Slides: 1 - Spatial Complexity and MCDA Introduction.pdf
Spatial complexity
A problem that scales in complexity with multiple dimensions
Influenced by several criteria, with different importance
Often multiple decision-makers, with different/conflicting preferences
A classic example of an SDSS problem is location allocation: choosing locations such that we can optimally provide facilities.
Spatial analysis
GIS
A system for spatial analysis Geographic information system - Wikipedia
Two approaches to modeling
- Statistical modeling
- Mathematical modeling
- Simulation
- Optimization
Spatial Simulation
- System components are mathematically defined
- Components are related to each other in a series of functional relationships
- Result: a mathematical description of a decision process
- model is solved repeatedly, using different parameters and different decision variables every time
- As these values are changed, a range of solutions are obtained for the problem and the ābestā solution can be chosen from that range.
Decision variables are the variables in a scenario that you, as a designer, have control over.
Spatial Optimization
In Spatial Optimization, we often try to maximize/minimize some quantity, which is the result of some decision variables.
This quantity is often referred to as the objective function or criterion function.
Alternatively, find some feasible alternative.
Simulation vs Optimization
Simulation modeling starts with actions and studies the effect.
Optimization starts with a definition of system objectives, and specify the actions that will best satisfy those objectives.
Semi-Structured Decisions
Structured decisions are relatively straight-forward. They can usually be optimized by optimizing one mathematical formula. There are no conflicting interests.
Unstructured decisions donāt have an optimal solution.
Semi-structured decisions fall somewhere inbetween or have aspects of both.
The structured part of the semi-structured problem may be amenable to automated solution by the use of a computer, while the unstructured aspects are tackled by decision makers.
Spatial Decision Support System
SDSS is meant to improve the effectiveness of decision making by incorporation decision makersā knowledge and experience into computer-based procedures.
Spatial MCDA
There are three GIS-MCDA methods
Conventional MCDA
- Mainly aspatial
Spatially Explicit MCDA
If:
- Outcomes vary when feasible alternatives are relocated
- Decision alternatives are spatially defined
Spatial multicriteria optimization
Specifically designed for modeling spatial systems and solving spatial problems such as:
- Site search
- Location allocation
- Routing
TODO
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