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Problems involving the processing of spatial data such as industrial site selection and land use allocation are multi- facetted challenges. Not only they often involve numerous technical requirements, but may also contain economical, social, environmental and political dimensions that may have conflicting values. Solutions for these problems involve highly complex spatial data analysis processes and frequently require advanced means to address physical suitability conditions, while considering the multiple socio-economic variables. Geographic Information Systems (GIS) and Multi-Criteria Decision- Making techniques (MCDM) are two common tools employed to solve these problems. However, each suffers from serious shortcomings. GIS, which deals mainly with physical suitability analysis, has very limited capability of incorporating the decision maker's preferences into the problem solving process. MCDM, which deals mainly with analyzing decision problems and evaluating the alternatives based on a decision maker's values and preferences, lacks the capability of handling spatial data (e. g., buffering and overlay) that are crucial to spatial analysis. The need for combining the strengths of these two techniques has prompted researchers to seek integration of GIS and MCDM. Current integration strategies (loose coupling and tight coupling) have their own limitations. Such limitations were successfully eliminated by using Component Object Model (COM) technology to integrate GIS and MCDM. An illustrative example was included to validate the capabilities of the presented integration strategy.
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