Supporting Event-based Geospatial Anomaly Detection with Geovisual

Analytics

Orland Hoeber

and Monjur Ul Hasan

Department of Computer Science, University of Regina, Regina, SK, S4S 0A2, Canada

Department of Computer Science, Memorial University of Newfoundland, St. John’s, NL, A1B 3X5, Canada

Keywords:

Geovisual Analytics, Event-based Anomaly Analysis, Spatial Event Representation, Anomaly Detection.

Abstract:

Collecting multiple geospatial datasets that describe the same real-world events can be useful in monitoring

and enforcement situations (e.g., independently tracking where a ﬁshing vessel travelled and where it reported

to have ﬁshed). While ﬁnding the obvious anomalies between such datasets may be a simple task, discover-

ing more subtle inconsistencies can be challenging when the datasets describe many events that cover large

geographic and temporal ranges. This paper presents a geovisual analytics approach to this problem domain,

automatically extracting potential event anomalies from the data, visualizing these on a map, and providing

interactive ﬁltering tools to allow expert analysts to discover and analyze patterns that are of interest. A case

study is presented, illustrating the value of the approach for discovering anomalies between commercial ﬁsh-

ing vessel movement data and their reported ﬁshing locations. Field trial evaluations conﬁrm the beneﬁts of

this geovisual analytics approach for supporting real-world data analyst needs.

1 INTRODUCTION

The analysis of event-based anomalies between mul-

tiple geospatial datasets is a challenging task, due

to the geotemporal complexity of the data (Dykes

and Mountain, 2003; Kraak and de Vlag, 2007;

MacEachren and Kraak, 2001). However, such anal-

yses can provide useful insights, helping analysts to

identify trends and patterns among the activities upon

which the data were collected, as well as identify-

ing potential problems with the data collection, data

processing, or even intentionally introduced inaccu-

racies. While simply visualizing the data in layers on

a map may allow for the obvious to become apparent,

discovering previously unknown anomalies within the

data can be challenging.

Although geospatial datasets may take multiple

forms with varying complexity, this research con-

siders geospatial events, independently described by

movement and geographical point datasets. These in-

dependent datasets represent the actions of the same

conceptual entities, but from different perspectives

(e.g., the movement of an entity over time, and the

location and time at which the entity performed some

notable action). When the temporal granularity be-

tween the datasets is synchronized, detecting anoma-

lies can be done with a simple distance calculation.

However, such synchronization is not guaranteed for

independently collected data sets. Thus, uncertainty

is introduced into the anomaly detection process, ne-

cessitating the need for human-centred analysis to

separate the meaningful anomalies from those that are

a result of the mismatch between the temporal scales.

Following a geovisual analytics approach, this re-

search combines automatic data processing methods

with information visualization and human-computer

interaction techniques, with the goal of supporting

data exploration, analytic reasoning, information syn-

thesis, and decision-making (Keim et al., 2008), with

special consideration given to the geotemporal as-

pects of the data (Andrienko et al., 2007a). After

matching the datasets and calculating the potential for

the events being anomalous, these are represented on

a map allowing the analyst to examine their relation-

ship with one another. Interactive ﬁltering tools are

provided to address information overload issues asso-

ciated with showing too much data at the same time.

These include spatial, temporal, and attribute-based

ﬁltering, along with novel anomaly threshold ﬁlters

that control how the datasets with different temporal

granularities are matched to one another. Interactively

manipulating these ﬁlters to focus on a particular type

of anomaly being investigated allows analysts to hide

uninteresting features of the data in order to isolate

Hoeber O. and Ul Hasan M..

Supporting Event-based Geospatial Anomaly Detection with Geovisual Analytics.

DOI: 10.5220/0005268000170028

In Proceedings of the 6th International Conference on Information Visualization Theory and Applications (IVAPP-2015), pages 17-28

ISBN: 978-989-758-088-8

 2015 SCITEPRESS (Science and Technology Publications, Lda.)

those that are of interest. Individual entities can be in-

spected in detail, showing their complete movement

path and linking them to other anomalies that may

have occurred for the same entity.

While many situations exist in which data is being

collected from multiple sources that relate to the same

conceptual entity, this particular research was moti-

vated by the challenges of comparing where ﬁshing

vessels have travelled and their reported ﬁshing loca-

tions. Anomaly detection in this domain can be used

for detecting data entry errors and instrument failures,

as well as for enforcement purposes. Because of the

signiﬁcant differences in temporal granularity (hourly

ﬁshing vessel position data and daily ﬁshing location

data), automatic methods are difﬁcult to tune in order

to ﬁnd the important anomalies and avoid an overload

of false-positives. Manual analysis often consists of

detailed visual inspection and comparison, requiring

signiﬁcant cognitive effort and focus. The geovisual

analytics approach described in this paper automates

the menial aspects of anomaly detection, allowing the

analysts to consider, examine, and explore among a

much larger number of potential anomalies than with

their existing methods. This is illustrated with a case

study, and supported with the results of ﬁeld trial eval-

uations with expert data analysts.

2 RELATED WORK

While much of the research on geovisualization and

geovisual analytics focuses on exploration within a

single geospatial dataset, some have studied meth-

ods for handling multiple related datasets. In addi-

tion to the traditional approach of layering multiple

datasets on the same map, more advanced approaches

have been explored. For example, it is possible to

transform and merge multiple geospatial datasets to-

gether into a common viewing framework (Treinish,

2000). Alternately, multiple coordinated views of the

different datasets may be provided (Johansson and

Jern, 2007; Mandiak et al., 2005), such that manipula-

tions in one representation (e.g., panning and zoom-

ing) affect the conﬁguration of the others. In cases

where the multiple datasets contain many different at-

tributes, parallel coordinate plots have been used to

ﬁlter the data and choose which aspects to show on

the map (Lundblad et al., 2009).

When the geospatial data represents the move-

ment of entities through space and time, new com-

plexities are introduced in the representation and anal-

ysis of such movement data (Kraak and de Vlag,

2007; Andrienko et al., 2012). While ﬂow lines can

support the interpretation of the movement paths, si-

multaneously representing the data from many enti-

ties often results in a visually complex display that

is difﬁcult to decode (Enguehard et al., 2013). Some

alternatives to addressing this problem include using

animation (Andrienko et al., 2000), taking advantage

of the third spatial dimension with space-time cubes

(Kapler and Wright, 2005; Kraak, 2003), perform-

ing automatic machine learning on the data to ex-

tract and represent the high-level features (Andrienko

et al., 2007b), and providing complex methods for in-

teractively ﬁltering the data to highlight the interest-

ing low-level features (Enguehard et al., 2013).

A further challenge with using movement data is

extracting events based on the motion and contextual

characteristics of an entity. Andrienko et al. (2011)

proposed a general approach to extract noteworthy

events from movement data, treating these as inde-

pendent objects. They suggest a conceptual model in

which movement is considered in relation to events of

diverse types and extents in space and time. With this

model, the relationships between movement events

and elements of the spatial and temporal contexts in

which those events are occurring can be visually rep-

resented and analyzed.

While the aforementioned works have sought to

ﬁnd similarities between elements within the geospa-

tial data, either automatically (Andrienko et al., 2011)

or based on interactive exploration (Enguehard et al.,

2013), few have approached the problem of ﬁnd-

ing differences, discrepancies, or anomalies. The

LAHVA system (Maciejewski et al., 2007) was de-

signed to extract events from human emergency room

data and veterinary hospital data, providing a visual

interface to allow analysts to detect similarities and

differences in order to identify disease outbreaks be-

fore they become epidemics. GTdiff (Hoeber et al.,

2011) took a small multiples approach to visually rep-

resenting the changes in a geospatial dataset, organiz-

ing a series of difference graphs in an inverted pyra-

mid structure to allow analysts to explore where and

when the data have changed. While these approaches

work well when the datasets are temporally synchro-

nized to one another, they do not address the situation

where there may be a mismatch in the granularity at

which the data was collected.

3 PROPOSED APPROACH

The focus of this research is to explore methods

for automatically extracting anomalies from indepen-

dently collected movement and geographical point

data that represent the activities of the same concep-

tual entities, and to visually represent this informa-

IVAPP2015-InternationalConferenceonInformationVisualizationTheoryandApplications

tion within an interactive interface that supports ex-

ploration and analytical reasoning about the anoma-

lies and their underlying sources. In order to real-

ize this goal, four critical elements are necessary: (1)

event extraction, (2) geospatial anomaly detection and

thresholding, (3) anomaly representation, and (4) in-

teractive ﬁltering and exploration. Here, we outline

the key elements of each of these aspects of our re-

search, along with a brief description of the imple-

mentation details.

3.1 Event Extraction

The event extraction process in this work is based on

the knowledge that the geospatial point dataset has

captured the existence of the events of interest. Us-

ing common identiﬁer ﬁelds between the two datasets

supports the matching of these events to their asso-

ciated entities in the movement dataset. The compli-

cating factor is the potential mismatch between the

temporal granularity of the datasets.

If the movement data is at a higher level of gran-

ularity than the geospatial point data, then a pair of

movement data points will be mapped to a single

event data point, representing where the entity was

before and after the event occurred. Alternately, if the

movement data is at a lower level of granularity than

the geospatial point data, then a series of movement

data points will be mapped to the event, representing

the path of the entity during the temporal range of the

event. In either case, there is a degree of uncertainty

in the matching of the data, which must be addressed

when seeking anomalies.

3.2 Geospatial Anomaly Detection and

Thresholding

Once the movement data and geospatial point data are

matched together in the context of the events, the task

then is to detect whether any geospatial anomalies ex-

ist within the events. The approach taken in this work

is to consider both the geographic distances between

the event and the entity location, and the amount of

time (i.e., the number of movement data points) the

entity spent within an acceptable distance threshold.

For example, in the case where there are multiple

movement data points captured at hourly intervals and

related to a single event that occurred sometime dur-

ing a six hour period, the event may be considered

normal if the entity was within a distance of 1 km for

three or more hours, and an anomaly otherwise.

Figure 1 illustrates three possibilities within this

scenario. Here, the movement data is shown with ar-

rows indicating the movement direction, and the lo-

(a) (b) (c)

Figure 1: Examples of potential anomalies between move-

ment paths and event locations.

cation of the event is shown as a point in space with

a circle indicating the threshold distance that is con-

sidered acceptable. In the ﬁrst case (Figure 1(a)), the

entity was not within an acceptable distance from the

event at any time. In the second case (Figure 1(b)),

the entity was within an acceptable distance from the

event, but only long enough for two data points to be

captured. In the third case (Figure 1(c)), the entity

was near the event location for four continuous hours.

As a result, in this example, we may consider the ﬁrst

and second cases anomalies, and the third normal.

A unique feature of this approach is the use of two

parameters for determining whether a speciﬁc event

is considered an anomaly, one based on space and the

other on time. Choosing appropriate threshold set-

tings for these parameters cannot be done automati-

cally, since they require domain-speciﬁc knowledge

regarding the actual activities of the entities. As a re-

sult, interactive support is provided to allow analysts

to manipulate these threshold parameters in order to

ﬁlter the set of potential anomalies to show those that

exhibit abnormal behaviour. Simple slider controls

are employed for this purpose, supporting indepen-

dent adjustment of the minimum distance from the

reported event and the amount of time the entity is

expected to have been within this distance radius.

3.3 Anomaly Representation

Representing anomalies that have been detected in the

context of the movement of entities requires visual en-

coding of multiple aspects of the data. These include

representing the movement paths of the entities, the

locations of the events, and the positional discrepan-

cies between the two. Anomaly visualization should

allow analysts to visually group data corresponding to

the events and the movement paths easily and quickly,

while at the same time, not overwhelm them with a

visual representation that is difﬁcult to interpret.

Since in the context of this work the positional dis-

crepancies between the datasets are the essence of the

anomalies, it is important to illustrate these clearly.

Taking advantage of the Gestalt Principle of Connect-

edness (Koffka, 1935; Palmer and Rock, 1994), each

anomaly is represented by the graphical connection

SupportingEvent-basedGeospatialAnomalyDetectionwithGeovisualAnalytics

of a line between the event location and the nearest

position on the movement path within the associated

timeframe. Representing anomalies in this way is a

powerful mechanism for expressing the relationships

between the data, supporting pre-attentive processing

(Ware, 2004). An added beneﬁt of this approach is

that the severity of the anomalies will automatically

be visually encoded, with more extreme anomalies

carrying more visual weight in the display due to the

length of the connecting lines.

It is also important to represent the paths of the

entities from the movement dataset in a way that can

readily be perceived and interpreted as a movement

path that is distinct from the connection to the event.

For this, ﬂow lines are used, with chevrons represent-

ing the locations of the movement data points along

with the direction to the next data point in the series.

Since the chevrons can add visual complexity to the

movement path when there are a large number to dis-

play, these can be interactively hidden or shown.

Representing such data on a geographical map in a

way that can readily be perceived and correctly inter-

preted is challenging (MacEachren and Kraak, 2001).

In particular, it is important to make careful choices

of the colours for the movement data ﬂow lines and

the anomaly lines used to connect these to the event

locations. Knowing the ambient colours used to rep-

resent the geographical features on the map can allow

for the selection of a set of colours that are percep-

tually distinct from the base map. For example, it is

common to represent oceans and land on a map us-

ing shades of blue and green, and other features using

black or grey. Following the Opponent Process The-

ory of Colour (Hering, 1964), this leaves yellow, red,

and white as perceptually distinct colours for repre-

senting aspects of the data.

An example showing a set of signiﬁcant anoma-

lies within an oceanic dataset is provided in Figure

2. Here, one can readily identify the movement paths

of the entities and the severity of the anomalies. In

addition the pattern of the anomalous behaviour can

also be observed and interpreted. While there remains

some overlap and clutter in this display, the selection

and highlighting features described in the following

section support an interactive disambiguation of the

anomalies.

It is also important to consider how to deal with

and represent situations where there are missing data

points in the movement dataset. This is a concern,

since it is common for movement datasets to be col-

lected remotely, using a regular time interval for log-

ging the data. When there are communication prob-

lems, data points may not be properly saved in the

dataset, leading to missing data. For these situations,

Figure 2: Anomalies are represented by the yellow line con-

necting where an event was reported and the white move-

ment path of the entity.

in order to maintain a consistent representation of the

movement activity, the missing data is interpolated on

a straight line between the known data points. Since

an anomaly may be detected as a result of this miss-

ing data and the interpolation method for ﬁlling in the

gaps, it is important to visually convey this interpola-

tion back to the analysts. This is done by replacing

the chevrons that represent the movement data points

with empty circles. However, because in some cases

a few missing data points may not have much of an

affect on the anomaly detection, an option is provided

to allow the analyst to determine how many missing

data points are necessary in order for these to be high-

lighted as distinct from the regular movement path

representation.

Since it can be difﬁcult to systematically inspect

each anomaly when they are represented only on

a map, a secondary representation is also provided.

Event anomalies are grouped by their entity identi-

ﬁer, and represented in a tree structure to facilitate in-

teractive selection. Each entity for which at least one

anomaly was detected is included as a node at the top-

level of the tree. Along with the entity identiﬁer, in-

formation about the number of anomalous events and

the total number of events are provided. Individual

events for a given entity are included as children of

the entity node, showing the timestamp and domain-

speciﬁc data about the event. A checkbox beside each

node in this tree structure allows for the visibility of

the anomalies to be toggled within the map display.

This tree structure and map operates as coordinated

views, dynamically updating what is shown in each

based on interactive ﬁltering and focusing. This is il-

lustrated in a screenshot of the entire interface display,

provided in Figure 3.

IVAPP2015-InternationalConferenceonInformationVisualizationTheoryandApplications

Figure 3: The interface for event-based geospatial anomaly detection and exploration includes a temporal ﬁlter (top-right),

map view of the movement paths and anomalies (bottom-right), tree representation of the entities and anomalies (top-left),

and interactive ﬁlters (bottom-left).

3.4 Interactive Filtering and

Exploration

Interactive ﬁltering is provided by four analyst-

controlled parameters: temporal extent, spatial extent,

anomaly thresholding, and ancillary data ﬁltering. By

ﬁltering out uninteresting or obvious anomalies, ana-

lysts are able to focus their attention on those that are

of particular interest. Any modiﬁcation of these pa-

rameters results in an interactive update of the anoma-

lies shown in all views of the system, allowing the

analysts to readily see the results of their actions.

The temporal and spatial ﬁltering features oper-

ate using commonplace interface controls and inter-

action mechanisms. A timeline is provided showing

the temporal range of the datasets. A temporal win-

dow control allows analysts to modify the upper and

lower bounds of the window, as well as pan the win-

dow over the temporal range. The spatial ﬁltering op-

erates within the geographic representation, allowing

analysts to zoom to the desired spatial scale and pan

to regions of interest.

As previously noted, the anomaly threshold con-

trols can be used to ﬁlter the data to only show those

that match the spatial and temporal threshold param-

eters. Two slider controls allow analysts to set the ac-

ceptable distance between the event location and the

associated movement data points, and the amount of

time the entity must have remained within this dis-

tance. All events that exceed these parameters are

considered anomalies and are displayed with the sys-

tem; all others (e.g., normal behaviour) are hidden.

In many cases, when an event occurs, there is ad-

ditional data that is collected in the context of this

event. For example, in the motivating case for this re-

search, when a ﬁshing event occurs, data on the catch

amount is logged. In the context of detecting anoma-

lies, we consider this ancillary data, and provide a

mechanism for the analysts to ﬁlter what is shown

based on this data. Another type of ancillary ﬁlter can

be used to hide data that is clearly not possible. For

the ﬁsheries case, this will remove anomalies where

the reported ﬁshing location is on land.

These interactive features for ﬁltering the data can

be used to address a common concern with geovisu-

alizaiton and geovisual analytics: the visual clutter

that arises when many data elements are displayed in

close spatial proximity (J

anicke et al., 2012). Tem-

poral, spatial, anomaly threshold, and ancillary data

ﬁltering all enable the analyst to interactively control

how much data is shown. An example of this is shown

in Figure 4, where a large number of anomalies is re-

duced to a manageable number through interactive ﬁl-

tering.

The highlighting of one or more anomalous events

is enabled by clicking on the anomalies within either

the map or tree views, supporting an interactive in-

spection of the data. Doing so changes the colour en-

coding of the movement path for the anomaly from

SupportingEvent-basedGeospatialAnomalyDetectionwithGeovisualAnalytics

Figure 4: Showing all potential anomalies within the

datasets can result in signiﬁcant visual clutter (top). Inter-

active ﬁltering and a narrowing deﬁnition of what it means

to be an anomaly can allow the analyst to focus on a small

number of anomalies to examine in detail (bottom).

white to red, both for the selected anomalous event,

as well as for any other anomalies for this same en-

tity. Additional contextual information of the move-

ment path of the highlighted entity within the selected

temporal range is added to the display as white lines

without directional markers, providing contextual in-

formation of where the entities travelled before, be-

tween, and after the anomalous events. In addition,

the visual intensity of the anomalies are provided at

three different levels: the highlighted event is shown

at a high level of intensity; other anomalies for this en-

tity are shown at the normal level of intensity; and all

remaining non-selected anomalies are shown at a low

level of intensity. This allows the analysts to readily

see what was selected, along with other contextual in-

formation about the entity, while fading the remaining

data to the background (see Figure 5).

4 CASE STUDY

In order to illustrate the features of the proposed ap-

proach for event-based geospatial anomaly detection,

a case study is presented in the context of commercial

ﬁshing datasets. The datasets were collected for the

Figure 5: Highlighting an anomaly changes the colour en-

coding of the path of the entity to red and increases the

brightness of the yellow link to the event location. Other

anomalies for this same entity remain at the normal visual

intensity, and all others are faded to allow the analyst to fo-

cus on what was selected.

inshore scallop ﬁsheries in Atlantic Canada over the

two year period of Jan 1, 2008 to Dec 31, 2009. One

of the stipulations for receiving a commercial ﬁshing

license in Canada is that each ﬁshing vessel must be

equipped with a Vessel Monitoring System (VMS),

which automatically and independently records the

GPS location of the vessel on an hourly basis. Upon

returning to the port after a multi-day ﬁshing voyage,

the vessel must report where it ﬁshed each day, as well

as the amount of ﬁsh caught in each location. This

data is logged in the MarFis database system, along

with details regarding the ﬁshing vessel identiﬁer and

license. The data analysis goal in this case study is to

explore and understand the spatial discrepancies be-

tween these two datasets that are meant to describe

the same conceptual entities (ﬁshing vessels) and their

noteworthy events (ﬁshing activities).

These particular datasets contain a substantial

amount of data that must be analyzed to identify

anomalies. From the MarFis data, 209 ﬁshing vessels

preformed a total of 18,030 ﬁshing events during the

two-year period, representing an average of 43.1 ﬁsh-

ing events per vessel per year. Within the VMS data,

1,967,341 data points were collected for these ﬁsh-

ing vessels, representing an average of approximately

196 days at sea per vessel per year. This discrepancy

between the average days of ﬁshing scallop and the

average days at sea is a result of the limits on the scal-

lop ﬁshing season and the common practice of ﬁsh-

ing vessels obtaining licenses to ﬁsh multiple species

throughout the year. Of note is the temporal mis-

match between the two datasets: the VMS data con-

tains hourly ﬁshing vessel locations, while the MarFis

data contains daily locations of where they ﬁshed. As

a result, extracting and analyzing anomalies is not as

straightforward as it would ﬁrst appear.

The current anomaly analysis practice among ﬁsh-

IVAPP2015-InternationalConferenceonInformationVisualizationTheoryandApplications

eries experts is to map each dataset independently,

and then manually inspect and compare the maps to

ﬁnd anomalies. Clearly, doing this for many vessels

over a long period of time and a large geographic

range is not feasible. As such, a ﬁrst analysis step is

to identify a small subset of vessels to study in detail.

The selection criteria may include choosing speciﬁc

vessels over a speciﬁc time period, specifying vessels

based on having been near some point of interest such

as a marine protected area, or even performing ran-

dom sampling for inspection purposes. With the data

ﬁltered in this way, the process of tracing the move-

ment of a particular vessel on one map and comparing

it to where it reportedly ﬁshed on another map is cog-

nitively complex and requires a great detail of focus

and attention. Even using GIS approaches that sup-

port multiple views and interactive layers does not al-

leviate the cognitive task of having to manually link

the data. Because of the effort involved, this type

of analysis is generally only done when there is al-

ready a clear indication of the existence of notewor-

thy anomalies within the data for a particular subset

of vessels. This means that this approach is seldom

used to discover anomalies, but is instead used to ver-

ify those that are already suspected or known.

Using this geovisual analytics approach for detect-

ing and discovering new anomalies in the data, a good

starting point is to view the entirety of the data with

a generous deﬁnition of what constitutes an anomaly.

For example, in the context of ﬁshing for scallop, nor-

mal ﬁshing events might be deﬁned as those for which

the vessel was within 40 km of the reported location

for more than 5 hours. The speciﬁc settings for such

an anomaly threshold ﬁlter would be based on the an-

alyst’s experience with the ﬁshing practice (e.g., how

long ﬁshing sessions normally last, as well as how far

they normally travel in this time) and the expected ac-

curacy within the data. With this particular dataset,

such a ﬁrst-pass ﬁlter eliminates 12,789 normal ﬁsh-

ing events, leaving 5,241 for further exploration and

ﬁltering. While showing these within the system re-

sults in a signiﬁcant amount of visual clutter (see Fig-

ure 6(a)), it does provide a high-level overview of the

extent and pattern of anomalies within the data. Even

from this cursory analysis of the data, it is clear that

there are a signiﬁcant number of cases for which the

ﬁshing location was reported on land. While this fact

may have been discovered simply by mapping all of

the ﬁshing locations within the MarFis data, the ex-

tent of the problem may not have been clear due to

the common practice of only inspecting a small num-

ber of vessels during a limited period of time.

To further explore among the anomalies, the ana-

lyst may choose to focus on data for a particular pe-

riod of time, and remove those anomalies that have

the ﬁshing location reported on land. Figure 6(b)

shows 43 anomalies detected among a total of 466

ﬁshing events in the month of September 2008. Fur-

ther reduction of the visual clutter is achieved by

hiding the directional markers on the ﬁshing vessel

movement data, since at this level of detail the vessel

direction is not contributing to insight into the data.

Based on the analyst’s knowledge of the ﬁshing

practice within this region, the anomaly threshold

could be conﬁgured to require the vessel to be closer

to the reported ﬁshing location (e.g., within 25 km),

but for less time (e.g., at least 2 hours). The analyst

may also wish to view only those ﬁshing events for

which a large catch amount was reported (e.g., 300 kg

or more). Doing so reduces the total number of ﬁshing

events to 328, of which 34 are detected as anomalies.

These are illustrated in Figure 6(c), from which three

separate geographical regions can be readily identi-

ﬁed in which anomalies are present: the northern bay

region, the central bay region and the southern region.

Noting the large number of anomalies in the

southern region, the analyst may use the pan and

zoom features of the map to focus on the anomalies

in this region. For this detailed analysis, the analyst

may also choose to enable the vessel direction indica-

tors. Within this spatial region, a total of 25 anoma-

lies remain for further exploration and examination

(see Figure 6(d)). Viewing these anomalies, the ana-

lyst can readily identify clusters of where the ﬁshing

vessels were spending their time, yet all of these ves-

sels are reporting their ﬁshing locations for these days

signiﬁcantly further south.

In order to support the analyst in understand-

ing the potential causes of these anomalies, individ-

ual vessels may be selected for detailed inspection

and evaluation. Upon making such selections, the

anomalies from the other vessels in the region are

dimmed, the selected anomalies and their vessel paths

are highlighted, and contextual information regarding

the paths of the vessels before and after the anoma-

lous event are included in the display. Further insight

into potential problems can also be provided by turn-

ing on the highlighting of missing VMS data points.

From this view of the anomalies (see Figure 6(e)), the

speciﬁc activities of the highlighted vessels can then

be inferred based on what is being shown. For ex-

ample, the vessel on the right indicated ﬁshing at the

exact same location on four separate days, but did not

come within 25 km of this location. In addition, there

is one particular period of 11 hours in which the ves-

sel’s VMS system was not responding. While this

may be an indication of equipment failure, it could

also have been a result of intentional equipment sabo-

SupportingEvent-basedGeospatialAnomalyDetectionwithGeovisualAnalytics

(a) All anomalies within a two-year period. (b) Filter to one month of data.

(e) Highlighting speciﬁc vessels. (f) Zooming out to provide context to the anomalies.

Figure 6: This series of screenshots of the map interface illustrate the interactive ﬁltering, exploration, and highlighting of

anomalies within a ﬁsheries dataset. Temporal ﬁltering, anomaly thresholding, and spatial zooming allow an analyst to explore

the patterns of anomalies, and discover particular vessels that require further investigation.

IVAPP2015-InternationalConferenceonInformationVisualizationTheoryandApplications

tage in order to disguise illegal activities. By contrast,

the vessel on the left made many trips back and forth

between a nearby port and a ﬁshing region just 15-20

km from port, but reported the ﬁshing location at least

double that distance. However, in this particular case,

the vessel moved around the ﬁshing region in a pattern

that is indicative of ﬁshing activity, but took efforts to

report locations that were distant from this location

and that slightly varied from one another. In both of

these cases, using the proposed approach gives the an-

alyst some insight into the activities surrounding the

anomalies, providing evidence to support supplemen-

tal investigation of these particular vessels.

Further insight and context regarding these ves-

sels and their anomalous ﬁshing events can be ob-

tained by zooming out to explore where else the ves-

sels have been travelling, broadening the temporal

range in which the data is shown, and including those

anomalies that are on land. In such a case, the analyst

may also wish to completely hide all other anomalous

events in order to avoid misinterpretation and visual

clutter. Figure 6(f) shows the movement paths and

anomalies for these two vessels over the entire scal-

lop ﬁshery region and a six-month period. From this

view, it is clear that data is normally reported prop-

erly when ﬁshing within the bay region (with the ex-

ception of a few data entry errors that put the ﬁsh-

ing location over land), but when the vessels travel

to the southern peninsula, they consistently misreport

their ﬁshing locations. From a ﬁsheries management

and enforcement perspective, this may be an indica-

tion that there is a need for more monitoring in the

southern region of the ﬁshery.

This case study illustrated how the visual and in-

teractive features of the system support not only an

exploration of the anomalies within the data, but also

analytical reasoning about the underlying behaviour

that has caused the anomalies. The system is highly

interactive, allowing analysts to easily focus on a ge-

ographic and temporal range of interest, as well as

set the parameters for what constitutes an anomaly.

Speciﬁc anomalies can be compared to one another

within the geographic context, for the same vessel as

well as across multiple different vessels. Patterns of

anomalies can be extracted, and using their existing

knowledge about the domain in question, analysts can

readily interpret these (e.g., systematic data quality

problems, intentional misrepresentations, potentially

illegal activities). In comparison to the existing prac-

tice of analyzing this ﬁsheries data, the use of this

geovisual analytics approach supports more than just

veriﬁcation of what is already known, but also discov-

ery and analysis of what was previously unknown.

5 FIELD TRIALS

Further validation of the proposed approach was ob-

tained via a set of ﬁeld trials conducted with real

world data analysts working in the ﬁsheries domain.

Rather than studying each of the novel elements of

this work in isolation, these ﬁeld trials take a holis-

tic approach to the evaluation, focusing on the sup-

port the software provides to the practice of event-

based geospatial anomaly detection and exploration.

The use of ﬁeld trials for evaluating systems such as

this are beneﬁcial when the data analysis activities are

complex, there are a limited number of experts avail-

able, and there is no reasonable system against which

to make a comparison. They provide real-world evi-

dence of the value the approach provides for support-

ing the actual activities of the target users (Lam et al.,

2012; Plaisant, 2004).

The target participants for these ﬁeld trails were

professional ﬁsheries data analysts working at Fish-

eries and Oceans Canada. Invitations were sent to

all the potential analysts who had prior familiar with

the analysis of VMS and MarFis data, and the scallop

ﬁsheries for which this data was collected. Five par-

ticipants voluntarily participated, of which four (Par-

ticipants A-D) had 4-6 years of experience with ana-

lyzing this data, and reported high degrees of prior fa-

miliarity with visualizing data with various software

packages. The ﬁfth (Participant E) had less experi-

ence with analyzing the data (1-2 years) and reported

moderate familiarity with the visualization methods

the others used.

Each participant was given a training session on

the use of the complete range of features of the soft-

ware. The participants were then invited to use the

software themselves in the exploration and analysis

of anomalies within the same two-year dataset de-

scribed in the case study. This use of the software

was driven by the participants’ interests and exper-

tise in the ﬁsheries domain, and was entirely open-

ended and self-directed. When necessary, the investi-

gator helped the participants to operate the software,

allowing them to perform their tasks at a level beyond

the novice level. Once the participants felt they had

sufﬁciently evaluated the anomalies within the data,

a post-study questionnaire was administered using a

survey instrument adapted from the Technology Ac-

ceptance Model (Davis, 1989). An interview was also

conducted, focusing on the participants’ qualitative

impressions of the software and the types of anoma-

lies they were able to discover during the course of

the study.

While using the software for their self-directed

data analysis activities, we observed the participants

SupportingEvent-basedGeospatialAnomalyDetectionwithGeovisualAnalytics

following the same pattern of ﬁltering and exploring

among the data. They started with setting the tempo-

ral range and conﬁguring the anomaly threshold ﬁlter,

and then zoomed to a geographical region of interest.

After observing the anomalies present in this region,

they each undertook further ﬁlter reﬁnement steps, in-

cluding setting the ancillary data ﬁlter, re-focusing the

spatial ﬁlter, adjusting the temporal ﬁlter, and reﬁn-

ing the anomaly threshold settings. Doing so brought

the number of anomalies displayed on the map down

to a manageable number, allowing the participants to

highlight particular vessels and evaluate the details of

the anomalies. In using the software, all of the partic-

ipants were able to ﬁnd and evaluate speciﬁc anoma-

lies, conducting detailed investigations of their activ-

ities before, during, and after the event in question to

try to make sense of what was happening.

While the participants were already highly experi-

enced in analyzing these data, most expressed their

surprise by the large number of anomalies present.

This ﬁnding highlights the difﬁculty they currently

experience in performing anomaly analysis between

these two datasets. While they have tools to map

the two datasets independently, matching the ﬁshing

events to the ﬁshing vessel movement paths, and then

studying the anomalies in detail, requires a signiﬁcant

amount of cognitive effort. Participant D noted that

“without tools like this... it is very difﬁcult for anyone

from enforcement to deal with this kind of problem.”

This participant further noted that doing this type of

analysis with their current software systems “is too

tedious.”

The post-study questionnaire administered after

the participants ﬁnished using the software focused on

the their perceptions of usefulness and ease of use of

various aspects of the software. For each feature, six

related questions were asked for each of these mea-

sures, with answers provided on a ﬁve-point Likert

scale. Although each participant had a different anal-

ysis goal while using the software, we observed that

they made full use of the features of the software to

support their analysis tasks. As such, we report here

the aggregated responses for each speciﬁc feature of

the software.

The perceived usefulness and ease of use of each

of the key features of the software are reported sep-

arately for each participant (A - E) in Table 1. Note

that since the anomaly representation feature is some-

thing that is observed but not manipulated, no data

was collected regarding its ease of use. While most

participants had a positive view of the usefulness of

the features of the system, one was neutral about

the anomaly representations and another was nega-

tive about the ancillary data ﬁlters. Given the open-

ended nature of the analysis activities, these negative

responses may be attributed to the feature not being

useful for the speciﬁc type of analysis the participant

was undertaking. In terms of ease of use, while the

responses were generally positive, some participants

reported negatively regarding the spatial and tempo-

ral ﬁlters and the anomaly highlighting. As prototype

software, some of this difﬁculty with using the system

may be attributed to its novelty and lack of sophisti-

cation in comparison to commercial-grade software.

The analysis of the interview responses revealed

a number of common themes. All of the participants

commented positively on the method for visually con-

veying the existence of an anomaly using a line con-

necting the event location and the vessel movement

path, and the method for visually highlighting se-

lected anomalies. They appreciated the value of these

methods for not only identifying anomalies, but also

for representing the degree of the spatial discrepancy

in the data. Participant C noted that “in terms of being

able to quickly visualize the differences in the data,

and the magnitude of discrepancies, this is huge.” Par-

ticipant B echoed this sentiment, stating that “it’s an

eye-opener for me. I didn’t realize that there are some

cases where there is such a discrepancy.” Participant

D noted the value of this approach for identifying

which vessels to investigate in further detail: “I am

not concerned [about the minor anomalies]. But if the

degree is big, then we will have major concern about

that.”

In terms of supporting the speciﬁc anomaly analy-

sis activities, and in comparison to their existing prac-

tice for this type of data analysis, all of the partici-

pants commented positively on the value of the ap-

proach. They indicated that with existing tools at

their disposal, their only option is to inspect the data

for potential anomalies on a case-by-case basis. Us-

ing this software, they identiﬁed the ability to view

the anomalies over a broad temporal and geographi-

cal scale as a signiﬁcant improvement. Participant D

highlighted these differences by stating that “this kind

of whole picture is very different than how I look at

the data right now. [This new approach] is deﬁnitely

useful.” Participant C noted that using this software

“is huge step up in terms of productivity and efﬁ-

ciency compare to what we are currently do, which

is basically... running three separate programs.”

Some participants highlighted a few basic usabil-

ity difﬁculties they had with using the software, and

noted additional features that could further enhance

the usefulness of the approach (e.g., data export fea-

tures, the ability to layer additional data on the map,

additional ﬁltering mechanisms). However, all of the

participants were enthusiastic about the possibility of

IVAPP2015-InternationalConferenceonInformationVisualizationTheoryandApplications

Table 1: Aggregated responses regarding percieved usefulness and perceived ease of use for each participant and each feature

of the software. Agreement is represented to the right, in light green for agreement and dark green for strong agreement;

neutral is in the middle in grey; disagreement is to the left in red, noting that there were no responses that were of strong

disagreement.

Feature Perceived Usefulness Perceived Ease of Use

Anomaly Representation

Anomaly Threshold Filters

Spatial and Temporal Filters

Ancillary Data Filters

Anomaly Highlighting

being able to have access to this software after the

ﬁeld trials were complete. Participant A noted that

“if I can have this software package, I would be a

happy [person].” Participant D said “its very useful

and something that is much needed in our current en-

vironment... it would be used almost as a daily tool

for me.”

6 CONCLUSION AND FUTURE

WORK

This paper described the geovisual analytics methods

we employed for analyzing geospatial anomalies be-

tween movement and event datasets representing the

same conceptual entities, but collected independently

and at different temporal granularities. A case study

for using this approach in the context of ﬁsheries

data analysis was presented, along with the ﬁndings

from ﬁeld trials conducted with expert ﬁsheries ana-

lysts. This research illustrates the great potential for

geovisual analytics to support the identiﬁcation, ex-

ploration, and analytical reasoning about event-based

geospatial anomalies.

While this research was motivated and conducted

in the context of ﬁsheries data analysis, the meth-

ods developed generalize to other data analysis do-

mains where there is a need to match and analyze the

anomalies between independently collected move-

ment and event datasets for common conceptual en-

tities in off-line or real-time. For example, the move-

ment paths measured from mobile phones and the lo-

cations where people use their credit cards at point-of-

sale machines can be analyzed to identify potentially

fraudulent charges, or tracking the movement of taxis

and the reported locations for pick-ups and drop-offs

can be used to detect the potential misreporting fares.

Future work includes the development and evalua-

tion of alternate approaches for identifying anomalies

between the movement and event location data, the

addition of a graph within the timeline to illustrate

when the anomalies are occurring, and the implemen-

tation of edge bundling approaches on the anomaly

representation lines to reduce the visual clutter. Ad-

ditional features to support the analytical reasoning

about the anomalies are also being investigated, such

as adding annotations, logging analysis sessions, sav-

ing anomaly threshold conﬁgurations, and linking the

data to other external resources.

SupportingEvent-basedGeospatialAnomalyDetectionwithGeovisualAnalytics

ACKNOWLEDGEMENTS

The authors wish to thank Fisheries and Oceans

Canada for making available the data used in the

case study. This work was supported by a Strategic

Projects Grant from the Natural Sciences and Engi-

neering Research Council of Canada held by the ﬁrst

author.

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