Name
Abstract | ||
|---|---|---|
Urban environments develop complex, non-obvious structures that are often hard to represent in the form of maps or guides. Finding the right place to go often requires intimate familiarity with the location in question and cannot easily be deduced by visitors. In this work, we exploit large-scale samples of usage information, in the form of mobile phone traces and geo-tagged Twitter messages in order to automatically explore and annotate city maps via kernel density estimation. Our experiments are based on one yearu0027s worth of mobile phone activity collected by Nokiau0027s Mobile Data Challenge (MDC). We show that usage information can be a strong predictor of semantic place categories, allowing us to automatically annotate maps based on the behavior of the local user base. |
Year | Venue | Field |
|---|---|---|
2016 | arXiv: Information Retrieval | Data mining,World Wide Web,Information retrieval,Computer science,Exploit,Mobile phone,City map,Mobile broadband,Kernel density estimation |
DocType | Volume | Citations |
Journal | abs/1601.05952 | 2 |
PageRank | References | Authors |
0.35 | 8 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Siddharth Sarda | 1 | 2 | 0.35 |
| Carsten Eickhoff | 2 | 365 | 39.21 |
| Thomas Hofmann | 3 | 3 | 1.72 |