Abstract
This paper presents an empirical application of an information theoretic approach to spatial hypothesis testing. Following the lead of Batty [1] this study employs the concept of expected information to test hypotheses concerning the distribution of urban population and population density in San Antonio for the years 1960 and 1970. Cast for the first time in a longitudinal context, major concerns of this work are the relative advantages, both theoretical and methodological, of certain entropy measures. Specifically, comparisons are made between the Shannon and the Kullback formulations. In this context of comparison, problems closely linked to what has been called the “entropy paradox” are identified and explained, suggesting important qualitative differences between these two measures.
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