Skip navigation

A framework for models of movement in geographic space

A framework for models of movement in geographic space

Wang, Jia ORCID: 0000-0003-4379-9724, Duckham, Matt and Worboys, Michael (2016) A framework for models of movement in geographic space. International Journal of Geographical Information Science (Special Issue: Analysis of Movement Data), 30 (5). pp. 970-992. ISSN 1365-8816 (Print), 1365-8824 (Online) (doi:https://doi.org/10.1080/13658816.2015.1078466)

[img]
Preview
PDF (Author's Accepted Manuscript)
15536_Wang_A framework for models of movement (AAM) 2016.pdf - Accepted Version

Download (1MB) | Preview

Abstract

This article concerns the theoretical foundations of movement informatics. We discuss general frameworks in which models of spatial movement may be developed. In particular, the article considers the object–field and Lagrangian–Eulerian dichotomies, and the SNAP/SPAN ontologies of the dynamic world, and classifies the variety of informatic structures according to these frameworks. A major challenge is transitioning between paradigms. Usually data is captured with respect to one paradigm but can usefully be represented in another. We discuss this process in formal terms and then describe experiments that we performed to show feasibility. It emerges that observational granularity plays a crucial role in these transitions.

Item Type: Article
Additional Information: This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Geographical Information Science in 20/8/2015, available online: http://www.tandfonline.com/doi/full/10.1080/13658816.2015.1078466
Uncontrolled Keywords: movement, field, object, Eulerian, Lagrangian, granularity
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Faculty / Department / Research Group: Faculty of Architecture, Computing & Humanities
Faculty of Architecture, Computing & Humanities > Department of Computing & Information Systems
Last Modified: 17 May 2019 11:03
Selected for GREAT 2016: None
Selected for GREAT 2017: GREAT b
Selected for GREAT 2018: None
Selected for GREAT 2019: GREAT 3
URI: http://gala.gre.ac.uk/id/eprint/15536

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics