PARADIGMS FOR SPATIAL AND SPATIO-TEMPORAL DATA MINING JOHN F. RODDICK School of Informatics and Engineering, Flinders University of South Australia, PO Box 2100, Adelaide 5001, South Australia BRIAN G. LEES Department of Resource Management and Environmental Science, Australian National University, Canberra, ACT 0200, Australia 1. Introduction With some significant exceptions, current applications for data mining are either in those areas for which there is little accepted discovery methodology or are being used within a knowledge discovery process that does not expect authoritative results but finds the discovered rules useful none-the-less. This is in contrast to its application in the fields applicable to spatial or spatio-temporal discovery which possess a rich history of methodological discovery and result evaluation. Examples of the former include market basket analysis which, in its simplest form, (q.v. (Agrawal, Imielinski and Swami 1993)) provides insight into the correspondence between items purchased in a retail trade environment, and web log analysis (qq.v. (Cooley, Mobasher and Srivastava 1997; Viveros, Wright, Elo-Dean and Duri 1997; Madria, Bhowmick, Ng and Lim 1999)), which attempts to derive a broad understanding of sequences of user activity on the internet. Examples of the latter includes time series analysis and signal processing (Weigend and Gershenfeld 1993; Guralnik and Srivastava 1999; Han, Dong and Yin 1999). The rules resulting from investigations in both of these areas may or may not be the result of behavioural or structural conditions but significantly it is the rule 1 itself, rather that the underlying reasons behind the rule, which is generally the focus of interest. An alternative approach is employed in the field of medical knowledge discovery which employs a procedure in which the results of data mining are embedded within a process that interprets the results as being merely hints towards further properly structured investigation into the reasons behind the rules (Lavrac 1999). This latter approach may also be usefully employed by knowledge discovery processes over geographic data. A third, and in some cases more useful approach may be appropriate for many of those areas for which spatial and spatio-temporal rules might be mined. This last approach accepts a (null) hypothesis and attempts to refine it (or disprove it) through the modification of the hypothesis as a result of knowledge discovery. This latter approach is carried out according to the principles of scientific experimentation and Appeared as Roddick, J. F. and Lees, B. G. (2001). Paradigms for Spatial and Spatio-Temporal Data Mining. Geographic Data Mining and Knowledge Discovery. Taylor and Francis. Research Monographs in Geographic Information Systems. Miller, H. and Han, J., Eds. 1 Although each form of data mining algorithm provides results with different semantics, we will use the term "rule" to describe all forms of mining output.