>>>Keynote Speakers -Dr. Stewart Fotheringham


Abstract:  This paper examines a parallel development to Big Data - that of Big Models.Big Models are characterized by having large numbers of parameters and in one sense they are the opposite of Big Data in that they maximise the information that can be obtained from relatively small amounts of data.However, Big Models and Big Data can be combined; in which case the number of estimated parameters creates a problem in itself which could be termed 'Big Results'.Two examples of Big Models are provided - one based on a novel extension of GWR; the other based on localised spatial interaction models.


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