Visual Analytics in Urban Computing: An Overview

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Added by Martin on 2021-03-16 17:52

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Holder of Rights: IEEE

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Creator(s): Yixian Zheng, Wenchao Wu, Yuanzhe Chen, Huamin Qu, Member, IEEE, and Lionel M. Ni, Fellow, IEEE

Description:
Nowadays, various data collected in urban context provide unprecedented opportunities for building a smarter city through
urban computing. However, due to heterogeneity, high complexity and large volumes of these urban data, analyzing them is not an
easy task, which often requires integrating human perception in analytical process, triggering a broad use of visualization. In this
survey, we first summarize frequently used data types in urban visual analytics, and then elaborate on existing visualization techniques
for time, locations and other properties of urban data. Furthermore, we discuss how visualization can be combined with automated
analytical approaches. Existing work on urban visual analytics is categorized into two classes based on different outputs of such
combinations: 1) For data exploration and pattern interpretation, we describe representative visual analytics tools designed for better
insights of different types of urban data. 2) For visual learning, we discuss how visualization can help in three major steps of automated
analytical approaches (i.e., cohort construction; feature selection & model construction; result evaluation & tuning) for a more effective
machine learning or data mining process, leading to sort of artificial intelligence, such as a classifier, a predictor or a regression model.
Finally, we outlook the future of urban visual analytics, and conclude the survey with potential research directions.

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Visual Analytics in Urban Computing: An Overview Nowadays, various data collected in urban context provide unprecedented opportunities for building a smarter city through urban computing. However, due to heterogeneity, high complexity and large volumes of these urban data, analyzing them is not an easy task, which often requires integrating human perception in analytical process, triggering a broad use of visualization. In this survey, we first summarize frequently used data types in urban visual analytics, and then elaborate on existing visualization techniques for time, locations and other properties of urban data. Furthermore, we discuss how visualization can be combined with automated analytical approaches. Existing work on urban visual analytics is categorized into two classes based on different outputs of such combinations: 1) For data exploration and pattern interpretation, we describe representative visual analytics tools designed for better insights of different types of urban data. 2) For visual learning, we discuss how visualization can help in three major steps of automated analytical approaches (i.e., cohort construction; feature selection & model construction; result evaluation & tuning) for a more effective machine learning or data mining process, leading to sort of artificial intelligence, such as a classifier, a predictor or a regression model. Finally, we outlook the future of urban visual analytics, and conclude the survey with potential research directions. Yixian Zheng, Wenchao Wu, Yuanzhe Chen, Huamin Qu, Member, IEEE, and Lionel M. Ni, Fellow, IEEE