Multi-criteria evaluation. Geography 570 B. Klinkenberg. Roadmap. Outline: Introduction Definitions Multi-criteria evaluation (MCE) Principles of MCE Example: MEC Multi-objective land allocation (MOLA) Example. Introduction. Land is a scarce resource
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Within 500m from Shepshed
Within 450m from roads
Slope between 0 and 2.5%
Land grade III
Suitable land, min 2.5 ha
Dynamic problems strongly simplified into a linear model
Static, lacks the time dimension
Controversial method – too subjective?
Gives a structured and traceable analysis
Possibility to use different evaluation factors makes it a good tool for discussion
Copes with large amounts of information
MCE – pros and cons
to a common range)
(which end of the
scale = good)
Used to standardize
the criterion scores
are inherently fuzzy
Graphs of the Fuzzy Memberships within IDRISI
(Based on Eastman 1999)
Cholera Health Risk Prediction in Southern Africa—the relation between temperature and risk
Below 28.5 there is no risk, above 37.5 it can’t rise.
Refer to description of ArcGIS extension ext_ahp.
S = ∑wixi x ∏cj
Gavin Fleming, Marna van der Merwe, Graeme McFerren, Kerry Murphy
CSIR, South Africa
Fuzzy Expert Systems and GIS for Cholera Health Risk Prediction in Southern Africa
Transmitted to humans:
Transmission to humans
Zooplankton: copepods & other crustaceans (fresh & saltwater systems)
Phytoplankton & Aquatic plants
Temperature, pH Fe+, salinity sunlight
Literature survey and expert workshops to:
Simulation model to:
Expert system to:
GIS and fuzzy logic to implement model thus defined
Occurrence of cholera in the past
Poor indication of epidemic reservoir
Average rainfall (mm/month)
Mean maximum daily surface temperature (C/day)
37 (<15C reduces growth and survival rates significantly)
Number of consecutive ‘hot’ months overlapping with the rainy season
Salinity for growth purposes (total salts, %).
Values between 5-25% considered to be optimal
Salinity for expression of toxigenity (total salts, %) (Häse and Barquera, 2001).
Values between 2-2.5% considered to be optimal
8.2 (< 4.6 with low temperatures reduce growth and survival rates significantly)
Fe+ (soluble and/or insoluble form)
Must be present (moderate amounts)
Moderate=0.1 to 0.5
Presence of phytoplankton and algae
Similar growth & survival factors. Photosynthesis also increases pH.
Presence of zooplankton
The simple presence of crustacean copepods enhances the survival of V. cholerae
Dissolved Oxygen daily cycles for every month of the year (mg/l)
Daily fluctuations provide a preliminary indication of algal blooms
Oxidation-Reduction Potential daily cycles for every month of the year
100m < Shepshed <1000m
Between 50m and 600m to roads
Slope between 1 and 5%
Land grade III and grade IV
Varying suitability, min 2.5 ha
Bright areas have highest suitability
The Boolean constrains leave no room for prioritisation, all suitable areas are of equal value, regardless of their position in reference to their factors.
Minimal fuzzy membership: the minimum suitability value from each factor at that location is chosen from as the "worst case" suitability. This can result in larger areas, with highly suitable areas.
Probabilistic fuzzy intersection: fewer suitable areas than the minimal fuzzy operation. This is due to the fact that this effectively is a multiplication. Multiplying suitability factors of 0.9 and 0.9 at one location yields an overall suitability of 0.81, whereas the fuzzy approach results in 0.9. Thus, it can be argued that the probabilistic operation is counterproductive when using fuzzy variables (Fisher, 1994). When using suitability values larger than 1 this does of course not occur.
Weighted Overlay: produces many more areas. This shows all possible solutions, regardless whether all factors apply or not, as long as at least one factor is valid for that area. This is so, because even if one factor is null, the other factors still sum up to a value. This also shows areas that are outside of the initial constraints.
(Department of Technology)
(Department of Geography & Planning)
Appalachian State University
Wind Farm Siting
(Turbine: 50m; Observer: 1.5m; Visual distance: 20mi)
2000 census block data
MOLA, Conflicting objectives: Protecting 6000 ha of agricultural land while leaving 1500 ha for industrial development
Carpet and agriculture in Kathmandu