ALOE http://aloe-project.de/AloeFeeds/action/atomFeedResourceActivities?resourceId=srQcC6o aloe-noreply@dfki.uni-kl.de Martin http://aloe-project.de/AloeView/action/userData?userId=lzwX6p3 This feed contains recent activities on resource "Visual Analytics in Urban Computing: An Overview" in [ALOE] http://aloe-project.de/AloeFeeds/action/atomFeedResourceActivities?resourceId=srQcC6o ALOE http://aloe-project.de/AloeFeeds/action/atomFeedResourceActivities?resourceId=srQcC6o aloe-noreply@dfki.uni-kl.de Martin http://aloe-project.de/AloeView/action/userData?userId=lzwX6p3 http://aloe-project.de/AloeFeeds/action/atomFeedResourceActivities?resourceId=srQcC6o:6:2021-03-16 17:52:39.0 March 16, 2021 5:52:00 PM CET: User <a href="http://aloe-project.de/AloeView/action/userData?userId=lzwX6p3"><b>Martin</b></a> contributed resource <a href="http://aloe-project.de/AloeView/action/resourceDetailed?resourceId=srQcC6o"><b>Visual Analytics in Urban Computing: An Overview</b></a> with the following metadata:<br/><br/><i>associatedDate</i>: <b></b><br/><i>creator</i>: <b>Yixian Zheng, Wenchao Wu, Yuanzhe Chen, Huamin Qu, Member, IEEE, and Lionel M. Ni, Fellow, IEEE</b><br/><i>description</i>: <b>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 &amp; model construction; result evaluation &amp; 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.</b><br/><i>language</i>: <b></b><br/><i>license</i>: <b></b><br/><i>publisher</i>: <b></b><br/><i>resourceType</i>: <b>resource-bookmark</b><br/><i>rightsHolder</i>: <b>IEEE</b><br/><i>tags</i>: <b>urbandata visualization sota data</b><br/><i>title</i>: <b>Visual Analytics in Urban Computing: An Overview</b><br/>