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Information theory, entropy and urban spatial structure

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2005
Esmer, Özcan
Urban planning has witnessed the profound changes in the methodologies of modelling during the last 50 years. Spatial interaction models have passed from social physics, statistical mechanics to non-spatial and spatial information processing stages of progress that can be designated as paradigm shifts. This thesis traces the Maximum Entropy (MaxEnt) approach in urban planning as pioneered by Wilson (1967,1970) and Spatial Entropy concept by Batty (1974) based on the Information Theory and its developments by Shannon (1948), Jaynes (1957), Kullback (1959) and by Tribus (1962,1969). Information-theoric methods have provided the theoretical foundation for challenging the uncertainty and incomplete information issues concerning the complex urban structure. MaxEnt, as a new logic, gives probabilities maximally noncommittal with regard to missing information. Wilson (1967,1970) has replaced the Newtonian analogy by the entropy concept from statistical mechanics to alleviate the mathematical inconsistency in the gravity model and developed a set of spatial interaction models consistent with the known information. Population density distribution as one of the determinants of the urban structure has been regarded as an exemplar to show the paradigm changes from the analysis of density gradients to the probabilistic description of density distributions by information-theoric methods. Spatial Entropy concept has introduced the spatial dimension to the Information Theory. Thesis applies Spatial Entropy measures to Ankara 1970 and 1990 census data by 34 zones and also obtains Kullback̕s Information Gain measures for population changes during the two decades. Empirical findings for Spatial Entropy measures show that overall Ankara-1970 and 1990 density distributions are ء̕Uneven̕̕ and the uniform distribution hypothesis is not confirmed. These measures also indicate a tendency