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ISPRS Int. J. Geo-Inf. 2015, 4(3), 1605-1626; doi:10.3390/ijgi4031605

Movement Pattern Analysis Based on Sequence Signatures

1
Department of Geography, Ghent University, Krijgslaan 281 (S8), Ghent 9000, Belgium
2
KERMIT, Department of Mathematical Modelling, Statistics and Bioinformatics, Ghent University, Coupure links 653, Ghent 9000, Belgium
3
Department of Telecommunications and Information Processing, Ghent University, Sint-Pietersnieuwstraat 41, Ghent 9000, Belgium
*
Author to whom correspondence should be addressed.
Academic Editor: Wolfgang Kainz
Received: 13 July 2015 / Revised: 3 August 2015 / Accepted: 27 August 2015 / Published: 2 September 2015
(This article belongs to the Special Issue Multi-Dimensional Spatial Data Modeling)
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Abstract

Increased affordability and deployment of advanced tracking technologies have led researchers from various domains to analyze the resulting spatio-temporal movement data sets for the purpose of knowledge discovery. Two different approaches can be considered in the analysis of moving objects: quantitative analysis and qualitative analysis. This research focuses on the latter and uses the qualitative trajectory calculus (QTC), a type of calculus that represents qualitative data on moving point objects (MPOs), and establishes a framework to analyze the relative movement of multiple MPOs. A visualization technique called sequence signature (SESI) is used, which enables to map QTC patterns in a 2D indexed rasterized space in order to evaluate the similarity of relative movement patterns of multiple MPOs. The applicability of the proposed methodology is illustrated by means of two practical examples of interacting MPOs: cars on a highway and body parts of a samba dancer. The results show that the proposed method can be effectively used to analyze interactions of multiple MPOs in different domains. View Full-Text
Keywords: moving point objects (MPO); movement patterns; qualitative trajectory calculus (QTC); sequence signature (SESI); similarity analysis moving point objects (MPO); movement patterns; qualitative trajectory calculus (QTC); sequence signature (SESI); similarity analysis
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Chavoshi, S.H.; De Baets, B.; Neutens, T.; Delafontaine, M.; De Tré, G.; de Weghe, N.V. Movement Pattern Analysis Based on Sequence Signatures. ISPRS Int. J. Geo-Inf. 2015, 4, 1605-1626.

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