Interactive Data Visualization

Chad A. Steed

doi:10.1016/B978-0-12-809715-1.00007-9

Interactive Data Visualization

Chad A. Steed

Data Science and Visualization

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

6 Scopus citations

Abstract

Interactive data visualization leverages human visual perception and cognition to improve the accuracy and effectiveness of data analysis. When integrated with data mining algorithms, data visualization systems combine human strengths with the computational power of machines to solve problems that neither approach can solve in isolation. In the intelligent transportation system domain, such systems are necessary to support decision making in large and complex data streams that originate from multiple sources, such as traffic cameras, vehicles, and traffic management centers. In this chapter, we provide an introduction to several key topics related to the design of data visualization systems. In addition to an overview of key techniques and strategies, we will discuss practical design principles. We conclude with a detailed case study involving the design of a multivariate visualization tool using automobile data.

Original language	English
Title of host publication	Data Analytics for Intelligent Transportation Systems
Publisher	Elsevier Inc.
Pages	165-190
Number of pages	26
ISBN (Electronic)	9780128098516
ISBN (Print)	9780128097151
DOIs	https://doi.org/10.1016/B978-0-12-809715-1.00007-9
State	Published - Apr 14 2017

Keywords

Data visualization
Human-computer interaction
Information visualization
Scientific visualization
Visual analytics

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1016/B978-0-12-809715-1.00007-9

Cite this

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title = "Interactive Data Visualization",

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AB - Interactive data visualization leverages human visual perception and cognition to improve the accuracy and effectiveness of data analysis. When integrated with data mining algorithms, data visualization systems combine human strengths with the computational power of machines to solve problems that neither approach can solve in isolation. In the intelligent transportation system domain, such systems are necessary to support decision making in large and complex data streams that originate from multiple sources, such as traffic cameras, vehicles, and traffic management centers. In this chapter, we provide an introduction to several key topics related to the design of data visualization systems. In addition to an overview of key techniques and strategies, we will discuss practical design principles. We conclude with a detailed case study involving the design of a multivariate visualization tool using automobile data.

KW - Data visualization

KW - Human-computer interaction

KW - Information visualization

KW - Scientific visualization

KW - Visual analytics

UR - http://www.scopus.com/inward/record.url?scp=85040600176&partnerID=8YFLogxK

U2 - 10.1016/B978-0-12-809715-1.00007-9

DO - 10.1016/B978-0-12-809715-1.00007-9

M3 - Chapter

AN - SCOPUS:85040600176

SN - 9780128097151

SP - 165

EP - 190

BT - Data Analytics for Intelligent Transportation Systems

PB - Elsevier Inc.

ER -

Interactive Data Visualization

Abstract

Keywords

UN SDGs

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