The Point-Descriptor-Precedence representation for point configurations and movements. Issue 7 (3rd July 2021)

• Record Type: Journal Article
• Authors: Qayyum, Amna; De Baets, Bernard; Baig, Muhammad Sulman; Witlox, Frank; De Tré, Guy; Van de Weghe, Nico
• Is Part Of: International journal of geographical information science. Volume 35:Issue 7(2021)
• Page Start: 1374
• Page End: 1391
• Publication Date: 2021-07-03
• Subjects: Qualitative representation -- curved directed line -- spatiotemporal data representation -- traffic research -- pattern detection; Geography -- Data processing -- Periodicals; Information storage and retrieval systems -- Periodicals; Géomatique -- Périodiques; Systèmes d'information -- Périodiques; 910.285
• DOI: 10.1080/13658816.2020.1864378
• Languages: English
• ISSNs: 1365-8816

Abstract

ABSTRACT: In this paper, we represent (moving) point configurations along a curved directed line qualitatively by means of a system of relational symbols based on two distance descriptors: one representing distance along the curved directed line and the other representing signed orthogonal distance to the curved directed line. The curved directed line represents the direction of the movement of interest. For instance, it could be straight as in the case of driving along a highway or could be curved as in the case of an intersection or a roundabout. Inspired by the Point Calculus, the order between the points on the curved directed line is described by means of a small set of binary relations ( <, =, > ) acting upon the distance descriptors. We call this representation the Point-Descriptor-Precedence-Static (PDPS ) representation at a time point and Point-Descriptor-Precedence-Dynamic (PDPD ) representation during a time interval. To illustrate how the proposed approach can be used to represent and analyse curved movements, some basic micro-analysis traffic examples are studied. Finally, we discuss some extensions of our work to highlight the practical benefits of PDP in identifying motion patterns that could be useful in GIS, autonomous vehicles, sports analytics, and gait analysis.