Maple – a Web Map Service for Verbal Visualisation using Tag Clouds Generated from Map Feature Frequencies - Advances in Cartography and GIScience - page 11

describes the whole map section, this can lead to misinterpretation. Hence,
this method may be more useful when displaying just one district, whereas
for the case of displaying a whole town, it might not provide significant in-
sights. It would be possible to solve this issue if the word cloud would not
be directly overlaid on the map but would be shown in a separate space of
the application.

Scale Cloud processing time

(sec)
1:36000 20 103 95 8
1:18000 5 37 29 8
1:9000 1.25 10 3 7
1:4500 0.32 7 0 7
1:2250 0.08 7 0 7
Table 1: Computational time for word cloud processing at different scales, test environ-
ment: AMD Dual Core Opteron 2.6 GHz, 1 GB RAM

Area (km²) Overall computing time
(sec)

Database query time
(sec)

Using the tag frequency in relation to vertices within a map area underes-
timates the relevance of large objects having few vertices and overesti-
mates the relevance of small objects having many vertices. This affects
lines as well as polygons which consume much map space with few verti-
ces and accordingly lines and polygons that consume little map space with
many vertices. This bias is relevant for the cases where the overlaid word
clouds are intended to visualise the main semantic information of a map
section like in Figures 8 and 9 . Instead of using tag frequencies, the esti-
mation of relevant tags within the word cloud visualisation can be
improved if length of lines and areas of polygons associated with specific
tags are used as a weight. For use cases where we only want to present
statistics of a dataset like in Figures 10 and 11 the vertex related frequency
estimation is sufficient.

4- Conclusions and Future Work

We have presented a method that able to present the main semantic infor-
mation included within a certain map section using a word cloud visualisa-
tion technique that visualises map feature frequencies on a map. Up to a
certain degree this technique is able to verbally characterise the real world
environment presented in a specific map section. Our demonstration is
based on the OSM dataset but the approach is also applicable to other