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dc.contributor.advisorNavidi, William Cyrus
dc.contributor.authorMcClary, Jenifer R.
dc.date.accessioned2019-10-15T17:46:32Z
dc.date.accessioned2022-02-03T13:16:21Z
dc.date.available2019-10-15T17:46:32Z
dc.date.available2022-02-03T13:16:21Z
dc.date.issued2019
dc.identifierMcClary_mines_0052N_11815.pdf
dc.identifierT 8803
dc.identifier.urihttps://hdl.handle.net/11124/173287
dc.descriptionIncludes bibliographical references.
dc.description2019 Fall.
dc.description.abstractOne of the most potentially useful methods for truly understanding our habits is by observing the data captured by our cellular phones. In 2009, Nokia launched the Lausanne Data Collection Campaign, which was designed to collect behavioral data from 170 volunteers through data collection software on contributed phones. In particular, GPS and WiFi connection data depicted the location of volunteers for the duration of their participation in the study. Through statistical analysis of the past location data for an individual over a prescribed duration of time, one can potentially predict the individual’s future movement with desirable degrees of confidence. This research investigates a methodology for conducting predictive analysis of individual movement based on past known movements through analysis of several known time series statistical methods. Based on this analysis, I provide recommendations for application of this method to other datasets.
dc.format.mediumborn digital
dc.format.mediummasters theses
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado School of Mines. Arthur Lakes Library
dc.rightsCopyright of the original work is retained by the author.
dc.subjectlocation prediction
dc.subjectseasonal SARIMA ARIMA
dc.subjectpredict criminal activity
dc.subjectautoregressive moving average
dc.titleStatistical methods for predicting individual movement patterns
dc.typeText
dc.contributor.committeememberMehta, Dinesh P.
dc.contributor.committeememberBandyopadhyay, Soutir
dc.contributor.committeememberBenigni, Matthew C.
thesis.degree.nameMaster of Science (M.S.)
thesis.degree.levelMasters
thesis.degree.disciplineApplied Mathematics and Statistics
thesis.degree.grantorColorado School of Mines


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