Investigating Cyclic Visit Pattern of Mobility Through Analysis of Geopositioning Data
- Authors
- Song, Hayoon; H.Y.; Hong, Suchan; S.
- Issue Date
- 2019
- Publisher
- SPRINGER INTERNATIONAL PUBLISHING AG
- Keywords
- Mobility pattern analysis; Mobility modeling; Temporal mobility; Cyclic mobility pattern; Recurrent location visit
- Citation
- Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v.11619 LNCS, pp.589 - 602
- Journal Title
- Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
- Volume
- 11619 LNCS
- Start Page
- 589
- End Page
- 602
- URI
- https://scholarworks.bwise.kr/hongik/handle/2020.sw.hongik/12735
- DOI
- 10.1007/978-3-030-24289-3_44
- ISSN
- 0302-9743
- Abstract
- Intuitions guide us that there are cyclic patterns for a person to visit a location, and there is a tendency of multiple cycles in visiting patterns. Nowadays, it is possible for a person to collect personal mobility data due to the help of smartphones and other portable devices. These devices collects raw geolocation (or geopositioning) data and the set of geolocation data can be analyzed in various ways. Based on location clusters distilled from raw geolocation data, we can establish mobility model of a person and investigate cyclic patterns of a person to visit location clusters. Based on the aggregate personal mobility models collected over several years, we calculated and analyzed the cluster revisiting time and visualized it as a graph. Regarding geolocation data for location clusters as set of time sequence, number of visiting cluster is measured in a unit of minutes. The number of visits from whole data is normalized in every 15 min. For various geolocation data set of a volunteer, cyclic patterns of a visit are examined in terms of autocorrelation, autocovariance and intervisiting time.
- Files in This Item
- There are no files associated with this item.
- Appears in
Collections - ETC > 1. Journal Articles
![qrcode](https://api.qrserver.com/v1/create-qr-code/?size=55x55&data=https://scholarworks.bwise.kr/hongik/handle/2020.sw.hongik/12735)
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.