Table of Contents
Visualising performances of Intelligent Location Optimisations in GIS
Contents
Background
Objectives
What are ILOs?
PPT Slide
What's wrong with what exists already ?
The ILOs features
Cont’d
Visualisation matters!
A good ILOs requires visualisation of results
Visualising ILOs performances
Assessment of the results (I)
Assessment of the results (II)
Data, Models, and algorithms
Cont’d
Visualisation of the results (I)
A general spatial search type of Genetic Algorithm solution
A general spatial search type of Random starting solution
A general spatial search type of Simulated Annealing solution
PPT Slide
3 Dimensional result of Genetic Algorithm solution ( ED data ]
3 Dimensional result of Simulated annealing solution ( ED data ]
3 Dimensional result of Monte Carlo solution [ ED data ]
3 Dimensional result of Tabu search solution using [ ED data ]
Accessibility subtraction of Random starting and Genetic algorithm solution performance ( ED data)
Accessibility subtraction of Random starting and Genetic algorithm solution performance (SURPOP GRID data)
ILOs solution performances
Conclusions
Cont’d
Further research for ILOs
PPT Slide
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Authors: Young-Hoon Kim and Stan Openshaw
Email:
pgky@geog.leeds.ac.uk
stan@geog.leeds.ac.uk
Home Pages:
http://www.geog.leeds.ac.uk/pgrads/y.kim/
http://www.geog.leeds.ac.uk/staff/s.openshaw/
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