PDF Version of this document Search Help Glossary

Lesson Navigation IconIntermediate Suitability Analysis

Unit Navigation IconFuzzy overlay

Unit Navigation IconMulti-objective analysis

LO Navigation IconConflicting and non-conflicting objectives

LO Navigation IconDecision heuristics

LO Navigation IconMOLA

LO Navigation IconCritical review of the MOLA approach

LO Navigation IconSelf Assessment

LO Navigation IconSelf Assessment

LO Navigation IconRecommended Reading

Unit Navigation IconSummary

Unit Navigation IconRecommended Reading

Unit Navigation IconGlossary

Unit Navigation IconBibliography

Unit Navigation IconMetadata

GITTA/CartouCHe news:

Go to previous page Go to next page

Multi-objective analysis

Suitability analysis of differing complexity

Geographic Information Systems (GIS) are ideal tools to monitor and manage natural resources. Thus, GIS is often used to support politicians and other decision makers in spatial planning issues. Such a spatial planning issue could be the estimation of the suitability of different sites for specific uses or the allocation of land to different uses. GIS has here the function of a decision support system (DSS), or more precisely a spatial decision support system (SDSS). This unit is built around a simple use case to illustrate the idea of suitability analysis and spatial decision support: A hypothetical alpine village, we will call it St. Gittal, seeks to identify potential habitats for the reintroduction of wild wolves.

The use of GIS for suitability analysis and decision support can have different levels of complexity. For many simple questions, it might be enough to combine spatial layers using Boolean overlay functions. Regions could be considered suitable if they are "forested" AND "unsettled". If some criteria would be more important than others, more complex weighted overlay functions may be used. "Unsettled" could thus be considered twice as important than "forested" and be assigned a weight of 2 in the suitability measure. Whenever uncertainty, ambiguity, and vagueness blur the trust in the input data, one might want to further raise the level of complexity introducing fuzzy concepts. In order to model uncertainty, the crisp class "forested" could be replaced by the fuzzy set "densely forested", i.e. a class without sharp boundaries assigning all sites a degree of membership to the set "densely forested".

Some suitability analysis tasks simply require the identification of land that meets some criteria, e.g. "identify sites suited for the reintroduction of the wolf". Such processes are called single objective, termmulti-criteria evaluations (MCE). Sometimes in contrast, the problem is to divide up regions of land according to their suitability for different objectives. This would be the case, if for example, wildlife conservationists and shepherds both required some land for their obviously differing needs. In this unit we focus on this last process, termed termmulti-objective evaluation (MOE). Since MOE integrates various procedures of minor complexity, it is rather complex. To make life easier, we use an intuitive, graphical and thus comprehensible approach to illustrate the basic ideas about MOE, the so called MOLA approach (Multi-Objective Land Allocation).

Learning Objectives

  • You can describe the basic concept of multi-objective evaluation (MOE)
  • You can explain the difference and the links between multi-criteria evaluation (MCE) and multi-objective evaluation (MOE)
  • You can sketch a MOLA decision diagram for two conflicting objectives
  • You can explain how heuristics can be used in a Spatial Decision Support System
Top Go to previous page Go to next page