Predicting Land Use of Italian Cities using Structural Semantic Models
Authors: Gianni Barlacchi, Bruno Lepri, Alessandro Moschitti (Università di Trento, TIM, Fondazione Bruno Kessler, Qatar Computing Research Institute, 2017)
Topic: Lexical and Semantic Resources and Analysis

Abstract
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 Location-Based 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. % up to 18% in Macro-F1.

Introduction
The growing availability of data from cities AUTHOR (e.g., traffic flow, human mobility and geographical data) opens 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 follow the approach proposed in AUTHOR and we use public textual descriptions of urban areas to design a novel machine learning representation. We represent urban areas as: (i) a bag-of-concepts (BOC), e.g., the terms Arts and Entertainment, College and University, Event, Food extracted from the Foursquare description of the area; and (ii) the same concepts above organized in a tree, which reflects the hierarchical organization of Foursquare activities. We combine BOC vectors with Tree Kernels (TKs) AUTHOR applied to concept trees (Geo-Tree) and use them in Support Vector Machines (SVMs). The Geo-Tree allows the model to learn complex structural and semantic patterns from the hierarchical conceptualization of an area. We show that TKs not only can capture semantic information from natural language text, e.g., as shown for semantic role labeling AUTHOR and question answering AUTHOR, but they can also learn from the hierarchy above to perform semantic inference, such as deciding which is the major activity of a land. We carried out a study on land use prediction of three Italian cities: Milan, Rome and Naples as follows: (i) we divided each city in squares of 200x200 meters; (ii) then, we classify the most predominant land use class (e.g., High Density Urban Fabric or Open Space and Outdoor), assigned by the city administration. The results show that GeoTKs achieve an impressive improvement over state-of-the-art classification approaches based on BOC., i.e., 21.2%, 13.6% and 54.3% of relative improvement in Macro-F1 over Milan, Rome and Naples datasets, respectively.

Conclusion
"In this paper, we have introduced Geo-Trees, a novel semantic representation based on a hierarchical classification of POIs, to better exploit geo-social data to the classification of the primary land use of an urban area. This is an important task as it gives the urban planners and policy makers the possibility to better administrate and renew a city in terms of infrastructures, resources and services. More in detail, we have built our classifiers with combinations of a kernel over BOC and TKs applied to Geo-Trees, thus exploiting hierarchical substructure of concepts as features. Our comparative study on three large Italian cities, Milan, Rome and Naples shows that our models can relatively improve the state of the art up to 11.9 absolute points in F1-score.