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Visually representing geo-temporal differences

Authors:
Orland Hoeber at University of Regina
Orland Hoeber
Garnett Carl Wilson
Garnett Carl Wilson
Garnett Carl Wilson
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Simon Lawrence Harding at Machine Intelligence Ltd.
Simon Lawrence Harding
  • Machine Intelligence Ltd.
René A. Enguehard at Memorial University of Newfoundland
René A. Enguehard
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Abstract

Data sets that contain geospatial and temporal elements can be challenging to analyze. In particular, it can be difficult to determine how the data have changed over spatial and temporal ranges. In this poster, we present a visual approach for representing the pair-wise differences between geographically and temporally binned data. In addition to providing a novel method for visualizing such geo-temporal differences, GTdiff provides a high degree of interactivity that supports the exploration and analysis of the data.
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Visually Representing Geo-Temporal Differences
Orland Hoeber∗
Department of Computer Science
Memorial University
Garnett Wilson†
Department of Computer Science
Memorial University
Simon Harding‡
Department of Computer Science
Memorial University
Ren´
e Enguehard§
Department of Geography
Memorial University
Rodolphe Devillers
Department of Geography
Memorial University
ABSTRACT
Data sets that contain geospatial and temporal elements can be chal-
lenging to analyze. In particular, it can be difficult to determine
how the data have changed over spatial and temporal ranges. In this
poster, we present a visual approach for representing the pair-wise
differences between geographically and temporally binned data.
In addition to providing a novel method for visualizing such geo-
temporal differences, GTdiff provides a high degree of interactivity
that supports the exploration and analysis of the data.
Index Terms: H.5.2 [Information Systems]: Information
Interfaces and Presentation—User Interfaces; I.3.6 [Computing
Methodologies]: Computer Graphics—Methodology and Tech-
niques;
1 INTRODUCTION
While geo-temporal data sets can be very rich, there are often com-
plexities associated with developing visual approaches for analyz-
ing and exploring the data [3, 5]. While the processes for visually
representing one or more data sets on a map are well-known [1],
constructing interactive approaches that address the specific needs
for a particular domain may not be straight-forward. Not only can
the data sets become very large [2], but the type of analysis required
by the user can be rather complex.
Many approaches to visualizing geo-temporal data sets make it
difficult for users to make meaningful comparisons between dif-
ferent spatial and temporal aspects of the data set. Our aim in
this research is to provide support for such activities by taking ad-
vantage of the human vision capabilities and employing interac-
tive exploration methods to show how data are changing through
geo-temporal differences. GTdiff calculates the differences in user-
defined spatial and temporal bins, and provides a visual represen-
tation that enables the user to identify and examine changes across
space and time. The goal is not to just provide a single view of the
data that can give users the answers they are seeking, but instead
to support their knowledge discovery activities through exploration
and analysis of the data.
2 GTDIFF
The primary goal in the creation of GTdiff was to support data
analysts in exploring and understanding how geospatial data sets
change over time. A prototype has been implemented as a Java ap-
plication, using a virtual globe generated by World Wind [4] for
the framework upon which the core geo-visual representations are
∗e-mail:hoeber@cs.mun.ca
†e-mail:gwilson@cs.mun.ca
‡e-mail:simonh@cs.mun.ca
§e-mail: rene@computer.org
e-mail: rdeville@mun.ca
layered. The interface of GTdiff supports three key interactive fea-
tures: temporal filtering and binning, spatial binning and the cre-
ation of geo-temporal difference graphs, and further spatial explo-
ration and filtering of the data (see Figure 1).
2.1 Temporal Filtering and Binning
GTdiff allows users to easily filter the data temporally, aggregating
what remains into a user-specified number of equal length temporal
bins. For example, an analyst may wish to filter the data set to
only include a five-year timeframe, and then group the data into
five one-year bins. Alternately, they may be interested in twelve
years of data, grouped into six two-year bins. As will be shown in
the description of the difference graphs in the section that follows,
this allows GTdiff to clearly illustrate changes that have occurred
between the temporal bins.
Each temporal bin is displayed side-by-side under the temporal
filter. These representations show a zoomed-out geospatial view
of the data. Their purpose is to support users in visual scanning
and comparison activities, and to allow users to select one or more
temporal bins to investigate in more detail. The value of a specific
attribute of the data under investigation is encoded in the volume
of a sphere placed at the specific location of the data point, using
equally spaced colours from a yellow-blue colour scale of mid-level
brightness to distinguish the separate temporal bins.
The spheres that encode the data allow users to readily make
volumetric comparisons between the numeric data at different lo-
cations and in different temporal bins. A simple shading model
enables the proper perception of the three-dimensional shape of the
spheres. They are rendered as semi-transparent objects in order to
address the occlusion problems that occur when a large sphere cov-
ers one or more smaller spheres at nearby locations.
2.2 Spatial Binning and Difference Graphs
In order to allow users to perceive how the data are changing be-
tween the temporal bins, spatial binning is necessary. Without spa-
tial binning, the only situation in which showing the differences
would be meaningful is when data points are at exactly the same
spatial location. Spatial binning groups data points that are near
one another to avoid this situation. A simple grid is used in the
binning process; the resolution is controllable by the users.
For each spatial bin, the difference between the values in each
pair of temporal bins are calculated. The maximum of the absolute
value of all of the differences is used as the extreme value in a
positive/negative scale. A divergent colour scale is used to visually
encode these differences within the visual representations of each
spatial bin. White represents a value of zero (no change), the degree
of saturation of green is used to represent positive values, and the
degree of saturation of red is used to represent negative values. In
order to assist with decoding, a legend is provided.
A visual representation of the difference between each pair of
temporal bins is provided in the form of a difference graph. The
difference graphs are organized in an inverted pyramid, where the
top layer shows the difference graphs for neighbouring pairs of tem-
poral bins, the second layer shows the difference graphs for pairs of
Figure 1: The main visual components of GTdiff provide support for temporal filtering and binning (top portion of the left screenshot; see Section
2.1), spatial binning and the representation of geo-temporal difference graphs (bottom portion of the left screenshot; see Section 2.2), and spatial
exploration and filtering of the data (right screenshot; see Section 2.3). The data shown is the catch weight of the cod fisheries off the coast
of Newfoundland, Canada. GTdiff has been used in this example to highlight the collapse of the fisheries (i.e., the reduction in catch weight as
identified by the red regions in the difference graphs and the small number and size of the yellow spheres that represent the catch data from the
end of the temporal range in relation to the blue spheres that represent the catch data from the beginning of the temporal range).
temporal bins with a one-bin gap, and so on until the final layer
shows the difference graph between the pair of temporal bins at the
extremes of the temporal range. As such, every possible pair-wise
comparison of temporal bins (i.e., the data above the inverted pyra-
mid) is shown simultaneously within this visual structure.
2.3 Spatial Exploration and Filtering
As users explore the data in the temporal bins and difference graphs,
they may wish to investigate specific elements further. One or more
temporal bins may be selected in order to conduct a detailed anal-
ysis of the data. Or a specific difference graph may be selected for
detailed inspection. In either case, the data is layered over satellite
imagery in order to provide users with spatial awareness of the data.
In order to enhance the ability of the users to perceive the fore-
ground data from the background contextual information, the sys-
tem was designed to ensure sufficient luminance contrast between
the two. The satellite imagery that makes up the background is
darkened by placing a semi-transparent layer over top of it. Bright
colours were chosen to represent the foreground information (i.e.,
the colour encoding for the temporal bins and difference graphs).
As such, the foreground information can be readily perceived as
being a separate layer placed over the background information.
The geospatial view supports spatial filtering of the data through
pan and zoom operations. As the user manipulates the location and
scale of the map, all other geospatial representations are updated.
In this way, users can manipulate the map in the geospatial view,
explore the features of the data within the temporal bins and dif-
ference graphs at the desired location and level of detail, and then
make further selections of interesting temporal bins and difference
graphs in support of their knowledge discovery tasks.
3 CONCLUSION
In this poster, we have outlined the features of GTdiff that support
the visual representation of geo-temporal differences. The system
was designed to allow users to interactively filter and bin the data
both temporally and spatially. The visual representations in the in-
verted pyramid of difference graphs highlights the temporal differ-
ences in the data within corresponding spatial bins, allowing users
to discover where and when significant changes have occurred.
Although the features of GTdiff were designed to support knowl-
edge discovery within fisheries-related data, the type of analysis it
supports may also be beneficial in the study of population statis-
tics (e.g., examining changing population demographics across re-
gions), business intelligence (e.g., studying national product sales
over time), and other domains where there are important spatial el-
ements and changing data values over time.
ACKNOWLEDGEMENTS
This work was supported by a Strategic Projects Grant from the
Natural Sciences and Engineering Research Council of Canada
(NSERC) held by the first and the last authors.
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IEEE Transactions on Visualization and Computer Graphics
  • J Wood
  • J Dykes
  • A Slingsby
  • K Clarke