(continued from Part III)
Last time I left off with showing you how individual documents would be blended into a landscape which is computed from a topic map.
So far I have ignored the topological structure of the topic map itself and computed the landscape only from the terms within the documents. But my ultimate goal is/was to visualize whole topic maps, not just the text corpus.
(continued from Part II)
The landscape we looked at last time only consisted of mountain ranges and valleys made up by the intensity and extensity of certain words (or word groups) within a set of documents. Documents which are all captured with a topic map.
(continued from Part I)
The last time we looked at a map which only contained two topics and where the documents attached to the two topics are completely disjunct:
One additional observation we can make is that the landscape has a rather peculiar structure: If you follow the JJJ mountain southwards towards the HHH ridge, you will see that that continues at the top.
The same holds for the east-west horizon: If you follow the EEE
Before we can look at the impact the topology of a topic map makes on the visualisation, we first consider only the text content itself. And to have a better control over it we will stick to purely synthetic maps, i.e. those where the text follows certain patterns we control.
Here is a visualisation of such a topic map:
Ok, ok, one map thing after the other:
A while back I ranted that topic maps are normally not visualized as (quasi) geographical maps. I argued that the map metapher is so natural to most of us that lifting it into a semantic space is worth a try.
Ah, and before I forget: Here is a trivial, but elegant piece of Perl code which utilizes the trusty GNUplot program to compute a 3D presentation of a given elevation model, together with its contours.
Something like this:
Invocation in Perl
To start the GNUplot binary we will open another process from the Perl program.
grass is a library of commands for manipulating, analyzing and displaying cartographic information. While the functionality itself has evolved now for many years, it was scattered over different organisations, programming languages and a zoo of different map formats.
grass' biggest achievement is to have brought all this functionality into a common framework and also to provide an interactive Tk application which allows amateurs like me to experiment with amazing features while remaining mostly