Classification in geographical information systems

Authors:
Salvatore Rinzivillo;Franco Turini
Affiliations:
University of Pisa, Italy;University of Pisa, Italy
Venue:
PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
Year:
2004

Citing 0
Cited 3

Extracting spatial association rules from spatial transactions

Proceedings of the 13th annual ACM international workshop on Geographic information systems
Knowledge discovery from spatial transactions

Journal of Intelligent Information Systems
Examples of integration of induction and deduction in knowledge discovery

Reasoning, Action and Interaction in AI Theories and Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

The paper deals with the problem of knowledge discovery in spatial databases. In particular, we explore the application of decision tree learning methods to the classification of spatial datasets. Spatial datasets, according to the Geographic Information System approach, are represented as stack of layers, where each layer is associated with an attribute. We propose an ID3-like algorithm based on an entropy measure, weighted on a specific spatial relation (i.e. overlap). We describe an application of the algorithm to the classification of geographical areas for agricultural purposes.