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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