The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B4. Beijing 2008 
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parameters, to define weighting measures and to decrease 
uncertainties resulting from the importance determination. 
3. CONTEXT AND FUZZY APPROACH 
3.1 Context Understanding 
It is possible consider context as follows. 
• context as the reflection of object or phenomena using 
different interpretation through the system of cognition: 
perception, conception and interpretation, 
• context as the reflection of selected facts is concerned with 
validity of statements and the system of argumentation, 
• context as the reflection concerning validity of statement 
using knowledge generating system - knowledge based 
context 
Using context it is possible to derive new quality of information 
that can be used to support decision. The integral part of control 
GIS [9] is the modelling where the information layers from real, 
artificial and virtual world are composed together to select 
optimal solution or verify given hypothesis or assumptions. 
The contextual design of spatial data and further development 
of geo-information technologies, image processing techniques 
and the possibilities of object history modelling together with 
the geographical networks environment will provide quite new 
and considerably wider possibilities of using GIS. 
Good message is that the increase of interactions and 
correlations implies decrease of differences between objects. 
From the point of uncertainty and contextual modelling the 
wide context (temporal, spatial, local, objective, attribute 
oriented, ...) influents in the sense of decreasing overall 
uncertainty of the results - the similar effect like the use of 
sequence of images in case of noise suppression - image 
reconstruction. 
3.2 Fuzzy Approach 
GIS architecture is open to incorporate new requirements of 
knowledge-based analysis and modelling, namely in connection 
with web designed spatial databases and temporal oriented 
approaches. The example of context implementation is 
composed classifier where the fuzzy elements are implemented. 
Composed classifier is composition of component classifiers, 
which predictions are connecting by combining classifier, 
unlike others simple classifiers. There are several architectures 
for possible combination of classifiers. 
The analysis of geographical data tends to tasks that are solved 
using defined sequences of operations and algorithms where the 
Figure 1. Outcome of the Fuzzy C- means 
Figure 2. Outcome of the Gustafson-Kessel Algorithm 
set of conditions is provided per parts. The amount of 
cumulative functions allows to capture the uncertainty and 
imprecision. The solution of zoning and tolerance extents where 
the fuzzy approaches are used the slivers are generated and 
together with overlay function arise the complexity of results. 
Figure 3. Overlay function and its consequences 
4. CONCLUSIONS 
In this paper, the problem of uncertainty of multisource data and 
muticriterial analysis is addressed and the use of wide spatial 
and temporal context and expert knowledge integration to 
decrease the uncertainty is discussed. The running development 
of information technologies, image processing techniques and 
knowledge-based databases, together with the geographical 
networks environment, will provide quite new and considerably 
wider possibilities of using GIS. Our decisions are becoming 
increasingly dependent on understanding of complex relations 
and phenomena in the world around and GIS technology is able 
to incorporate new requirements.