Edited by Robert Stimson
Chapter 26: Graphical models and Bayesian networks as a spatial analytical tool
The role of location is central to spatially integrated social science in which the focus is to enhance our understanding, explanation and prediction of complex human behaviours and social processes (Goodchild and Janelle, 2004). A key approach to addressing these foci is the use of models that facilitate the synthesis of complex spatial data, many of which are probabilistic in nature and can be analysed through graphical models. The purpose of graphical models is to combine probability theory with graph theory allowing for increased interpretability of underlying assumptions, obtaining computational tractability of models and the inferring of causality. In the field of spatially integrated social science there are two key contributions of graphical models: · The first is the construction of sophisticated spatial models allowing a complex synthesis of spatial information. · The second is the capacity to infer causal relationships, the benefit here is that in social science it is rarely applicable to run large-scale experimentation in contrast to, for example, medical trials; causal graphical models however still allow causal relationships to be inferred as if an experimentation was undertaken. Recent innovation in both of these areas is of potentially great interest in developing more sophisticated methods for spatially integrated social science.
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