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dc.contributor.authorLong, Jed Andrew
dc.identifier.citationLong , J A 2018 , ' Modeling movement probabilities within heterogeneous spatial fields ' , Journal of Spatial Information Science , vol. 2018 , no. 16 , pp. 85-116 .
dc.identifier.otherPURE: 252041645
dc.identifier.otherPURE UUID: 1592cbf4-13e9-4676-aeb0-11fefc32d5eb
dc.identifier.otherScopus: 85049112325
dc.identifier.otherWOS: 000436339800005
dc.description.abstractRecent efforts have focused on modeling the internal structure of space-time prisms to estimate the unequal movement opportunities within. This paper further develops this area of research by formulating a model for field-based time geography that can be used to probabilistically model movement opportunities conditioned on underlying heterogeneous spatial fields. The development of field-based time geography draws heavily on well-established methods for cost-distance analysis, common to most GIS software packages. The field-based time geographic model is compared with two alternative approaches that are commonly employed to estimate probabilistic space-time prisms - (truncated) Brownian bridges and time geographic kernel density estimation. Using simulated scenarios it is demonstrated that only field-based time geography captures underlying heterogeneity in output movement probabilities. Field-based time geography has significant potential in the field of wildlife tracking (an example is provided), where Brownian bridge models are preferred, but fail to adequately capture underlying barriers to movement.
dc.relation.ispartofJournal of Spatial Information Scienceen
dc.rights© 2018 by the author(s). Open Access article. Licensed under Creative Commons Attribution 3.0 License.en
dc.subjectSpace-time prismen
dc.subjectLeast-cost path analysisen
dc.subjectMovement analysisen
dc.subjectResistance surfaceen
dc.subjectGPS trackingen
dc.subjectG Geography (General)en
dc.subjectGA Mathematical geography. Cartographyen
dc.titleModeling movement probabilities within heterogeneous spatial fieldsen
dc.typeJournal articleen
dc.description.versionPublisher PDFen
dc.contributor.institutionUniversity of St Andrews.School of Geography & Sustainable Developmenten
dc.contributor.institutionUniversity of St Andrews.Bell-Edwards Geographic Data Instituteen
dc.description.statusPeer revieweden

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