Cartographic visualization of human trajectory data: overview and analysis

Tiago Gonçalves, Ana Paula Afonso, Bruno Martins: Cartographic visualization of human trajectory data: overview and analysis. In: Journal of Location Based Services, 9 (2), pp. 138-166, 2015.

Abstract

With the prevalence of mobile computing systems and location based services, large amounts of spatio-temporal data are nowadays being collected, representing the mobility of people performing various activities. However, despite the increasing interest in the exploration of these data, there are still open challenges in various application contexts, e.g. related to visualisation and human–computer interaction. In order to support the extraction of useful and relevant information from the spatio-temporal and the thematic properties associated with human trajectories, it is crucial to develop and study adequate interactive visualisation techniques. In addition to the properties of the visualisations themselves, it is important to take into consideration the types of information present within the data and, more importantly, the types of tasks that a user might need to consider in order to achieve a given goal. The understanding of these factors may, in turn, simplify the development and the assessment of a given interactive visualisation. In this paper, we present and analyse the most relevant concepts associated to these topics. In particular, our analysis addresses the main properties associated with (human) trajectory data, the main types of visualisation tasks/objectives that the users may require in order to analyse that data and the high-level classes of techniques for visualising trajectory data. In addition, this paper also presents an overview on a user study, conducted in function of this analysis, to compare two classes of visualisation techniques, namely static maps and space-time cubes, regarding their adequacy in helping users completing basic visualisation tasks.

BibTeX (Download)

@article{doi:10.1080/17489725.2015.1074736,
title = {Cartographic visualization of human trajectory data: overview and analysis},
author = {Tiago Gonçalves and Ana Paula Afonso and Bruno Martins},
url = {http://dx.doi.org/10.1080/17489725.2015.1074736},
doi = {10.1080/17489725.2015.1074736},
year  = {2015},
date = {2015-09-30},
journal = {Journal of Location Based Services},
volume = {9},
number = {2},
pages = {138-166},
abstract = {With the prevalence of mobile computing systems and location based services, large amounts of spatio-temporal data are nowadays being collected, representing the mobility of people performing various activities. However, despite the increasing interest in the exploration of these data, there are still open challenges in various application contexts, e.g. related to visualisation and human–computer interaction. In order to support the extraction of useful and relevant information from the spatio-temporal and the thematic properties associated with human trajectories, it is crucial to develop and study adequate interactive visualisation techniques. In addition to the properties of the visualisations themselves, it is important to take into consideration the types of information present within the data and, more importantly, the types of tasks that a user might need to consider in order to achieve a given goal. The understanding of these factors may, in turn, simplify the development and the assessment of a given interactive visualisation. In this paper, we present and analyse the most relevant concepts associated to these topics. In particular, our analysis addresses the main properties associated with (human) trajectory data, the main types of visualisation tasks/objectives that the users may require in order to analyse that data and the high-level classes of techniques for visualising trajectory data. In addition, this paper also presents an overview on a user study, conducted in function of this analysis, to compare two classes of visualisation techniques, namely static maps and space-time cubes, regarding their adequacy in helping users completing basic visualisation tasks.},
keywords = {2D Map, Data Visualisation, Moving Objects, Space-Time Cube, Spatio-temporal Data},
pubstate = {published},
tppubtype = {article}
}