Land Use Classification with Point of Interests and Structural Patterns
In this paper, we present a framework for performing automatic analysis of Land Use Zones based on Location-Based Social Networks (LBSNs). We model city areas using a hierarchical structure of POIs extracted from foursquare. We encode such structures in kernel machines, e.g., Support Vector Machines, using a new Tree Kernel, i.e., the Hierarchical POI Kernel (HPK), which can take the importance of the individual POIs into account during the substructure matching. This way, HPK projects structures in the space of all their possible substructures such that each dimension corresponds to a semantic structural feature, weighted according to the discriminative power of POIs . We generated five different datasets for the following cities: New York City, Barcelona, Lisbon, Amsterdam, and Milan, where we trained and tested our models. The results show that our approach largely outperforms previous work and standard baseline built on simple features, such as counts of different POIs. Finally, we analyze the nature of the different cities by applying a reverse kernel engineering algorithm, which extracts the most meaningful substructures from the HPK space.
Branch: CSE Domain: Data Mining
Developed In: Java