Bulens, J.D.

Vullings, L.A.E.

Wessels, C.G.A.M.

English

Wageningen Yield 

12th AGILE International Conference on Geographic Information Science 2009 page 1 of 3 
Leibniz Universität Hannover, Germany 
 
Fuzziness to Reduce Uncertainty  
L.A.E. Vullings1, C.G.A.M Wessels2, J.D. Bulens1 
1: Wageningen-UR, Alterra, Wageningen, The Netherlands; wies.vullings@wur.nl 
2: Nexpri bv, Utrecht, The Netherlands; C.Wessels@nexpri.nl 
 
 
INTRODUCTION 
 
In the Netherlands space is a valuable asset. Sometimes new locations have to be found for an 
certain function in an certain area, but the area is claimed already completely by other functions that 
exclude the new function. An example is the case study that was carried out in the GeO3 project. The 
case study was initiated by the Dutch ministry of Agriculture and the Dutch Cadastre and dealt with 
finding a location for cultivation under glass within certain area in the Netherlands. The area is 
claimed by rural plans of the municipalities and by zones of the Natura2000 areas and the ecological 
main structure and leaves hardly any space for a large area of cultivation under glass. These three-km-
zones around Natura2000 areas and ecological main structures are in fact continuous objects that are 
discretely defined (often by law). Although the zones concern mainly ammonia emissions, it is 
generally interpreted as ‘not to be used for all agrarian activities’. However, cultivation under glass is 
not a agrarian activities that emits amounts of ammonia compared to intensive pig farming. One of 
the main environmental influences that is caused by cultivation under glass is sound. Sound is a very 
different environmental factor than  ammonia emission and for that reason it should be treated 
differently. 
This situation causes uncertainty. One knows that one is legally bounded by the 3-km zoning, but 
one also knows that the reason behind the zoning (ammonia emission) is not an issue for the function 
cultivation under glass. For this reason this situation was dealt with as a case study in the GeO3 
project. The objective of the GeO3 project was to study various characteristics of uncertainty in 
spatial planning, to define them and to suggest solutions to deal with them. The project resulted in a 
taxonomy for uncertainty in spatial planning (Vullings et al. 2007). One of the sources of uncertainty 
is objects that are continuous phenomena (noise, emission, risk, zones etc.), but are treated like 
discrete objects. A solution for dealing with this kind of uncertainty could be to use the fuzzy set 
theory. 
The objective of this case study was to research whether fuzzy logic could be used to deal with 
continuous phenomena that are generally represented as discrete objects and whether using fuzzy 
logic would result in more space for the location of cultivation under glass than with the conventional 
discrete analyses. 
 
FUZZY SET THEORY 
 
The concept of fuzzy logic was introduced in 1965 by Zadeh (Zadeh, 1965) as an extension to  
Boolean logic. The principal of Boolean logic is that something is true (1) or false (0). This idea is 
abandoned in fuzzy logic; because in fuzzy logic something can also be partly true and partly false. 
Therefore an object can be part of an object class (1) or not (0), but it can also be partly a member of 
this object class (every value between 0 and 1).  
 
12th AGILE International Conference on Geographic Information Science 2009 page 2 of 3 
Leibniz Universität Hannover, Germany 
 
 
 
Figure 1: Transition areas based on crisp buffer (left) and on fuzzy buffers (right). 
 
The rate at which a fuzzy object participates in an object class is called the membership of this 
object to the object class. Duindam concluded that it is preferred to determine a new membership 
function for modeling fuzzy objects in spatial planning: Planning Object Membership function (POM) 
(Duindam 2006). 
For this case study a POM demonstrator was realized. The POM demonstrator is an extension of 
ArcGIS and is able to generate, visualize and analyze fuzzy objects. As main factor for finding 
suitable locations is chosen for noise hindrance for nature areas and housing locations caused by 
traffic of products and basics to and from cultivation under glass. To generate fuzzy zones the 
parameters of the POM function for the influence of sound had to be set. After generating the fuzzy 
zones the zones were divided into three classes with different policy implications.  Both setting the 
parameters and defining the classes was done by experts. 
Figure 2 shows that a larger area is available as placement area with the fuzzy method than with 
the conventional method . 
12th AGILE International Conference on Geographic Information Science 2009 page 3 of 3 
Leibniz Universität Hannover, Germany 
 
 
Figure 2: Left: Natura2000 areas with a discrete buffer of 3 km. Right Natura2000 areas with a 
red zone ( no cultivation under glass), orange zone (No cultivation under glass, unless…), yellow 
zone ( cultivation under glass possible, if…). 
 
CONCLUDING REMARKS 
 
The case study showed that this method leads to finding more space for placement of cultivation 
under glass within the study area. However the case study also showed that dealing with 
environmental factors like noise in a quantitative way asks for a different approach within spatial 
planning. To set the parameters of the POM function and to classify the zones was not easy, not even 
for the experts. Normally environmental factors are dealt with in a more intuitive way, to valorize 
these relationships is new and scary. The advantage of the use of the POM demonstrator is that it 
showed the implications of choices on the fly, which lead to useful discussions and decisions about 
environmental relationships.  Another aspect is that the fuzzification of the Natura2000 zones is 
legally not possible yet, but this case was used to proof the usefulness of the method. The participants 
of the case study thought the method has potential, they were interested in this different approach and 
liked to pursue further development.   
 
BIBLIOGRAPHY 
 
Duindam, A.J., 2006. Fuzziness in spatial planning data – an exploration in uncertainty. Centre for 
geo-information, WUR. Research report GIRS-2006-20 
Fisher, P., A. Comber and R. Wadsworth (2005). Approaches to Uncertainty in Spatial Data. Pp. 9-64 
in Qualité de l’information géographique, (eds. Rodolphe Devillers and Robert Jeansoulin), 
IGAT, Hermes, France 
Vullings, W., M. de Vries, et al., 2007, Dealing with uncertainty in spatial planning. 10th AGILE 
International Conference on Geographic Information Science. Aalborg University, Denmark. 
Zadeh, L. A., 1965. Fuzzy sets. Information and Control 8(3): 338-353. 


Fuzziness in spatial planning data – an exploration in uncertainty. Centre for geo-information,

Fuzzy sets.

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