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English. We propose a hierarchical semantic representation of urban areas extracted from a social network to classify the most predominant land use, which is a very common task in urban computing. We encode geo-social data from LocationBased Social Networks with standard feature vectors and a conceptual tree structure that we call Geo-Tree. We use the latter in kernel machines, which can thus perform accurate classification, exploiting hierarchical substructure of concepts as features. Our comparative study on three datasets extracted from Milan, Rome and Naples shows that Tree Kernels applied to Geo-Trees are very effective improving the state of the art. Italiano. In questo lavoro, proponiamo un nuovo modello semantico per la rappresentazione di aree urbane utilizzando dati da social media. In particolare, modelliamo tale informazione con una struttura ad albero che abbiamo chiamato GeoTree. Questa viene utilizzata, in combinazione con un vettore di feature classico, nelle kernel machine per fare classificazione della destinazione di uso delle aree urbane. Abbiamo valutato il nostro approccio su tre grandi metropoli italiane quali Milano, Roma e Napoli. I risultati mostrano come i Geo-Tree, applicati ai Tree Kernel, riescono a raggiungere risultati di molto superiori ad altri modelli attualmente stato dell’arte.

Predicting Land Use of Italian Cities using Structural Semantic Models