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. 2022 Oct 18;9(1):3–39. doi: 10.1007/s41095-022-0275-7

A survey of urban visual analytics: Advances and future directions

Zikun Deng 1, Di Weng 2,✉, Shuhan Liu 1, Yuan Tian 1, Mingliang Xu 3,4, Yingcai Wu 1,✉
PMCID: PMC9579670  PMID: 36277276

Abstract

Developing effective visual analytics systems demands care in characterization of domain problems and integration of visualization techniques and computational models. Urban visual analytics has already achieved remarkable success in tackling urban problems and providing fundamental services for smart cities. To promote further academic research and assist the development of industrial urban analytics systems, we comprehensively review urban visual analytics studies from four perspectives. In particular, we identify 8 urban domains and 22 types of popular visualization, analyze 7 types of computational method, and categorize existing systems into 4 types based on their integration of visualization techniques and computational models. We conclude with potential research directions and opportunities.

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Keywords: visual analytics, smart city, spatiotemporal data analysis, urban analytics

Acknowledgements

This work was supported by National Natural Science Foundation of China (62072400), the Collaborative Innovation Center of Artificial Intel-ligence by MOE and Zhejiang Provincial Government (ZJU), and the Zhejiang Lab (2021KE0AC02).

Declaration of competing interest

The authors have no competing interests to declare that are relevant to the content of this article.

Footnotes

Zikun Deng received his B.S. degree in transportation engineering from Sun Yat-sen University in 2018. He is currently pursuing a doctoral degree with the State Key Lab of CAD & CG, Zhejiang University. His research interests include spatiotemporal data mining, visualization, and urban visual analytics.

Di Weng is a researcher at Microsoft Research Asia. He received his Ph.D. degree in computer science from the State Key Lab of CAD & CG, Zhejiang University in 2021 and his B.S. degree in computer science from Shandong University in 2016. His research interests include data mining, visualization, and visual analytics of large-scale urban data.

Shuhan Liu received her B.S. degree in computer science from Zhejiang University in 2021. She is currently pursuing a doctoral degree with the State Key Lab of CAD & CG, Zhejiang University. Her research interests include spatiotemporal data mining, visualization, and industrial data visual analytics.

Yuan Tian is currently an undergraduate in the State Key Lab of CAD & CG, Zhejiang University. Her research interests include visualization and visual analytics.

Mingliang Xu is a professor in the School of Information Engineering of Zhengzhou University, China, and currently is the director of the Center for Interdisciplinary Information Science Research. He is the vice general secretary of ACM SIGAI China. He received his Ph.D. degree in computer science and technology from the State Key Lab of CAD & CG at Zhejiang University. His current research interests include computer graphics and artificial intelligence.

Yingcai Wu is a professor in the State Key Lab of CAD & CG, Zhejiang University. His main research interests are in information visualization and visual analytics, with focuses on urban computing, sports science, immersive visualization, and narrative visualization. He received his Ph.D. degree in computer science from the Hong Kong University of Science and Technology. He was a postdoctoral researcher in the University of California, Davis from 2010 to 2012, a researcher in Microsoft Research Asia from 2012 to 2015, and a ZJU100 Young Professor at Zhejiang University from 2015 to 2020.

Contributor Information

Zikun Deng, Email: zikun_rain@zju.edu.cn.

Di Weng, Email: diweng@microsoft.com.

Shuhan Liu, Email: shliu@zju.edu.cn.

Yuan Tian, Email: ytian@zju.edu.cn.

Mingliang Xu, Email: iexumingliang@zzu.edu.cn.

Yingcai Wu, Email: ycwu@zju.edu.cn.

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