings

et al., 2009) or city planning (Makse et al., 1995).

https://orcid.org/0000-0002-7885-5768

https://orcid.org/0000-0002-4511-5763

https://orcid.org/0000-0001-5562-1996

https://orcid.org/0000-0002-6308-6484

https://orcid.org/0000-0001-9044-2707

However, the inconsistency and incompleteness of

the retrieved data may lead to various challenges in

ﬁnding mobility patterns exclusively through modern

practices that rely solely on data mining (Krings et al.,

2009; Makse et al., 1995).

To address these challenges, we developed AN-

TENNA, a visual analytics tool for movement data,

the project which departs from cooperation with Al-

tice Labs – a multinational telecommunication com-

pany. The main goal of this project is to identify

and analyse mobility patterns to promote the use of

measures for sustainable urban mobility. The mo-

bile phone records, which consists of cell connec-

tions, allowed us to create a visualization tool that

provides the means to (i) identify the most com-

mon trajectories; (ii) perceive how many people move

from/towards a speciﬁed reference location; (iii) iden-

tify how many people do the same trajectory, or have

the same origin/destination points; (iv) derive and

categorise different geographic locations of interest;

and (v) identify areas of greater afﬂuence throughout

time. Before delving into technical details, we sug-

gest to watch the demonstration of the tool on this

link https://bit.ly/3CT16Vu.

Our approach tackles several challenges of trans-

forming raw cell phone data into analyzable and

explorable representations. Namely, the difﬁculties

range from cleaning the cell phone data records to

extracting sequential events (Vajakas et al., 2015),

Silva, P., Maçãs, C., Correia, J., Machado, P. and Polisciuc, E.

ANTENNA: A Tool for Visual Analysis of Urban Mobility based on Cell Phone Data.

DOI: 10.5220/0010902200003124

In Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 3: IVAPP, pages

88-100

ISBN: 978-989-758-555-5; ISSN: 2184-4321

from converting these events into sequences of trips

and activity locations (Zheng et al., 2009) to match-

ing these trips and activity locations to the geographic

map (Quddus et al., 2007). At the visualization end,

it is still not trivial to represent massive amounts of

mobility ﬂow in a clutter-free display (Von Landes-

berger et al., 2015). Each of the challenges individu-

ally has already been addressed by the research com-

munity. However, integrating all the involving stages

into a single analytic pipeline is demanding: either it

is computationally costly to process the data on the

ﬂy, or the trajectory reconstruction lacks precision, or

the visualization is slow to render a high number of

trajectories, or the visualization technique fails in re-

vealing needed patterns.

In summary, the contributions of this paper are

the following: (i) the characterization of the analyt-

ical problem (Section 4); (ii) the reporting on the ar-

chitecture (Section 3) and design of the ANTENNA

tool (Section 5); and (iii) the evaluation (Section 7).

Concerning the contributions to visual analytics and

visualization, we emphasise the combination of exist-

ing visualization techniques, such as hexagonal grids

and the usage of gradient/thickness of a line to rep-

resent directionality, applied to high-density urban

mobility data in visual analytics context. Further-

more, our architecture enables data transformation

(i.e., from raw data to visual representation) to exe-

cute in useful times. Another important contribution

of the presented work is the design of glyphs and their

use for higher-level readings. The glyphs allow the

user to retrieve important insights about multiple as-

pects of mobility, such as the ratio between pass-by

and stay points, the geographic zone-associated be-

haviours, and the indication of the start and end of

aggregated trajectories. Finally, we highlight the in-

teraction method for constructing visual queries.

2 VISUAL ANALYTICS OF

MOVEMENT

The visual analytics of movement has its own state-

of-the-art techniques, which are well-established ap-

proaches concerning visual analysis of movement

data (Andrienko and Andrienko, 2013). Andrienko

and Andrienko identiﬁed four directions of visual

analysis: (i) looking at trajectories, (ii) looking in-

side trajectories, (iii) bird’s-eye view on movement,

and (iv) investigating movement in context.

The ﬁrst category, looking at trajectories, is char-

acterized by the strong focus on trajectories of mov-

ing objects analyzed as a whole. The visualization

methods within this category support exploration of

the spatial and temporal properties through different

graphical means. Representing trajectories with lin-

ear symbols (lines) in static and animated maps (An-

drienko et al., 2000; Enguehard et al., 2013; Kr

uger

et al., 2013) and space-time cubes (STC) (Kraak,

2003; Kapler and Wright, 2005) is the most common

technique. Besides, arcs can be used instead of lin-

ear symbols to show ﬂow (Chua et al., 2014; Polis-

ciuc et al., 2015). The most obvious drawback of

these methods is that the display may suffer from vi-

sual occlusions and clutter (Bach et al., 2017). Clus-

tering trajectories, more known as progressive clus-

tering method, is another popular technique to deal

with large amounts of data (Rinzivillo et al., 2008;

Andrienko et al., 2007) and to reduce visual clut-

ter (Chua et al., 2016). As such, ANTENNA takes

the advantage of aggregation techniques while using

linear symbols to represent trajectories.

Concerning the variation of movement character-

istics along the trajectory, the methods from the sec-

ond category, looking inside trajectories, are used to

support the detection and localization of segments

with speciﬁc movement characteristics. Attribute

values can be represented by colouring or shad-

ing (Tominski et al., 2012; Spretke et al., 2011), as

well as placing glyphs onto the segments (Ware et al.,

2006). Furthermore, a trajectory can be considered as

a sequence of spatial events (Andrienko et al., 2011a),

and techniques such as presented in (Andrienko et al.,

2011b) can be used to extract and cluster movement

data, revealing spatial dynamics. We apply variable

colouring and shading in ANTENNA to distinguish

sections of trajectories with major activity inﬂux.

The techniques under bird’s-eye view on move-

ment mainly focus on providing an overview of

the distribution of movement in space and time

through the means of generalization and aggrega-

tion. The most known techniques fall under the ﬂow

maps (Dent, 1999), thematic maps that are used to

represent aggregated ﬂow without neglecting the ex-

act trajectory (Wood et al., 2011; Cornel et al., 2016;

Polisciuc et al., 2016a). Origin-destination (OD) ma-

trix can also be used to represent the ﬂow of mov-

ing objects (Guo, 2007). Small multiple maps is an-

other technique to show ﬂows from/to one location by

colouring the other locations (Guo et al., 2006; Wood

et al., 2010). The work of (Lu et al., 2015) uses OD

clustering along with circular glyph that provides a

cluster-wise visual analysis of OD patterns. Further-

more, the movement can be aggregated by territory

divisions and represented on a ﬂow map without in-

tersections by linking adjacent territories with a half-

arrow symbol (Chua et al., 2016). Apart from clus-

tering techniques, kernel density estimation method

ANTENNA: A Tool for Visual Analysis of Urban Mobility based on Cell Phone Data

can be applied on trajectories to display density ﬁelds

on a map by using colour or shading (Polisciuc et al.,

2016a) and an illumination model (Scheepens et al.,

2011). In ANTENNA we implemented an aggrega-

tion method that is based on a hexagonal grid, which

subdivides into hexagonal areas that are served as ag-

gregation bins. The aggregated information is then

shown using circular glyphs.

Movement data can also be analyzed within the

context (e.g., spatial or temporal), focusing on the

relations and interaction between moving objects

and the environment (Tomaszewski and MacEachren,

2010). Interaction techniques, for instance “staining”

(i.e., the user marks a certain area of the context and

relationships with moving objects emerge) (Bouvier

and Oates, 2008) or by computing distances of mov-

ing objects to a selected element and visualizing the

result on a timeline (Orellana et al., 2009), can be em-

ployed to explore movement in context. Similar ideas

are presented in (Andrienko et al., 2011a), which con-

sist of computing spatial and temporal distances from

moving objects to items in the environment and rep-

resenting them as attributes linked to trajectory posi-

tions. ANTENNA provides the user with a function-

ality that consists of updating the map according to

the hovered bar on the timeline, which in turn is a

representation of aggregated data shown on the map.

3 DATA AND SYSTEM

OVERVIEW

In this section, we present details about the dataset

and pre-processing steps carried out to prepare the

data. We provide an overview of the underlying sys-

tem and its architecture. Also, we brieﬂy describe the

data transformation pipeline and the stages involved.

3.1 Data

The dataset was provided by the Altice telecommu-

nication operator, containing per-user sequences of

cell connection events. The overall timespan of the

data ranges from December 27 of 2018 to January 3

of 2019, and geographically covers the entire area of

Aveiro city in Portugal. Each event indicates the be-

ginning and the end of the connection to a cell tower.

The data entries have an average interval of 10 min-

utes between them. In some cases, simultaneous con-

nections to two cell towers can appear. This result in

an added temporal uncertainty associated to user po-

sitioning in space-time. Further, the cell towers are

characterised by their GPS location, and the sector of

coverage (angle and radius), measuring 0

◦

from the

REST API

Worker

Visualization

Filter and clean

Database

Trajectory extractor

Mapmatching

Client Server

Data

Processed

Data

Raw

Data

Data to Visualize

Process

Launcher

Query

Data

Figure 1: Representation of the ANTENNA architecture.

The important modules are visualization, REST API, the

datasource, and the worker.

north in the clock wise direction. Multiple towers can

appear within the range of other towers, which fur-

ther decrease the positioning accuracy. The dataset is

comprised of 20Gb of information, making real-time

processing challenging. We address these problems

with our system design in the following section 3.2.

3.2 System Overview

The ANTENNA was implemented using modern

technologies for distributed data storage and process-

ing, following a three-tier architecture: presentation

tier (frontend), application tier (backend), and data-

source tier. The visualization pipeline can be de-

scribed as follows: (i) the user deﬁnes the visual query

using a graphical user interface; (ii) the visual query is

submitted to the Backend; (iii) the received request is

processed and passed to a worker unit that is executed

asynchronously; (iv) the worker processes the data in

a constant communication with the datasources, also

in a distributed fashion; and (vi) the result is returned

to the frontend where it is visualized in an interactive

web page (Figure 1). The rationale behind our design

is to allow the system to be able to process multiple

simultaneous requests in a parallel and scalable way

and to read from dynamic distributed data sources.

Backend. Regarding the application tier, it is re-

sponsible for data cleaning, trajectory extraction, map

matching, and geographic location labeling. The

main heavy processes occur in the worker modules,

which are executed by the Apache Spark engine on

a computer cluster. Each worker performs temporal

and spatial aggregations based on the query parame-

ters (detailed in Section 5.1) and the available data. In

short, temporal aggregation consists of transforming

temporal events into sequential time intervals, from

which trips can be derived. The method employs sev-

eral techniques for noise reduction (e.g., ping-pong

removal, aggregation of the concurrent events).

As for the spatial aggregation, we deﬁned two

types, which to some extent are related to the scale of

geographic generalization: (i) road level; (ii) cells of

IVAPP 2022 - 13th International Conference on Information Visualization Theory and Applications

a hexagonal grid. On the road level, we map-matched

the extracted sequences of the trips and locations and

transformed them into the arrays of vertices that con-

stitute road segments. For the spatial aggregation of

the second type, we employ the method of the hierar-

chical hexagonal grid that constructs cells of variable

granularity depending on the spatial distribution of

data points (Polisciuc et al., 2016b). Having the grid

computed, we aggregate all the data points by each

cell of the grid, derive corresponding statistics and ar-

range them in time-series. Furthermore, we store all

the information in a graph structure, such that each

node is centered in its respective cell, and each edge

represents the mobility ﬂow (see Section 5.3.1).

Finally, we try to deduce the usage of each geo-

graphic location in terms of pass-by and stay points.

Furthermore, the stay points are further labelled as

arrival or departure locations. In simple terms, the

method is based on the stay duration at a certain lo-

cation. The stay is considered when a user remains at

the same location (i.e., within the range of each tower

cell) for a period greater than 30 minutes, the duration

that is discussed in (Widhalm et al., 2015). Further-

more, the stay points are further classiﬁed as arrival

or departure, depending on the trip direction (i.e., in-

bound or outbound). Any other location is considered

a pass-by point. It is important to mention that our

approach does not considers modes of transportation

either the types of activities performed at each loca-

tion, which can inﬂuence the results.

Frontend. The presentation tier, which is the focus

of this article, is divided into two pages: the query

management page and the visualization page. In the

former, the user can create, delete, and select previ-

ously submitted queries. In the latter, the user can

visualise the query’s results (see Section 5).

4 TASKS AND DESIGN

REQUIREMENTS

The collaboration with the analysts at Altice Labs.

helped us to derive a set of tasks that will support

them in achieving their goals. The main goal is to

understand the amount of people which, at any given

time, move from location A to location B. The result-

ing tasks, which will be used as primary guidelines

for designing ANTENNA, are the following:

T1 Identify the Trafﬁc Flow within a City. Iden-

tiﬁcation of the most likely roads used within a

city. For this task, a higher level of detail was

achieved by projecting sequences of trips onto the

Portuguese roads. The road segments are selected

depending on their proximity to the connection

between locations A and B;

T2 Identify Periods of Time with Different Traf-

ﬁc Volumes. Identiﬁcation of important periods

of time according to the trafﬁc activity. To facili-

tate this task, all trips were aggregated by different

time periods and represented in a timeline with the

aid of bar charts;

T3 Analyse the Trips between Larger Geographic

Areas. Identiﬁcation of the trips between differ-

ent cities. This task requires a higher aggrega-

tion of locations, as its main goal is to give an

overview of the trajectories. To aggregate the dif-

ferent locations, a dynamic hexagonal grid was

implemented that, depending on the zoom level,

can have more or less granularity (Polisciuc et al.,

2016b);

T4 Analyse the Trips from, to, or between Speciﬁc

Areas. This task refers to the aggregation and vi-

sualization of all trajectories depending on their

departing, arriving, or both points. This task is

deﬁned in the visual query page and visualized in

the visualization page;

T5 Distinguish Urban Locations. Characterisation

of the different locations depending on the tra-

jectory’s characteristics if the users only pass by,

stay, or leave certain cell towers.

From these tasks, it was possible to determine the

visualization design requirements:

DR1 Enable the User to Zoom and Pan the Map. To

have different levels of detail, the user should be

able to pan the map and zoom in areas with higher

densities of transitions;

DR2 Enable the Interaction with the Timeline. To

analyze the differences in trafﬁc ﬂow according to

time, the visualization should provide the means

to select different periods of time and to visualise

them on the map;

DR3 Distinguish the Directionality of the Trajecto-

ries. To comprehend how people move, a visual

cue should be given so it is possible to distinguish

different directions;

DR4 Visualise the Mobility between Cities. The

user should be able to perceive the movements

between locations, being these aggregated at a

higher level (hexagonal grid) or a lower level

(road map);

DR5 Characterise the Geographic Areas According

to Their Mobility Impact. Visual cues should

be given so the user can understand the type of

mobility in different locations.

ANTENNA: A Tool for Visual Analysis of Urban Mobility based on Cell Phone Data

Figure 2: Two timeline designs: (A) represent the distribution of activities aggregate per time intervals; (B) shows the distri-

bution of trips made as a group, also aggregated by temporal blocks.

5 THE ANTENNA

At a higher level, our visualization tool provides dif-

ferent ways of interacting with the data. As already

described in Section 3, the starting point is the deﬁni-

tion of the visual query and submission to the query

queue. After the completion, the backend returns the

processed data, as described in Section 3.2. These

can be represented based on two different methods

of aggregation on an interactive map (DR1). Along

with the aggregation mode, a glyph symbol is applied

to characterise different urban areas. Furthermore, a

panel placed on the left of the screen is provided to

display a legend and the query details. Finally, a time-

line, placed at the bottom of the screen, provides the

means to analyse speciﬁc periods of time in greater

detail. All these stages in the analytical pipeline are

described in detail in the following subsections.

5.1 Visual Query

The query is deﬁned by graphical means and con-

tains the following parameters: (i) time range, more

speciﬁcally start and end date and time; (ii) aggre-

gation time intervals; (iii) spatial aggregation for

trajectories—by hexagonal grid or road network; (iv)

origin/destination (OD) selection; and (v) group ag-

gregation mode; among other parameters that can be

consulted in Figure 3. The user can also deﬁne other

spatial ﬁlters by choosing to aggregate the data into

groups and show those groups activities instead. For

the same time interval, a group is considered as such

when the number of performed individual activities

between location A and location B is greater than a

predeﬁned group threshold number.

Finally, the administrative units are used as ori-

gin/destination ﬁlters to visualise only the activities

that start at the origin and/or end at destination areas.

There are three levels of administrative units: (i) dis-

tricts, (ii) municipalities, and (iii) civil parishes. Fur-

thermore, we implemented circular OD ﬁlters, which

act as selectors of the origin and destination areas.

The user can specify the location and radius of the

circle by clicking and dragging the mouse, when the

Free option is selected (Figure 3).

5.2 Interactive Timeline

A timeline is made available to navigate through the

query’s time interval, enabling the visualization of

different periods of time (DR2). This way, the users

can get a more comprehensive understanding of the

trajectories’ geographical ﬂow through time. The

timeline is constructed based on the speciﬁed aggre-

gation mode: (i) a total number of activities (trips);

(ii) a total number of activities as a group. We con-

sider group trips the trips that are taken by a group of

four and more people (e.g., bus rides). The timeline is

deﬁned by several blocks that divide the time range in

a set of time intervals, the duration of which is speci-

ﬁed in the visual query (e.g., 15min, 30min, 1h) (Fig-

ure 2). The width of each block varies in proportion to

Figure 3: The query management page. The user deﬁnes

query parameters using the panel on the left and the inter-

active map.

IVAPP 2022 - 13th International Conference on Information Visualization Theory and Applications

Figure 4: Grid aggregation mode. Aggregated activities are

encoded with lines and the characteristics of the geographic

locations are summarised by the glyph nodes. At the bot-

tom, the timeline shows the temporal distribution of activi-

ties.

available horizontal space on the screen and the num-

ber of the bars within the queried time range (i.e., the

user indicates the beginning and end date and time

in the Time Selection section ( Figure 3)). When the

ﬁrst aggregation mode is selected, the height of each

block is deﬁned by the number of activities within

each respective time interval (Figure 2 (A)). When vi-

sualising the data with the group mode selected, the

timeline blocks are subdivided into two parts: (i) one

top grey block, representing the average number of

users per group; and (ii) one bottom cyan block, rep-

resenting the number of groups for that time interval

(Figure 2 (B)). Finally, the map updates as the user

interacts with a time block, leaving only the trips that

where made during the selected time interval.

5.3 Trajectory Visualization

As mentioned in Section 3.2, the pipeline of data

transformation includes a map matching stage, where

all the individual trips are projected either onto the

road map or a hexagonal grid, depending on the

queried aggregation mode. The retrieved data was de-

picted using different visualization techniques.

5.3.1 Grid Aggregation

For this spatial aggregation, we employed the method

of the hierarchical hexagonal grid (Polisciuc et al.,

2016b). This method of hexagonal binning constructs

cells of variable granularity depending on the spatial

distribution of data points. This is important, since

the density and distribution of data points may vary

within urban areas (e.g., downtown vs. periphery of

a city). Having the grid computed, we aggregate all

the data points by each cell of the grid, which will be

used as the nodes of the ﬁnal bi-directed graph. The

edges in the graph represent aggregated trips neglect-

ing the exact route taken. Each edge is constituted by

a time-series which includes computed statistics with

respect to query parameters.

In the grid aggregation mode, the locations of the

cell towers are projected onto an invisible hexagonal

grid, and new locations are derived (i.e., centres of

the grid cells). Therefore, the centre of each hexago-

nal cell can represent one or more cell towers that are

placed within the grid. This strategy was deﬁned to

reduce visual clutter as this hexagonal grid can pro-

vide a higher level of analysis of geographical data

(DR4) and enable the overview rather than exact esti-

mation of the data values (Figure 4).

Edges. The individual trips are grouped deﬁning the

edges of the graph. The representation takes the form

of two adjacent straight lines, one for each direction.

The lines are coloured according to their directional-

ity: in red for North-South and in purple for South-

North directions (DR3). This colour palette was cho-

sen according to its contrasting colour combination,

promoting its distinction and consequent reading. We

intensiﬁed the directionality perception with an opac-

ity gradient that increases towards its endpoint, the

technique studied in (Holten and Van Wijk, 2009;

Holten et al., 2011). In the case of opposite direction-

ality, the edges are placed side-by-side, an approach

adapted from (Chua et al., 2016). The thickness of

the lines encodes the number of trips made by distinct

users between two nodes—edge activity.

Figure 5: Label displayed by interacting with an edge. It

shows the activities distribution over time and additional

statistical data.

Finally, a dynamic label is provided by interacting

with an edge (Figure 5). The label contains a compact

version of the timeline, as well as additional statistics

such as total, minimum, maximum, and average num-

ber of trips.

ANTENNA: A Tool for Visual Analysis of Urban Mobility based on Cell Phone Data

Nodes. To represent different types of activities

(i.e., pass-by or stay), we rendered the nodes as pie

charts (for the sake of simplicity we use the terms pie

chart and glyph interchangeably). The pie chart is di-

vided into three slices, corresponding to three types

of activities (DR5). The angle of each slice encodes

the number of individual trips that contributed to the

corresponding type of activity. The colours represent

the following: grey for pass-by points, cyan for ar-

rival stay point (inbound trips), and yellow for depar-

ture stay points (outbound trips). Recall that an arrival

stay point implies that the person arrived at a location

and stayed for a duration longer than 30 minutes. The

inverse happens for the departure stay points. Any

other locations are marked as pass-by locations.

In addition to the pie chart, a central orange circle

is used to depict local activities. Local activity is con-

sidered when a trip is made within the grid cell. Re-

call that each grid cell can embrace large geographic

regions, depending on the zoom level. The radius of

the circle is deﬁned according to its ratio of activities

within each cell, i.e., the number of local activities

divided by the total amount of activities at the node.

This is done so the size of the orange circle scales

proportionally with the size of the pie chart.

Finally, the radius of the pie chart is deﬁned by the

total number of activities, including the local ones.

The glyphs sizes were globally normalised to avoid

overlapping and readability issues (Figure 6).

Figure 6: Glyph design. The cyan, yellow, and grey colours

encode arrival, departure, and pass-by locations, respec-

tively. The orange represent local activities.

In some cases, the glyphs may not contain all

types of activities described. Thus, different higher-

level readings and geographic location characteriza-

tion can be achieved by looking at the glyphs (Fig-

ure 7). By representing these patterns, we seek to un-

veil urban characteristics, such as its inhabitant’s type

of movements and region topologies (e.g., housing,

commercial, leisure) (Zeng et al., 2016).

5.3.2 Road Aggregation

To enable a more detailed analysis of the most used

roads (DR4), we perform aggregation of the trips at

the road level. For that, we project the extracted

trajectories onto the Portuguese road network, us-

ing a distance based map matching approach (Qud-

dus et al., 2007). The geometry data was retrieved

Figure 7: Example of different glyphs representing geo-

graphic areas with different types of mobility behaviour:

(from left to right) arrival/departure location with local ac-

tivities, mostly a pass-by location, arrival and pass-by loca-

tion, mixed behaviour with little local activity, arrival only,

and mixed behaviour with some local activities.

Figure 8: Schematic representation of an edge. Line colour

depicts direction, while semi-circles represent the start and

end points. Line width encodes the number of unique trips.

from OpenStreetMap provider and stored in Postgres

database within the datasource tier, such that real-

time processing is possible. The matching road seg-

ments are returned to the presentation tier along with

the attached quantitative information (the number of

distinct trips), and are used as a geometry descriptor

for rendering of the trajectories.

We used red and purple solid colours, for rep-

resenting south-north and north-south directions, re-

spectively. The directionality is dictated by the rel-

ative position of the start and end points. If the end

point of the trajectory is further north than its start-

ing point, the trajectory is coloured in purple, oth-

erwise, it is coloured in red, which is aligned with

(DR3). The line thickness takes the same meaning as

the grid projection, representing the number of unique

trips made in a given direction. Furthermore, we use

transparency to highlight the impact of the most used

roads, i.e., the higher the number of trips, the more

opaque the trajectories will be. We also resorted to

opacity to discriminate possible overlapping trajec-

tory paths (Bach et al., 2018).

As for the start and end points, we represent

them with a semicircle painted with yellow and cyan

colours, respectively. This is done to overpass the

reading issues caused by trajectory overlapping. Our

goal was to facilitate the identiﬁcation of the start and

end points, as well as the directionality DR5. Fur-

thermore, the semicircles are positioned in such a way

that their combination in the locations that are exclu-

sive for both starting and ending points can form a

full circle (see Figure 8). These circles suggest the

locations of shared activities (arrival/departure) (Fig-

ure 9).

IVAPP 2022 - 13th International Conference on Information Visualization Theory and Applications

Figure 9: Schematic representation of an edge. Line colour

depicts direction, while semi-circles represent the start and

end points. Line width encodes the number of unique trips.

6 USAGE SCENARIOS

In this section, we outline three usage scenarios, cov-

ering both aggregation modes, exposing their main

advantages and purposes for high-level analysis to

study inter-urban movements and lower level analy-

sis, focused for more accurate assessments of urban

mobility

6.1 Scenario 1: Inter-urban Movements

To analyse how people travel from two different cities

known for their New Year’s Eve festivities, we vi-

sualised the trips made from Oporto to Aveiro, in

last 4 days of 2018 (Figure 4). We used the origin-

destination ﬁlters during the query creation and set

Oporto as the origin district (Figure 4, cyan area) and

Aveiro as the district of arrival (Figure 4, orange area).

We chose the grid aggregation since the intention

was to understand the ﬂows between the two urban

areas and not the exact roads used (T4). This way we

could reduce the visual clutter by aggregating over-

lapping trajectories into a single edge. The time inter-

val of 1 hour was deﬁned empirically after previous

tests with different time intervals.

By interacting with the timeline, it was possible

to detect daily patterns throughout the ﬁrst three days

(increased activity at morning hours, lunchtime (12h–

14h), and end of work hours (18h–20h) but also iden-

tify an increase in activity and movement towards

Barra’s beach. Through this interaction, it was pos-

sible to identify different trafﬁc volumes in different

periods of time (T2), understand the transitions be-

tween cities (T3) and detect uncommon behaviours.

The demo videos for the three usage scenarios can be

found here https://bit.ly/3BTcoaY

6.2 Scenario 2: Intra-urban Movements

To analyse what are the main exit routes of a certain

city (i.e., Aveiro) (Figure 10), we applied a circular

ﬁlter (cyan circle in Figure 10) and deﬁned the geo-

graphical area in which the trips must begin. To have

an overview of the movements between the different

city neighbourhoods, we opted for the grid aggrega-

tion and the minimum time granularity (15 minutes).

By analysing the visualization, we could identify

four main exit areas for the city of Aveiro and its main

origin and arrival regions, trough the glyphs’ compo-

sitions. We can observe an arrival region further north

of the city, where its glyphs are composed mostly by

the cyan slice and a departure region further south,

where its glyphs are composed mostly by a yellow

slice (Figure 6). This may suggest the identiﬁcation

of working and residential areas, respectively (T5).

Through the timeline, it is possible to verify that,

at dawn, there were no activities within the city cen-

tre. Movements start at 9h, which corresponds to the

time of entry into work. In contrast, we see an activ-

ity reduction during the rest of the morning (working

hours). Near lunchtime (12h), there is an increasing

activity towards the rightmost cell that is placed near

a shopping, which may indicate that people head to

the shopping mall’s restaurants (T2).

6.3 Scenario 3: Group Movements

To make a more detailed analysis and perceive which

roads are the most used (T1) during the hours of en-

try to work, we deﬁned a reduced time interval (8h–

10h) and chose the road aggregation. We selected the

group mode option to view only the shared trajecto-

ries made by distinct users within the same time win-

dow and understand where mass activities happened,

suggesting congested routes (Figure 11).

With the obtained trajectories, we could perceive

homogeneous group activities throughout the city. We

noticed a considerable set of trajectories ending in the

northernmost region of the city centre, indicated by

the major number of arrival activities (Figure 11, cyan

semi-circles), suggesting the existence of a commer-

cial area. Through the transparency of trajectories, we

were able to identify the most used roads at this time

of day, seeing an increase in group activities through-

out the morning and a decrease towards the end of the

morning (working hours).

It was also possible to perceive an interesting ac-

tivity showing the transition from other city, near

a municipal stadium, to the municipal stadium of

Aveiro suggesting the occurrence of a sporting event.

Analysing trajectories starting outside the city of in-

ANTENNA: A Tool for Visual Analysis of Urban Mobility based on Cell Phone Data

Figure 10: Result of a visual query depicting the trajectories originated within the cyan circular area, located in the centre of

Aveiro district. The query details may be consulted in the side sheet, located at the top-left corner of the screen.

Figure 11: Result of a visual query depicting the trajectories in road mode for the city of Aveiro. The trajectories are

aggregated by grouped activities. The query details can be consulted in the side sheet, located at the top-left corner.

terest may be useful to understand which access roads

may be used to enter the city and thus strategically im-

prove them.

7 USER EVALUATION

In this section, we cover our user evaluation starting

with our methodology followed by the description of

the tasks, and ending with the study of ﬁndings.

7.1 Methodology

To evaluate the effectiveness of ANTENNA, we con-

ducted user testing with 20 participants (15 male and

5 female) aged between 21 and 28 years, recruited

from the University of Coimbra, Portugal. The par-

ticipants were students from several ﬁelds of data sci-

ence such as Image Processing, Data Visualization,

IVAPP 2022 - 13th International Conference on Information Visualization Theory and Applications

Machine Learning but also Biomedical Engineering

and Multimedia Design. The main goal of this test

is to evaluate whether the participants can understand

both aggregation modes and assess correct informa-

tion through their visual elements. The tests are com-

posed of ﬁve tasks, which must be completed by in-

teracting with ANTENNA. To understand if the inter-

action techniques inﬂuence the use and interpretation

of the visualization, we gathered the participants’ oral

comments during and after the execution of the tasks.

At the beginning of each test, an introduction was

made to contextualise our tool and its main purposes.

Also, a brief explanation of the visual elements for

both aggregation modes was held through screen-

shots. Then, a sheet containing the tasks was given

to the participants. The tasks were performed with

speciﬁc queries, deﬁned beforehand. All tests were

performed with the same setup, in which the partici-

pants interacted with the visualization through a 24”

monitor. If a task is composed of two queries, the

queries are presented separately during the test, so the

response to one does not inﬂuence the other. All tasks

were executed in the same order. The answers for all

tasks were timed and participants were encouraged to

think out loud, so we could comprehend what they

used to answer each question and to understand their

line of thought.

7.2 Tasks

To evaluate all the visual elements of the visualiza-

tion, we based the test’s tasks on the task abstractions

stipulated in Section 4. The ﬁrst two tasks focus on

road aggregation. To validate the trajectory represen-

tation, in Task 1, we asked the participants to indi-

cate the most used roads. To validate the timeline

design and functionality, in Task 2, we asked the par-

ticipants to indicate which roads are the most used

in the time interval with more activities. Task 2 was

performed with two queries, one with group aggre-

gation (Task 2-Groups) and the other without (Task

2-No Groups). Task 3 focuses on grid aggregation.

To evaluate its effectiveness for high-level analyses

and validate the edges and nodes representations, we

presented the trajectories between two districts and

asked the participants to indicate main points of ac-

cess for both districts. Task 4 evaluates the usefulness

of the road aggregation over the grid aggregation. For

that purpose, two identical queries were used (Task

4-Grid and Task 4-Road), being their only difference

the type of aggregation. For both queries, we asked

the participants to identify areas with high levels of

movements and compared the results. Task 5 focuses

on the glyph. We used a query that presented various

0 50 100 150 200

Task 1

Execution Time (sec)

Task 2

(No Groups)

Task 2

(Groups)

Task 3

Task 4

(Grid)

Task 4

(Road)

Task.5

Figure 12: Box-plot of the execution times for each task.

types of behaviours. To perceive the glyph’s effective-

ness and whether insights about the urban topology

could be inferred (e.g., residence, commercial, pas-

sage), we asked the participants to identify a speciﬁc

glyph fulﬁlling a set of requirements and to charac-

terise three urban areas with distinct glyph compo-

sitions. The participants answered freely and were

not constrained, allowing us to obtain the most in-

formation possible. To verify the consistency within

the given answers or if a major deviation occurred, a

ground truth answer was deﬁned for each task.

7.3 Findings

The participants provided positive insights about the

aggregation modes and the visual tool itself. In Fig-

ure 12, we can see a positively skewed distribution of

the participants’ execution times since their median is

closer to the bottom of the box, revealing a high con-

centration of results with short execution times. Most

participants revealed no major difﬁculties in complet-

ing the tasks, however, their learning curve and ex-

ploration times varied to a great extent, originating

outliers (Figure 12). Some participants gave quick an-

swers without exploring the visualization while others

explored it thoroughly before providing their answers.

The learning curve can be analysed by the time taken

in the ﬁrst three tasks, as their goals were the same

but for different scenarios. Being the ﬁrst task the par-

ticipants’ ﬁrst point of contact with the visualization,

it took additional time to interpret it. The execution

times for the following two tasks improved consider-

ably, even presenting more complex scenarios to anal-

yse, corroborating a smooth and fast learning curve.

Task 3 presents higher values of execution times as it

is the most ambiguous question in the test due to the

interpretation of an access point. From Figure 13, we

can see that it is Task 3 that contains the most distinct

number of answers. Concerning the remaining tasks,

the majority of the participants completed them with

no difﬁculties and in less time, requiring much less

inspection. For all tasks, the majority of the partici-

ANTENNA: A Tool for Visual Analysis of Urban Mobility based on Cell Phone Data

25%

50%

75%

100%

Answer Distribution

Task 1 Task 2

(Groups) (Grid) (Road)(No Groups)

Task 2 Task 3 Task 4 Task 4 Task 5

Correct Answers

Figure 13: Answer distribution for each task. Dark grey

bars represent answers within the set of correct answers.

The count value for each answer is inside the bars.

Task 1 Task 2

(Groups) (Grid) (Road)(No Groups)

Task 2 Task 3 Task 4 Task 4 Task 5

Nº of Answers

Users Distribution per Nº Answers

21 3 4 6

Figure 14: Participants distribution per number of answers

for each task. The number of participants is inside each bar.

pants indicated, at least, one correct answer, with the

exception of task 5, where one participant answered

incorrectly.

In Figure 13 we represent the number of different

answers for each task and we can see that most an-

swers are within the set of correct answers. It is im-

portant to note that the majority of the tasks implied

the ordered listing of the visualization elements (e.g.,

“Identify the most used roads”). For this reason, some

participants continued to point out elements that are

not within the correct answer set even though it is no

longer required. In Figure 14, we can see the partici-

pants distribution per number of given answers. Tasks

1 and 3 have the most distinct number of answers sup-

porting the results retrieved from the distributions for

these tasks (Figure 12). For both queries of Task 4

the majority of participants gave the same number of

answers. Analysing both Figure 13 and Figure 14, we

can see that most participants gave the expected num-

ber of answers, with the exception of Task 1, Task 3,

and Task 4-Grid. Through these results, we can verify

that all participants who provided a fewer number of

answers than the desired one, contained only correct

answers. In general, the participants had no difﬁculty

in completing the timeline’s tasks.

Most participants found the tool useful for com-

pleting the tasks and its learning curve quite comfort-

able, as they quickly began to interpret the visualiza-

tions correctly. One participant referred that the grid

aggregation was better for a ﬁrst analysis phase, pro-

viding an overview of the data, and the road aggre-

gation better to conduct a more focused analysis. In

terms of difﬁculties, all participants stated that the im-

possibility of interacting with the trajectories within

speciﬁc periods of time in the timeline complicated

the analysis. Furthermore, they found the timeline

confusing when representing group activities as the

grey bars (depicting the average number of group ele-

ments) were difﬁcult to notice. Concerning the fading

gradient, one participant interpreted their directional-

ity in reverse. The glyphs were the most complex ele-

ments due to the participants’ diverse interpretations.

8 DISCUSSION

In this section, we discuss the usefulness and advan-

tages of both aggregation modes and the effectiveness

of ANTENNA for the scenarios presented. Then, the

results from the user tests are analysed to assess the

users understanding of the application.

Usage Scenarios. Section 6 enabled the under-

standing of real usage scenarios for both aggregation

modes and their combination with other parameters,

such as spatial and temporal aggregation ﬁlters. It

is possible to identify different purposes for each ag-

gregation mode. The grid aggregation was found as

more efﬁcient for an overview analysis. Its main ad-

vantages are: (i) the reduction of visual clutter; (ii)

the visual highlight of areas according to the type of

activity (e.g., residential, commercial, passage); and

(iii) the highlight of inter-region ﬂow patterns. Also,

the glyphs provided a quick and general interpretation

of its region behaviours. The road aggregation was

found as more efﬁcient for more detailed and precise

analysis. Its main advantages are: (i) the understand-

ing of road afﬂuence; (ii) the highlight of everyday

movement patterns (i.e., travels to work, recreation);

and (iii) the highlight of intra-region ﬂow patterns.

Also, the representations of the start and end points

of the trips facilitated the reading of directionality.

The timeline enabled a more precise examination

of the trips in time and space. The interactive label

enabled a more thorough analysis and understanding

of afﬂuence patterns.

User Evaluation. Given the success of the user

evaluation and the positive feedback of the partici-

pants, we consider that the visualization presents the

data correctly and that its interaction is intuitive. Both

IVAPP 2022 - 13th International Conference on Information Visualization Theory and Applications

aggregation modes were well received and easily in-

terpreted by the participants. The last task revealed

interesting insights about the glyphs due to their var-

ied interpretations. For example, one participant did

not consider the grey area of the glyph as being repre-

sentative of information, as it was too similar to the

map base colour. Also, some participants had dif-

ﬁculties in characterising the areas according to the

glyphs representation. However, the participants re-

ported that after some adaptation, the glyphs could

characterise different areas, enabling them to distin-

guish residential from work areas.

9 CONCLUSIONS

We have presented a design study of ANTENNA,

a visual analytics tool for urban mobility analysis.

Through the collaboration with a telecommunication

provider, we had access to a dataset concerning cell

phone connections. The main goal of this work is to

summarise and ease the comprehension of the data,

enabling the company’s analysts to understand the

inter- and/or intra-urban mobility. Through our col-

laboration, we were able to deﬁne the main tasks and

design requirements for the presented tool. We imple-

mented two aggregation modes to respond to one set

of tasks for higher-level analysis and one for a more

focused and thorough analysis. To demonstrate the

advantages of each aggregation, we provided three us-

age scenarios. We conducted user evaluations with

20 participants to evaluate the effectiveness of our

tool. Finally, a thorough discussion was made over

the results from usage scenarios and user evaluations.

We contributed to the state of the art in urban mo-

bility analysis, by creating a visual analytics tool that

provides two visualization modes suitable for a wide

range of mobility analysis scenarios. As future work,

some visual encodings may be improved, and addi-

tional interaction techniques integrated. For example,

the timeline should provide the ability to lock the vi-

sualization in the selected period of time, enabling the

analysis of the visualization in more detail for the de-

sired time interval.

ACKNOWLEDGEMENTS

The work is supported by the Portuguese Foundation

for Science and Technology (FCT), under the grant

SFRH/8D/144283/2019.

REFERENCES

Andrienko, G., Andrienko, N., and Heurich, M. (2011a).

An event-based conceptual model for context-aware

movement analysis. International Journal of Geo-

graphical Information Science, 25(9):1347–1370.

Andrienko, G., Andrienko, N., Hurter, C., Rinzivillo, S.,

and Wrobel, S. (2011b). From movement tracks

through events to places: Extracting and character-

izing signiﬁcant places from mobility data. In 2011

IEEE conference on visual analytics science and tech-

nology (VAST), pages 161–170. IEEE.

Andrienko, G., Andrienko, N., and Wrobel, S. (2007). Vi-

sual analytics tools for analysis of movement data.

ACM SIGKDD Explorations Newsletter, 9(2):38–46.

Andrienko, N. and Andrienko, G. (2013). Visual analyt-

ics of movement: An overview of methods, tools and

procedures. Information visualization, 12(1):3–24.

Andrienko, N., Andrienko, G., and Gatalsky, P. (2000).

Supporting visual exploration of object movement. In

Proceedings of the working conference on Advanced

visual interfaces, pages 217–220.

Bach, B., Dragicevic, P., Archambault, D., Hurter, C.,

and Carpendale, S. (2017). A descriptive framework

for temporal data visualizations based on generalized

space-time cubes. In Computer Graphics Forum, vol-

ume 36, pages 36–61. Wiley Online Library.

Bach, B., Perin, C., Ren, Q., and Dragicevic, P. (2018).

Ways of visualizing data on curves.

Bouvier, D. J. and Oates, B. (2008). Evacuation traces mini

challenge award: Innovative trace visualization stain-

ing for information discovery. In 2008 IEEE Sym-

posium on Visual Analytics Science and Technology,

pages 219–220. IEEE.

Calabrese, F., Diao, M., Di Lorenzo, G., Ferreira Jr, J.,

and Ratti, C. (2013). Understanding individual mo-

bility patterns from urban sensing data: A mobile

phone trace example. Transportation research part C:

emerging technologies, 26:301–313.

Chua, A., Marcheggiani, E., Servillo, L., and Moere, A. V.

(2014). Flowsampler: Visual analysis of urban ﬂows

in geolocated social media data. In International Con-

ference on Social Informatics, pages 5–17. Springer.

Chua, A., Servillo, L., Marcheggiani, E., and Moere, A. V.

(2016). Mapping cilento: Using geotagged social me-

dia data to characterize tourist ﬂows in southern italy.

Tourism Management, 57:295–310.

Cornel, D., Konev, A., Sadransky, B., Horv

ath, Z., Bram-

billa, A., Viola, I., and Waser, J. (2016). Composite

ﬂow maps. In Computer Graphics Forum, volume 35,

pages 461–470. Wiley Online Library.

Dent, B. (1999). Cartography: Thematic Map Design.

Number v. 1 in Cartography: Thematic Map Design.

WCB/McGraw-Hill.

Enguehard, R. A., Hoeber, O., and Devillers, R. (2013). In-

teractive exploration of movement data: A case study

of geovisual analytics for ﬁshing vessel analysis. In-

formation Visualization, 12(1):65–84.

Guo, D. (2007). Visual analytics of spatial interaction

patterns for pandemic decision support. Interna-

ANTENNA: A Tool for Visual Analysis of Urban Mobility based on Cell Phone Data

tional Journal of Geographical Information Science,

21(8):859–877.

Guo, D., Chen, J., MacEachren, A. M., and Liao, K. (2006).

A visualization system for space-time and multivariate

patterns (vis-stamp). IEEE transactions on visualiza-

tion and computer graphics, 12(6):1461–1474.

Holten, D., Isenberg, P., Van Wijk, J. J., and Fekete, J.-D.

(2011). An extended evaluation of the readability of

tapered, animated, and textured directed-edge repre-

sentations in node-link graphs. In 2011 IEEE Paciﬁc

Visualization Symposium, pages 195–202. IEEE.

Holten, D. and Van Wijk, J. J. (2009). A user study on vi-

sualizing directed edges in graphs. In Proceedings of

the SIGCHI conference on human factors in comput-

ing systems, pages 2299–2308. ACM.

Kapler, T. and Wright, W. (2005). Geotime information vi-

sualization. Information visualization, 4(2):136–146.

Kraak, M.-J. (2003). The space-time cube revisited from

a geovisualization perspective. In Proc. 21st Inter-

national Cartographic Conference, pages 1988–1996.

Citeseer.

Krings, G., Calabrese, F., Ratti, C., and Blondel, V. D.

(2009). Urban gravity: a model for inter-city telecom-

munication ﬂows. Journal of Statistical Mechanics:

Theory and Experiment, 2009(07):L07003.

uger, R., Thom, D., W

orner, M., Bosch, H., and Ertl,

T. (2013). Trajectorylenses–a set-based ﬁltering and

exploration technique for long-term trajectory data. In

Computer Graphics Forum, volume 32, pages 451–

460. Wiley Online Library.

Lin, M. and Hsu, W.-J. (2014). Mining gps data for mobility

patterns: A survey. Pervasive and mobile computing,

12:1–16.

Lu, M., Wang, Z., Liang, J., and Yuan, X. (2015). Od-

wheel: Visual design to explore od patterns of a cen-

tral region. In 2015 IEEE Paciﬁc Visualization Sym-

posium (PaciﬁcVis), pages 87–91. IEEE.

Makse, H. A., Havlin, S., and Stanley, H. E. (1995). Mod-

elling urban growth patterns. Nature, 377(6550):608.

Orellana, D., Wachowicz, M., Andrienko, N., and An-

drienko, G. (2009). Uncovering interaction patterns in

mobile outdoor gaming. In 2009 International Con-

ference on Advanced Geographic Information Sys-

tems & Web Services, pages 177–182. IEEE.

Polisciuc, E., Cruz, P., Amaro, H., Mac¸

as, C., Carvalho, T.,

Santos, F., and Machado, P. (2015). Arc and swarm-

based representations of customer’s ﬂows among su-

permarkets. In IVAPP, pages 300–306.

Polisciuc, E., Cruz, P., Amaro, H., Mac¸as, C., and Machado,

P. (2016a). Flow map of products transported among

warehouses and supermarkets. In VISIGRAPP (2:

IVAPP), pages 179–188.

Polisciuc, E., Mac¸

as, C., Assunc¸

ao, F., and Machado, P.

(2016b). Hexagonal gridded maps and information

layers: a novel approach for the exploration and anal-

ysis of retail data. In SIGGRAPH ASIA 2016 Sympo-

sium on Visualization, page 6. ACM.

Quddus, M. A., Ochieng, W. Y., and Noland, R. B. (2007).

Current map-matching algorithms for transport appli-

cations: State-of-the art and future research directions.

Transportation research part c: Emerging technolo-

gies, 15(5):312–328.

Rinzivillo, S., Pedreschi, D., Nanni, M., Giannotti, F., An-

drienko, N., and Andrienko, G. (2008). Visually

driven analysis of movement data by progressive clus-

tering. Information Visualization, 7(3-4):225–239.

Scheepens, R., Willems, N., Van de Wetering, H., An-

drienko, G., Andrienko, N., and Van Wijk, J. J. (2011).

Composite density maps for multivariate trajectories.

IEEE Transactions on Visualization and Computer

Graphics, 17(12):2518–2527.

Spretke, D., Bak, P., Janetzko, H., Kranstauber, B., Mans-

mann, F., and Davidson, S. (2011). Exploration

through enrichment: a visual analytics approach for

animal movement. In Proceedings of the 19th ACM

SIGSPATIAL International Conference on Advances

in Geographic Information Systems, pages 421–424.

Tomaszewski, B. and MacEachren, A. M. (2010). Geo-

historical context support for information foraging

and sensemaking: Conceptual model, implementa-

tion, and assessment. In 2010 IEEE Symposium on

Visual Analytics Science and Technology, pages 139–

146. IEEE.

Tominski, C., Schumann, H., Andrienko, G., and An-

drienko, N. (2012). Stacking-based visualization of

trajectory attribute data. IEEE Transactions on visu-

alization and Computer Graphics, 18(12):2565–2574.

Vajakas, T., Vajakas, J., and Lillemets, R. (2015). Tra-

jectory reconstruction from mobile positioning data

using cell-to-cell travel time information. Interna-

tional Journal of Geographical Information Science,

29(11):1941–1954.

Von Landesberger, T., Brodkorb, F., Roskosch, P., An-

drienko, N., Andrienko, G., and Kerren, A. (2015).

Mobilitygraphs: Visual analysis of mass mobility

dynamics via spatio-temporal graphs and clustering.

IEEE transactions on visualization and computer

graphics, 22(1):11–20.

Ware, C., Arsenault, R., Plumlee, M., and Wiley, D. (2006).

Visualizing the underwater behavior of humpback

whales. IEEE Computer Graphics and Applications,

26(4):14–18.

Widhalm, P., Yang, Y., Ulm, M., Athavale, S., and

Gonz

alez, M. C. (2015). Discovering urban ac-

tivity patterns in cell phone data. Transportation,

42(4):597–623.

Wood, J., Dykes, J., and Slingsby, A. (2010). Visualisation

of origins, destinations and ﬂows with od maps. The

Cartographic Journal, 47(2):117–129.

Wood, J., Slingsby, A., and Dykes, J. (2011). Visualizing

the dynamics of london’s bicycle-hire scheme. Carto-

graphica: The International Journal for Geographic

Information and Geovisualization, 46(4):239–251.

Zeng, W., Fu, C.-W., M

uller Arisona, S., Erath, A., and Qu,

H. (2016). Visualizing waypoints-constrained origin-

destination patterns for massive transportation data.

In Computer Graphics Forum, volume 35, pages 95–

107. Wiley Online Library.

Zheng, Y., Zhang, L., Xie, X., and Ma, W.-Y. (2009). Min-

ing interesting locations and travel sequences from

gps trajectories. In Proceedings of the 18th interna-

tional conference on World wide web, pages 791–800.

IVAPP 2022 - 13th International Conference on Information Visualization Theory and Applications

100