Exploration through enrichment: a visual analytics approach for animal movement.

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Exploration through Enrichment: A Visual Analytics
Approach for Animal Movement
David Spretke
University of Konstanz
spretke@dbvis.inf.uni-
konstanz.de
Peter Bak
IBM Research Haifa
Peter.Bak@il.ibm.com
Halldor Janetzko
University of Konstanz
janetzko@dbvis.inf.uni-
konstanz.de
Bart Kranstauber
Max Planck Institute for
Ornithology
kranstauber@orn.mpg.de
Florian Mansmann
University of Konstanz
mansmann@dbvis.inf.uni-
konstanz.de
Sarah Davidson
Max Planck Institute for
Ornithology
davidson@orn.mpg.de
ABSTRACT
The analysis of trajectories has become an important field in ge-
ographic visualization, as cheap GPS sensors have become com-
monplace and, in many cases, valuable information can be derived
either from the data themselves or their metadata if processed and
visualized in the right way. However, showing the “right” infor-
mation to highlight dependencies or correlations between different
measurements remains a challenge, because the technical intrica-
cies of applying a combination of automatic and visual analysis
methods prevents the majority of domain experts from analyzing
and exploring the full wealth of their movement data. This pa-
per presents an exploration through enrichment approach, which
enables iterative generation of metadata based on exploratory find-
ings and is aimed at enabling domain experts to explore their data
beyond traditional means.
1.INTRODUCTION
Animal tracking has become a powerful means to study the ecol-
ogy of many kinds of animals. Technology improvements continu-
ously increase the number of species that can be tracked, the tem-
poral resolution and accuracy of location measurements, and the
ability to use other sensors to record additional information about
the animals and their surroundings. After the technical challenge
of recording the positions of animals has been mastered, initiatives
such as Movebank1or Global Tagging of Pelagic Predators2are
working to solve standardization, metadata and data provenance
issues to make such studies reproducible and comparable to each
other. These initiatives are also offering an increasing number of
linkages to external global environmental datasets (e.g., weather
data).
However, the analysis interfaces widely available are still basic,
1http://movebank.org
2http://www.gtopp.org
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and applying a combination of automatic and visual analysis steps
to enrich and explore animal movement trajectories requires signif-
icant coding experience. Therefore, researchers’ valuable analysis
time is often spent with time-intensive data cleaning and coding ac-
tivities that are needed to verify a few hypotheses, rather than with
exploring the full wealth of the recorded data sets.
Visual analytics that combine automated and visual analyses can
help to empower the animal tracking community and to foster new
insight into the ecology and movement of tracked animals. In this
paper we propose a unique integrated system that empowers its
users to interactively explore all attributes of animal tracking data
sets through iterative data enrichment. The visualization, interac-
tive exploration and data enrichment features are the essential com-
ponents that allow the user to link the raw data to hypotheses and
provide a feedback loop to improve the data upon which new in-
sight can be gained.
2. RELATED WORK
Since raw movement data are both very complex, as they repre-
sent rough approximations of complex activities, and at the same
time semantically poor, it is necessary to develop appropriate anal-
ysis techniques. Andrienko et. al. [1] combine density-based clus-
tering techniques on a sample of trajectories with a user-driven vi-
sual refining process to extract cluster representants. Afterwards a
classifier is built to enable classification of the remaining trajecto-
ries.
A similar but fully automatic method for trajectory segmentation
and sampling of moving objects is proposed by Panagiotakis et. al.
[5]. As an alternative, a partition-and-group framework for cluster-
ing of trajectories was introduced by Lee et. al. [4], which enables
the discovery of common sub-trajectories. Furthermore, the work
of Lee et. al. [3] focuses on the classification of trajectories using a
feature generation framework for trajectory data.
Dealing with movement data that have semantic context attached
leads to the field of multi-dimensional data analysis, as the addi-
tional context data can be seen as multi-dimensional time series.
The Attribute Explorer [6] or the XmdvTool [7] show the benefits
of interactive dynamic queries. By an interactive feedback of the
result of a query, dynamic queries allow the user to quickly visual-
ize and understand the connections between dimensions.
We followed these ideas by allowing the user to interactively select
time intervals or intervals of attribute values to highlight interest-
ing parts of the data and combine it with capable data enrichment
features on movement data.

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KnowledgeData
Visualization
Data
Enrichment
Interactive
Exploration
Hypotheses
Figure 1: Exploration through Enrichment
3.THEEXPLORATIONTHROUGHENRICH-
MENT APPROACH
The basic idea about our novel exploration through enrichment
approach is depicted in Figure 1. The normal exploration work-
flow starts by visualizing a particular data set and then offering
interaction capabilities for exploring the full wealth of the shown
data. The user can instantaneously derive useful knowledge from
this process or form hypotheses about the phenomena that she dis-
covered in the data. In the next step, these new hypotheses can
be rejected, confirmed or need to be checked in detail by starting
the workflow over again. However, often the data needed to verify
or explore the newly formed hypotheses is not available and must
be created in a pain-staking external data enrichment process. In
contrast to this, our tool integrates this vital enrichment procedure
into the analysis interface. In summary, it is the interplay of the
user with the system through interactive exploration and data en-
richment that extends the analysis capabilities beyond traditional
approaches.
4.ANIMAL ECOLOGY EXPLORER
Thissectionintroducesourvisualanalyticstool, theAnimalEcol-
ogyExplorer, whichisanimplementationoftheexplorationthrough
enrichment approach proposed in this paper. Figure 2 gives an
overview of the visual interface of our tool, consisting a data loader
window for selecting the animal tracking study; the data tracks (top
left); the data attributes (below); several enrichment features, such
as the Movebank weather interface, the segmentation window and
the segment clustering window; map representations in the cen-
ter and several configurable line charts and compressed time series
graphs in the form of horizon graphs [2]. Brushing and linking
is used to explore and relate behavioral or ecological phenomena.
The following subsections describe the interactive exploration and
data enrichment components of our system in detail.
4.1Interactive Exploration
The Animal Ecology Explorer includes many components for
interactive exploration of trajectory data and their associated meta-
data. In particular, these components are a geographic map inter-
face combined with a track simplification method, several charts
for metadata exploration and an interaction concept for relating the
displayed data to each other.
TrajectoryVisualization: Inordertovisualizegeospatialdatasets
of moving animals, we naturally start with a map representation
showing all selected trajectories. This map representation is based
on the Java OpenStreetMap Framework; in addition to allowing
layers to be drawn on top of the map, this framework offers geo-
graphic background tiles of satellite imagery (e.g., Microsoft Bing
Aerial Map), elevation maps (e.g., OSM Cycle Map) or abstract ge-
ographic and political maps (e.g., TilesAtHome or Mapnik). This
framework also provides basic interaction capabilities (e.g. zoom-
ing, panning, etc.).
A major challenge is that drawing trajectories of moving objects
on a map often causes the problem of overplotting. Animals com-
monly stay in relatively small areas over extended periods of time,
which results in regions with highly overlapping tracks. Further-
more, visualizing several tracks at the same time will result in even
more visual clutter. In order to reduce this effect we apply a cluster-
ing algorithm to each single track to paint a simplified track (with
less overplotting) on the map based on the current zoom level. In
addition to showing the positions of the recorded data points, we
can encode other variables in the data set by colorizing the tracks.
The option to visualize one or two variables along the track is pro-
vided by colorizing the inner and outer portions of the track line.
Using this approach we can view and compare two different vari-
ables along the track and view the relationships between these vari-
ables and location.
Metadata Exploration: Analyzing animal tracking data is very
challenging as there are many variables that could explain the be-
havior and movement of an animal. We supply researchers with an
overview time series visualization and detailed line charts for fur-
ther investigations.
The compressed time series visualization in the form of a Horizon
Graph [2], shown in the top part of Figure 2 detailing the altitude
sensor measurement of gulls over a period of 1.5 years, uses a two-
band color representation of selected attributes. If a value is above
the average of the shown time series, it will be visualized by a blue
hue, whereas a red hue is used for below-average values. Our rep-
resentation is capable of comparing the same attributes for different
animals or comparing different attributes for the same animal. This
enables the user to see correlations between different attributes of
the same individual and correlations between the same attribute of
different individuals. While the absolute measurements are harder
to estimate than in a line chart, the Horizon Graph is more scalable
since it easily and clearly illustrates a lot of data without overplot-
ting effects.
We implemented the line charts so that the user can view at max-
imum two animals and two attributes in the same chart. Color is
used to differentiate between different animals, and dashed or solid
lines denote the respective attributes. Color linkage thus enables
cross-comparisons between the geographic extent of the individual
animal’s movement and the attributes displayed in the line charts.
Interaction: An essential part of our visual analytics tool is the
interaction concept, which enables clear and efficient analysis of
movement data. We connect all corresponding line charts and pixel
visualizationswiththegeographicmaprepresentationusinglinking
and brushing. By moving the mouse cursor within a line chart or
pixel visualization, the user will automatically highlight the nearest
point in the active chart and its corresponding points in all other
shown visualizations, including the map. Furthermore, we provide
dynamic filtering to select areas of interest in the map and highlight
the corresponding regions in the line charts and pixel visualization.
A very helpful technique for analyzing tracks is the dynamic fil-
tering of attribute values and/or time/distance intervals, which can
be done by marking regions within the plots to specify ranges of
both time and attribute values for highlighting. Dragging the time
slider with a defined range is a very helpful feature for biologists
to interactively replay animal movement behavior. When attribute
values are represented on the color attribute of the tracks, we omit
color opaqueness, since combining the two color schemes together
might lead to misinterpretations of opaque variants of the colors
when matching it to the scale. In such a case, only the interactive
movement of the slider reveals the directionality of movement.

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Figure 2: Visual interface of the Animal Ecology Explorer showing migrating gulls. On the left side the study and the tracked animals
can be selected. A a horizon graph (top) and line chart (bottom) visualize attributes of individuals either over time or cumulated
distance. The center depicts two zoom- and pannable map interfaces showing the segmentation of gulls’ migration trajectories
resulting in dozens of segments for every trajectory (center left) and clustering of segments with k-means (center right). Note the
clearly distinguishable behavior for migration (purple) and for resting (azure with red border).
4.2 Data Enrichment
The unique feature of the Animal Ecology Explorer is the close
integrationofdataenrichmentwiththeinteractiveexplorationcom-
ponents. However, the presented three components are just some of
the many possible options to enrich trajectory data.
BasicDerivedAttributeCalculations: Trajectories, asrawdata,
areusuallytime-stampedlocationinformations. Thesearecleansed
and preprocessed before being loaded into the Animal Ecology
Explorer. While loading the tracks the tool derives additional at-
tributes, such as speed, distance, duration, time-related attributes,
etc. Once the trajectories are loaded, the tool creates a visual rep-
resentation of their location on a cartographic map.
Data Access Modules for External Data: Access to external
databases requires a lot of customization to meet the required data
format and protocol of the data provider. In our case, we interface
the Movebank API to enrich all coordinates of a set of trajectories
with their associated weather conditions, such as temperature, wind
speed and direction, geopotential height at different pressure levels,
surface temperature, cloud coverage, or precipitation rate. For the
end user, this is displayed as a system dialog with one checkbox
for each weather parameter and hitting the “enrich” button results
in new attributes in the data attributes window, which can then be
used in the visualizations.
Formula Editor: Since our data model is mostly based on one
record per GPS position, common movement parameters requir-
ing more than one coordinate are automatically derived and then
assigned to the coordinates. Our formula editor is a straight for-
ward approach to derive attributes from the existing ones. To avoid
complicated indexing syntax, we therefore restricted these methods
to be either only applicable to single records or the whole dataset
for aggregation operations (e.g., min, max and average). However,
since basic calculations such as speed are already assigned to each
GPS record, complex derived attributes can be calculated. Wind
support for birds, for example, can be calculated using wind direc-
tion and strength as well as the birds flight direction.
Trajectory Segmentation and Annotation: The semantic an-
notation of trajectories is a fundamental task in understanding be-
havior from movement data. However, it is also a very challenging
task, sinceGPStrackingdevicesreflectrealworldbehavior, andare
therefore very noisy, sometimes random, but mostly domain and
context dependent. In order to achieve a semantic annotation that
reflects the behavior of the objects under investigation, we suggest
a tight integration of interactive visualization and automatic algo-
rithms for information extraction. The role of the user is therefore
to interact with the data and the algorithms through a visual inter-
face to enhance the discovery process with his domain knowledge.
Trajectory Segmentation: Segmentation is conducted by set-
ting threshold parameters as splitting criterion in the segmentation
panel next to the data loader (see lower left part of Figure 2). The
user can determine ranges of speed, distance, and/or duration, ac-

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cording to which a trajectory is split into two or more segments.
As an example, during bird migration, a resting period of at least
45 minutes, and a continuous flight distance of less than 2 km, in-
dicates an interruption of the migration flow. This interruption can
indicate sleeping places, feeding, etc.
The segmentation itself is carried out in a fully automatic manner
by generating parameter driven queries to the spatial database. The
setting of the parameters, however, is a fully manual task, carried
out by the user. Every change of a setting results in an immedi-
ate response from the database, after which the data is re-rendered
on the map. Colors are used to distinguish between consecutive
segments by iterating through a set of qualitative colors.
Data Clustering: In many cases the definition of behavior is
highly complex and based on multidimensional attributes. For such
cases, the Animal Navigator provides a highly enhanced clustering
feature. Clustering can be conducted by selecting one of dozens of
standard algorithms and setting its specific parameters. The results
of the clustering are immediately shown with additional statistical
and quality information of the algorithmic performance. The user
can iteratively optimize the parameter settings of the algorithm un-
til the results are satisfactory. As a result of clustering, segments
of trajectories are assigned to a single behavior type. Each behav-
ior is described by a set of attributes and can now be semantically
annotated.
5.EVALUATION
In this section, we describe a real-world case study about animal
tracking that shows how our proposed tool is used by the domain
experts co-authoring this paper. In this case study our investigation
focuses on the migration behavior of gulls. A large-scale study was
conducted to record the movement patterns of gulls during migra-
tion in the winter 2009 and 2010. There were 63 gulls (lesser black-
backed gull, Larus fuscus) equipped with GPS receivers, recording
on average 800 location signals during one migration period. These
gulls breed in Europe and migrate annually from northern Europe
to central Africa. During migration these birds can fly tremen-
dous distances. Our investigation aimed to locate regions of resting
places during their migration and try to find temporal differences in
their migration patterns. The results of our investigation are shown
in Figure 2. The segmentation window for setting parameters based
on the loaded tracks and attributes is shown on the lower left corner.
Results of the segmentation are displayed in the left map, and the
right map shows the final results of the clustering and annotation.
We first segmented the trajectories by setting thresholds for speed
(< 4 km/h), distance (< 1 km) or duration (< 60 minutes). This
resulted in 1,900 segments for the 63 trajectories. We then clus-
tered these segments using a k-means algorithm (k=3). The clus-
tering was conducted on the average-speed and day-time attributes
of the data. For clustering we rely on the methods available in the
WEKA3toolkit. There is a wide range of possible algorithms (e.g.,
DBScan) to match domain-specific requirements.
This segmentation resulted in three different clusters, which were
annotated after investigating the cluster properties: 1. Migration
during day time: described long distance flights with consistent
speed, occurring during day time. 2. Migration during the night:
describedlongdistanceflightswithconsistentspeed, occurringdur-
ing the night. In this cluster, segments had at least one location
with a time-stamp at night. 3. No migration segments: described
as short distance flights with large speed difference of consecutive
segments or no-movement periods (resting) during the time period.
Animals spend most of the year in this movement mode as they
3http://www.cs.waikato.ac.nz/ml/weka/
forage, rest and breed in a relatively restricted range.
These results illustrate a visual analytics approach to the semantic
annotation of trajectories. Using this approach, domain experts can
efficiently describe behavioral differences as movement patterns.
6.CONCLUSIONS
In this paper we described a novel exploration through enrich-
ment approach for trajectory analysis, which tightly integrates data
enrichment features into the data analysis and exploration work-
flow. To demonstrate the applicability of this approach we pre-
sented the Animal Ecology Explorer, which is an interactive tool
for confirmatory and exploratory analysis and enrichment of move-
ment data. While each of its visual and analytical components is
not novel in itself, our contribution is the tight integration of these
components in a single analysis tool to not only help researchers to
focus on data analysis rather than laborious coding, but also to em-
power them to make deep insights while exploring enriched move-
ment data.
7.ACKNOWLEDGMENT
We thank the Movebank team Kamran Safi, Roland Kays and
Martin Wikelski for the many rounds of feedback and for sharing
their tracking data. This work was funded by the German Research
Foundation (DFG) within the projects “Visual Spatiotemporal Pat-
tern Analysis of Movement and Event Data” (ViaMod) and “Move-
Bank Virtual Research Environment” (MoveVRE).
8.ADDITIONAL AUTHORS
Additional authors: Manuel Mueller (University of Konstanz,
Manuel.Mueller@uni-konstanz.de)
9.
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