Analyzing student travel patterns with augmented data visualizations

Each 3D column visualization is coupled with a bar chart, representing the distribution of respondents by institutions with “OCAD University” selected as the subset. This representation illustrates Transit modes, Commute frequency and Commute time for OCAD University students. The bar charts generated in Story Facets give aggregate quantitative information, while the 3D visualization gives a corresponding spatial understanding, therefore enhancing the qualitative dimension of the data.
Abstract
Visualization and visual analytics tools can provide critical support for experts and stakeholders to understand transportation flows and related human activities. Correlating and representing quantitative data with data from human actors can provide explanations for patterns and anomalies. We conducted research to compare and contrast the capabilities of several tools available for visualization and decision support as a part of an integrated urban informatics and visualization research project that develops tools for transportation planning and decision making. For this research we used the data collected by the StudentMoveTO (Toronto) survey which was conducted in the fall of 2015 by Toronto's four universities with the goal of collecting detailed data to understand travel behaviour and its effect on the daily routines of the students. This paper discusses the usefulness of new software which can allow designers to build meaningful narratives integrating 3D representations to assist in Geo-spatial analysis of the data.
Materials
DOI | BibTeX
Authors
Carl Skelton
Manpreet Kaur Juneja
Jeremy Bowes
Steve Szigeti
Minsheng Zheng
Marcus Gordon
Sara Diamond
Citation
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Analyzing student travel patterns with augmented data visualizations

Carl Skelton, Manpreet Kaur Juneja, Cody Dunne, Jeremy Bowes, Steve Szigeti, Minsheng Zheng, Marcus Gordon, and Sara Diamond. Proc. ACM Conference Companion Publication on Designing Interactive Systems—DIS. 2017. DOI: 10.1145/3064857.3079140

DOI | BibTeX


Cody Dunne, Vis Lab — Northeastern University
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