Discussed is the merit of inductive learning as a tool with which to discover geographic knowledge in data-rich environments, and particularly as an analysis tool in spatial decision-making theory. The capability and applicability of Quinlan's C4.5 decision tree induction algorithm to the class of problems involving the choice among discrete travel destinations within an urban area are analyzed. The C4.5 algorithm and its relation to other decision tree induction algorithms and to spatial behavioral modeling are described, and its implementation on spatial behavior data from the Minneapolis-St. Paul metropolitan area is illustrated.