Movement Pattern Analysis Based on Sequence Signatures

Seyed Chavoshi,Tijs Neutens,Guy De Tré,Bernard De Baets,Nico De Weghe,Matthias Delafontaine

doi:10.3390/ijgi4031605

Seyed Chavoshi, Tijs Neutens + Show 4 more

Open Access

https://doi.org/10.3390/ijgi4031605

Copy DOI

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.

Highlights

The increasing deployment of location-aware devices has given rise to an unprecedented wealth of trajectory information, documenting the movements of various types of moving objects, including vehicles [1], animals [2], bank notes [3], sportspersons [4], visitors at mass events [5], and tourists [6]
Based on concepts from geographic knowledge discovery (GKD) [50] for extracting meaningful information, discovering interesting patterns, and interpreting them in a plausible way, we propose a technique for analyzing the relative movements of multiple disjoint moving point objects (MPOs) using qualitative trajectory calculus (QTC)
We focus on the usefulness of QTC in identifying relative movement patterns of pairs of MPOs

Summary

Introduction

The increasing deployment of location-aware devices has given rise to an unprecedented wealth of trajectory information, documenting the movements of various types of moving objects, including vehicles [1], animals [2], bank notes [3], sportspersons [4], visitors at mass events [5], and tourists [6]. Adopting a qualitative approach implies that continuous information is being discretized by landmarks that classify neighboring open intervals into discrete quantity spaces [14]. Key to this approach is that a distinction is introduced only if it is relevant to the research context at hand [15,16]. The impetus for developing qualitative formalisms is that qualitative information aligns better with human intuition, communication and decision making than quantitative information [17,18]

Methods

Discussion

Conclusion