The growing availability of data from cities (e.g., traffic flow, human mobility and geographical data) open new opportunities for predicting and thus optimizing human activities. For example, the automatic analysis of land use enables the possibility of better administrating a city in terms of resources and provided services. However, such analysis requires specific information, which is often not available for privacy concerns. In this paper, we propose a novel machine learning representation based on the available public information to classify the most predominant land use of an urban area, which is a very common task in urban computing. In particular, in addition to standard feature vectors, we encode geo-social data from Location-Based Social Networks (LBSNs) into a conceptual tree structure that we call Geo-Tree. Then, we use such representation in kernel machines, which can thus perform accurate classification exploiting hierarchical substructure of concepts as features. Our extensive comparative study on the areas of New York and its boroughs shows that Tree Kernels applied to Geo-Trees are very effective improving the state of the art up to 18% in Macro-F1.
Structural Semantic Models for Automatic Analysis of Urban Areas / Barlacchi, Gianni; Rossi, Alberto; Lepri, Bruno; Moschitti, Alessandro. - ELETTRONICO. - 10536:(2017), pp. 279-291. (Intervento presentato al convegno The 28th European Conference on Machine Learning tenutosi a Skopje, Macedonia nel 18-22 September, 2017) [10.1007/978-3-319-71273-4_23].
Structural Semantic Models for Automatic Analysis of Urban Areas
Gianni Barlacchi;Bruno Lepri;Alessandro Moschitti
2017-01-01
Abstract
The growing availability of data from cities (e.g., traffic flow, human mobility and geographical data) open new opportunities for predicting and thus optimizing human activities. For example, the automatic analysis of land use enables the possibility of better administrating a city in terms of resources and provided services. However, such analysis requires specific information, which is often not available for privacy concerns. In this paper, we propose a novel machine learning representation based on the available public information to classify the most predominant land use of an urban area, which is a very common task in urban computing. In particular, in addition to standard feature vectors, we encode geo-social data from Location-Based Social Networks (LBSNs) into a conceptual tree structure that we call Geo-Tree. Then, we use such representation in kernel machines, which can thus perform accurate classification exploiting hierarchical substructure of concepts as features. Our extensive comparative study on the areas of New York and its boroughs shows that Tree Kernels applied to Geo-Trees are very effective improving the state of the art up to 18% in Macro-F1.File | Dimensione | Formato | |
---|---|---|---|
2017 ECML.pdf
Solo gestori archivio
Tipologia:
Post-print referato (Refereed author’s manuscript)
Licenza:
Tutti i diritti riservati (All rights reserved)
Dimensione
9.79 MB
Formato
Adobe PDF
|
9.79 MB | Adobe PDF | Visualizza/Apri |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione