Utilizing Geospatial Information in Cellular Data Usage for Key Location Prediction

Simple item page
dc.contributor.authorYu, Yinan
dc.contributor.authorMa, Baojun
dc.contributor.authorChen, Hailiang
dc.contributor.authorYen, Benjamin
dc.date.accessioned2017-12-28T00:43:14Z
dc.date.available2017-12-28T00:43:14Z
dc.date.issued2018-01-03
dc.description.abstractPrevious research on the identification of key locations (e.g., home and workplace) for a user largely relies on call detail records (CDRs). Recently, cellular data usage (i.e., mobile internet) is growing rapidly and offers fine-grained insights into various human behavior patterns. In this study, we introduce a novel dataset containing both voice and mobile data usage records of mobile users. We then construct a new feature based on the geospatial distribution of cell towers connected by mobile users and employ bivariate kernel density estimation to help predict users’ key locations. The evaluation results suggest that augmented features based on both voice and mobile data usage improve the prediction precision and recall.
dc.format.extent8 pages
dc.identifier.doi10.24251/HICSS.2018.123
dc.identifier.isbn978-0-9981331-1-9
dc.identifier.urihttp://hdl.handle.net/10125/50010
dc.language.isoeng
dc.relation.ispartofProceedings of the 51st Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectDecision Support for Smart Cities
dc.subjectcellular data usage, geospatial information, home and workplace, precision marketing, kernel density estimation
dc.titleUtilizing Geospatial Information in Cellular Data Usage for Key Location Prediction
dc.typeConference Paper
dc.type.dcmiText

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
paper0123.pdf
Size:
973.61 KB
Format:
Adobe Portable Document Format
Download

Collections

Decision Support for Smart Cities