In your textbook you will find an overview of models and a discussion on the various ways that models can be integrated with a GIS. You should consider how the term model, as used in the previous notes on Representing Geography, is being used in a very different manner here (that is, a data model is a statement of how the world looks, while a model, as considered here, is an expression of how the world is believed to work). So, one model is like a template (i.e., a data model) while the other is an expression of a process. There are a variety of models that traditionally have been used (e.g., analog versus digital; static vs dynamic simulation, agent-based, cellular).
For the purposes of this lecture, and in order to provide some additional background on a methodology that many of you may use in your project, I will concentrate on one important group of spatial modeling methods–multi-criteria methods. Although multi-criteria / multi-objective decision analysis is a complex topic, you should become familiar with these methods given how useful they are in many situations. As noted above, your textbook provides a broad review of spatial modeling in general. Other forms of modeling include boolean overlay, linear programming , network analysis, agent-based modeling, cellular automata, and spatial interaction modeling.
Learning Objectives
- Know what modeling means in the context of GIS;
- Be familiar with the important types of models and their applications;
- Be familiar with the software environments in which modeling takes place;
- Understand the needs of modeling and how these needs are being addressed
by current trends in GIS software.
Recommended Readings
Text: Chapters 13: Spatial Data Analysis, Chapter 14: Spatial Analysis and Inference, and Chapter 15: Spatial Modeling with GI Systems
Slides for the spatial modeling with GIS lecture.
Alternative text: GIS Basics: Chapter 6, 7 and 8.
Additional Readings
Chapter 3: Methodological Context in the Geospatial Analysis online text is also worth reviewing.
Useful Resources
Spatial Simulation: Explore Pattern and Process
Wolfram MathWorld: Cellular Automaton
References
Cohon, Jared l., 1978. Multiobjective Programming and Planning, Academic Press, Mathematics in Science and Engineering, Vol. 140
Courtney, J.F., Jr., T.D. Klastorin and T.W. Ruefli, 1972. "A Goal Programme Approach to Urban-Suburban Location Preference, " Management Science 18:258-268.
Dane. C.W., N.C. Meador and J.B. White, 1977. "Goal Programming in Land Use Planning," Journal of Forestry 75:325-329.
Diamond, J.T. and Wright, J.R., 1988. Design of an integrated spatial system for multiobjective land-use planning, Environment and Planning B: Planning and Design, Vol. 15, pp 205-214.
Fisher, P., 1995. (Ed.). Diffusion and Use of Geographic Information Technologies, Kluwer Academic Publishers.
Lee, S.M., 1972. Goal Programming for Decision Analysis, Auerbach, Philadelphia. A general introduction to Goal Programming.
Malczewski, Jacek, 1999. GIS and Multicriteria decision analysis, John Wiley & Sons, New York.
Nijkamp, P. and Rietveld, P., 1986. Multiple Objective decision analysis in regional economics. In P. Nijkamp (Ed.). Handbook of Regional Economics, North Holland Publishing Co., Amsterdam.
Voogd, H., 1983. Multicriteria Evaluation for Urban and Regional Planning, Pion, London.
Keywords
analytical hierarchy process (AHP), boolean {logic, analysis}, factors & constraints, factor standardization / normalization, heuristic, multi-criteria / multi-objective evaluations, sensitivity analysis