Entropy, Theory Construction and Spatial Analysis

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Abstract
The framework of Bayesian inference is proposed as a structure for unifying those highly disparate approaches to entropy modelling that have appeared in geography to date, and is used to illuminate the possibilities and shortcomings of some of these models. The inadequacy of most descriptive entropy statistics for measuring the information in a spatially-autocorrelated map is described. The contention that entropy maximization in itself provides theoretical justification for spatial models is critically evaluated. It is concluded that entropy should, first and foremost, be regarded as a technique to expand our methods of statistical inference and hypothesis testing, rather than one of theory construction.