Geoscience Reference
In-Depth Information
Figure 11
shows a map overlaid with the names of the OSM editors that
have been used within this area. It is remarkable that there are considerable
differences between different regions as different local OSM mapping
communities seem to prefer different OSM editors to edit data. These
results may turn out to be interesting for the mapping community especially
for the development and documentation of OSM edit tools.
The tag cloud visualisation method allows the analysis of both object
types
and object
values
.
Figure 10
is an example for object type visualisation and
Table 1
shows typical computing times for word cloud processing at dif-
ferent scales within an area of high density of OSM objects. Test environ-
ment was a machine equipped with an AMD Dual Core Opteron 2.6 GHz
processor and 1 GB RAM. Times for processing of the word cloud from
the tag frequency list are nearly scale-independent, whereas times for data-
base queries increase exponentially with decreasing scale. For the proto-
type, a copy of the part of the OSM database that covers the area of
Germany was used. This includes currently just 5% of all data of the data-
base. However there are still about 40 million entities in table 'nodes', 5
million entities in table 'ways' and 80.000 entities in table 'relation' that
need to be queried. Additionally, there are about 8 million entities in table
'node_tags', 14 million entities in table 'way_tags', and 300.000 entities in
table 'relation_tags', which need to be analysed for every word cloud
request.
Word clouds are intuitively perceptible and by their nature do not suffer
from the labelling problem of bar charts, tree maps or bubble charts.
Furthermore they are able to present the gist of a word corpus. Cons are
that long words as well as words with many ascenders and descenders get
undue attention and that it is not possible to read exact values. The layout
algorithm of a word cloud is more sophisticated than the layout algorithm
of a tag cloud as it uses the typographical whitespace more efficiently.
The big advantage of using a standardised WMS implementation is that a
multitude of existing WMS clients can directly integrate the results. Our
implementation even allows making use of the getMap transparency
parameter and hence an overlay that does not completely hide the examined
map is possible.
A disadvantage of the word cloud visualisation displayed directly on a map
is that map readers are used to associate text shown on a map with the
directly underlying situation. In the case of an overlaid word cloud that
