Name
Abstract | ||
|---|---|---|
Human mobility analysis is emerging as a more and more fundamental task to deeply understand human behavior. In the last decade these kind of studies have become feasible thanks to the massive increase in availability of mobility data. A crucial point, for many mobility applications and analysis, is to extract interesting locations for people. In this paper, we propose a novel methodology to retrieve efficiently significant places of interest from movement data. Using car drivers' systematic movements we mine everyday interesting locations, that is, places around which people life gravitates. The outcomes show the empirical evidence that these places capture nearly the whole mobility even though generated only from systematic movements abstractions. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/978-3-319-15201-1_19 | Lecture Notes in Computer Science |
Field | DocType | Volume |
Data science,Recommender system,Empirical evidence,Systems engineering,Computer science,Mobility analysis,Point of interest | Conference | 8938 |
ISSN | Citations | PageRank |
0302-9743 | 2 | 0.41 |
References | Authors | |
12 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Riccardo Guidotti | 1 | 112 | 24.81 |
| Anna Monreale | 2 | 581 | 42.49 |
| Salvatore Rinzivillo | 3 | 673 | 44.49 |
| Dino Pedreschi | 4 | 3083 | 244.47 |
| Fosca Giannotti | 5 | 2948 | 253.39 |