Abstract
Anonymized Call Detail Records (CDRs) contain positional information of large populations and therefore have been extensively analyzed to understand human mobility. Due to the temporally sparse and spatially coarse nature of the data, most of these studies have focused on primitive aspects of movements such as travel distance and speed. Incorporating underlying geographic information in these analyses would allow analysts to put these movements into context and to gain deeper insight into how metropolitan areas function. In this paper, we present a set of procedures for inferring mobile users' mobility patterns while retaining the context of underlying geography. We apply these methods to our case study on New York City anonymized CDRs. We find that our methods verify current areal semantics and commuting rush-hour patterns, and we also derive further implications regarding geographic, demographic, and other effects on human mobility.
| Original language | English (US) |
|---|---|
| Title of host publication | 10th International Conference on Advances in Mobile Computing and Multimedia, MoMM 2012 - Proceedings |
| Pages | 151-160 |
| Number of pages | 10 |
| DOIs | |
| State | Published - Dec 1 2012 |
| Event | 10th International Conference on Advances in Mobile Computing and Multimedia, MoMM 2012 - Bali, Indonesia Duration: Dec 3 2012 → Dec 5 2012 |
Other
| Other | 10th International Conference on Advances in Mobile Computing and Multimedia, MoMM 2012 |
|---|---|
| Country | Indonesia |
| City | Bali |
| Period | 12/3/12 → 12/5/12 |
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Keywords
- call detail records
- data mining
- human mobility
- mobile computing
ASJC Scopus subject areas
- Software
- Human-Computer Interaction
- Computer Vision and Pattern Recognition
- Computer Networks and Communications
Cite this
Inferring human mobility patterns from anonymized mobile communication usage. / Tanahashi, Yuzuru; Rowland, James R.; North, Stephen; Ma, Kwan-Liu.
10th International Conference on Advances in Mobile Computing and Multimedia, MoMM 2012 - Proceedings. 2012. p. 151-160.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Inferring human mobility patterns from anonymized mobile communication usage
AU - Tanahashi, Yuzuru
AU - Rowland, James R.
AU - North, Stephen
AU - Ma, Kwan-Liu
PY - 2012/12/1
Y1 - 2012/12/1
N2 - Anonymized Call Detail Records (CDRs) contain positional information of large populations and therefore have been extensively analyzed to understand human mobility. Due to the temporally sparse and spatially coarse nature of the data, most of these studies have focused on primitive aspects of movements such as travel distance and speed. Incorporating underlying geographic information in these analyses would allow analysts to put these movements into context and to gain deeper insight into how metropolitan areas function. In this paper, we present a set of procedures for inferring mobile users' mobility patterns while retaining the context of underlying geography. We apply these methods to our case study on New York City anonymized CDRs. We find that our methods verify current areal semantics and commuting rush-hour patterns, and we also derive further implications regarding geographic, demographic, and other effects on human mobility.
AB - Anonymized Call Detail Records (CDRs) contain positional information of large populations and therefore have been extensively analyzed to understand human mobility. Due to the temporally sparse and spatially coarse nature of the data, most of these studies have focused on primitive aspects of movements such as travel distance and speed. Incorporating underlying geographic information in these analyses would allow analysts to put these movements into context and to gain deeper insight into how metropolitan areas function. In this paper, we present a set of procedures for inferring mobile users' mobility patterns while retaining the context of underlying geography. We apply these methods to our case study on New York City anonymized CDRs. We find that our methods verify current areal semantics and commuting rush-hour patterns, and we also derive further implications regarding geographic, demographic, and other effects on human mobility.
KW - call detail records
KW - data mining
KW - human mobility
KW - mobile computing
UR - http://www.scopus.com/inward/record.url?scp=84873452105&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84873452105&partnerID=8YFLogxK
U2 - 10.1145/2428955.2428988
DO - 10.1145/2428955.2428988
M3 - Conference contribution
SN - 9781450313070
SP - 151
EP - 160
BT - 10th International Conference on Advances in Mobile Computing and Multimedia, MoMM 2012 - Proceedings
ER -