Mining Association Rules in Spatio-Temporal Data: An Analysis of Urban Socioeconomic and Land Cover Change

Abstract

This research demonstrates the application of association rule mining to spatio-temporal data. Association rule mining seeks to discover associations among transactions encoded in a database. An association rule takes the form A→B where A (the antecedent) and B (the consequent) are sets of predicates. A spatio-temporal association rule occurs when there is a spatio-temporal relationship in the antecedent or consequent of the rule. As a case study, association rule mining is used to explore the spatial and temporal relationships among a set of variables that characterize socioeconomic and land cover change in the Denver, Colorado, USA region from 1970–1990. Geographic Information Systems (GIS)-based data pre-processing is used to integrate diverse data sets, extract spatio-temporal relationships, classify numeric data into ordinal categories, and encode spatio-temporal relationship data in tabular format for use by conventional (non-spatio-temporal) association rule mining software. Multiple level association rule mining is supported by the development of a hierarchical classification scheme (concept hierarchy) for each variable. Further research in spatio-temporal association rule mining should address issues of data integration, data classification, the representation and calculation of spatial relationships, and strategies for finding ‘interesting’ rules.