Stefan J
, Christian Heine
, Ralf Stockmann
and Gerik Scheuermann
Image and Signal Processing Group, Institute for Computer Science, University of Leipzig, Leipzig, Germany
ottingen State and University Library, University of G
ottingen, G
ottingen, Germany
Fraunhofer Institute for Intelligent Analysis and Information Systems IAIS, Sankt Augustin, Germany
Visual Data Exploration, Geovisualization, Comparative Visualization.
The amount of online data annotated with geospatial and temporal metadata has grown rapidly in the recent
years. Social networks like Flickr and Twitter are popular providers of masses of such data, but are hard to
browse. Many systems exist that can show the data’s geospatial distribution in a map widget and their temporal
distribution in a time widget, allowing these widgets to become dynamic-query-like filters for data. We present
a web application that combines several existing approaches and supports comparison of multiple, potentially
large result sets of textual queries in a geospatial and temporal context with extended interaction capabilities.
We validate our approach with several case studies and our tool’s learnability in a field experiment.
Although the amount and types of data available
through public web resources is seemingly endless,
the main method of finding information is still largely
performed by text queries. Because of the typically
enormous amount of results for text queries, popular
search engines rank their results using a blend of rele-
vance and popularity. The results often include items
the user is not interested in, requiring him to refine
the query by adding additional or using different key-
words, but repeated refinement can lead to frustration.
While the amount of data is increasing, they also
become more structured. Metadata as well as struc-
tural relationships between results can give rise to
a contextual overview of the data, allowing to filter
them. Contextual overview can take many forms: top-
ical, geospatial, and temporal being some of the more
popular. Websites such as Flickr and Twitter provide
rich data sources annotated with geospatial and tem-
poral metadata. Users already familiar with searching
in geographic environments like Google Maps, can
find results presented directly on a map rather than in
a list. There are also early prototypes for mapping the
events and characters of cultural heritage onto maps
and interactive timelines (e.g. Timeplot
), driven by
enthusiasts using the Google Maps API
or the IN-
SPIRE framework
These enthusiasts approached us to implement a
web application that enables the synergetic explo-
ration of multiple topical queries in a geospatial and
temporal context. They want to be able to juxta-
pose and compare spatial distribution and temporal
trends of multiple queries. We searched for other sys-
tems on which we could base our design. Among
our major influences are a web visualization of The
Guardian (Rogers, 2010), working with many dat-
apoints, but supporting only one dataset and lim-
ited interaction, GeoVISTA CrimeViz (Roth et al.,
2010), which shows three datasets at the highest
zoom but aggregates them at lower zoom levels, and
VisGets (D
ork et al., 2008), which supports single
datasets with a wide variety of interactions. A gen-
eral concern with these systems, which we selected
as representatives of current systems for geospatial
and temporal exploration, is also the clutter that arises
when many data elements are spatially close together.
This becomes especially harmful when the glyphs’
attributes, e.g. which topical query the items arose
from, differ. Also the time widgets in these systems
can be improved as they typically do not support flex-
ible queries like May 2009 to May 2010.
Jänicke S., Heine C., Stockmann R. and Scheuermann G..
DOI: 10.5220/0003833406130625
In Proceedings of the International Conference on Computer Graphics Theory and Applications (IVAPP-2012), pages 613-625
ISBN: 978-989-8565-02-0
2012 SCITEPRESS (Science and Technology Publications, Lda.)
In this paper we present the design of a novel web
application that:
combines showing and comparing multiple result
sets in a geospatial and temporal context,
improves geospatial presentation of items by
zoom-dependent aggregation of items that avoids
allows more flexible selections within the map
and time widget, and
works client-side to allow user-provided data and
improve response time.
There has been a lot of research on the usage of visual
interfaces to interact with large data resources. Tukey
illustrates the benefit of integrating interactive visual
tools into the data exploration process to expedite the
undirected search for structures and trends (Tukey,
1977). This so called Visual Data Exploration process
follows the Information Seeking Mantra (Shneider-
man, 1996): Overview first, zoom and filter, details-
on-demand. This means, the first presentation of the
information is an overview over the whole data, fol-
lowed by the search for and drill down on interesting
patterns, exploiting capabilities of the human visual
system. The search for a specific information, which
starts with a vague imagination, and in a stepwise
process leads to understanding is called exploratory
search (Marchionini, 2006).
Much research in the field of Visual Data Explo-
ration was dedicated to the analysis of data with ge-
ographical and temporal metadata. An overview over
tools, principles, challenges, and the concept of the
analysis of geotemporal data are given by Andrienko
and Andrienko (Andrienko and Andrienko, 2006; An-
drienko and Andrienko, 2005).
Baldonado (Baldonado et al., 2000) proposed
guidelines for the usage of multiple views in visual-
ization to increase the user’s ability to receive deeper
insights if the data is shown under multiple aspects.
Causal relationships can be found easier as well as
unforeseen connections. Because of space restrictions
we cannot address all but a few representative imple-
mentations that exploit synergetic effects from using
linked views for geospatial-temporal data.
VisGets is a web application that displays the
result of one query in four linked widgets (D
et al., 2008), working much like specialized dynamic
queries (Shneiderman, 1994) for spatial and temporal
filtering. A location widget shows the result items as
small glyphs overlaid on a geographic map. A time
widget shows the distribution of results in bins repre-
senting years, months, and days. A tag widget shows
a limited set of frequent words in alphabetic order and
a size proportional to their importance. A results wid-
get shows small textual or image thumbnails of re-
sults arranged as a table. VisGets offers query refine-
ment in space (selecting one glyph), time (selecting
a year, month or day bar), and by topic (selecting a
tag). Each refinement affects the presentation in all
other widgets. VisGets also provides many mecha-
nisms to interact with the results of a single query, but
a comparison of different queries is not possible.
GeoVISTA CrimeViz (Roth et al., 2010) for crime
analysis enables comparison of different crime inci-
dent types. The map widget shows individual cir-
cles on higher zoom levels. However, on lower zoom
levels, the incidents of all types are aggregated into
hexagonal bins, thereby disabling comparison. The
concept of binning items and plotting their total poses
a problem, since the resulting continuous map over-
lay does not preserve the user’s geospatial orientation.
On the CrimeViz timeline, the number of incidents
per time period of different types are stacked, which
shows the sum of incidents and makes obvious dif-
ferences between the incident types and global trends
visible. Wang et al. (Wang et al., 2008) also uses
a specific stacked graph the ThemeRiver (Havre
et al., 2000) for the visual analysis of global ter-
rorism activities. We avoid stacked graphs, because
we do not want to sum up unrelated categorical data.
Furthermore, it is hard to accurately interpret trends
that lie atop other curves (Heer et al., 2010). LISTA-
Viz (Hardisty and Klippel, 2010) uses the concept
of small multiples (Tufte, 2001) for the analysis of
spatio-temporal autocorrelations on maps and time-
bars to allow comparison. The usage of small mul-
tiples makes comparison of our data, which typically
has a highly irregular spatial distribution, difficult.
Maps can also benefit data of historic origin. Tsi-
pidis et al. (Tsipidis et al., 2011) demonstrate the
usefulness of interactive geospatial-temporal visual-
izations for the exploration of archaeological data.
The HESTIA project (Barker et al., 2010) investi-
gates the differences between imagined geographic
distances and real distances in ancient Mediterranean
space by mapping data onto contemporary maps: the
Herodotus Timemap. It is a geo-temporal visualiza-
tion that shows several location types (settlements,
regions, physical features) mentioned in various text
passages. The user is able to browse through time,
space and book chapters. However, there is neither
aggregation in space nor time, causing visual clut-
ter. The system also lacks filtering of locations by
dynamic refinement in space and time.
IVAPP 2012 - International Conference on Information Visualization Theory and Applications
In this section, we describe the design of our system,
starting from the input and the filtering methods to the
representation inside multiple linked views and finish
with the interaction techniques for query refinement.
The presentation consists of a map widget showing
the position of query results as small circles suitably
aggregating items for the current zoom, a time widget
showing the distribution of results in a time span, and
a detail widget showing textual contents or thumb-
nails of data items arranged in a table.
The input to our system is the result of one to
four result sets from a classical keyword or term-
based topical search. Each item in the input sets is
required to have a longitude and latitude as well as a
time stamp or time span annotation. In our datasets
the number of items that lack these data is sufficiently
low so that we can simply omit them from the map
and time widget.
The system provides spatial filtering based on
containment queries in the form of circles and poly-
gons as well as temporal filtering based on intersec-
tion queries with a given time span. Initially, the sys-
tem starts with a minimally enclosing rectangle over
all items’ positions and a time span that stretches over
all items’ time stamps and time spans.
We choose colors to codify results from different
datasets because of their effective use to discrimiate
categorial aspects of data (Bertin, 1983). We pre-
sume map colors to be mostly dark, cold, or unsat-
urated, and select very light colors for deselected and
very saturated colors for selected circles to ensure that
overlaid circles are not masked by map colors. We use
a fixed number of colors, as the human ability to dis-
tinguish colors is limited. This also limits our system
to four input datasets. From the 12-color palette sug-
gested by Ware (Ware, 2004) for categorial usage, we
chose to use only four colors because we wish to en-
sure that mixtures of these colors are distinguishable
in our time-widget. The four colors used for datasets
1 through 4 are red, blue, green, and yellow. The order
was chosen to reduce the chances of misinterpretation
by color-blind people. We followed the recommen-
dation of Harrower (Harrower and Brewer, 2003) for
diverging colors in thematic maps. The user can also
change the colors from their default values.
3.1 Map Widget
The dominant widget in our visualization in terms of
screen space is the map widget. Following the defini-
tions of Harris (Harris, 1999), the map widget consists
of a geographical map, which is overlaid with bub-
ble glyphs. We use circles of varying size for glyphs,
and each circle represents one or more items of the
dataset. We chose a circle representation on the map
to display data densities instead of, for instance, a heat
map, for multiple reasons. Multiple result sets re-
quire multiple heat maps, which in turn would require
mixing color maps, against which Ware argues (Ware,
2004). Also, humans are more accurate judging areas
than they are judging color tones, making areas a bet-
ter candidate for quantitative values (Cleveland and
McGill, 1984). Using glyphs also allows to group
glyphs and to make every data item individually ac-
cessible through its graphical representant.
In addition, we disallow the glyphs to overlap in
order to avoid visual clutter. To achieve this goal, we
merge circles based on their size, distances, and the
current zoom level in an iterative process. We ag-
gregate by proximity rather than geographic regions
because regions can have a bad area-to-diameter ra-
tio and the glyph resulting from the aggregation may
extend beyond the border, leading to ambiguities.
Aggregation for One Dataset. In the following let
N denote the number of items, k the number of sup-
ported zoom levels, and N
the number of circles at
zoom level l (0 l < k). r
is the radius used for
circles representing one item. We define r
dent on the average font size of labels of common web
mapping services (Google Maps, Bing Maps,. . . ) so
that small circles have salience equal to labels. The
maximum radius is defined as r
= r
+ log
and A
are the resulting circle areas. The area
of an arbitrary circle c
is interpolated linearly de-
pending on the number n
of data elements, that are
assigned to it:
N 1
= πr
= (1 φ
+ φ
The weight in φ
ensures larger area differences for
higher zoom levels. The effect is, that differences be-
tween smaller circles are more salient.
To ensure non-overlapping circles, we perform a
variant of the hierarchical agglomerative clustering
algorithm. Two separate circles c
and c
need to be
merged if their Euclidean distance d
i j
after Mercator
projection is smaller than the sum of their radii r
. From this ratio we compute dissimilaries ψ
i j
merge circles c
and c
into a new circle c
i j
placed at
the barycenter of all their data elements if
i j
i j
+ r
+ ε
< 1.
The gap value ε denotes the smallest permissible dis-
tance between two separate circle boundaries.
(a) Individual circles. (b) Aggregated circles.
Figure 1: Aggregation of 307 points. The underly-
ing Guardian dataset shows key incidents during the
Afghanistan conflict. (a) Each circle with radius r
sents one incident. The high degree of overplotting makes
it hard to access each circle individually, and to determine
the precise conflict centers. (b) 307 elements were aggre-
gated to 43 non-overlapping circles. Larger circles clearly
indicate conflic centers.
The aggregation of the circles starts with N cir-
cles from which a proximity matrix Ψ is generated.
Initially, each single circle represents all items at a
common point. Iteratively, we merge the two circles
with the lowest value for ψ until there is no value be-
low 1. Each merge step causes two deletions and one
insertion of rows and columns in Ψ
i j
. We end with
circles for the highest zoom level k 1 and pro-
ceed similarly to compute the N
circles at zoom
level l 1 from the circles at zoom level l for each
0 l < k. We perform this agglomerative hierarchical
clustering procedure, which takes O(N
) time for
each zoom level l (with N
= N), until we have found
a non-overlapping circle set for zoom level l = 0. An
example result is given in Figure 1.
Aggregation for Multiple Datasets. In the case of
multiple (m = 2, 3, 4) input sets, we compose multiple
circles c
, . . . , c
into a more complex glyph – a circle
group whose bounding circle b will be used in the
aggregation. We use a modified version of the congru-
ent circle packing method of Kravitz (Kravitz, 1967):
after computing the radius of each c
with Equation 1
we place m template circles t
at the same distance
from a hypothetical center z that touch each other at
their boundary. We then place the circles c
inside the
template circles t
and move them as far as possible to-
wards z without them leaving t
. In the case m = 4 the
biggest and second biggest circle are positioned oppo-
site each other. We then construct a minimal bound-
ing circle like in Figure 2.
Although the circle packing method requires a lot
of screen space, it leaves all similarly located ele-
ments very close and quantitative differences that are
reflected by varying diameters of the corresponding
circles are easy to spot. Furthermore, all circles can
Figure 2: Example circle packings.
(a) Country level. (b) Region level.
(c) City level. (d) Borough level.
Figure 3: Level of detail of placename tag clouds. The used
Flickr dataset shows photos which are tagged with “Italy”.
(a) most of the places are “Italy” and “Vatican City”, (b)
administrative divisions of Italy, (c) most photos for capital
Rome, (d) Colosseum photos in Rome’s district San Paolo.
be accessed individually. We also chose packed cir-
cles over pie charts, because even though they can
present ratios of the data at one point very good, it
is difficult to compare the size of pie slices between
pie charts of different size.
Placename Tag Cloud. Each of the map’s circles is
optionally associated with a set of placenames includ-
ing their frequency. A details-on-demand tag cloud
for a circle shows the most frequent placenames, their
font scaled proportional to their frequency. This fea-
ture provides a preview of how a glyph arising from
agglomeration would split if zoomed in. If the data
offers different levels of detail for a place, we choose
the label dependent on the current zoom level. We dis-
tinguish four semantic levels: country, region (e.g., a
state or a countryside), city, and borough (e.g, a dis-
trict, specific place or an address of the given city). If
one of levels is missing, we take the next coarser level
if possible, otherwise the next finer level. An example
of the placename tag cloud is given in Figure 3.
Historical Maps. Because some of our applicants
wish to study datasets with historical context, we al-
low to show political borders for specific points of
time. In addition to a contemporary map we offer 23
different historical maps from 2000 BC to 1994 AD
provided by Thinkquest
. Because a dataset may span
a time range for which multiple maps are available
IVAPP 2012 - International Conference on Information Visualization Theory and Applications
and there is no concept of “average political border”
we show the map closest to the median time stamp
occuring in the dataset as default and allow the user
to switch to any map at his convenience.
3.2 Time Widget
Using terminology of Harris (Harris, 1999), we con-
struct the time widget as a segmented area graph, that
has time as unit for the x-axis. The segmented area
graph is a line chart where the area under the line is
filled. T
, . . . , T
partition the interval T = [t
of the given dataset into intervals of regular duration:
either seconds, minutes, hours, days, weeks, months,
quarters, years, or decades. The resolution is chosen
to maximize the number of intervals without exceed-
ing 400. Short units typically arise from steadily up-
dated data sources and large units arise from data with
historic content. The resolution unit changes automat-
ically when the user zooms inside the time widget.
Whereas the x-axis of the time widget is directly
defined by T
, . . . , T
, the y-direction shows the num-
ber of data items that fall in each interval using bin-
ning. For data items with time stamps the counting
is straight-forward, for data items with time spans
we add a value proportional to the amount of over-
lap with each bin. Although this can lead to an over-
representation of items with long time spans in the
time widget, we found this to be no problem. For the
datasets we considered, either the effect was benign
as the time spans had approximately the same dura-
tion or items with longer time spans were also more
Time Binning for Multiple Datasets. In the com-
parative setting we perform the bin counting per
group. In the final visualization the bins’ sizes are
shown as overlapping segmented area graphs rather
than bar charts because the former is better suited
to direct comparison of the groups’ time distribution.
We use the same colors for the groups as in the map
widget and shade the area under each line using a
semi-transparent version of that color. This ensures
that each curve is visible and also hints at the area
that would have been present in the bar chart. Despite
the use of blending, the limitation to four colors as
well as the stacked area graphs’ shape helps limit and
resolve ambiguities.
3.3 Detail Widget
For inspection of single data items that match the cur-
rent filtering we present small textual or image thumb-
nails presented as a table in the detail widget. This
widget is the only one which does not include any ag-
gregation, but results are presented on multiple pages
if they exceed a certain fixed number.
3.4 User Interaction
Our application offers multiple ways for the user to
interact with the data. Each widget provides native
navigation that results in updates of the other widgets.
The map and the time widgets provide simple zoom
and pan. Because of the way we aggregate data in
these widgets an animation between zoom levels or
time resolution changes is not performed. When fo-
cusing or selecting items in one view the correspond-
ing items are highlighted in the other widgets too.
The selections are linked. In particular, a selec-
tion within the map widget triggers a proportional se-
lection on the time widget. This is done by overlay-
ing the area graph with a bar chart that shows, how
many elements of a timeslice are associated with the
selected location. A selection on the timeplot triggers
a proportional selection on the map as well. There-
fore, we overlay a deselected circle with a circle with
the highlight color and a radius, that reflects the pro-
portion of elements, which are inside the selected time
Highlight. A glyph or time step that the mouse hov-
ers over is highlighted along in all other views. It is
a preview for selection. Hovering over a circle on the
map frames the circle with its associated placename
tag cloud. For a hovered bin in the time widget, the
number of associated elements and the corresponding
date is shown.
Selection. Each highlight can be turned into a se-
lection by a simple click. Every widget provides its
own means of selection and offers a broad set of fur-
ther user interactions, e.g., any selection can be turned
into a query refinement by a context menu item.
In the map widget the user can select each cir-
cle individually. Thereby, the placename tag cloud
is frozen and the different place labels can be high-
lighted as well as selected.
It is also possible to draw shapes such as circles
or polygons. Selections can be refined by dragging
shapes or control points. As a special selection the
user may also click on a country effecting the selec-
tion of all the items inside its borders. In historical
contexts, using maps of the time period of interest are
of great benefit to this task.
The time widget allows both the clicking on one
bin and the selection of a time range using a mouse
drag gesture. A toolbar is then shown, that offers to
modify the left or right border of the selected time era
and to add a “gray zone” which blends between se-
lected and deselected elements. Finally, the user can
manually move the selected time window. We update
the map widget automatically to facilitate a direct re-
flection how locations change over time. A play but-
ton starts an animation mode and the selected time
span loops smoothly over the entire time range. This
mode directs the user’s attention towards changes in-
side the map.
The boxes in the detail widget reflect highlight
and selection by setting the border and background,
respectively, to the dataset’s color. Additionally, the
table can be used for the selection and deselection of
single items, to allow further refinement to the user.
Intra Dataset Comparison. One of the major fea-
tures of the detail widget is the ability to export ele-
ments of a selection of a dataset as a new individual
input set. Hence, the temporal comparison of differ-
ent geographical regions of one dataset as well as the
geographical comparison of different time periods is
We implemented our design in a prototype applica-
tion in the context of the European project (Purday,
2009) EuropeanaConnect
. The system’s architecture
is given in Figure 4(a).
The Web-server is mainly used for data process-
ing. Dependent on the requested data source, the
server retrieves the requested data and constructs a
KML-file with a specific format. For each item that
fits the given query, we add a Placemark tag to the
KML root. Each Placemark is filled with a name, lo-
cation in form of latitude and longitude and either a
valid TimeStamp or TimeSpan. Optionally, a place-
mark node can be enriched with a location info (ad-
dress), that will be used for the placename tag clouds.
Slashes are used to separate their levels of detail. A
description entry can be used to commit a CDDATA
section that includes HTML content to offer detailed
information for the placemark. Finally, the KML-file
will be sent to the client as a response to the XML-
The client browser parses the retrieved KML-file,
creates JavaScript objects and fills the widgets with
content. We decided to construct a pure JavaScript
client, because it is supported by every browser with-
out any additional package. Another reason for
(a) Architecture.
(b) Screenshot.
Figure 4: System overview.
choosing JavaScript for our main application is the
rapidly growing browser support in terms of perfor-
mance. All interactions, including data refinement are
performed on the client side. The great advantage of
this system structure is, that each modification of a
dataset within the client browser triggers only some
functions on the client side. This benefits the response
time of modifications, since the browser does not have
to wait for the server. We allow the user to load his
own KML-files. To the best of our knowledge our tool
is the first to support this feature.
For the widgets on the client side we make use
of two OpenSource JavaScript libraries. A modified
Simile Widgets Timeplot
instance organizes the seg-
mented area graphs and OpenLayers
visualizes the
circles and arranges the different map layers. We of-
fer some popular maps as well as the historic maps,
IVAPP 2012 - International Conference on Information Visualization Theory and Applications
(a) CrimeViz (low zoom level): Incident types arson, homi-
cide and sex abuse.
(b) Our system (low zoom level): Incident types arson (red),
homicide (blue) and sex abuse (green) in circle groups.
(c) Selected incidents in 3rd police district. (d) Compared crimes stolen car (red), burglary (blue), theft
(green) and robbery (yellow).
(e) Co-located incidents at 27th of June. (f) Stolen car and burglary incidents in neigh-
borhood 13 (top) and 39 (bottom).
Figure 5: Crime incidents in Washington D.C. Figure 5(a) reproduced with permission from (Roth et al., 2010)
which are explained in Section 3.1. We use a web
server instance of the GeoServer
, that provides the
tiles of the historic maps. A screenshot of our appli-
cation can be seen in Figure 4(b).
To demonstrate the flexibility of our system, we
linked dynamic web sources as well as static data sets
with our system and compared our visualization to ex-
isting ones. Finally, we performed a field experiment
(McGrath, 1995) to evaluate our design.
5.1 Crime Incidents
We compared our visualization to the CrimeViz appli-
cation. CrimeViz can only show incidents of one year
at once; we chose 2009 for comparison. In CrimeViz
the total of the frequencies of 3 incident types ar-
son, homicide and sex abuse is displayed in form of
hexagonal bins of different saturated red color hues on
lower zoom levels (Figure 5(a)). Thereby, the over-
lay causes a loss of map context in strongly saturated
areas. In our system places with many incidents are
salient through circle groups with different sizes (Fig-
ure 5(b)). Without the need to zoom or filter, we can
directly see that there was no homicide and arson inci-
dent in the north western neighborhoods. This cannot
be seen in CrimeViz. Furthermore, the time resolu-
tion in our system is more precise (days) compared
to CrimeViz (weeks). There are also no spatial filter
mechanisms offered by CrimeViz. Within our system,
we are able to filter incidents geographically by defin-
ing polygon or circle container shapes, or directly by
selecting an administrative area, e.g., a police district
(Figure 5(c)).
The used crime database
offers a total of eight
different crime incident types. CrimeViz used only
the three types with smallest number of incidents: ar-
son (59), homicide (140), sex abuse (361). Other
types theft (9,262), stolen car (4,852), robbery
(4,389), burglary (3,670), assault with deadly weapon
(2,623) are not considered. Since each circle has
one spatial representative in CrimeViz and there is no
spatial aggregation on higher zoom levels, overplot-
ting would be a fatal problem for the larger datasets.
The additional crime types allow us to compare
crime incidents which may be more related to each
other than arson, homicide, and sex abuse. In Fig-
ure 5(d) we see the geospatial and temporal compar-
ison of stolen cars, burglaries, thefts, and robberies.
By hovering over the glyphs in the city center, we
find out that there are relatively few stolen car (20%)
and burglary incidents (23%) in comparison to thefts
(45%) and robberies (35%). Moreover, we find pat-
terns for thefts and robberies near populated places
like metro stations or shopping centers. By exploring
individual days, we discover that a stolen car incident
is often grouped with a theft, burglary or robbery in-
cident (Figure 5(e)). The number of correlations in-
creases further by choosing a two day time range.
With the comparison feature, crime analysts could
improve the detection of connected crime incidents of
different crime types. Furthermore, our tool in combi-
nation with crime data could be used to drive the de-
cision making for apartment search. Interesting ques-
tions could be “Where is a secure region in terms of
burglary?” or “Where should I also rent a garage for
my car?” Figure 5(f) indicates Neighborhood 13 as
substantially safer than Neighborhood 39 with respect
to stolen cars and burglaries.
5.2 Guardian Data
The Guardian datablog
is another open source for
a multitude of datasets with geospatial-temporal data.
On 23rd of October 2010, The Guardian published
the Iraq war logs. The data contains information
about approximately 60,000 incidents during the Iraq
conflict from 2004 to 2009. Every incident claimed
at least one casualty; 109,000 total casualties are
mapped. Each incident is annotated with a time
stamp, latitude, longitude, incident type, and number
of casualties for each casualty type. The Guardian
also published a visualization (Rogers, 2010), where
each incident is represented by one circle on a map
(Figure 6(a)). The visualisation is cluttered as the re-
sult of overlapping glyphs. Figure 6(b) shows the geo-
graphical distribution of incidents with our system us-
ing the same color for glyphs. In both visualizations
regions with a low glyph density work well, e.g., a
series of horizontal circles from Baghdad (center) to
Jordan (east) highlights the importance of this con-
necting road. However, the conflict centers, Baghdad,
Al Mausi, and Al Basrah, cannot only be discrimated
easily in our visualization.
The temporal distribution shows a peak of inci-
dents between August 2006 and February 2007 (Fig-
ure 7(a)). The decreasing number of incidents after-
wards indicates the success of the Operation Impos-
ing Law, which started in February 14th 2007. The
Guardian map shows information about the incidents’
number of victims only in popups. We split the data
into four sets, depending on the four given casualty
IVAPP 2012 - International Conference on Information Visualization Theory and Applications
(a) Guardian map. (b) Our map widget.
Figure 6: Iraq War Logs: Incidents distribution. Figure 6(a) reproduced with permission from (Rogers, 2010). (a) Three
conflict centers can be made out: Baghdad in the center, Al Mausi in the north, and Al Basrah in the south-east. Due to
overplotting, it is difficult to determine which of these three regions suffers the most from incidences. The original visualiza-
tion omits some incidences for unknown reasons. (b) Baghdad in the center is clearly the region with most incidents (23.783
incidents, approx. 5,600 incidents in Al Mausi, 2,400 in Al Basrah, as simple mouse-over reveals). All incidences are shown.
types (civilian, enemy, iraq forces, coalition forces)
for comparison.
A comparison between incidents and civilian vic-
tims reveals two informations: (1) several incidents
without civilian casualties in the north-west of Bagh-
dad (Figure 7(b)), (2) incidents with many civilian
casualties, e.g., an incident which caused 158 civil-
ian casualties in the south of Karbala at April, 14th
2007 (Figure 7(c)). Another of these incidents is an
IED Explosion in the north of the Baghdad’s neigh-
borhood Rasheed that caused 94 civilian casualties at
February, 3rd 2007. The overplotting in the Guardian
Map masks this incident: it is hidden by an event with
one victim (Figure 7(e)). Our spatial aggregation pro-
cedure merges both incidents (Figure 7(d)); the inci-
dents’ details can be found in the detail widget.
The comparison of all casualty types shows that
most of the victims were civilians (65,649), and the
only period with more enemy than civilian casual-
ties was at the beginning of the conflict in 2004 (Fig-
ure 7(f)). There was also less fighting at the road to
Jordan in 2004. The roads to Mosul (north) and to
Syria (north-west) were more contested at that time.
We are also able to reject the statement of the Iraq
Military from March 14th 2007, that there were only
265 civilian casualties in Baghdad since the start of
Operation Imposing Law. Figure 7(g) shows the time
range from February 14th to March 13th with a total
of 997 civilian casualties and a peak on March 5th,
caused by explosions around noon in Tigris.
5.3 Dynamic Data Sources
Humanities scholars within the eAQUA project
uchler et al., 2008; Heyer et al., 2011) use our sys-
tem to explore the geographic distribution and propa-
gation of words extracted from texts in ancient Greek.
The TLG research center at the University of Cali-
fornia created the corpus, which is one of the most
important resources that offers around 7200 Greek
works of over 1800 Greek authors. Text mining al-
gorithms extract words of strongly inflected ancient
Greek and tag them with temporal and geospatial in-
formation. The metadata spans the eastern Mediter-
ranean with Greece, Italy, Turkey, and North Egypt
and 18 centuries starting from 8 BC. The resulting
data divisions are shown in a suitable historic context
by automatically displaying adequate historic maps.
Figure 8 shows occurences of the words “Plato” and
Aristotle” on the map of 400 AD.
We integrated our system into the online digital
catalogue of Europeana. It contains over 15 million
entries (images, texts, sounds, videos), which are dig-
itized resources of Europe’s libraries, museums, and
archives. Most of the items have geospatial (e.g., pub-
lication place) and temporal (e.g., publication year)
(a) Peak of incidents 2006/07. (b) Incidents (blue) and civilian (red) casualties.
(c) Incident near Kabala. (d) IED explosion caused 94 casualties. (e) Criminal event at same place (Guardian).
(f) More enemy (blue) than civilian (red) incidents 2004. (g) 997 civilian casualties (top), explosions on March
5th 2007 (bottom).
Figure 7: Iraq war logs: analysis. Figure 7(e) reproduced with permission from (Rogers, 2010).
IVAPP 2012 - International Conference on Information Visualization Theory and Applications
(a) Occurences during middle platonism. (b) Occurences during neoplatonism
Figure 8: Occurences of the words “Plato” (red) and Aristotle” (blue) in ancient Greek texts: In the first period Middle
Platonism (1st to 3rd century) – we see a widely spread distribution of “Plato” and Aristotle” (Figure 8(a)) in the Greek-Ionic
region. The second period – Neoplatonism (4th to 6/7th century) – shows a lot of occurences in the territories of Athens and
Constantinople (Figure 8(b)). This indicates a correlated movement of both topics from rural regions to metropolises.
metadata. In the detail widget we use thumbnails,
which link to the Europeana source page. Figure 9
shows the distribution of occurences of different his-
toric watermark types (tokens of letter authenticity).
5.4 Field Experiment
To evaluate our design and spot areas of usabil-
ity improvements we conducted a field experiment.
We asked 15 individuals to partake; 7 men and 3
women followed our call. The participant’s occupa-
tion ranged from pupil to pensioner and their age from
13 to 71 years. The participant’s educational back-
ground varied, but none had notable computer sci-
ence background. Each test took place at the partici-
pant’s typical location where he accesses the internet.
The average session duration was approximately two
hours. A session consisted of three parts: a tutorial,
solving tasks with the system, and an interview.
First, each participant was asked to work through a
tutorial, which consists of 10 short flash videos linked
from the application. The tutorial introduced the wid-
gets and features of the system. To understand each
feature, the users were encouraged to solve basic per-
ceptual and manipulation tasks. Parts of the tutorial
could be skipped or repeated, but we asked the partic-
ipants not to ask questions to the present conductor of
the study, who only observed, whether the pace, clar-
ity, and volume of the tutorial was appropriate.
After finishing the tutorial, each participant was
given a form with 15 prepared questions, which he
had to answer using the system. During that time the
participant could replay tutorials or ask the study con-
ductor for help, but participants only used the oppor-
tunity to verify their understanding of the questions.
The questions had an increasing complexity, and the
solutions required the use of all features mentioned in
the tutorial. Questions could be answered in any or-
der and skipped if considered to be too difficult. The
solutions had different types: single or multiple data
items, a specific time, place, placename, and even
combinations of them. An example question asked
to verify the Iraq police statement on the success of
Operation Imposing Law (see Section 5.2). For some
questions the users had to estimate solutions or find
trends in space and time.
The fraction of correct answers was 75.3%. Even
the oldest subject attempted 8 of 15 questions and all
of his answers were correct. Some answers were in-
correct but “almost” right (10.0%); there was often
just an single overlooked glyph or time step. Only a
small portion of answers were wrong (6.7%) or not
solved (8.0%). By comparing the questionnaires, we
discovered, that the ratio of correct answers was inde-
pendent from the internet usage per day and the reg-
ularly visited platforms. Users with lesser experience
required more time to finish the tutorial, and they also
took more time in answering the questions, but the
ratio of correct answers was comparable. Even expe-
riences with web mapping services were not relevant
for the correctness of the given answers.
Finally, the subjects were asked openly how they
liked the application, followed by which features they
liked or disliked, and what they would change. The
general reception of the application was very pos-
itive. Although they stated to require the tutorial,
which introducted only elementary interactions with
single widgets, they learned and used the synergetic
effects the more they used the tool. We also noted
that the majority of the subjects did not make use of
the time animation control, but those who took it to
solve the questions, claimed it to be a useful feature.
Suggested improvements were mostly of technical na-
ture, e.g., avoiding overlapping boxes inside the time
widget and reducing the startup time of the system
for very large datasets. Not all of them were using the
browsers we reccomended for fast JavaScript execu-
tion. They also proposed to use pie charts rather than
circle packing, to provide placename tag clouds for
circle groups, selections for continents, and a speed
adjustment for the time animation feature. Some
tasks, for instance the selection of multiple countries,
have been implemented in the mean time.
Figure 9: Watermarks tagged with “Snake” (blue), “Bird”
(red) and “Cloche” (green). Compared to Bird, Cloche
and Snake watermarks were used more often. Whereas the
Cloche watermark is spatially distinct and often used in the
14th and 15th century, the Snake watermark was used be-
tween 1450 and 1700, mostly in northern Italy and central
Germany. Domain experts attribute this to the popularity of
the Snake watermark as the heraldic device of the Milan dy-
nasty of the Visconti, and later on as Swabian papermakers’
hallmark of excellence.
We presented a novel approach and web applica-
tion to show, compare, and explore multiple topical
query results in a geographical and temporal context.
We were able to utilize, combine, and improve ap-
proaches from several related works. In comparison
to CrimeViz (Roth et al., 2010), which also offers
comparitive visualization, we found a consistent so-
lution for displaying results of different queries in the
geospatial dimensions. We display them separately
without aggregating them into the same representa-
tives for coarser zoom levels. Furthermore, with our
method we are able to solve the spatial overlap prob-
lem, which is also an issue in the Guardian visualiza-
tion for the Iraq war logs (Rogers, 2010). Compared
to the similar system VisGets (D
ork et al., 2008),
which only works for one set of results, we also made
use of the linked widgets approach (map, timeline,
detail widget) to extend the users exploration abili-
ties. Furthermore, we enriched the filter capabilities
in both geospatial (e.g., selecting all results inside a
country) and temporal dimension (e.g., selecting dy-
namic time ranges). In contrast to VisGets, where
most user interactions lead to a server request, we
placed nearly all system logic inside the client to ef-
fectively improve the response times.
Our case studies show that visually comparable
datasets extend the exploration and analysis abilities
of the user in an effective way. It helps to detect
equalities and varieties between distinct data contents
that unveil their relationships in space and time. Our
method is limited to four datasets at a time mainly
to ensure that the colors used for discrimination are
properly distinguishable, the splitting of circles does
not waste too much screen space, and the overlapping
segmented area graphs do not occlude each other too
In the field experiment, we could establish through
the feedback of the participants, that our design is
easy to learn and suitable to the given task of com-
paring multiple input sets in geospatial and temporal
dimensions. In addition, the aggregation of items of
distinct sets were evaluated as intuitive and the func-
tionalities we offer to interact with the data were easy
to adopt with the help of the provided tutorial – inde-
pendent on the class of age, education level, and the
amount of participants’ internet usage. We also re-
ceived some hints on how to improve our application.
In the future we will direct our attention to the ex-
tension of our method to very large datasets by search-
ing for client-server communication where most of
the data remains on the server but a working set is
transmitted to the client for quick interaction. We aim
to extend our system to extract and show trajectories
of distinct sets containing movement data, potentially
leading to new insights.
The dynamic visualization of recent Twitter feeds
is also one of our future objectives. We hope to see
how topics proliferate. Preparing examples which
automatically update their results by querying social
platforms in equal time intervals could even show the
live change of topics’ relations.
We like to thank our colleagues Christian Mahnke,
Marco B
uchler, and Muhammed Faisal Cheema for
fruitful discussions and our field experiment partici-
IVAPP 2012 - International Conference on Information Visualization Theory and Applications
pants for their time. We are also indebted to Cornelius
uller for preparing datasets. We also thank Simon
Rogers and Robert E. Roth for providing screenshots
of their systems. This research was funded by the Eu-
ropean project EuropeanaConnect.
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