Defining measures for location visiting preference
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Song, Hayoon | - |
dc.contributor.author | H.Y. | - |
dc.contributor.author | Choi, Dong-yun | - |
dc.contributor.author | D.Y. | - |
dc.date.available | 2021-03-17T10:45:59Z | - |
dc.date.created | 2021-02-26 | - |
dc.date.issued | 2015 | - |
dc.identifier.issn | 1877-0509 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hongik/handle/2020.sw.hongik/13801 | - |
dc.description.abstract | For better location based service or better analysis of human mobility pattern, measures for presenting frequently visiting locations are usually required. In this paper, we will establish related measures for specific meaningful locations. Location points as well as Location clusters are objects of the measurements. In order to represent the degree of a specific location visit, the degree of location visit called Position Frequency (PF), and Inverse Location Frequency (ILF) are defined. In order to represent the degree of location area (cluster) visit, Inverse Cluster Frequency (ICF) is established. Moreover, along with the frequency of location visit, the duration of location visit is also considered. Therefore Position Duration (PD), Inverse Location Duration (ILD), and Inverse Cluster Duration (ICD) are defined. Using R language, real positioning data set collected by volunteers are analyzed in order to demonstrate the usefulness of these measures. The definitions of measures and the application of measures will be presented. (C) 2015 The Authors. Published by Elsevier B.V. | - |
dc.publisher | ELSEVIER SCIENCE BV | - |
dc.title | Defining measures for location visiting preference | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Song, Hayoon | - |
dc.identifier.doi | 10.1016/j.procs.2015.08.324 | - |
dc.identifier.scopusid | 2-s2.0-84954096018 | - |
dc.identifier.wosid | 000373842900018 | - |
dc.identifier.bibliographicCitation | Procedia Computer Science, v.63, pp.142 - 147 | - |
dc.relation.isPartOf | Procedia Computer Science | - |
dc.citation.title | Procedia Computer Science | - |
dc.citation.volume | 63 | - |
dc.citation.startPage | 142 | - |
dc.citation.endPage | 147 | - |
dc.type.rims | ART | - |
dc.type.docType | Proceedings Paper | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Health Care Sciences & Services | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Interdisciplinary Applications | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Theory & Methods | - |
dc.relation.journalWebOfScienceCategory | Health Care Sciences & Services | - |
dc.subject.keywordAuthor | Human Location Preference | - |
dc.subject.keywordAuthor | Measures of Location Visit | - |
dc.subject.keywordAuthor | Position Frequency | - |
dc.subject.keywordAuthor | Inverse Location Frequency | - |
dc.subject.keywordAuthor | Inverse Cluster Frequency | - |
dc.subject.keywordAuthor | Position Duration | - |
dc.subject.keywordAuthor | Inverse Location Duration | - |
dc.subject.keywordAuthor | Inverse Cluster Duration | - |
dc.subject.keywordAuthor | Positioning Data Analytics | - |
dc.subject.keywordAuthor | Location Base Service | - |
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