A decision can be defined as a choice that is made between two or more alternatives. Any decision making problem falls on a continuum that ranges from completely structured to unstructured decisions. The structured decisions can be programmed and solved by a computer. The structured problems are repetitive and routine, and the computer can solve the structured problem without requiring any intervention from a decision maker. The unstructured decisions must be solved by decision makers without assistance from a computer. In this case the decision makers use their experience. (Malczewski 1999). Furthermore, for most decision situations, the spatial decision problems are ill structured because of the variety of interest groups and uncertainties associated with assessment and evaluation of the distribution of the quality and quantity of impacts at alternative locations (Malczewski 1999). These semi-structured problems are often multidimensional, have goals and objectives that are not completely defined, and have a large number of alternative solutions (Gao et al. 2004).
Considering the above comments, it is more obvious that the spatial decision making process is basically a Multi-criteria Decision Making Process (MCDMP). In particular, spatial multi-criteria decision problems, typically involve a set of geographically-defined alternatives (events) from which a choice of one or more alternatives is made, with respect to a given set of evaluation criteria (Malczewski 1999). Spatial multi-criteria analysis requires information on criterion values and the geographical locations of alternatives in addition to the decision makers’ preferences with respect to a set of evaluation criteria.
The multi-criteria problem is at the core of decision support. MC-SDSS can be viewed as a spatial Decision Support System (DSS). The essential difference between these two concepts is that MC-SDSSs emphasize the multi-criteria character of spatial decision making. In general, the main feature of an MC-SDSS is the integration of GIS capabilities and Multi-criteria Decision Making (MCDM) techniques (Malczewski 1999). According to Ascough et al. (2002), MC-SDSSs offer a flexible, problem solving environment where the decision problem can be explored, understood and redefined; Tradeoffs between multiple and conflicting objectives are investigated and priority actions are set.
References
1. Ascough JC, Rector HD, Hoag DL, McMaster GS, Vandenberg BC, Shaffer MJ, Weltz MA, Ahuja LR (2002). “Multi criteria spatial decision support systems: overview, applications, and future research directions”. Online Proceedings of the International Environmental Modelling and Software Society Conference on Integrated Assessment and Decision Support 3:175–180. http://www.iemss.org/iemss2002/
2. Gao S, Sundaram D, Paynter J (2004). “Flexible support for spatial decision making”. 37th Annual International Conference on System Sciences, Honolulu, Hawaii
http://ec.europa.eu/regional_policy/sources/docgener/studies/study_en.htm
3. Malczewski J (1999). “GIS and multicriteria decision analysis”. New York: John Wiley & Sons, Inc