Automatic identification of relevant places from cellular network data. Mamei, M., Colonna, M., & Galassi, M. Pervasive and Mobile Computing, 1, 2016.
Automatic identification of relevant places from cellular network data [link]Website  abstract   bibtex   
We present a methodology to automatically identify users' relevant places from cellular network data1. In this work we used anonymized Call Detail Record (CDR) comprising information on where and when users access the cellular network. The key idea is to effectively cluster CDRs together and to weigh clusters to determine those associated to frequented places. The approach can identify users' home and work locations as well as other places (e.g., associated to leisure and night life).We evaluated our approach threefold: (i) on the basis of groundtruth information coming from a fraction of users whose relevant places were known, (ii) by comparing the resulting number of inhabitants of a given city with the number of inhabitants as extracted by the national census. (iii) Via stability analysis to verify the consistency of the extracted results across multiple time periods. Results show the effectiveness of our approach with an average 90% precision and recall.
@article{
 title = {Automatic identification of relevant places from cellular network data},
 type = {article},
 year = {2016},
 identifiers = {[object Object]},
 keywords = {cdr,cellular,location,wireless},
 websites = {http://dx.doi.org/10.1016/j.pmcj.2016.01.009},
 month = {1},
 id = {ec8de2ac-8e0b-3ff3-910c-80f85c7516d1},
 created = {2018-07-12T21:32:39.096Z},
 file_attached = {false},
 profile_id = {f954d000-ce94-3da6-bd26-b983145a920f},
 group_id = {b0b145a3-980e-3ad7-a16f-c93918c606ed},
 last_modified = {2018-07-12T21:32:39.096Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {mamei:places},
 source_type = {article},
 private_publication = {false},
 abstract = {We present a methodology to automatically identify users' relevant places from cellular network data1. In this work we used anonymized Call Detail Record (CDR) comprising information on where and when users access the cellular network. The key idea is to effectively cluster CDRs together and to weigh clusters to determine those associated to frequented places. The approach can identify users' home and work locations as well as other places (e.g., associated to leisure and night life).We evaluated our approach threefold: (i) on the basis of groundtruth information coming from a fraction of users whose relevant places were known, (ii) by comparing the resulting number of inhabitants of a given city with the number of inhabitants as extracted by the national census. (iii) Via stability analysis to verify the consistency of the extracted results across multiple time periods. Results show the effectiveness of our approach with an average 90% precision and recall.},
 bibtype = {article},
 author = {Mamei, Marco and Colonna, Massimo and Galassi, Marco},
 journal = {Pervasive and Mobile Computing}
}
Downloads: 0