=Paper=
{{Paper
|id=None
|storemode=property
|title=Visualizing and Animating Large-scale Spatiotemporal Data with ELBAR Explorer
|pdfUrl=https://ceur-ws.org/Vol-1272/paper_126.pdf
|volume=Vol-1272
|dblpUrl=https://dblp.org/rec/conf/semweb/MazumdarK14
}}
==Visualizing and Animating Large-scale Spatiotemporal Data with ELBAR Explorer==
https://ceur-ws.org/Vol-1272/paper_126.pdf
Visualizing and Animating Large-scale
Spatiotemporal Data with ELBAR Explorer
Suvodeep Mazumdar1 and Tomi Kauppinen2,3
1
Department of Computer Science,
University of Sheffield, 1 Portobello, S1 4DP, United Kingdom
s.mazumdar@sheffield.ac.uk
2
Cognitive Systems Group, University of Bremen, Germany
3
Department of Media Technology, Aalto University School of Science, Finland
tomi.kauppinen@uni-bremen.de
Abstract. Visual exploration of data enables users and analysts observe
interesting patterns that can trigger new research for further investiga-
tion. With the increasing availability of Linked Data, facilitating support
for making sense of the data via visual exploration tools for hypothesis
generation is critical. Time and space play important roles in this be-
cause of their ability to illustrate dynamicity, from a spatial context. Yet,
Linked Data visualization approaches typically have not made efficient
use of time and space together, apart from typical rather static mul-
tivisualization approaches and mashups. In this paper we demonstrate
ELBAR explorer that visualizes a vast amount of scientific observational
data about the Brazilian Amazon Rainforest. Our core contribution is
a novel mechanism for animating between the different observed values,
thus illustrating the observed changes themselves.
Keywords: Visual Analytics, Information Visualization, Linked Data
1 Introduction
Making sense of spatiotemporal data is a crucial step in providing insight for
critical actionable decisions. Linked Data is no exception in this. With the in-
creased availability of potentially interesting information the task is to support
decision makers by illustrating significant patterns of data. This way data be-
comes narrative and can share a story [3].
In this paper we demonstrate the combined use of spatial and temporal as-
pects of Linked Data and illustrate how very heterogeneous phenomena can
be illustrated over time and space. Our contribution is an explorer that takes
spatiotemporal Linked Data as an input via SPARQL queries and enables explo-
ration of variables via animations. To evaluate and illustrate the use of the tool
we make use of openly published data about the Brazilian Amazon Rainforest.
This data, including its economical, social and ecological dimensions, serves to
show the potential of visualizing Linked Data by animations over time.
Section 2 explains both the ELBAR explorer and the spatiotemporal data
we used for evaluation. Section 3 discusses the use of ELBAR explorer with a
2 Spatial and Temporal Exploration of Linked Data
concrete scenario. Section finishes the paper with concluding remarks and future
work ideas.
2 Animating Large-scale Temporal Data with ELBAR
In this demonstration, ELBAR4 makes use of the openly available Linked Brazil-
ian Amazon Rainforest Data5 [2] which captures and shares environmental ob-
servations (like deforestation) together with information about social phenomena
(like amount of population) and economical phenomena (like market prices of
products). The data has been aggregated to 25 km x 25 km grid cells [1] and
extended with open governmental data and linked to DBPedia. ELBAR uses
the paradigm of visual animations to illustrate changes over time on maps. The
core idea is that employing such means to navigate multiple dimensions of data
supports analysts in generating hypotheses for further investigation.
Fig. 1. Explorer for Linked Brazilian Amazon Rainforest (ELBAR). The interface con-
tains four sections: A – Filters, B – Map, C – Info window, D – Graph
Fig. 1 presents a screenshot of the ELBAR explorer. The filters (Section A)
provide mechanisms for selecting variables (e.g. deforestation rates) for further
inspection. SPARQL queries are built from the interactions done by the users
and sent to SPARQL endpoints. Results from the endpoint are processed and
converted to visual elements on maps (Section B) and graphs (Section D)6 . The
relevant observations (as retrieved from a triple store) are then visually encoded
and overlaid on a map, based on their spatial positions. The information window
(Section C) is then updated with further information regarding the filter being
selected. Clicking on visual elements of the graph (Section D) highlights the
individual sections on the map.
4
A demo of ELBAR is presented at http://linkedscience.org/demos/elbar
5
http://linkedscience.org/data/linked-brazilian-amazon-rainforest/
6
Users are also presented with Preset Animations, that can be previously defined or
extracted from the data
Visualizing and Animating Large-scale Spatiotemporal Data with ELBAR 3
3 Scenario for the Use of ELBAR
3.1 Visualizing the Phenomena
Assuming a user would like to understand a certain phenomena over time (like
deforestation) she/he will select a property (like deforestation rate) to visualise it
on the map. The explorer then presents the corresponding values of the property,
color-coded and overlaid on the map as well as a distribution of the values of
the property as a graph. The graph (see the the bottom right of Figure 1) is
interactive and supports mouse events such as zoom (by clicking and dragging
to select a section in the graph) or left clicks.
Fig. 2. User interactions on graphs translate to highlight visual elements on the map,
aiding in providing context
Zooming the graph provides a finer grained view of the distribution to fa-
cilitating selection of individual data points. Clicking on the graph selects the
respective section on the map and highlights it. Such interactions support un-
derstanding of how different phenomena are distributed. They also potentially
illustrate their spatial proximity and patterns they form. The example illustrated
in Figure 2 shows the distribution of a property (ACUM 2008 - accumulated per-
centage of deforestation in The Brazilian Amazon Rainforest until 2008) on the
graph (left) and the map (right). The graph is then zoomed to the highest few
points and then explored using mouse clicks. Clicking on individual datapoints
indicate their spatial references by marking up the respective section on the
map. Clicking on all the points (with a high value of ACUM 2008) on Section A
(middle) highlights the respective section A on the map (right). However, with
almost similar high values, the section B and C highlight sections in other areas.
3.2 Animating the Phenomena over Time
Comparing two properties for example (selected in Section A), accumulated de-
forestation in 1997 (ACUM 1997) and accumulated deforestation in 2008 (ACUM
2008) in such cases is a helpful feature that provides animated transitions to as-
sist users observe change. The result is then parsed and the values are visually
encoded into the relevant sections on the map, with a transition7 defined that
7
https://github.com/mbostock/d3/wiki/Transitions
4 Spatial and Temporal Exploration of Linked Data
Fig. 3. Generating hypothesis for the high deforetation in the North East Amazon. 1:
accumulated deforestation in 1997, 2: accumulated deforestation in 2008, 3: distance
from the nearest road, 4: distance from nearest municipality.
iterates between the two visual encodings, thereby creating an animated effect.
Since no other visual feature is altered in the user’s field of view apart from color
encoding, the user can easily observe the evolution of deforestation. The analyst
then attempts to understand why the North East Amazon contains the highest
amount of deforestation. Different properties such as distance from nearest road
(bottom left, Fig. 3) and distance from the nearest municipality (bottom right,
Fig. 3) indicate the clear segregation of the North Eastern region, which could
support an analyst to hypothesise that the remoteness of these areas help in
illegal deforestation activities.
4 Conclusions
In this paper, we presented ELBAR explorer that employs generic visualization
and animation techniques to support analysts and decision makers explore spa-
tial and temporal data. We demonstrate the system and the architecture, along
with a description of the data. This will be accompanied with a guided exam-
ple of how such techniques can be used, and we also invite our demonstration
attendees, both online and onsite, to build custom transitions.
Future work will include development and evaluation of ELBAR with differ-
ent kinds of spatiotemporal data. Moreover, we investigate other novel mech-
anisms for exploring the spatiotemporal and topical aspects of Linked Data.
We foresee the potential for the use of the community is the expansion of the
background data, such as census data from individual municipalities and other
authorities could support for gaining insight of social, economical and ecological
processes.
References
1. de Espindola, G.M.: Spatiotemporal trends of land use change in the Brazilian
Amazon. Ph.D. thesis, National Institute for Space Research (INPE), São José dos
Campos, Brazil (2012)
2. Kauppinen, T., de Espindola, G., Jones, J., Sanchez, A., Gräler, B., Bartoschek, T.:
Linked Brazilian Amazon Rainforest Data. Semantic Web Journal 5(2) (2014)
3. Segel, E., Heer, J.: Narrative visualization: Telling stories with data. Visualization
and Computer Graphics, IEEE Transactions on 16(6), 1139–1148 (2010)