Name
Abstract | ||
|---|---|---|
The large amount of phone call records from mobile operators in a city can inform us how many people are present in any given area and how many are entering or leaving. Each phone call record usually contains the caller and callee IDs, date and time, and the base station where the phone calls are made. As mobile phones are widely used in our daily life, many human behaviors can be revealed by analyzing mobile phone data. In this paper, we propose a comprehensive visual analysis system which can be used to analyze the population's mobility patterns from millions of phone call records. Our system consists of three major components: 1) visual analysis of user groups in a base station; 2) visual analysis of the mobility patterns on different user groups making phone calls in certain base stations; 3) visual analysis of handoff phone call records. Some well-established visualization techniques such as parallel coordinates and pixel-based representations have been integrated into our system. We also develop a novel visualization schemes, Voronoi-diagram-based visual encoding to reveal the unique features of mobile phone data. We have applied our system to real mobile phone data collected in a large city and obtained some interesting findings regarding people's mobility pattern. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1145/2016656.2016669 | VINCI |
Keywords | Field | DocType |
mobility pattern,voronoi-diagram-based visual encoding,handoff phone call record,mobile phone,base station,phone call,phone call record,visual analysis,real mobile phone data,mobile phone data,parallel coordinates,application,voronoi diagram,human behavior,data collection | Mobile identification number,Population,World Wide Web,Mobile phone tracking,Mobile station,Timing advance,GSM services,Phone,Human–computer interaction,Mobile phone,Geography | Conference |
Citations | PageRank | References |
6 | 0.45 | 13 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jiansu Pu | 1 | 61 | 4.28 |
| Panpan Xu | 2 | 227 | 11.73 |
| Huamin Qu | 3 | 2033 | 115.33 |
| Weiwei Cui | 4 | 976 | 41.22 |
| Siyuan Liu | 5 | 544 | 37.89 |
| Lionel M. Ni | 6 | 9462 | 802.67 |