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.