Learning Spatial Relationships: Some Approaches

R I McKay & R A Pearson

Computer Science Dept, University College,
Australian Defence Force Academy

Email: (rim;rap)@cs.adfa.oz.au

P A Whigham

Division of Land and Water Resources
Commonwealth Scientific & Industrial Research Organisation

Email: paw@cs.adfa.oz.au

Presented at the second annual conference of GeoComputation ‘97 & SIRC ‘97, University of Otago, New Zealand, 26-29 August 1997

Abstract

We consider three approaches to learning natural resource models involving spatial relationships, based respectively on decision tree learning, genetic programming and induc-tive logic programming. In each case, the results of spatial learning on a natural resource problem are compared with the results of non-spatial learning from the same data, and improvements in predictivity or simplicity of the models are noted. We argue also that it is highly desirable that spatial learning systems for natural resource problems in-corporate mechanisms for the user specification of learning