Post on 27-Apr-2023
Identifying Specific Spatial Tasks through
Clustering and Geovisual Analysis
Ali Tahir School of Computer Science and
Informatics, University College Dublin
Dublin 4, Ireland
Email: ali.tahir@ucd.ie
Gavin McArdle National Centre for Geocomputation
National University of Ireland Maynooth
Maynooth, Co. Kildare, Ireland
Email: gavin.mcardle@nuim.ie
Michela Bertolotto
School of Computer Science and
Informatics, University College Dublin
Dublin 4, Ireland
Email: michela.bertolotto@ucd.ie
Abstract—As people’s mobility has increased so too has the use of web maps and other geo-technologies for navigational purposes. Daily usage of these technologies, either embedded in smart phones or through advanced Web GIS, involves carrying
out specific spatial tasks. Such spatial tasks can be of various types, context and also consider the physical environment in which the task is being performed. By capturing and analysing
users map interactions, common behaviour along with interests and dislikes can be identified and used as a basis on which to study map personalisation and adaptation. Completing a spatial
tasks essentially corresponds to a mouse trajectory represented as a series of mouse cursor positions on a web map. The challenge is to extract meaning from this spatio-temporal dataset.
With the synergy of exploratory visualisation and data-mining techniques, we present a novel approach to automatically identify and validate a number of spatial tasks forming complex mouse
trajectories within a given study area. The task validation justifies trajectory clustering techniques as well as assisting in attaching semantics to the visual interpretation. This research opens further
avenues for studying map personalisation. Keywords—spatial tasks; mouse trajectories; user profiles;
spatial clustering; OPTICS clustering; Web GIS
I. IN T RO D U CT I O N
The increasing mobility of humans and simultaneous growth
of geospatial technologies have had a profound impact on
society behaviour. As a result portable devices, such as smart
phones, have been embedded with technology required by the
mobile population (GPS, cameras and the internet). Similarly,
smart applications from the geographic domain such as web
maps and Location-based Services (LBS) have been adapted
for a population on the move. Furthermore, familiar terms
such as Location-based Social Networks (LBSN), Geosocial
Networks (GSM) and Volunteered Geographic Information
(VGI), chart the evolution of the social network to a mobile
platform, where people share geographic information about
their movement. In all cases, the applications generate large
volumes of user data which can be classed as Geographic
Information (GI) and contribute to the generation of large
spatio-temporal datasets. When users interact with such data
via web-maps the volume of data displayed can be overwhelm-
ing. As a result, there is a genuine need to adapt web map
content through recommendation and personalisation. In order
to achieve this, user interactions with a map, which generate
trajectories via eye and mouse movements, can be visually
analysed to gain insight into user behaviours and determine
their interests.
Movement data is generally described by a trajectory which
is a series of geographic points with associated time-stamps.
This data can be broadly classified into free movement [1] [2],
constrained movement with respect to some network [3][4]
and indoor movement [5]. As the movement patterns differ
depending on context, analysis of the movement data has to
be adapted accordingly. For example in case of a car user, the
road characteristics have to be considered while the underlying
terrain for pedestrian and animal movement is important.
Movement data is not limited to the physical environment.
Mouse, eye and touch gestures on a computer screen are
all forms of movement which generate specific trajectories.
When interacting with a spatial application, such as a web
map, these trajectories can be analysed to examine Human-
Computer Interaction. In our scenario, we are interested in
studying the mouse movements (a type of free movement) of
users interacting with a web map application. The resulting
trajectories can be represented as: MouseTraj → {x, y, time,
scale} where scale represents the zoom level of the map at a
given time. Map scale provides additional information about a
trajectory (for example, where a particular sequence of mouse
pointers were recorded). Furthermore, a trajectory can also
have associated semantics such as the region or area where it
was recorded [6] [7].
Our research focuses on mouse trajectories which corre-
spond to spatial tasks performed by single or multiple users.
A spatial task can be a simple locate and find task or a more
complex task based on a scenario. Irrespective of the task, a
user has to perform navigation on a map in order to locate
and identify map items. This navigation generates movement
patterns which reveals their intentions. Over a period of
time, user interactions generate huge datasets, especially when
several users over multiple sessions are considered. These
trajectories can be visualised in order to gain insight into user
behaviour, however classical visualisation techniques cannot
deal with such massive datasets as cluttering and occlusion
can occur. The field of Geovisual Analytics [8] is concerned
with developing specific techniques that can support the visual-
isation of such large spatio-temporal datasets through methods
such as clustering and aggregation. The visual analysis of the
results facilitates the identification of patterns, interests and
assists in attaching semantics to trajectories.
In this paper we present a visual analysis approach sup-
ported by trajectory clustering to deal with a mouse movement
dataset obtained by recording user actions as they completed
specific spatial tasks. The spatial tasks are validated using
visualisation and clustering techniques. The results show that
our approach is effective for clustering mouse trajectories
in order to identify specific types of spatial tasks through a
geovisual approach.
The paper is organized as follows. Section II presents some
related work. Section III discusses the characteristics of a
mouse trajectory while Section IV presents the spatial tasks
used in our evaluation. Section V elaborates on experiments
and related discussion. Finally conclusions and direction for
future work are listed in Section VI.
II. RE L AT E D WO RK
This section considers implicit user profiling, geovisual an-
alytic tools, especially those for analysing movement datasets,
and techniques for determining the similarity of trajectories
and clustering which are all relevant to the techniques which
we utilise.
Implicit profiling, unlike explicit profiling, consists of the
system unobtrusively monitoring user interactions with an
underlying interface. The data obtained then permits the per-
sonalisation of information. Implicit profiling is successfully
employed in several (non-spatial) Web-based systems where
user actions such as bookmarking, link clicking and printing
act as indicators of interest in web-page content [9]. Similar
interest indicators have been identified recently in the spatial
domain via interactions (clicking, mouse resting positions)
with map interfaces and content [10], [11]. In our work we
support the building of user profiles by visually analysing
web map usage patterns and provide suitable visualisation
methods to present personalised contents to users based on
their implicitly generated profiles.
Mouse interactions are a good indicator of interest and pre-
vious research in the analysis of mouse interactions with maps
produced a stand-alone research prototype, GIIViz, which
focussed on adapting map content based on the mouse clicks
from a single user [11]. We extend this work and incorporate
mouse trajectories as an element of user interaction analysis.
Our tool VizAnalysisTools [12] incorporates GeoVisual Ana-
lytics for trajectories from single as well as multiple users by
exploiting new visualisation techniques and applying spatial
clustering and aggregation algorithms. Preliminary work was
performed in [13], where an approach was presented to show
how the mouse trajectories can be clustered.
CommonGIS is another tool for exploratory data analysis
and has specific modules for movement datasets [14]. SEC-
ONDO 1 is an open source generic Database Management
System (DBMS) which can implement various data models. It
consists of three main components, a kernel, a query optimizer
1 http://dna.fernuni-hagen.de/secondo/
and a Graphical User Interface (GUI). It has proved to be
a good tool for analysing moving objects. While these tools
provide stand-alone platforms or limited web capability, we
offer an interoperable and web service architecture to deal
with movement data analysis.
Determining appropriate similarity metrics for comparing
multi-characteristic trajectories as well as appropriate tech-
niques for clustering are important areas of research at
present. While the general structure of trajectories are the
same (latitude, longitude, time-stamp), they can differ in their
characteristics (shape, size, volume, semantics and spatio-
temporal descriptors). Clustering methods, applied to various
data involving multiple dimensions, can be categorised as
partitioning, hierarchical, density-based, grid-based, model-
based, constrain-based and clustering high-dimensional data
[15]. Several clustering techniques have been studied for
determining trajectory similarity [16]. Density-based trajectory
clustering [17], trajectory segmentation and sampling [18],
partition and group framework for discovery of common sub-
trajectories [19], trajectory clustering based on future genera-
tion framework [20] and spectral clustering [21] represent the
commonly used approaches. When trajectories are collected in
real time, they usually suffer low resolutions of measurements,
which make noise tolerance a highly considerable feature
[17]. Each technique has its own capability to deal with
various parameters and such variability. In the case of mouse
trajectories, we found density-based clustering methods to be
efficient for finding noise and outliers as well as discovering
clusters of an arbitrary shape which are apparent features
of mouse trajectories. In particular, we have opted to apply
Ordering Points To Identify the Clustering Structure (OPTICS)
[22], a widely used density-based clustering method from
DBSCAN family, to analyse mouse trajectories.
The distance function required to determine the similarity
between trajectories is an important input to any clustering
algorithm. Morris and Trivedi [16] have evaluated several
approaches including: distance similarity metrics based on
fixed length measures called Principle Component Analysis
(PCA) subspace and Hu euclidean, time-normalised distances
which are also suitable for trajectories of unequal length called
longest common subsequence, dynamic time warping, and
modified hausdorff. We have adapted a common destination
distance measure [17] for our specific case to deal with mouse
movements. However we term ’common destination’ as ’close
destination’ as mouse movements are very random. In the real
situation it is difficult to assume that a mouse trajectories of
different users will exactly rest at the same location in the same
way that pedestrians might end up in the same building upon
completing a specific task. Therefore a more liberal description
of a common destination is required.
III. MOU S E TRAJE CTO RI E S
A. Overview
A mouse trajectory is formed as a result of user interaction
with a map. The positions of the mouse cursor are recorded
and related events are logged and stored in a spatial database.
Mouse
Trajectory
Database
Visualisation Web
Service
Pattern
Identification
User sessions/
spatial tasks
Decision
Making
Users
Interaction
History
Clustering and
Aggregation Web
Service
Visual
Semantics
B. Clustering
The movement of a mouse trajectory can be termed as free
movement which is not constrained by an underlying network.
Therefore it is quite likely that the generated data will be
large and contain a certain amount of noise. With the help of
a Geovisual tool which we developed [12], the geographical
extent of mouse trajectories can be visually analysed on a
web map along with other statistical data (mouse speed,
mouse hesitation and map scale). However, large datasets may
clutter the visual display and hence visualisation alone cannot
tackle this situation. To resolve this, Geovisual Analytics [8]
are required. For our purpose we have opted to choose the
OPTICS [22] clustering algorithm. This technique locates a
core distance and reachability distance of an object (trajectory Figure 1. A geovisual trajectory clustering approach
TABLE I. SPAT I A L TA S K S
Task No Task
Type Task Name
1 STLS How many motor ways are there in Ireland 2 STLS Find the total number of exits on M50
motorway in Dublin 3 STLO Name the southern cities of Ireland starting
with letter ’W’ 4 STLO Write the names of areas close to the start-
ing and ending of M50 motorway in Dublin 5 STLO Which is the nearest hospital to Heuston
train station in Dublin 6 STLI Name the possible tourist attraction closest
to Saint Stephen’s Green Park in Dublin city center
7 STLD What is the name of the largest park in Dublin
8 STLS Write the total number of bridges on Liffey River in Dublin starting from Grattan bridge towards the eastern coast
9 STLS How many vehicle entrances are there on the University College Dublin (UCD) cam- pus. UCD is located on N11 in south Dublin
10 STLO Locate Connolly train station in Dublin. What is the name of the nearest canal to
this station
The additional attributes which are captured along with the
mouse positions are a time-stamp for each location, the speed
in pixels per seconds, the duration of mouse pause (rest) at
each position and the map scale where the given position
was recorded. These additional parameters are significant
when studying map personalisation. They can reveal users’
interests in a particular portion of a map and classify users
(novice, experienced, etc.). Rendering a mouse trajectory can
be challenging due to dynamic nature of users interest. For
example, when a user moves the mouse cursor off a map to
toggle between various geographic layers, a gap between the
subsequent mouse cursor positions is created and techniques
to overcome this are required. In such a situation it may
be advantageous to visualise the relationship between map
and non-map movements to gain a deeper insight into user
intentions.
in our case) with respect to its predecessor. This technique
produces an ordering of the dataset which can be seen with the
help of a graph called a reachability plot (Fig. 2). Additionally,
it stores a core distance and a reachability distance. The x-
axis shows the number of objects in a database while the
y-axis presents a reachability distance. From this interactive
and intuitive graph, clustering can be obtained by choosing an
appropriate threshold value of reachability distance on which
to group trajectories.
C. Approach
A geovisual trajectory clustering approach is illustrated in
Fig. 1, while a detailed architecture and description are pre-
sented in [12]. User interaction history is recorded and stored
in a spatial database as the user interacts with a web map. Due
to the use of an interoperable web architecture, web services
can request user sessions in the form of mouse trajectories
from the database. At the next level, mouse trajectories are
stored with their associated spatio-temporal information as
well as characteristic information such as map scale, speed
and duration. The visualisation web service can request any
number of users and their corresponding sessions to visualise.
Similarly, the clustering and aggregation service provides
mechanisms to perform classification of mouse trajectories. All
the aggregated clusters can be visualised simultaneously which
helps inform decision making for the analyst. Furthermore,
patterns can be visually identified and semantics attached with
the help of the tool.
IV. SPAT I A L TA S K S
Spatial tasks can be broadly categorised into a search and
locate task, an orientation task, deciphering, symbol meaning,
a route finding task and a more complex scenario based
task consisting of series of subtasks [23]. Additionally, map
navigation tasks can also be categorised as Precise versus
Fuzzy tasks [24]. Precise navigation tasks can be completed
non-visually where a map is the product of the user’s request.
On the contrary, fuzzy navigation tasks are completed visually
where a map is the tool for completing the users request. Due
to the awareness and availability of geo-spatial and mobile
technologies it has become more popular for the general public
to perform routine map navigational tasks.
Figure 2. A reachability plot showing valleys in the graph depict good density clusters. The non-valleys show less dense clusters
For our purpose we have designed ten spatial tasks and
divided them in four distinct categories. Spatial Task Locate
and Scan (STLS) defines spatial tasks which involve locating
geographical objects while scanning and navigating on a
map in order to find the answer. The second type is termed
as Spatial Task Locate with respect to Orientation (STLO)
where the objective is to locate an item with the help of
an orientation, for example considering directions (such as to
locating an item north of object X). The third type is called
Spatial Task Locate Interest (STLI) where a task is open ended
and users provide an answer based on their interest. This
category is interesting as users are explicitly asked to show
their interests with the help of map browsing actions. The final
category is Spatial Task Locate and Decipher (STLD) where an
item has to be located based on deciphering (process to decode
an object). For example, this relates to the characteristics of
geographical objects such as largest, smallest and nearest. Our
main aim is to observe and investigate usage patterns based
on these spatial tasks. There may be additional types of spatial
tasks which can be included however the ten tasks we have
defined (Table. I) are sufficient to study mouse movement
patterns.
To complete and answer the spatial tasks, the users in-
teracted with a Web-based map. They were free to perform
navigation in their own style using standard map operations
as the whole process was unsupervised. The spatial tasks
were carefully chosen to clearly convey the goal of each task.
Furthermore, they were designed to include a variety of user
interactions. Table. I shows the type of tasks and their complete
description.
Figure 3. All trajectories visualised Figure 4. Clustered mouse trajectories
V. EX P E RI M E N TA L RE S U LT S AN D DI S CU S S I O N
A. Experiment Setup
Ireland was selected as the study area due to the authors’
familiarity with it and their ability to design meaningful spatial
tasks as a result. The spatial tasks were distributed across the
complete Irish map, however the majority of spatial tasks were
designed involving the city of Dublin, due to the complex
nature of the road network and quantity of spatial features.
Most of the test subjects who participated in experiments were
physically located in Dublin however not necessarily familiar
with its geography. The central goal of the study is to analyse
mouse trajectories (corresponding to specific spatial tasks) in
order to infer how users perform such tasks. Feedback was
collected after the tasks which provides additional indicators
such as familiarity with the map and the given study area. This
feedback can be correlated with the results of the tasks.
The experiments were conducted using a web platform
which records every mouse movement and interaction with
the map. The trials were carried out unsupervised as opposed
to a controlled environment. This removes pressure from
participants and the feeling that they need to meet certain ex-
pectations, which are issues with supervised experiences such
as Think Aloud [25]. A total of 12 participants (10 males and
2 females) volunteered for this trial, 11 had previously used
interactive web maps such as Google Maps. For this trial we
wish to collect meaningful interaction data which is reflected
in the choice of mostly users with web map experience. Of
the 120 tasks completed by participants, 117 were used for
evaluation purposes. In some sessions participants knew the
answers immediately and did not perform navigation for a
particular task and so these tasks were removed from our
analysis. The remaining data was stored and analysed using
the approach discussed above.
B. Clustering Algorithm Parameters
To cluster the data, the OPTICS clustering algorithm re-
quires two parameters in order to generate the results. The
first one is a distance threshold (ɛ), while the second is
the minimum neighbours (minNbs). The distance threshold
determines how close two trajectories are from each other.
The minimum neighbours on the other hand are the minimum
number of objects which should form a cluster. The larger the
Figure 5. Clusters correspond to spatial tasks a) Task 5 b) Task 10 c) Task 6 d) Task 7 (See Table. I)
Figure 6. Clusters correspond to spatial tasks a) Task 9 b) Task 3 c) Task 2 , 4 and 8 d) Task 1 (See Table. I)
Figure 7. Cluster cardinalities relative to spatial tasks
value of these two parameters, the better the results that are
returned [22]. Based on the reachability plots and statistical
information, we selected a distance threshold value of 100
kilometres and minimum neighbours of 5 as input to clustering
algorithm. The large distance threshold is required since the
spatial tasks involved interacting with a map of the whole
island of Ireland.
C. Visual Analysis
User interaction data corresponding to the mouse trajecto-
ries for all completed tasks is shown in Fig. 3. Clustering
was applied to resolve the cluttered nature of the display
while also classifying the trajectories. Based on the input
parameters discussed in Section V-B, the OPTICS clustering
algorithm produced the reachability plot illustrated in Fig. 2.
This plot shows the re-ordered mouse trajectories on the x-axis
while the y-axis presents associated reachability distances. The
noticeable features in the plot are the four valleys which show
the density of data. These valleys correspond to clusters which
represent several spatial tasks. Fig. 4 presents the classified
trajectories after clustering was performed. Similar groups
were assigned the same colours for visualisation purposes.
While this image marginally improves the cluttering issue,
it is still difficult to solicit meaning and classify trajectories.
However, since all clusters are represented as Keyhole Markup
Language (KML) layers, they can be toggled on and off using
a layer view. For visual interpretation, it is advantageous to
show a subset of the trajectories at a time by switching layers
on and off. Fig. 5 and 6 provide more semantic information
when visualised at certain map scales such as an identification
of a location or area where there is a more user activity
on a map. This visual semantic approach assists in spatial
task verification. Additionally, we can clearly observe that the
clusters are obtained based on trajectory density.
Fig. 7 illustrates a bar chart showing the number of tra-
jectories (cardinalities) on the y-axis, while the x-axis shows
the cluster number and the associated spatial task which the
trajectories in a given cluster predominantly belong to. In total
there were 12 users and 10 spatial tasks so that each task
contains 12 trajectories. Therefore, in an ideal situation, 10
clusters, each containing 12 trajectories (as its cardinality)
should be revealed in order to validate the tasks. However,
only 117 trajectories were usable and this is reflected in the
results detailed below and in Fig. 7.
Fig. 7 presents four clusters (3, 4, 5, 7) corresponding to
spatial task (5, 7, 9, 3) as shown in Table. I with cardinalities
(8, 9, 10, 11) respectively. These four clusters are also evident
as four valleys in the reachability plot (see Fig. 2) and
show that in these 4 cases, the approach very successfully
grouped similar spatial tasks together. Although there is some
misclassification, clusters (1, 2, 8) with cardinalities (16, 16,
18) correspond to specific spatial tasks due to predominance
of trajectories from spatial tasks (6, 10, 1) respectively in
these clusters. Cluster 8 (task 1), according to the reachability
plot contains noise. Task 1 involved finding the total number
of motorways in Ireland. Due to the nature of this task,
trajectories were widely spread around the map of Ireland (see
Fig. 6 (d)) and hence they did not fulfil the clustering input
parameter requirements and so were termed as noise according
to OPTICS. Despite the presence of noise, the clustering
technique was successfully able to verify the spatial task. The
maximum number of trajectories (29) are observed in cluster
6. This cluster (see Fig. 6 (c)) corresponds predominantly to
spatial tasks 2 and 4 with some trajectories from task 8. This
was caused due to overlap in the area in which these spatial
tasks took place.
VI. CO N CL U S I O N A N D FU T U RE WO RK
User interactions in the form of mouse trajectories can re-
veal users interest and dislikes. These behaviours are very rel-
evant for map personalisation. In this paper we demonstrated
a spatial task validation approach with the help of geovisual
trajectory clustering. Our technique proved to be effective and
successfully grouped 117 trajectories into 8 clusters based on
their spatial closeness. The clusters correspond to the tasks
which were used to generate the trajectories. The scenario can
be made more powerful with the addition of temporal simi-
larity metrics and ideally, a combination of both approaches
will reveal users’ similarity. The approach currently relies on
a manual stage where the clusters are visually identified from
the valleys in a reachability plot. We would like to explore
automatic techniques to extract such information. Additionally,
we aim to perform clustering analysis on multiple map scales
unlike the scale invariant approach. Progressive clustering will
also be applied to find similarities between each spatial task
by applying several distance measures [17].
ACK N OW L E D G M E N T
Research presented in this paper was funded by a Strategic
Research Cluster grant (07/SRC/I1168) by Science Foundation
Ireland under the National Development Plan. The authors
gratefully acknowledge this support.
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