Is inductive machine learning just another wild goose (or might it lay the golden egg)?

Abstract

The research reported here contrasts the roles, methodologies and capabilities of statistical methods with those of inductive machine learning methods, as they are used inferentially in geographical analysis. To this end, various established problems with statistical inference applied in geographical settings are reviewed, based on Gould's (1970) critique. Possible solutions to the problems outlined by Gould are suggested via reviews of: (i) improved statistical methods, and (ii) recent inductive machine learning techniques. Following this, some newer problems with inference are described, emerging from the increased complexity of geographical datasets and from the analysis tasks to which we put them. Again, some solutions are suggested by pointing to newer methods. By way of results, questions are posed, and answered, relating to the changes brought about by adopting inductive machine learning methods for geographical analysis. Specifically, these questions relate to analysis capabilities, methodologies, the role of the geographer and consequences for teaching and learning. Conclusions argue that there is now a strong need, motivated from many perspectives, to give geographical data a stronger voice, thus favouring techniques that minimize the prior assumptions made of a dataset.