A Decision Tree for Multi-Layered Spatial Data - Université de Lille Accéder directement au contenu
Communication Dans Un Congrès Année : 2002

A Decision Tree for Multi-Layered Spatial Data


Spatial data mining fulfils real needs of many geomatic applications. It allows the geomatics community to take advantage of the growing availability of geographically referenced data and benefit from this rich information resource. This article addresses spatial data classification for using decision trees. A new method called SCART which differs from conventional decision trees by considering the specifics of geographical data, namely their organisation in thematic layers, and their spatial relationships is proposed. SCART is an extension of CART methods in two ways. On the one hand, the algorithm considers several thematic layers as in the so-called relational data mining area, and on the other hand, it extends discriminating criteria to address concerns about the neighbourhood. As such, the algorithm determines which combination of attribute values and spatial relationships of neighbouring objects provide the best criterion.
Fichier principal
Vignette du fichier
A_Decision_Tree_for_Multi_Layered_Spatia.pdf (189.16 Ko) Télécharger le fichier
Origine Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-04371584 , version 1 (03-01-2024)




Nadjim Chelghoum, Karine Zeitouni, Azedine Boulmakoul. A Decision Tree for Multi-Layered Spatial Data. 10th International Symposium on Spatial Data Handling, Jul 2002, Ottawa, Canada. pp.1-10, ⟨10.1007/978-3-642-56094-1_1⟩. ⟨hal-04371584⟩
6 Consultations
2 Téléchargements



Gmail Mastodon Facebook X LinkedIn More