Integrating GIS and MCDM Methods

By: Piotr Jankowski

Summary:

In the early 1980s geographical information system (GIS) software emerged commercially as a new information processing technology offering unique capabilities of automating, managing, and analyzing a variety of spatial data. GIS has been depicted as a decision support technology with many applications of GIS developed over the last decade providing information necessary for decision-making in many diverse areas.  One perspective on developing better decision support capabilities of GIS can be identified based on integration of GIS and specialized analytical models.  This perspective improves the decision support capabilities on the expansion of GIS descriptive, prescriptive, and predictive capabilities by integrating GIS software with other general software packages and with specialized analytical models like environmental and socioeconomic models.

This article’s term MCDM is used in reference to multiple attribute decision making (MADM) which is concerned with choice from a moderate/small size set of discrete actions, involving choosing, based on the decision criteria and criteria priorities, from a moderate/small size set of alternatives.

MCDM techniques can be classified according to the level of cognitive processing demanded from the DM and the method of aggregating criterion scores and the DM's priorities.  Two classes of MCDM techniques can be distinguished:  compensatory and non-compensatory.  The compensatory approach is based on the assumption that the high performance of an alternative achieved on one or more criteria can compensate for the weak performance of the same alternative on other criteria.  The compensatory approach is cognitively demanding since it requires the DM to specify criterion priorities expressed as cardinal weights or priority functions.  Under the non-compensatory approach, a low criterion score for an alternative cannot be offset by another criterion's high score. The non-compensatory approach is cognitively  less demanding than  the  compensatory  approach  since  it requires, at the most,  the ordinal  ranking of  criteria  based on  the DM's priorities.  Both the classes of techniques can be further broken down as illustrated in the article. 

DMs can then use five different choice strategies that can be matched with characteristics of different MCDM techniques.  There are:

1.  Screening of absolute rejects: elimination of clearly dominated alternatives as the first step before any further choice deliberation

1. Satisficing principle: the DM will consider all the alternatives  that satisfy conjunctively or disjunctively the minimum  performance levels

1. First-reject: the DM wants to use exclusively the conjunctive elimination rule to reject  all the alternatives that do not pass  minimum  threshold values

1. Stepwise elimination: The DM narrows down the choice re-evaluating  the set of remaining alternatives every time one of  the alternatives is eliminated

1. Generation of linear ordering: the DM wants to generate a ranking of alternatives from the most preferred to the least preferred one

The first four choice strategies can be implemented using exclusively the non-compensatory MCDM techniques with the last strategy requires the full processing approach so it can be implemented using the compensatory MCDM techniques. 

A few of the MCDM techniques can be implemented directly in GIS using the operators of the database query language. Others, however, can be implemented more efficiently using external programs and integrating these programs with GIS. Two strategies for integrating GIS and MCDM are proposed. The first strategy called the loose coupling strategy suggests linking GIS and MCDM techniques using a file exchange mechanism. The second strategy called the tight coupling strategy suggests linking GIS and MCDM techniques using a shared database.

Critique:

Overall, this article was very interesting in how it broken down the various aspects of MCDM into categories, techniques within these categories, choice strategies paired with the techniques, and finally the recommended strategies.  At points, the article was somewhat dense with in depth mathematical explanations for utilizing the various techniques, but was nonetheless very clear in how to perform the various listed techniques of MCDM.   The role of MCDM in GIS is to look for suitable alternatives while helping the DM assign priority weights to decision criteria, evaluate the suitable alternatives, and visualize the results of choice.  While often several results satisfy the minimum threshold values, MCDM techniques are often required to further reduce the alternatives and select the best choice.  Thus, introducing MCDM techniques into the GIS context will likely improve GIS decision support capabilities.  Additionally, the article notes that further research is needed on sensitivity analysis topics in an integrated GIS-MCDM system and facilitating group decision making in the GIS-MCDM context.

Source:

Piotr Jankowski (1995) Integrating geographical information systems and multiple criteria decision-making methods, International Journal of Geographical Information Systems, 9:3, 251-273, DOI: 10.1080/02693799508902036

http://dx.doi.org/10.1080/02693799508902036