=Paper=
{{Paper
|id=Vol-1136/paper3
|storemode=property
|title=Visualization Techniques of Trajectory Data: Challenges and Limitations
|pdfUrl=https://ceur-ws.org/Vol-1136/paper3.pdf
|volume=Vol-1136
|dblpUrl=https://dblp.org/rec/conf/agile/GoncalvesAM13
}}
==Visualization Techniques of Trajectory Data: Challenges and Limitations==
https://ceur-ws.org/Vol-1136/paper3.pdf
Visualization Techniques of Trajectory Data:
Challenges and Limitations
Tiago Gonçalves 1 , Ana Paula Afonso 1 , and Bruno Martins 2
1
Faculdade de Ciências da Universidade de Lisboa,
2
Instituto Superior Técnico, INESC-ID
tgoncalves@lasige.di.fc.ul.pt,apa@di.fc.ul.pt,bruno.g.martins@ist.utl.pt
Abstract. As a consequence of the prevalence of mobile computing and
location based services, huge amounts of movement data are nowadays
being collected. While the research interest on the analysis of trajectory
data has also significantly increased, there are still several open chal-
lenges in areas related to geographic information systems. Despite the
existence of several techniques for the visualization of movement data, it
is still unclear how usable and useful these techniques are, how can they
be improved, and in which tasks they should be used. In this paper, we
highlight the current limitations in the visual exploration of trajectory
data, and present the ongoing research aiming to address those issues.
For that, we propose the development of taxonomies addressing visu-
alization tasks, techniques, and data, based on empirical data, through
systematic comparative usability studies, and present an overview of the
current results.
1 Introduction
The intrinsic relation between what we do and where we are emphasizes the
importance of the analysis of geo-referred data [1]. This fostered the community’s
interest and enabled the collection of huge amounts of spatio-temporal data
representing the trajectories of people, animals, and natural phenomena [2].
Trajectory data is an important factor on many processes and activities in
several areas of research, like traffic analysis, or the identification of behaviours
and moving patterns. By definition, a trajectory consists in the evolution of a
moving object’s spatial properties over time [3]. Typically, this type of data is
represented as a time-stamped oriented series of location points, P = hxn , yn , tn i
or P = hxn , yn , zn , tn i, that compose a trajectory T = {P1 , P2 , ...Pn }, where
xi , yi , and zi represent, respectively, the geographic coordinates of latitude,
longitude and altitude, at time ti . Moreover, each point, or group of points, may
contain additional attributes, derived or associated from the data, representing
other types of information. These subsets of the data, often called thematic, may
represent, for instance, the object’s speed at a given instant, its current state, or
its category. Therefore, time, space, and thematic attributes play an important
role when analysing these data [4].
2 Visualization Techniques of Trajectory Data: Challenges and Limitations
In order to support the extraction of useful and relevant information from
these large datasets, it is crucial to develop and study adequate data visual-
ization techniques. In the context of geographic information systems, several
techniques have been proposed to help on the visualisation and exploration of
the spatio-temporal properties of trajectory data [5] [6] [7]. Despite the various
approaches, these techniques can be grouped into four main high-level categories,
namely: (i) static maps, (ii) space-time cubes, (iii) animated maps, and (iv) small
multiples [7].
However, despite the useful results obtained, several challenges and open
issues can still be identified. In particular, in the field of visualization and human
computer interaction, it is still unclear how usable these techniques really are,
and in which tasks they should be used.
In this paper, we highlight the main limitations regarding the visual explo-
ration of trajectory data, and describe the work in progress addressing these
issues. The following sections address an overview of the existing work, followed
by the description of the main limitations in the visualization of trajectory data,
and finally, the description of the ongoing research’s objectives and methods
addressing these limitations.
2 Related Work
The following section presents some relevant concepts associated with the visual
exploration of trajectory data, addressing the characterization of trajectory data,
and the methods used to process and, later, visualize the data.
According to Peuquet [8], spatio-temporal data and, in particular, trajec-
tory data can be characterized by three main components: space (where), time
(when), and topics (what). In turn, several questions can be made by combining
these components, namely: (i) when+where → what, to state the properties of
an object at a given time; (ii) when+what → where, to state the location(s) of
object(s) at given time(s); and (iii) where+what → when, to state the time or
set of times when an object or more was at a certain spatial area.
Other authors, such as Andrienko et al. [9], argue that, asides from these fac-
tors, analysts should also take into consideration search levels, elementary and
general, depending if the focus of the analysis is one or more objects, respec-
tively, and cognitive operations, such as identify and compare. By combining
these levels with two main search targets (when and what+where), the authors
identified four types of questions: (i) Elementary When and What+Where, to
describe characteristics of an object at given time (e.g. where is person A at
9 am?); (ii) Elementary When and General What+Where, to describe a situ-
ation at a given time moment (e.g. Are persons A and B close to each other
at 9 am?); (iii) General When and Elementary What+Where, to describe the
dynamics of the characteristics of an object over time at a certain location (e.g.
which buildings did person A visited during the day?); and (iv) General When
and What+Where, to describe the evolution of the overall situation over time
(e.g. who visited the most locations between 9 and 10 am?).
Visualization Techniques of Trajectory Data: Challenges and Limitations 3
To properly answer these questions, adequate visualization techniques are
needed. This often means mapping temporal information representing feature
changes or dynamics of an object [10]. These can be obtained through the adap-
tation of certain visual variables [11], including, among others, colour, size, shape
and orientation, that can be used over different marks, namely, points, lines, ar-
eas, surfaces and volumes. Through the adaptation of these visual marks, it is
possible to represent the characteristics and variations of spatial, temporal, and
thematic attributes.
In addition, due to the sheer amount of data present in trajectory datasets,
purely visual approaches may be insufficient [7]. This requires visualization meth-
ods to be preceded and/or combined with data processing techniques to reduce
the amount of data, including methods such as data cleaning, filtering, and/or
smoothing techniques.
Data cleaning consists of the deletion of erroneous dataset entries [2]. Data
filtering methods consist of the selection of a subset of the dataset, based on
the spatial, the temporal, and/or the thematic proprieties of the data. Data
smoothing methods transform the dataset by reducing the level of detail of
a trajectory, for instance, using algorithms such as Douglas-Pecker, to reduce
the number of points in a trajectory [12]. An additional solution consists of
aggregation methods to merge subsets of the data, based on their similarity (e.g.
direction of movement, position in time/space).
After cleaned/filtered, these raw trajectories can be enriched with more data,
becoming semantic trajectories [13]. This extra information is used to represent,
among others, the mode of transportation of the moving object, or the activities
performed (e.g. working, shopping) [14]. These can be obtained by inferring data
from the original dataset (e.g. determine the dislocation mode of the object based
on its speed) or by combining them with other datasets (e.g. determine that a
person is eating, since it is close to a restaurant).
In order to properly visualize these transformed trajectory datasets several
visualization methods were presented.The following sections present an overview
of the most used techniques for spatio-temporal, and trajectory data visualiza-
tion.
2.1 Static Maps
Two dimensional static maps are one of the most common approaches for rep-
resenting any type of information of a geographical location.
The analysis of the existing literature reveals a wide range of options to repre-
sent trajectory data information on static maps. This happens due to the several
possible combinations of visual variables into complex symbols [15] and/or the
use of data processing techniques [16].
Typically, lines/arrows are used to represent the spatio-temporal properties of
an object’s movement from a starting to a destination point, while specific sym-
bols with pre-defined shapes may represent different events occurring in space
(see Figure 1). Similarly, other visual attributes like colour, line thickness, or
transparency may be used to represent an event’s category or recency [10].
4 Visualization Techniques of Trajectory Data: Challenges and Limitations
Fig. 1. Two-dimensional static map representation. Colours are used to represent the
period of the day, while line width is used to represent the object’s approximated speed.
2.2 Space-Time Cube Map
With the increasing evolution of computer graphics, three dimensional visualiza-
tions have become more common and are seen as a promising way to represent
complex information [17].
The space-time cube map is a visualization technique that emphasizes the
idea that time and space are inseparable, by using the three dimensions to rep-
resent both attributes [17]. Typically, the x − y axis of the cube are used to
represent spatial information, while the third dimension, the z-axis, is used to
represent time. Usually, time increases along the z-axis, implying that the higher
the information is within the cube, the most recent it is (see Figure 2) [18].
In this type of map, time is represented as a spatial position, therefore, it is
not required other visual variables to convey that information [18]. However, this
technique may be affected by the human perceptual issues of three dimensional
environments [18]. These may induce the occlusion of information, and prob-
lems of interpretation, in particular, of metric properties [18], regarding three
dimensional perspectives. Therefore, it is recommended the use of interactive
techniques that allow the user to change the point of view within the space-time
cube [18].
2.3 Animated Maps and Small-Multiples
Although different in presentation, animated and small multiple maps follow a
somewhat similar approach in the way the data is handled. In both techniques,
data is divided between several maps, each one representing the state of a phe-
nomenon, at a different time period. However, while animated approaches display
Visualization Techniques of Trajectory Data: Challenges and Limitations 5
Fig. 2. Space-time cube representation. Colours are used to represent the period of the
day, while line width is used to represent the object’s approximated speed.
these maps (frames) as a sequence in a single view, small multiple maps present
them juxtaposed to each other (see Figures 3 and 4).
Preliminary research argues that animated displays can help revealing spatio-
temporal patterns that are not evident with common static representations [19].
Besides, unlike static approaches, these have an additional dimension that can
be used to present information. However, the longer the animation, the higher
the amount of data to be presented. Since one frame will not be always visible,
this may raise some cognitive and perceptual limitations [20]. Controls over
the animation can also be given to the user, allowing him to move towards or
backwards on the animation, thus allowing frames to be visible again.
On the other hand, although small multiple maps minimize cluttering issues
by splitting the information through separated maps, and allow the comparison
of multiple time periods at the same time, these depend on the screen size,
which determines the number of maps that can be presented simultaneously.
Moreover, small multiples are discontinuous spatial presentations, which may
require a larger effort from the viewer when a large number of maps is used,
since it requires the user to mentally connect all maps into an ordered sequence.
3 Current Challenges and Limitations
Recently, several approaches addressing the visualization of trajectory data have
been reported. However, despite the results obtained, we can still identify several
challenges and open issues.
Andrienko et al. [9] stressed that existing visualizations often neglect the
temporal properties of the data, which may undermine the visualizations. In
addition, they emphasize that everyone is a spatio-temporal analyst and, thus,
6 Visualization Techniques of Trajectory Data: Challenges and Limitations
Fig. 3. Animated map representation. Each map represents an instant in time, and is
displayed once.
Fig. 4. Small multiple map representation. In this example, each map represents one
day of recorded movement. All maps are visible.
it should not be assumed that the analysis of spatio-temporal data will always be
conducted by a specialist, therefore, making the community of users potentially
unlimited.
On the other hand, although the number of visualization studies has in-
creased, usability has been, somewhat, neglected [21]. When evaluating a cer-
tain visualization, most usability studies tend to be either limited to case studies
and/or focus on highly expert users of the system [21].
In addition, previous studies suggest an uncertainty regarding the techniques
and the procedures used on comparative evaluations [22][23]. In fact, the results
of some studies are, sometimes contradictory, in particular, when dealing with
animated representations [22]. Tverky et al. [23], in particular, suggest that there
is no evidence to conclude that animated representations are more helpful than
others, since previous studies either fail to assure that informationally similar
visualizations are used, or have unbalanced procedures, with different techniques.
Moreover, some authors suggest possible factors that may have an impact on the
comparison of these visualization techniques [5] [6].
On the other hand, given the diverse results obtained in terms of the adequacy
with different techniques, some authors highlight the need for new taxonomies
of tasks and data, based on empirical findings in different types of tasks [17].
Based on these limitations, it is important to study more deeply the visu-
alization of trajectory data and explore the empirical data, based on the users’
performance, to better support this knowledge.
Visualization Techniques of Trajectory Data: Challenges and Limitations 7
4 Proposed Approach and Expected Results
Despite the existence of several studies addressing the importance of the vi-
sual analysis of trajectory data, several limitations have been identified, which
emphasize that the knowledge associated with these techniques is still scarce.
The results of previous studies suggest some techniques’ adequacy for cer-
tain types of tasks (and data) over others. However, while some studies present
mixed conclusions, others highlight some uncertainty regarding the procedures,
and possible interfering factors in some experiments. Although some taxonomies,
partially, address some of these issues [7], these are rarely based on empiri-
cal data. As such, to minimize these limitations, we propose the development
of taxonomies of tasks, data, and techniques for trajectory data, through the
systematic empirical assessment of the existing main groups of techniques and
possible impacting factors over them, and, consequently, the development of an
evaluation framework.
Usually themes such as the description of the data, the visualization tech-
niques, and the types of tasks/operations over spatio-temporal data tend to be
addressed separately. However, we argue that these are actually related to each
other. Similarly to the work of Andrienko et al. [9], cognitive tasks like the iden-
tification, or the comparison of certain data features can be done considering
the spatio-temporal and thematic properties of the data. Moreover, these can
also be associated with the properties of visual variables, that determine how
adequate a variable is to represent a certain type of data [11].
Consequently, as one of the first steps in this research, we have defined a
taxonomy addressing these various components. While the first connection be-
tween these components (tasks-data) provides a set of tasks that can be used
in a comparative assessment between techniques, the second connection (tasks-
variables) highlights the visual methods that can be used to better represent the
required information for certain tasks. Nevertheless, despite these initial results,
we argue that this taxonomy can still be expanded by addressing techniques for
the visualization of trajectory data, based on the results of comparative studies.
When exploring trajectory data, several factors may have an effect over the
users’ performance. Based on the considerations of previous research, we expect
useful results from the empirical assessment of some factors, such as: (i) den-
sity/complexity of information, (ii) type of dataset, (iii) the familiarity of the
user with the spatial location, (iv) the size of the visualization.
In order to assess these factors, it is necessary to conduct comparative eval-
uations between the existing techniques, following the tasks identified in the
previously mentioned taxonomy.
It is unlikely to find a visualization technique that outperforms every other [7],
however, it is plausible to assume that these evaluations may reveal new prob-
lems with the techniques, new factors to be explored, and, by consequence, new
variations to be tested [24]. It is expected an overall reduction in the user’s per-
formance with a higher information density and/or complexity, since the larger
the dataset, the higher the probability of over-plotting/cluttering of information.
Similarly, the more types of information to represent, the more visual variables
8 Visualization Techniques of Trajectory Data: Challenges and Limitations
may be needed to encode all of the information. Consequently, this may increase
the cognitive workload needed to interact with the visualization.
On the other hand, with the advances in mobile technology, users have be-
come more proficient in interacting with smaller screens. This raises several
challenges, since the smaller the screen, the less information a visualization may
contain. In turn, this may hinder small multiple map techniques, since the num-
ber of possible visible maps is reduced. However, whether and/or how these
factors significantly affect the user’s performance with other techniques is still
unclear.
In order to obtain relevant results, it is necessary to have some users to
participate on the experiments. Since everyone is a spatio-temporal analyst [9],
and thus, the community of users of spatio-temporal visualizations is potentially
unlimited, we argue that the conduction of comparative studies with (suppos-
edly) inexperienced users may not only reveal important results, but may also
be considered as necessary.
Previous studies argue that novice users can be particularly helpful, due to
three main reasons [5]. First, it is difficult to find expert user in a reasonable
quantity that allow to obtain reliable results. Second, proving a visualization’s
usefulness for novice users, may provide a useful empirical building block to help
researchers recruiting less experienced users. Finally, if novice users are able to
properly interact with a visualization, there should be no reason to assume that
expert users would not be able to do the same (if not better).
In fact, our previous research goes in agreement with these considerations [25].
Following the previously mentioned taxonomy, and through the use of several
processing data techniques, we developed and tested ST-TrajVis, an application
for the visualization of trajectory data. The results suggest that the techniques
used, such as the space time cube, are easy to learn and intuitive to use, even for
less experienced users. More importantly, it highlighted the need to conduct fur-
ther studies addressing the effects of the number of trajectories, the complexity
of the data representation, and the exploration of usable and noticeable methods
to better interact with the visualizations.
Ultimately, our aim is to develop an evaluation framework of trajectory data
visualizations. In fact, the process of this research addresses several components
expected on an evaluation framework, namely: a set of representative visualiza-
tion tasks, associated with common (trajectory) data properties (through the
definition of the taxonomies); the identification of possible users of these visu-
alizations, of factors that may have an effect over the user’s performance, and
metrics to evaluate them (through the comparative assessment of visualization
techniques); and also a set of relevant results, from previous experiences, that
may contribute for future research/analysis.
5 Conclusions and Future Work
The visual exploration of trajectory data has become an important element on
several activities, which raised the interest and the collection of several trajectory
Visualization Techniques of Trajectory Data: Challenges and Limitations 9
datasets. Despite the various studies addressing this type of data, there are still
many open challenges that cross different research areas, such as visualization
and human-computer interaction.
While several visualization approaches have been reported, the lack of con-
crete (and empirical) knowledge regarding the usability and usefulness of these
techniques, alongside the potentially unlimited users are still relevant issues.
In this paper, we highlighted the main limitations of the visual exploration of
trajectory data, and presented the ongoing research addressing those issues. We
argue that, through systematic comparative user studies, it will be possible to:
(i) develop taxonomies of tasks, data, and visualization techniques, that allow
spatio-temporal analysts to understand, and evaluate whether a certain visual-
ization technique is useful for a given task; (ii) empirically compare the different
visualization techniques; (iii) effectively identify possible factors that may have
an impact over the users’ performance, when interacting with techniques for the
visualization of trajectory data; (iv) improve existing visualization techniques.
As the first steps in our work, based on the analysis of previous studies, we
developed a taxonomy addressing trajectory data components, cognitive visual-
ization tasks, and visual variables. Although, we argue that this taxonomy needs
to be expanded, it contributed to the creation and assessment of a small visual-
ization application that allowed for a preliminary interaction with novice users,
in terms of spatio-temporal data analysis, and highlighted several important
challenges.
Current ongoing work consists on the expansion of the referred taxonomy to
address visualization techniques for trajectory data, and its use on the compar-
ative user-based evaluation of representative prototypes of the various visualiza-
tion techniques.
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