Conventional methods and models for spatial data analysis are based on ‘hard’ (quantitative, cardinally-measured) information. Approaches such as location-allocation models, optimization models, entropy models, spatial assignment models and regional growth models all re flect the past trend to cast complex and multidimensional spatial interaction patterns in the framework of a cardinal metric system. In recent years, significant progress has been made in the analysis of ‘soft’, qualitative or categoricaily-measured data. In the fields of both parametric and non-parametric statistics and econometrics, a wide variety of techniques and models have been designed which treat qualitative variables in an appropriate manner. All these methods and techniques aim at taking into account the limitations caused by measuring variables on a non-metric scale, and try to avoid ~he use of non-permissible numerical operations on qualitative variables. These endeavors have resulted in new catch phrases for the analysis of qualitative data, such as ‘soft econometrics’, etc. Researchers in the areas of regional and urban economics, geography and planning have become increasingly aware of the necessity to in corporate qualitative data and a wide array of methods for quali tative data are now being applied. Applications have included, for instance, spatial consumer choice behavior, locational perceptions and preferences, contingency table analysis, spatial scenario analysis, qualitative impact analysis, project and plan evaluations, spatial conflict analysis, and so forth.