Name
Abstract | ||
|---|---|---|
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 four different datasets for the following cities: 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 apply a mining algorithm to extract the most relevant features (tree fragments) from the implicit TK space according to the weights the kernel machine assigned to them. Our approach can produce an explicit set of representative features that can be used to classify and characterize urban areas. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/TKDE.2020.2967381 | IEEE Transactions on Knowledge and Data Engineering |
Keywords | DocType | Volume |
Urban computing,machine learning,kernel machines | Journal | 33 |
Issue | ISSN | Citations |
9 | 1041-4347 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gianni Barlacchi | 1 | 21 | 5.59 |
| Bruno Lepri | 2 | 981 | 72.52 |
| Alessandro Moschitti | 3 | 3262 | 177.68 |