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dc.contributor.authorLong, Jed Andrew
dc.contributor.authorNelson, T.A.
dc.contributor.authorNathoo, F.S.
dc.date.accessioned2014-09-16T16:01:05Z
dc.date.available2014-09-16T16:01:05Z
dc.date.issued2014
dc.identifier.citationLong , J A , Nelson , T A & Nathoo , F S 2014 , ' Toward a kinetic-based probabilistic time geography ' , International Journal of Geographical Information Science , vol. 28 , no. 5 , pp. 855-874 . https://doi.org/10.1080/13658816.2013.818151en
dc.identifier.issn1365-8816
dc.identifier.otherPURE: 69343916
dc.identifier.otherPURE UUID: 3b53b0bb-3fa1-4b26-9a76-7264473fb7e0
dc.identifier.otherScopus: 84899047207
dc.identifier.urihttp://hdl.handle.net/10023/5419
dc.description.abstractTime geography represents a powerful framework for the quantitative analysis of individual movement. Time geography effectively delineates the space–time boundaries of possible individual movement by characterizing movement constraints. The goal of this paper is to synchronize two new ideas, probabilistic time geography and kinetic-based time geography, to develop a more realistic set of movement constraints that consider movement probabilities related to object kinetics. Using random-walk theory, the existing probabilistic time geography model characterizes movement probabilities for the space–time cone using a normal distribution. The normal distribution has a symmetric probability density function and is an appropriate model in the absence of skewness – which we relate to an object’s initial velocity. Moving away from a symmetric distribution for movement probabilities, we propose the use of the skew-normal distribution to model kinetic-based movement probabilities, where the degree and direction of skewness is related to movement direction and speed. Following a description of our model, we use a set of case-studies to demonstrate the skew-normal model: a random walk, a correlated random walk, wildlife data, cyclist data, and athlete movement data. Our results show that for objects characterized by random movement behavior, the existing model performs well, but for object movement with kinetic properties (e.g., athletes), the proposed model provides a substantial improvement. Future work will look to extend the proposed probabilistic framework to the space–time prism.
dc.language.isoeng
dc.relation.ispartofInternational Journal of Geographical Information Scienceen
dc.rights© 2013 Taylor & Francis. This is an Accepted Manuscript of an article published in the International Journal of Geographical Information Science on 2 September 2013, available online: http://www.tandfonline.com/10.1080/13658816.2013.818151en
dc.subjectTime geographyen
dc.subjectKineticsen
dc.subjectProbabilityen
dc.subjectMobile objectsen
dc.subjectPersonal movement modelsen
dc.subjectG Geography (General)en
dc.subject.lccG1en
dc.titleToward a kinetic-based probabilistic time geographyen
dc.typeJournal articleen
dc.description.versionPostprinten
dc.contributor.institutionUniversity of St Andrews.Geography & Sustainable Developmenten
dc.contributor.institutionUniversity of St Andrews.Centre for Geoinformaticsen
dc.contributor.institutionUniversity of St Andrews.Bell-Edwards Geographic Data Instituteen
dc.identifier.doihttps://doi.org/10.1080/13658816.2013.818151
dc.description.statusPeer revieweden


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