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Computational Visual Media 2023, 9(1): 3-39 https://doi.org/10.1007/s41095-022-0275-7
Review Article | Open Access | Issue | Published: 18 October 2022

A survey of urban visual analytics: Advances and future directions

Show Author's Information Zikun Deng1,  Di Weng2( ),  Shuhan Liu1,  Yuan Tian1,  Mingliang Xu3,4,  Yingcai Wu1( )
State Key Lab of CAD & CG, Zhejiang University, Hangzhou310058, China
Microsoft Research Asia, Beijing 100080, China
School of Information Engineering, Zhengzhou University, Zhengzhou, China
Henan Institute of Advanced Technology, Zhengzhou University, Zhengzhou 450001, China
Keywords:
visual analytics, smart city, spatiotemporal data analysis, urban analytics
Cite this article:
Deng Z, Weng D, Liu S, et al. A survey of urban visual analytics: Advances and future directions. Computational Visual Media, 2023, 9(1): 3-39. https://doi.org/10.1007/s41095-022-0275-7
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Abstract Full text About this article

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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About this article

A survey of urban visual analytics: Advances and future directions

Show Author's information Zikun Deng1, Di Weng2( ), Shuhan Liu1, Yuan Tian1, Mingliang Xu3,4, Yingcai Wu1( )
State Key Lab of CAD & CG, Zhejiang University, Hangzhou310058, China
Microsoft Research Asia, Beijing 100080, China
School of Information Engineering, Zhengzhou University, Zhengzhou, China
Henan Institute of Advanced Technology, Zhengzhou University, Zhengzhou 450001, China

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.

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

References(218)

[1]
Zheng, Y.; Capra, L.; Wolfson, O.; Yang, H. Urban computing. ACM Transactions on Intelligent Systems and Technology Vol. 5, No. 3, Article No. 38, 2014.
DOI Google Scholar
[2]
Pan, Z.; Liang, Y.; Wang, W.; Yu, Y.; Zheng, Y.; Zhang, J. Urban traffic prediction from spatio-temporal data using deep meta learning. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 1720-1730, 2019.
DOI
[3]
Zheng, Y.; Yi, X.; Li, M.; Li, R.; Shan, Z.; Chang, E.; Li, T. Forecasting fine-grained air quality based on big data. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2267-2276, 2015.
DOI
[4]
He, T. F.; Bao, J.; Ruan, S. J.; Li, R. Y.; Li, Y. H.; He, H.; Zheng, Y. Interactive bike lane planning using sharing bikes’ trajectories. IEEE Transactions on Knowledge and Data Engineering Vol. 32, No. 8, 1529-1542, 2020.
Google Scholar
[5]
Weng, D.; Chen, R.; Zhang, J. H.; Bao, J.; Zheng, Y.; Wu, Y. C. Pareto-optimal transit route planning with multi-objective Monte-Carlo tree search. IEEE Transactions on Intelligent Transportation Systems Vol. 22, No. 2, 1185-1195, 2021.
DOI Google Scholar
[6]
Li, Y.; Bao, J.; Li, Y.; Wu, Y.; Gong, Z.; Zheng, Y. Mining the most influential k-location set from massive trajectories. In: Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, Article No. 51, 2016.
[7]
Zheng, Y. X.; Wu, W. C.; Chen, Y. Z.; Qu, H. M.; Ni, L. M. Visual analytics in urban computing: An overview. IEEE Transactions on Big Data Vol. 2, No. 3, 276-296, 2016.
DOI Google Scholar
[8]
Liu, S. X.; Cui, W. W.; Wu, Y. C.; Liu, M. C. A survey on information visualization: Recent advances and challenges. Visual Computer Vol. 30, No. 12, 1373-1393, 2014.
DOI Google Scholar
[9]
Chen, W.; Guo, F. Z.; Wang, F. Y. A survey of traffic data visualization. IEEE Transactions on Intelligent Transportation Systems Vol. 16, No. 6, 2970-2984, 2015.
DOI Google Scholar
[10]
Andrienko, G.; Andrienko, N.; Chen, W.; Maciejewski, R.; Zhao, Y. Visual analytics of mobility and transportation: State of the art and further research directions. IEEE Transactions on Intelligent Transportation Systems Vol. 18, No. 8, 2232-2249, 2017.
DOI Google Scholar
[11]
Guo, Y.; Guo, S. N.; Jin, Z. C.; Kaul, S.; Gotz, D.; Cao, N. A survey on visual analysis of event sequence data. IEEE Transactions on Visualization and Computer Graphics , 2021.
DOI Google Scholar
[12]
Lu, L.; Cao, N.; Liu, S.; Ni, L.; Yuan, X.; Qu, H. Visual analysis of uncertainty in trajectories. In: Advances in Knowledge Discovery and Data Mining. Lecture Notes in Computer Science, Vol. 8443. Tseng, V. S.; Ho, T. B.; Zhou, Z. H.; Chen, A. L. P.; Kao, H. Y. Eds. Springer Cham, 509-520, 2014.
[13]
Chen, S. M.; Wang, Z. C.; Liang, J.; Yuan, X. R. Uncertainty-aware visual analytics for exploring human behaviors from heterogeneous spatial temporal data. Journal of Visual Languages & Computing Vol. 48, 187-198, 2018.
DOI Google Scholar
[14]
Chen, S. M.; Yuan, X. R.; Wang, Z. H.; Guo, C.; Liang, J.; Wang, Z. C.; Zhang, X. L.; Zhang, J. Interactive visual discovering of movement patterns from sparsely sampled geo-tagged social media data. IEEE Transactions on Visualization and Computer Graphics Vol. 22, No. 1, 270-279, 2016.
DOI Google Scholar
[15]
Huang, Z. S.; Zhao, Y.; Chen, W.; Gao, S. J.; Yu, K. J.; Xu, W. X.; Tang, M.; Zhu, M.; Xu, M. A natural-language-based visual query approach of uncertain human trajectories. IEEE Transactions on Visualization and Computer Graphics Vol. 26, No. 1, 1256-1266, 2020.
Google Scholar
[16]
Poco, J.; Doraiswamy, H.; Vo, H. T.; Comba, J. L. D.; Freire, J.; Silva, C. T. Exploring traffic dynamics in urban environments using vector-valued functions. Computer Graphics Forum Vol. 34, No. 3, 161-170, 2015.
DOI Google Scholar
[17]
Lu, M.; Wang, Z. C.; Yuan, X. R. TrajRank: Exploring travel behaviour on a route by trajectory ranking. In: Proceedings of the IEEE Pacific Visualization Symposium, 311-318, 2015.
DOI
[18]
Liu, H.; Gao, Y.; Lu, L.; Liu, S. Y.; Qu, H. M.; Ni, L. M. Visual analysis of route diversity. In: Proceedings of the IEEE Conference on Visual Analytics Science and Technology, 171-180, 2011.
DOI
[19]
Chu, D.; Sheets, D. A.; Zhao, Y.; Wu, Y. Y.; Yang, J.; Zheng, M. G.; Chen, G. Visualizing hidden themes of taxi movement with semantic transformation. In: Proceedings of the IEEE Pacific Visualization Symposium, 137-144, 2014.
[20]
Ma, Y. X.; Lin, T.; Cao, Z. D.; Li, C.; Wang, F.; Chen, W. Mobility viewer: An Eulerian approach for studying urban crowd flow. IEEE Transactions on Intelligent Transportation Systems Vol. 17, No. 9, 2627-2636, 2016.
DOI Google Scholar
[21]
Wu, F. R.; Zhu, M. F.; Zhao, X.; Wang, Q.; Chen, W.; Maciejewski, R. Visualizing the time-varying crowd mobility. In: Proceedings of the SIGGRAPH Asia Visualization in High Performance Computing, Article No. 15, 2015.
[22]
Wang, F.; Chen, W.; Zhao, Y.; Gu, T. Y.; Gao, S. Y.; Bao, H. J. Adaptively exploring population mobility patterns in flow visualization. IEEE Transactions on Intelligent Transportation Systems Vol. 18, No. 8, 2250-2259, 2017.
DOI Google Scholar
[23]
Steptoe, M.; Krüger, R.; Garcia, R.; Liang, X.; Maciejewski, R. A visual analytics framework for exploring theme park dynamics. ACM Transactions on Interactive Intelligent Systems Vol. 8, No. 1, Article No. 4, 2018.
DOI Google Scholar
[24]
Zeng, W.; Fu, C. W.; Müller Arisona, S.; Schubiger, S.; Burkhard, R.; Ma, K. L. A visual analytics design for studying rhythm patterns from human daily movement data. Visual Informatics Vol. 1, No. 2, 81-91, 2017.
DOI Google Scholar
[25]
Liu, D.; Xu, P.; Ren, L. TPFlow: Progressive partition and multidimensional pattern extraction for large-scale spatio-temporal data analysis. IEEE Transactions on Visualization and Computer Graphics Vol. 25, No. 1, 1-11, 2019.
DOI Google Scholar
[26]
Andrienko, G.; Andrienko, N.; Mladenov, M.; Mock, M.; Pölitz, C. Discovering bits of place histories from people’s activity traces. In: Proceedings of the IEEE Symposium on Visual Analytics Science and Technology, 59-66, 2010.
DOI
[27]
Von Landesberger, T.; Brodkorb, F.; Roskosch, P.; Andrienko, N.; Andrienko, G.; Kerren, A. MobilityGraphs: Visual analysis of mass mobility dynamics via spatio-temporal graphs and clustering. IEEE Transactions on Visualization and Computer Graphics Vol. 22, No. 1, 11-20, 2016.
DOI Google Scholar
[28]
Liu, Q. Q.; Li, Q.; Tang, C. F.; Lin, H. B.; Peng, Z.; Li, Z. W.; Chen, T. Visual analysis of car-hailing reimbursement data for overtime. In: Proceedings of the EuroVis (Posters), 21-23, 2020.
[29]
Chen, W.; Huang, Z. S.; Wu, F. R.; Zhu, M. F.; Guan, H. H.; Maciejewski, R. VAUD: A visual analysis approach for exploring spatio-temporal urban data. IEEE Transactions on Visualization and Computer Graphics Vol. 24, No. 9, 2636-2648, 2018.
DOI Google Scholar
[30]
Lu, M.; Lai, C. F.; Ye, T. Z.; Liang, J.; Yuan, X. R. Visual analysis of multiple route choices based on general GPS trajectories. IEEE Transactions on Big Data Vol. 3, No. 2, 234-247, 2017.
DOI Google Scholar
[31]
Gu, T. L.; Zhu, M. F.; Chen, W.; Huang, Z. S.; Maciejewski, R.; Chang, L. Structuring mobility transition with an adaptive graph representation. IEEE Transactions on Computational Social Systems Vol. 5, No. 4, 1121-1132, 2018.
DOI Google Scholar
[32]
Kim, S.; Jeong, S.; Woo, I.; Jang, Y.; Maciejewski, R.; Ebert, D. S. Data flow analysis and visualization for spatiotemporal statistical data without trajectory information. IEEE Transactions on Visualization and Computer Graphics Vol. 24, No. 3, 1287-1300, 2018.
DOI Google Scholar
[33]
Lu, M.; Liang, J.; Wang, Z. C.; Yuan, X. R.Exploring OD patterns of interested region based on taxi trajectories. Journal of Visualization Vol. 19, No. 4, 811-821, 2016.
DOI Google Scholar
[34]
Lu, M.; Wang, Z. C.; Liang, J.; Yuan, X. R. OD-Wheel: Visual design to explore OD patterns of a central region. In: Proceedings of the IEEE Pacific Visualization Symposium, 87-91, 2015.
DOI
[35]
Zeng, W.; Fu, C. W.; Müller Arisona, S.; Erath, A.; Qu, H. Visualizing waypoints-constrained origin-destination patterns for massive transportation data. Computer Graphics Forum Vol. 35, No. 8, 95-107, 2016.
DOI Google Scholar
[36]
Andrienko, G.; Andrienko, N.; Fuchs, G.; Wood, J. Revealing patterns and trends of mass mobility through spatial and temporal abstraction of origin-destination movement data. IEEE Transactions on Visualization and Computer Graphics Vol. 23, No. 9, 2120-2136, 2017.
DOI Google Scholar
[37]
Zeng, W.; Shen, Q.; Jiang, Y.; Telea, A. Route-aware edge bundling for visualizing origin-destination trails in urban traffic. Computer Graphics Forum Vol. 38, No. 3, 581-593, 2019.
DOI Google Scholar
[38]
Shi, L.; Huang, C. C.; Liu, M. J.; Yan, J.; Jiang, T.; Tan, Z. H.; Hu, Y.; Chen, W.; Zhang, X. UrbanMotion: Visual analysis of metropolitan-scale sparse trajectories. IEEE Transactions on Visualization and Computer Graphics Vol. 27, No. 10, 3881-3899, 2021.
DOI Google Scholar
[39]
Ferreira, N.; Poco, J.; Vo, H. T.; Freire, J.; Silva, C. T. Visual exploration of big spatio-temporal urban data: A study of New York City taxi trips. IEEE Transactions on Visualization and Computer Graphics Vol. 19, No. 12, 2149-2158, 2013.
DOI Google Scholar
[40]
Zhou, Z. G.; Meng, L. H.; Tang, C.; Zhao, Y.; Guo, Z. Y.; Hu, M. X.; Chen, W. Visual abstraction of large scale geospatial origin-destination movement data. IEEE Transactions on Visualization and Computer Graphics Vol. 25, No. 1, 43-53, 2019.
DOI Google Scholar
[41]
Chen, W.; Xia, J.; Wang, X.; Wang, Y.; Chen, J.; Chang, L. RelationLines: Visual reasoning of egocentric relations from heterogeneous urban data. ACM Transactions on Intelligent Systems and Technology Vol. 10, No. 1, Article No. 2, 2019.
DOI Google Scholar
[42]
Wu, W. C.; Xu, J. Y.; Zeng, H. P.; Zheng, Y. X.; Qu, H. M.; Ni, B.; Yuan, M.; Ni, L. M. TelCoVis: Visual exploration of co-occurrence in urban human mobility based on telco data. IEEE Transactions on Visualization and Computer Graphics Vol. 22, No. 1, 935-944, 2016.
DOI Google Scholar
[43]
Zheng, Y. X.; Wu, W. C.; Zeng, H. P.; Cao, N.; Qu, H. M.; Yuan, M. X.; Zeng, J.; Ni, L. M. TelcoFlow: Visual exploration of collective behaviors based on telco data. In: Proceedings of the IEEE International Conference on Big Data, 843-852, 2016.
DOI
[44]
Yu, L.; Wu, W.; Li, X. H.; Li, G. X.; Ng, W. S.; Ng, S. K.; Huang, Z.; Arunan, A.; Watt, H. M. iVizTRANS: Interactive visual learning for home and work place detection from massive public transportation data. In: Proceedings of the IEEE Conference on Visual Analytics Science and Technology, 49-56, 2015.
DOI
[45]
Zeng, W.; Fu, C. W.; Müller Arisona, S.; Schubiger, S.; Burkhard, R.; Ma, K. L. Visualizing the relationship between human mobility and points of interest. IEEE Transactions on Intelligent Transportation Systems Vol. 18, No. 8, 2271-2284, 2017.
DOI Google Scholar
[46]
Krueger, R.; Thom, D.; Ertl, T. Semantic enrichment of movement behavior with foursquare: A visual analytics approach. IEEE Transactions on Visualization and Computer Graphics Vol. 21, No. 8, 903-915, 2015.
DOI Google Scholar
[47]
Al-Dohuki, S.; Wu, Y. Y.; Kamw, F.; Yang, J.; Li, X.; Zhao, Y.; Ye, X.; Chen, W.; Ma, C.; Wang, F. SemanticTraj: A new approach to interacting with massive taxi trajectories. IEEE Transactions on Visualization and Computer Graphics Vol. 23, No. 1, 11-20, 2017.
DOI Google Scholar
[48]
Kamw, F.; Al-Dohuki, S.; Zhao, Y.; Eynon, T.; Sheets, D.; Yang, J.; Ye, X.; Chen, W. Urban structure accessibility modeling and visualization for joint spatiotemporal constraints. IEEE Transactions on Intelligent Transportation Systems Vol. 21, No. 1, 104-116, 2020.
DOI Google Scholar
[49]
Feng, Z. Z.; Li, H. T.; Zeng, W.; Yang, S. H.; Qu, H. M. Topology density map for urban data visualization and analysis. IEEE Transactions on Visualization and Computer Graphics Vol. 27, No. 2, 828-838, 2021.
DOI Google Scholar
[50]
Wu, W. C.; Zheng, Y. X.; Qu, H. M.; Chen, W.; Gröller, E.; Ni, L. M. BoundarySeer: Visual analysis of 2D boundary changes. In: Proceedings of the IEEE Conference on Visual Analytics Science and Technology, 143-152, 2014.
[51]
Huang, X. K.; Zhao, Y.; Ma, C.; Yang, J.; Ye, X. Y.; Zhang, C. TrajGraph: A graph-based visual analytics approach to studying urban network centralities using taxi trajectory data. IEEE Transactions on Visualization and Computer Graphics Vol. 22, No. 1, 160-169, 2016.
DOI Google Scholar
[52]
Deng, Z. K.; Weng, D.; Xie, X.; Bao, J.; Zheng, Y.; Xu, M. L.; Chen, W.; Wu, Y. Compass: Towards better causal analysis of urban time series. IEEE Transactions on Visualization and Computer Graphics Vol. 28, No. 1, 1051-1061, 2022.
DOI Google Scholar
[53]
Wang, Z. C.; Ye, T. Z.; Lu, M.; Yuan, X. R.; Qu, H. M.; Yuan, J.; Wu, Q. Visual exploration of sparse traffic trajectory data. IEEE Transactions on Visualization and Computer Graphics Vol. 20, No. 12, 1813-1822, 2014.
DOI Google Scholar
[54]
Guo, H. Q.; Wang, Z. C.; Yu, B. W.; Zhao, H. J.; Yuan, X. R. TripVista: Triple Perspective Visual Trajectory Analytics and its application on microscopic traffic data at a road intersection. In: Proceedings of the IEEE Pacific Visualization Symposium, 163-170, 2011.
DOI
[55]
Zeng, W.; Fu, C. W.; Arisona, S. M.; Qu, H. M. Visualizing interchange patterns in massive movement data. Computer Graphics Forum Vol. 32, No. 3pt3, 271-280, 2013.
DOI Google Scholar
[56]
Wang, F.; Chen, W.; Wu, F. R.; Zhao, Y.; Hong, H.; Gu, T. Y.; Wang, L.; Liang, R.; Bao, H. A visual reasoning approach for data-driven transport assessment on urban roads. In: Proceedings of the IEEE Conference on Visual Analytics Science and Technology, 103-112, 2014.
DOI
[57]
Zheng, Y. X.; Wu, W. C.; Qu, H. M.; Ma, C. Y.; Ni, L. M. Visual analysis of bi-directional movement behavior. In: Proceedings of the IEEE International Conference on Big Data, 581-590, 2015.
DOI
[58]
Sun, G. D.; Chang, B. F.; Zhu, L.; Wu, H.; Zheng, K.; Liang, R. H. TZVis: Visual analysis of bicycle data for traffic zone division. Journal of Visualization Vol. 22, No. 6, 1193-1208, 2019.
DOI Google Scholar
[59]
Jin, Z. C.; Cao, N.; Shi, Y.; Wu, W. C.; Wu, Y. C. EcoLens: Visual analysis of ecological regions in urban contexts using traffic data. Journal of Visualization Vol. 24, No. 2, 349-364, 2021.
DOI Google Scholar
[60]
Lee, C.; Kim, Y.; Jin, S.; Kim, D.; Maciejewski, R.; Ebert, D.; Ko, S. A visual analytics system for exploring, monitoring, and forecasting road traffic congestion. IEEE Transactions on Visualization and Computer Graphics Vol. 26, No. 11, 3133-3146, 2020.
DOI Google Scholar
[61]
Pi, M. Y.; Yeon, H.; Son, H.; Jang, Y. Visual cause analytics for traffic congestion. IEEE Transactions on Visualization and Computer Graphics Vol. 27, No. 3, 2186-2201, 2021.
DOI Google Scholar
[62]
Andrienko, G.; Andrienko, N.; Hurter, C.; Rinzivillo, S.; Wrobel, S. Scalable analysis of movement data for extracting and exploring significant places. IEEE Transactions on Visualization and Computer Graphics Vol. 19, No. 7, 1078-1094, 2013.
DOI Google Scholar
[63]
Andrienko, G.; Andrienko, N.; Hurter, C.; Rinzivillo, S.; Wrobel, S. From movement tracks through events to places: Extracting and characterizing significant places from mobility data. In: Proceedings of the IEEE Conference on Visual Analytics Science and Technology, 161-170, 2011.
DOI
[64]
Wang, Z. C.; Lu, M.; Yuan, X. R.; Zhang, J. P.; van de Wetering, H. Visual traffic jam analysis based on trajectory data. IEEE Transactions on Visualization and Computer Graphics Vol. 19, No. 12, 2159-2168, 2013.
DOI Google Scholar
[65]
Deng, Z. K.; Weng, D.; Liang, Y. X.; Bao, J.; Zheng, Y.; Schreck, T.; Xu, M.; Wu, Y. Visual cascade analytics of large-scale spatiotemporal data. IEEE Transactions on Visualization and Computer Graphics Vol. 28, No. 6, 2486-2499, 2022.
Google Scholar
[66]
Andrienko, N.; Andrienko, G.; Patterson, F.; Stange, H. Visual analysis of place connectedness by public transport. IEEE Transactions on Intelligent Transportation Systems Vol. 21, No. 8, 3196-3208, 2020.
DOI Google Scholar
[67]
Palomo, C.; Guo, Z.; Silva, C. T.; Freire, J. Visually exploring transportation schedules. IEEE Transactions on Visualization and Computer Graphics Vol. 22, No. 1, 170-179, 2016.
DOI Google Scholar
[68]
Zeng, W.; Fu, C. W.; Arisona, S. M.; Erath, A.; Qu, H. M. Visualizing mobility of public transportation system. IEEE Transactions on Visualization and Computer Graphics Vol. 20, No. 12, 1833-1842, 2014.
DOI Google Scholar
[69]
Weng, D.; Zheng, C. B.; Deng, Z. K.; Ma, M. Z.; Bao, J.; Zheng, Y.; Xu, M.; Wu, Y. Towards better bus networks: A visual analytics approach. IEEE Transactions on Visualization and Computer Graphics Vol. 27, No. 2, 817-827, 2021.
DOI Google Scholar
[70]
Di Lorenzo, G.; Sbodio, M.; Calabrese, F.; Berlingerio, M.; Pinelli, F.; Nair, R. AllAboard: Visual exploration of cellphone mobility data to optimise public transport. IEEE Transactions on Visualization and Computer Graphics Vol. 22, No. 2, 1036-1050, 2016.
DOI Google Scholar
[71]
Liu, Q. Q.; Li, Q.; Tang, C. F.; Lin, H. B.; Ma, X. J.; Chen, T. J. A visual analytics approach to scheduling customized shuttle buses via perceiving passengers’ travel demands. In: Proceedings of the IEEE Visualization Conference, 76-80, 2020.
DOI
[72]
Piringer, H.; Buchetics, M.; Benedik, R. AlVis: Situation awareness in the surveillance of road tunnels. In: Proceedings of the IEEE Conference on Visual Analytics Science and Technology, 153-162, 2012.
DOI
[73]
Pu, J. S.; Liu, S. Y.; Ding, Y.; Qu, H. M.; Ni, L. T-watcher: A new visual analytic system for effective traffic surveillance. In: Proceedings of the IEEE 14th International Conference on Mobile Data Management, 127-136, 2013.
[74]
Liao, Z. C.; Yu, Y. Z.; Chen, B. Q. Anomaly detection in GPS data based on visual analytics. In: Proceedings of the IEEE Symposium on Visual Analytics Science and Technology, 51-58, 2010.
DOI
[75]
Gou, L.; Zou, L. C.; Li, N. X.; Hofmann, M.; Shekar, A. K.; Wendt, A.; Ren, L. VATLD: A visual analytics system to assess, understand and improve traffic light detection. IEEE Transactions on Visualization and Computer Graphics Vol. 27, No. 2, 261-271, 2021.
DOI Google Scholar
[76]
He, W. B.; Zou, L. C.; Shekar, A. K.; Gou, L.; Ren, L. Where can we help? A visual analytics approach to diagnosing and improving semantic segmentation of movable objects. IEEE Transactions on Visualization and Computer Graphics Vol. 28, No. 1, 1040-1050, 2022.
DOI Google Scholar
[77]
Jamonnak, S.; Zhao, Y.; Huang, X. Y.; Amiruzzaman, M. Geo-context aware study of vision-based autonomous driving models and spatial video data. IEEE Transactions on Visualization and Computer Graphics Vol. 28, No. 1, 1019-1029, 2022.
DOI Google Scholar
[78]
Hou, Y. J.; Wang, C. S.; Wang, J. H.; Xue, X. Y.; Zhang, X. L.; Zhu, J.; Wang, D.; Chen, S. Visual evaluation for autonomous driving. IEEE Transactions on Visualization and Computer Graphics Vol. 28, No. 1, 1030-1039, 2022.
DOI Google Scholar
[79]
Zeng, W.; Lin, C. Q.; Lin, J. C.; Jiang, J. C.; Xia, J. Z.; Turkay, C.; Chen, W. Revisiting the modifiable areal unit problem in deep traffic prediction with visual analytics. IEEE Transactions on Visualization and Computer Graphics Vol. 27, No. 2, 839-848, 2021.
DOI Google Scholar
[80]
Andrienko, N.; Andrienko, G.; Rinzivillo, S. Leveraging spatial abstraction in traffic analysis and forecasting with visual analytics. Information Systems Vol. 57, 172-194, 2016,
DOI Google Scholar
[81]
Yuan, J.; Zheng, Y.; Zhang, C. Y.; Xie, X.; Sun, G. Z. An interactive-voting based map matching algorithm. In: Proceedings of the IEEE 11th International Conference on Mobile Data Management, 43-52, 2010.
DOI
[82]
Lou, Y.; Zhang, C. Y.; Zheng, Y.; Xie, X.; Wang, W.; Huang, Y. Map-matching for low-sampling-rate GPS trajectories. In: Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, 352-361, 2009.
DOI
[83]
Qu, H. M.; Chan, W. Y.; Xu, A. B.; Chung, K. L.; Lau, K. H.; Guo, P. Visual analysis of the air pollution problem in Hong Kong. IEEE Transactions on Visualization and Computer Graphics Vol. 13, No. 6, 1408-1415, 2007.
DOI Google Scholar
[84]
Li, C. H.; Baciu, G.; Wang, Y. Z.; Chen, J. J.; Wang, C. B. DDLVis: Real-time visual query of spatiotemporal data distribution via density dictionary learning. IEEE Transactions on Visualization and Computer Graphics Vol. 28, No. 1, 1062-1072, 2022.
DOI Google Scholar
[85]
Wu, Y. C.; Weng, D.; Deng, Z. K.; Bao, J.; Xu, M. L.; Wang, Z. Y.; Zheng, Y.; Ding, Z.; Chen, W. Towards better detection and analysis of massive spatiotemporal co-occurrence patterns. IEEE Transactions on Intelligent Transportation Systems Vol. 22, No. 6, 3387-3402, 2021.
DOI Google Scholar
[86]
Li, J.; Chen, S.; Zhang, K.; Andrienko, G. L.; Andrienko, N. V. COPE: interactive exploration of co-occurrence patterns in spatial time series. IEEE Transactions on Visualization and Computer Graphics, 25(8):2554-2567, 2019.
DOI Google Scholar
[87]
Deng, Z. K.; Weng, D.; Chen, J. H.; Liu, R.; Wang, Z. B.; Bao, J.; Zheng, Y.; Wu, Y. AirVis: Visual analytics of air pollution propagation. IEEE Transactions on Visualization and Computer Graphics Vol. 26, No. 1, 800-810, 2020.
Google Scholar
[88]
Guo, F. Z.; Gu, T. L.; Chen, W.; Wu, F. R.; Wang, Q.; Shi, L.; Qu, H. Visual exploration of air quality data with a time-correlation-partitioning tree based on information theory. ACM Transactions on Interactive Intelligent Systems Vol. 9, No. 1, Article No. 4, 2019.
DOI Google Scholar
[89]
Shen, Q. M.; Wu, Y. H.; Jiang, Y. Z.; Zeng, W.; Lau, A. K. H.; Vianova, A.; Qu, H. Visual interpretation of recurrent neural network on multi-dimensional time-series forecast. In: Proceedings of the IEEE Pacific Visualization Symposium, 61-70, 2020.
DOI
[90]
Gautier, J.; Brédif, M.; Christophe, S. Co-visualization of air temperature and urban data for visual exploration. In: Proceedings of the IEEE Visualization Conference, 71-75, 2020.
DOI
[91]
Li, J.; Zhang, K.; Meng, Z. P. Vismate: Interactive visual analysis of station-based observation data on climate changes. In: Proceedings of the IEEE Conference on Visual Analytics Science and Technology, 133-142, 2014.
DOI
[92]
Quinan, P. S.; Meyer, M. Visually comparing weather features in forecasts. IEEE Transactions on Visualization and Computer Graphics Vol. 22, No. 1, 389-398, 2016.
DOI Google Scholar
[93]
Liao, H. S.; Wu, Y. C.; Chen, L.; Hamill, T. M.; Wang, Y. H.; Dai, K.; Zhang, H.; Chen, W. A visual voting framework for weather forecast calibration. In: Proceedings of the IEEE Scientific Visualization Conference, 25-32, 2015.
DOI
[94]
Accorsi, P.; Lalande, N.; Fabrègue, M.; Braud, A.; Poncelet, P.; Sallaberry, A.; Bringay, S.; Teisseire, M.; Cernesson, F.; Ber, F. L. HydroQual: Visual analysis of river water quality. In: Proceedings of the IEEE Conference on Visual Analytics Science and Technology, 123-132, 2014.
DOI
[95]
Maciejewski, R.; Tyner, B.; Jang, Y.; Zheng, C.; Nehme, R. V.; Ebert, D. S.; Cleveland, W. S.; Ouzzani, M.; Grannis, S. J.; Glickman, L. T. LAHVA: Linked animal-human health visual analytics. In: Proceedings of the IEEE Symposium on Visual Analytics Science and Technology, 27-34, 2007.
DOI
[96]
Malik, A.; Maciejewski, R.; Elmqvist, N.; Jang, Y.; Ebert, D. S.; Huang, W. A correlative analysis process in a visual analytics environment. In: Proceedings of the IEEE Conference on Visual Analytics Science and Technology, 33-42, 2012.
DOI
[97]
Wei, D. T.; Li, C. L.; Shao, H. N.; Tan, Z. J.; Lin, Z. X.; Dong, X. J.; Yuan, X. SensorAware: Visual analysis of both static and mobile sensor information. Journal of Visualization Vol. 24, No. 3, 597-613, 2021.
DOI Google Scholar
[98]
Liu, D. Y.; Weng, D.; Li, Y. H.; Bao, J.; Zheng, Y.; Qu, H. M.; Wu, Y. SmartAdP: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Transactions on Visualization and Computer Graphics Vol. 23, No. 1, 1-10, 2017.
DOI Google Scholar
[99]
Weng, D.; Chen, R.; Deng, Z. K.; Wu, F. R.; Chen, J. M.; Wu, Y. C. SRVis: Towards better spatial integration in ranking visualization. IEEE Transactions on Visualization and Computer Graphics Vol. 25, No. 1, 459-469, 2019.
DOI Google Scholar
[100]
Weng, D.; Zhu, H. M.; Bao, J.; Zheng, Y.; Wu, Y. C. HomeFinder revisited: Finding ideal homes with reachability-centric multi-criteria decision making. In: Proceedings of the CHI Conference on Human Factors in Computing Systems, Paper No. 247, 2018.
DOI
[101]
Li, Q.; Liu, Q. Q.; Tang, C. F.; Li, Z. W.; Wei, S. C.; Peng, X. R.; Zheng, M. H.; Chen, T. J.; Yang, Q. Warehouse vis: A visual analytics approach to facilitating warehouse location selection for business districts. Computer Graphics Forum Vol. 39, No. 3, 483-495, 2020.
DOI Google Scholar
[102]
Li, C. L.; Dong, X. J.; Yuan, X. R. Metro-wordle: An interactive visualization for urban text distributions based on wordle. Visual Informatics Vol. 2, No. 1, 50-59, 2018.
DOI Google Scholar
[103]
Cao, N.; Lin, C. G.; Zhu, Q. H.; Lin, Y. R.; Teng, X.; Wen, X. D. Voila: Visual anomaly detection and monitoring with streaming spatiotemporal data. IEEE Transactions on Visualization and Computer Graphics Vol. 24, No. 1, 23-33, 2018.
DOI Google Scholar
[104]
Chae, J.; Thom, D.; Bosch, H.; Jang, Y.; Maciejewski, R.; Ebert, D. S.; Ertl, T. Spatiotemporal social media analytics for abnormal event detection and examination using seasonal-trend decomposition. In: Proceedings of the IEEE Conference on Visual Analytics Science and Technology, 143-152, 2012.
DOI
[105]
Li, Q.; Lin, H. B.; Wei, X. G.; Huang, Y. K.; Fan, L. X.; Du, J.; Ma, X.; Chen, T. MaraVis: Representation and coordinated intervention of medical encounters in urban marathon. In: Proceedings of the CHI Conference on Human Factors in Computing Systems, 1-12, 2020.
DOI
[106]
Maciejewski, R.; Rudolph, S.; Hafen, R.; Abusalah, A.; Yakout, M.; Ouzzani, M.; Cleveland, W. S.; Grannis, S. J.; Ebert, D. S. A visual analytics approach to understanding spatiotemporal hotspots. IEEE Transactions on Visualization and Computer Graphics Vol. 16, No. 2, 205-220, 2010.
DOI Google Scholar
[107]
Lukasczyk, J.; Maciejewski, R.; Garth, C.; Hagen, H. Understanding hotspots: A topological visual analytics approach. In: Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems, Article No. 36, 2015.
DOI
[108]
Garcıa, G.; Silveira, J.; Poco, J.; Paiva, A.; Nery, M. B.; Silva, C. T.; Adorno, S.; Nonato, L. G. CrimAnalyzer: Understanding crime patterns in são Paulo. IEEE Transactions on Visualization and Computer Graphics Vol. 27, No. 4, 2313-2328, 2021.
DOI Google Scholar
[109]
Malik, A.; Maciejewski, R.; Towers, S.; McCullough, S.; Ebert, D. S. Proactive spatiotemporal resource allocation and predictive visual analytics for community policing and law enforcement. IEEE Transactions on Visualization and Computer Graphics Vol. 20, No. 12, 1863-1872, 2014.
DOI Google Scholar
[110]
Malik, A.; Maciejewski, R.; Maule, B.; Ebert, D. S. A visual analytics process for maritime resource allocation and risk assessment. In: Proceedings of the IEEE Conference on Visual Analytics Science and Technology, 221-230, 2011.
DOI
[111]
MacEachren, A. M.; Jaiswal, A.; Robinson, A. C.; Pezanowski, S.; Savelyev, A.; Mitra, P.; Zhang, X.; Blanford, J. I. SensePlace2: GeoTwitter analytics support for situational awareness. In: Proceedings of the IEEE Conference on Visual Analytics Science and Technology, 181-190, 2011.
DOI
[112]
Maciejewski, R.; Hafen, R.; Rudolph, S.; Larew, S. G.; Mitchell, M. A.; Cleveland, W. S.; Ebert, D. S. Forecasting hotspots-A predictive analytics approach. IEEE Transactions on Visualization and Computer Graphics Vol. 17, No. 4, 440-453, 2011.
DOI Google Scholar
[113]
Afzal, S.; Maciejewski, R.; Ebert, D. S. Visual analytics decision support environment for epidemic modeling and response evaluation. In: Proceedings of the IEEE Conference on Visual Analytics Science and Technology, 191-200, 2011.
DOI
[114]
Meghdadi, A. H.; Irani, P. Interactive exploration of surveillance video through action shot summarization and trajectory visualization. IEEE Transactions on Visualization and Computer Graphics Vol. 19, No. 12, 2119-2128, 2013.
DOI Google Scholar
[115]
Huang, K. T. Mapping the hazard: Visual analysis of flood impact on urban mobility. IEEE Computer Graphics and Applications Vol. 41, No. 1, 26-34, 2021.
DOI Google Scholar
[116]
Li, Q.; Liu, Y. J.; Chen, L.; Yang, X. C.; Peng, Y.; Yuan, X. R.; Wijerathne, M. L. L. SEEVis: A smart emergency evacuation plan visualization system with data-driven shot designs. Computer Graphics Forum Vol. 39, No. 3, 523-535, 2020.
DOI Google Scholar
[117]
Miranda, F.; Hosseini, M.; Lage, M.; Doraiswamy, H.; Dove, G.; Silva, C. T. Urban mosaic: Visual exploration of streetscapes using large-scale image data. In: Proceedings of the CHI Conference on Human Factors in Computing Systems, 2020.
DOI
[118]
Shen, Q. M.; Zeng, W.; Ye, Y.; Arisona, S. M.; Schubiger, S.; Burkhard, R.; Qu, H. StreetVizor: Visual exploration of human-scale urban forms based on street views. IEEE Transactions on Visualization and Computer Graphics Vol. 24, No. 1, 1004-1013, 2018.
DOI Google Scholar
[119]
Arietta, S. M.; Efros, A. A.; Ramamoorthi, R.; Agrawala, M. City forensics: Using visual elements to predict non-visual city attributes. IEEE Transactions on Visualization and Computer Graphics Vol. 20, No. 12, 2624-2633, 2014.
DOI Google Scholar
[120]
Miranda, F.; Doraiswamy, H.; Lage, M.; Wilson, L.; Hsieh, M.; Silva, C. T. Shadow accrual maps: Efficient accumulation of city-scale shadows over time. IEEE Transactions on Visualization and Computer Graphics Vol. 25, No. 3, 1559-1574, 2019.
DOI Google Scholar
[121]
Zhu, M. F.; Chen, W.; Xia, J. Z.; Ma, Y. X.; Zhang, Y. K.; Luo, Y. T.; Huang, Z.; Liu, L. Location2vec: A situation-aware representation for visual exploration of urban locations. IEEE Transactions on Intelligent Transportation Systems Vol. 20, No. 10, 3981-3990, 2019.
DOI Google Scholar
[122]
Zeng, W.; Ye, Y. VitalVizor: A visual analytics system for studying urban vitality. IEEE Computer Graphics and Applications Vol. 38, No. 5, 38-53, 2018.
DOI Google Scholar
[123]
Qu, H. M.; Wang, H. M.; Cui, W. W.; Wu, Y. C.; Chan, M. Y. Focus+context route zooming and information overlay in 3D urban environments. IEEE Transactions on Visualization and Computer Graphics Vol. 15, No. 6, 1547-1554, 2009.
DOI Google Scholar
[124]
Ferreira, N.; Lage, M.; Doraiswamy, H.; Vo, H.; Wilson, L.; Werner, H.; Park, M.; Silva, C. T. Urbane: A 3D framework to support data driven decision making in urban development. In: Proceedings of the IEEE Conference on Visual Analytics Science and Technology, 97-104, 2015.
DOI
[125]
Miranda, F.; Doraiswamy, H.; Lage, M.; Zhao, K.; Gonçalves, B.; Wilson, L.; Hsieh, M.; Silva, C. T. Urban pulse: Capturing the rhythm of cities. IEEE Transactions on Visualization and Computer Graphics Vol. 23, No. 1, 791-800, 2017.
DOI Google Scholar
[126]
Sun, G. D.; Liang, R. H.; Wu, F. L.; Qu, H. M. A web-based visual analytics system for real estate data. Science China Information Sciences Vol. 56, No. 5, 1-13, 2013.
DOI Google Scholar
[127]
Wang, H.; Lu, Y. F.; Shutters, S. T.; Steptoe, M.; Wang, F.; Landis, S.; Maciejewski, R. A visual analytics framework for spatiotemporal trade network analysis. IEEE Transactions on Visualization and Computer Graphics Vol. 25, No. 1, 331-341, 2019.
DOI Google Scholar
[128]
Zhang, J. W.; Yanli, E.; Ma, J.; Zhao, Y. H.; Xu, B. H.; Sun, L. T.; Chen, J.; Yuan, X. Visual analysis of public utility service problems in a metropolis. IEEE Transactions on Visualization and Computer Graphics Vol. 20, No. 12, 1843-1852, 2014.
DOI Google Scholar
[129]
Li, J.; Chen, S. M.; Chen, W.; Andrienko, G.; Andrienko, N. Semantics-space-time cube: A conceptual framework for systematic analysis of texts in space and time. IEEE Transactions on Visualization and Computer Graphics Vol. 26, No. 4, 1789-1806, 2020.
DOI Google Scholar
[130]
Andrienko, G.; Andrienko, N.; Bosch, H.; Ertl, T.; Fuchs, G.; Jankowski, P.; Thom, D. Thematic patterns in georeferenced tweets through space-time visual analytics. Computing in Science & Engineering Vol. 15, No. 3, 72-82, 2013.
DOI Google Scholar
[131]
Lu, Y. F.; Hu, X.; Wang, F.; Kumar, S.; Liu, H.; Maciejewski, R. Visualizing social media sentiment in disaster scenarios. In: Proceedings of the 24th International Conference on World Wide Web, 1211-1215, 2015.
[132]
Cao, N.; Lin, Y. R.; Sun, X. H.; Lazer, D.; Liu, S. X.; Qu, H. M. Whisper: Tracing the spatiotemporal process of information diffusion in real time. IEEE Transactions on Visualization and Computer Graphics Vol. 18, No. 12, 2649-2658, 2012.
DOI Google Scholar
[133]
Wu, Y. C.; Liu, S. X.; Yan, K.; Liu, M. C.; Wu, F. Z. OpinionFlow: Visual analysis of opinion diffusion on social media. IEEE Transactions on Visualization and Computer Graphics Vol. 20, No. 12, 1763-1772, 2014.
DOI Google Scholar
[134]
Xu, P. P.; Wu, Y. C.; Wei, E. X.; Peng, T. Q.; Liu, S. X.; Zhu, J. J. H.; Qu, H. Visual analysis of topic competition on social media. IEEE Transactions on Visualization and Computer Graphics Vol. 19, No. 12, 2012-2021, 2013.
DOI Google Scholar
[135]
Sun, G. D.; Wu, Y. C.; Liu, S. X.; Peng, T. Q.; Zhu, J. J. H.; Liang, R. H. EvoRiver: Visual analysis of topic coopetition on social media. IEEE Transactions on Visualization and Computer Graphics Vol. 20, No. 12, 1753-1762, 2014.
DOI Google Scholar
[136]
Chen, S. M.; Chen, S.; Wang, Z. H.; Liang, J.; Yuan, X. R.; Cao, N.; Wu, Y. D-Map: Visual analysis of ego-centric information diffusion patterns in social media. In: Proceedings of the IEEE Conference on Visual Analytics Science and Technology, 41-50, 2016.
DOI
[137]
Hu, M. D.; Liu, S. X.; Wei, F. R.; Wu, Y. C.; Stasko, J.; Ma, K. L. Breaking news on twitter. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 2751-2754, 2012.
[138]
Knittel, J.; Koch, S.; Tang, T.; Chen, W.; Wu, Y. C.; Liu, S. X.; Ertl, T. Real-time visual analysis of high-volume social media posts. IEEE Transactions on Visualization and Computer Graphics Vol. 28, No. 1, 879-889, 2022.
DOI Google Scholar
[139]
Han, S. Y.; Ye, S. J.; Zhang, H. X. Visual exploration of Internet news via sentiment score and topic models. Computional Visual Media Vol. 6, No. 3, 333-347, 2020.
DOI Google Scholar
[140]
Chen, S. M.; Lin, L. J.; Yuan, X. R. Social media visual analytics. Computer Graphics Forum Vol. 36, No. 3, 563-587, 2017.
DOI Google Scholar
[141]
Wu, Y. C.; Cao, N.; Gotz, D.; Tan, Y. P.; Keim, D. A. A survey on visual analytics of social media data. IEEE Transactions on Multimedia Vol. 18, No. 11, 2135-2148, 2016.
DOI Google Scholar
[142]
Doraiswamy, H.; Ferreira, N.; Damoulas, T.; Freire, J.; Silva, C. T. Using topological analysis to support event-guided exploration in urban data. IEEE Transactions on Visualization and Computer Graphics Vol. 20, No. 12, 2634-2643, 2014.
DOI Google Scholar
[143]
Liu, H. Y.; Chen, X. H.; Wang, Y. D.; Zhang, B.; Chen, Y. P.; Zhao, Y.; Zhou, F. Visualization and visual analysis of vessel trajectory data: A survey. Visual Informatics Vol. 5, No. 4, 1-10, 2021.
DOI Google Scholar
[144]
Peña-Araya, V.; Bezerianos, A.; Pietriga, E. A comparison of geographical propagation visualizations. In: Proceedings of the CHI Conference on Human Factors in Computing Systems, 1-14, 2020.
DOI
[145]
Huang, Z. S.; Lu, Y. F.; Mack, E. A.; Chen, W.; Maciejewski, R. Exploring the sensitivity of choropleths under attribute uncertainty. IEEE Transactions on Visualization and Computer Graphics Vol. 26, No. 8, 2576-2590, 2020.
DOI Google Scholar
[146]
Zhang, Y. F.; Maciejewski, R. Quantifying the visual impact of classification boundaries in choropleth maps. IEEE Transactions on Visualization and Computer Graphics Vol. 23, No. 1, 371-380, 2017.
DOI Google Scholar
[147]
Ying, L.; Tangl, T.; Luo, Y. Z.; Shen, L.; Xie, X.; Yu, L. Y.; Wu, Y. GlyphCreator: Towards example-based automatic generation of circular glyphs. IEEE Transactions on Visualization and Computer Graphics Vol. 28, No. 1, 400-410, 2022.
DOI Google Scholar
[148]
Wang, H. X.; Ni, Y. N.; Sun, L.; Chen, Y. Y.; Xu, T.; Chen, X. H.; Su, W. H.; Zhou, Z. G. Hierarchical visualization of geographical areal data with spatial attribute association. Visual Informatics Vol. 5, No. 3, 82-91, 2021.
DOI Google Scholar
[149]
Schöttler, S.; Yang, Y. L.; Pfister, H.; Bach, B. Visualizing and interacting with geospatial networks: A survey and design space. Computer Graphics Forum Vol. 40, No. 6, 5-33, 2021.
DOI Google Scholar
[150]
Chen, Z. T.; Wang, Y. F.; Sun, T. C.; Gao, X.; Chen, W.; Pan, Z. G.; Qu, H.; Wu, Y. Exploring the design space of immersive urban analytics. Visual Informatics Vol. 1, No. 2, 132-142, 2017.
DOI Google Scholar
[151]
Sun, G. D.; Liu, Y.; Wu, W. B.; Liang, R. H.; Qu, H. M. Embedding temporal display into maps for occlusion-free visualization of spatio-temporal data. In: Proceedings of the IEEE Pacific Visualization Symposium, 185-192, 2014.
[152]
Sun, G. D.; Liang, R. H.; Qu, H. M.; Wu, Y. C. Embedding spatio-temporal information into maps by route-zooming. IEEE Transactions on Visualization and Computer Graphics Vol. 23, No. 5, 1506-1519, 2017.
DOI Google Scholar
[153]
Carenini, G.; Loyd, J. ValueCharts: Analyzing linear models expressing preferences and evaluations. In: Proceedings of the Working Conference on Advanced Visual Interfaces, 150-157, 2004.
DOI
[154]
Gratzl, S.; Lex, A.; Gehlenborg, N.; Pfister, H.; Streit, M. LineUp: Visual analysis of multi-attribute rankings. IEEE Transactions on Visualization and Computer Graphics Vol. 19, No. 12, 2277-2286, 2013.
DOI Google Scholar
[155]
Tang, J. X.; Zhou, Y. H.; Tang, T.; Weng, D.; Xie, B. Y.; Yu, L. Y.; Zhang, H.; Wu, Y. A visualization approach for monitoring order processing in E-commerce warehouse. IEEE Transactions on Visualization and Computer Graphics Vol. 28, No. 1, 857-867, 2022.
DOI Google Scholar
[156]
Wang, J. C.; Cai, X. W.; Su, J. J.; Liao, Y.; Wu, Y. C. What makes a scatterplot hard to comprehend: Data size and pattern salience matter. Journal of Visualization Vol. 25, No. 1, 59-75, 2022.
DOI Google Scholar
[157]
Nguyen, Q. V.; Miller, N.; Arness, D.; Huang, W. D.; Huang, M. L.; Simoff, S. Evaluation on interactive visualization data with scatterplots. Visual Informatics Vol. 4, No. 4, 1-10, 2020.
DOI Google Scholar
[158]
Yang, Y. L.; Dwyer, T.; Goodwin, S.; Marriott, K. Many-to-many geographically-embedded flow visualisation: An evaluation. IEEE Transactions on Visualization and Computer Graphics Vol. 23, No. 1, 411-420, 2017.
DOI Google Scholar
[159]
Shu, X. H.; Wu, J.; Wu, X. K.; Liang, H. Y.; Cui, W. W.; Wu, Y. C.; Qu, H. DancingWords: Exploring animated word clouds to tell stories. Journal of Visualization Vol. 24, No. 1, 85-100, 2021.
DOI Google Scholar
[160]
Liu, S. Y.; Pu, J. S.; Luo, Q.; Qu, H. M.; Ni, L. M.; Krishnan, R. VAIT: A visual analytics system for metropolitan transportation. IEEE Transactions on Intelligent Transportation Systems Vol. 14, No. 4, 1586-1596, 2013.
DOI Google Scholar
[161]
Zhu, H. Y.; Zhu, M. F.; Feng, Y.; Cai, D.; Hu, Y. Z.; Wu, S. L.; Wu, X.; Chen, W. Visualizing large-scale high-dimensional data via hierarchical embedding of KNN graphs. Visual Informatics Vol. 5, No. 2, 51-59, 2021.
DOI Google Scholar
[162]
Andrienko, N.; Andrienko, G. Spatial generalization and aggregation of massive movement data. IEEE Transactions on Visualization and Computer Graphics Vol. 17, No. 2, 205-219, 2011.
DOI Google Scholar
[163]
Li, Y. H.; Bao, J.; Li, Y. H.; Wu, Y. C.; Gong, Z. G.; Zheng, Y. Mining the most influential k-location set from massive trajectories. IEEE Transactions on Big Data Vol. 4, No. 4, 556-570, 2018.
DOI Google Scholar
[164]
Liang, Y.; Jiang, Z.; Zheng, Y. Inferring traffic cascading patterns. In: Proceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, Article No. 2, 2017.
DOI
[165]
Li, R. Y.; Ruan, S. J.; Bao, J.; Li, Y. H.; Wu, Y. C.; Hong, L.; Zheng, Y. Efficient path query processing over massive trajectories on the cloud. IEEE Transactions on Big Data Vol. 6, No. 1, 66-79, 2020.
DOI Google Scholar
[166]
Li, R. Y.; He, H. J.; Wang, R. B.; Huang, Y. C.; Liu, J. W.; Ruan, S. J.; He, T.; Bao, J.; Zheng, Y. JUST: JD urban spatio-temporal data engine. In: Proceedings of the IEEE 36th International Conference on Data Engineering, 1558-1569, 2020.
[167]
Mei, H. H.; Chen, W.; Wei, Y. T.; Hu, Y. Z.; Zhou, S. Y.; Lin, B. R.; Zhao, Y.; Xia, J. RSATree: Distribution-aware data representation of large-scale tabular datasets for flexible visual query. IEEE Transactions on Visualization and Computer Graphics Vol. 26, No. 1, 1161-1171, 2020.
DOI Google Scholar
[168]
Liu, C.; Wu, C.; Shao, H. N.; Yuan, X. R. SmartCube: An adaptive data management architecture for the real-time visualization of spatiotemporal datasets. IEEE Transactions on Visualization and Computer Graphics Vol. 26, No. 1, 790-799, 2020.
Google Scholar
[169]
Lins, L.; Klosowski, J. T.; Scheidegger, C. Nanocubes for real-time exploration of spatiotemporal datasets. IEEE Transactions on Visualization and Computer Graphics Vol. 19, No. 12, 2456-2465, 2013.
DOI Google Scholar
[170]
Doraiswamy, H.; Vo, H. T.; Silva, C. T.; Freire, J. A GPU-based index to support interactive spatio-temporal queries over historical data. In: Proceedings of the IEEE 32nd International Conference on Data Engineering, 1086-1097, 2016.
DOI
[171]
Pahins, C. A. L.; Stephens, S. A.; Scheidegger, C.; Comba, J. L. D. Hashedcubes: Simple, low memory, real-time visual exploration of big data. IEEE Transactions on Visualization and Computer Graphics Vol. 23, No. 1, 671-680, 2017.
DOI Google Scholar
[172]
Scheepens, R.; Willems, N.; van de Wetering, H.; Andrienko, G.; Andrienko, N.; van Wijk, J. J. Composite density maps for multivariate trajectories. IEEE Transactions on Visualization and Computer Graphics Vol. 17, No. 12, 2518-2527, 2011.
DOI Google Scholar
[173]
Tominski, C.; Schumann, H.; Andrienko, G.; Andrienko, N. Stacking-based visualization of trajectory attribute data. IEEE Transactions on Visualization and Computer Graphics Vol. 18, No. 12, 2565-2574, 2012.
DOI Google Scholar
[174]
Chen, R.; Shu, X. H.; Chen, J. H.; Weng, D.; Tang, J. X.; Fu, S. W.; Wu, Y. Nebula: A coordinating grammar of graphics. IEEE Transactions on Visualization and Computer Graphics , 2021.
DOI Google Scholar
[175]
Shneiderman, B. The eyes have it: A task by data type taxonomy for information visualizations. In: Proceedings of the IEEE Symposium on Visual Languages, 336-343, 1996.
[176]
Wu, Y. C.; Chen, Z. T.; Sun, G. D.; Xie, X.; Cao, N.; Liu, S. X.; Cui, W. StreamExplorer: A multi-stage system for visually exploring events in social streams. IEEE Transactions on Visualization and Computer Graphics Vol. 24, No. 10, 2758-2772, 2018.
DOI Google Scholar
[177]
Sedlmair, M.; Meyer, M.; Munzner, T. Design study methodology: Reflections from the trenches and the stacks. IEEE Transactions on Visualization and Computer Graphics Vol. 18, No. 12, 2431-2440, 2012.
DOI Google Scholar
[178]
Eirich, J.; Bonart, J.; Jäckle, D.; Sedlmair, M.; Schmid, U.; Fischbach, K.; Schreck, T.; Bernard, J. IRVINE: A design study on analyzing correlation patterns of electrical engines. IEEE Transactions on Visualization and Computer Graphics Vol. 28, No. 1, 11-21, 2022.
DOI Google Scholar
[179]
Wu, Y. C.; Lan, J.; Shu, X. H.; Ji, C. Y.; Zhao, K. J.; Wang, J. C.; Zhang, H. iTTVis: Interactive visualization of table tennis data. IEEE Transactions on Visualization and Computer Graphics Vol. 24, No. 1, 709-718, 2018.
DOI Google Scholar
[180]
Holten, D. Hierarchical edge bundles: Visualization of adjacency relations in hierarchical data. IEEE Transactions on Visualization and Computer Graphics Vol. 12, No. 5, 741-748, 2006.
DOI Google Scholar
[181]
Liu, S. X.; Wu, Y. C.; Wei, E. X.; Liu, M. C.; Liu, Y. StoryFlow: Tracking the evolution of stories. IEEE Transactions on Visualization and Computer Graphics Vol. 19, No. 12, 2436-2445, 2013.
DOI Google Scholar
[182]
Zhao, Y.; Jiang, H. J.; Chen, Q. A.; Qin, Y. Q.; Xie, H. X.; Wu, Y. T.; Liu, S.; Zhou, Z.; Xia, J.; Zhou, F. Preserving minority structures in graph sampling. IEEE Transactions on Visualization and Computer Graphics Vol. 27, No. 2, 1698-1708, 2021.
DOI Google Scholar
[183]
Zhou, Z. G.; Shi, C.; Shen, X. L.; Cai, L. H.; Wang, H. X.; Liu, Y. H.; Zhao, Y.; Chen, W. Context-aware sampling of large networks via graph representation learning. IEEE Transactions on Visualization and Computer Graphics Vol. 27, No. 2, 1709-1719, 2021.
DOI Google Scholar
[184]
Zhou, Z. G.; Zhang, X. L.; Yang, Z. D.; Chen, Y. Y.; Liu, Y. H.; Wen, J.; Wen, J.; Chen, B.; Zhao, Y.; Chen, W. Visual abstraction of geographical point data with spatial autocorrelations. In: Proceedings of the IEEE Conference on Visual Analytics Science and Technology, 60-71, 2020.
DOI
[185]
Yuan, J.; Xiang, S. X.; Xia, J. Z.; Yu, L. Y.; Liu, S. X. Evaluation of sampling methods for scatterplots. IEEE Transactions on Visualization and Computer Graphics Vol. 27, No. 2, 1720-1730, 2021.
DOI Google Scholar
[186]
Wang, G. Z.; Guo, J. J.; Tang, M. J.; de Queiroz Neto, J. F.; Yau, C.; Daghistani, A.; Karimzadeh, M.; Aref, W. G.; Ebert, D. S. STULL: Unbiased online sampling for visual exploration of large spatiotemporal data. In: Proceedings of the IEEE Conference on Visual Analytics Science and Technology, 72-83, 2020.
DOI
[187]
Zheng, F. L.; Wen, J.; Zhang, X.; Chen, Y. Y.; Zhang, X. L.; Liu, Y. N.; Xu, T.; Chen, X.; Wang, Y.; Su, W. Visual abstraction of large-scale geographical point data with credible spatial interpolation. Journal of Visualization Vol. 24, No. 6, 1303-1317, 2021.
DOI Google Scholar
[188]
Stolper, C. D.; Perer, A.; Gotz, D. Progressive visual analytics: User-driven visual exploration of in-progress analytics. IEEE Transactions on Visualization and Computer Graphics Vol. 20, No. 12, 1653-1662, 2014.
DOI Google Scholar
[189]
Pezzotti, N.; Lelieveldt, B. P. F.; van der Maaten, L.; Hollt, T.; Eisemann, E.; Vilanova, A. Approximated and user steerable tSNE for progressive visual analytics. IEEE Transactions on Visualization and Computer Graphics Vol. 23, No. 7, 1739-1752, 2017.
DOI Google Scholar
[190]
Li, J. K.; Ma, K. L. P4: Portable parallel processing pipelines for interactive information visualization. IEEE Transactions on Visualization and Computer Graphics Vol. 26, No. 3, 1548-1561, 2020.
DOI Google Scholar
[191]
Li, J. K.; Ma, K. L. P5: Portable progressive parallel processing pipelines for interactive data analysis and visualization. IEEE Transactions on Visualization and Computer Graphics Vol. 26, No. 1, 1151-1160, 2020.
Google Scholar
[192]
Schulz, C.; Nocaj, A.; Goertler, J.; Deussen, O.; Brandes, U.; Weiskopf, D. Probabilistic graph layout for uncertain network visualization. IEEE Transactions on Visualization and Computer Graphics Vol. 23, No. 1, 531-540, 2017.
DOI Google Scholar
[193]
Liu, M. C.; Liu, S. X.; Zhu, X. Z.; Liao, Q. Y.; Wei, F. R.; Pan, S. M. An uncertainty-aware approach for exploratory microblog retrieval. IEEE Transactions on Visualization and Computer Graphics Vol. 22, No. 1, 250-259, 2016.
DOI Google Scholar
[194]
Gortler, J.; Schulz, C.; Weiskopf, D.; Deussen, O. Bubble treemaps for uncertainty visualization. IEEE Transactions on Visualization and Computer Graphics Vol. 24, No. 1, 719-728, 2018.
DOI Google Scholar
[195]
Baumgartl, T.; Petzold, M.; Wunderlich, M.; Hohn, M.; Archambault, D.; Lieser, M.; Dalpke, A.; Scheithauer, S.; Marschollek, M.; Eichel, V.; et al. In search of patient zero: Visual analytics of pathogen transmission pathways in hospitals. IEEE Transactions on Visualization and Computer Graphics Vol. 27, No. 2, 711-721, 2021.
DOI Google Scholar
[196]
Zheng, Y.; Liu, F. R.; Hsieh, H. P. U-Air: When urban air quality inference meets big data. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1436-1444, 2013.
DOI
[197]
Liao, B.; Zhang, J.; Wu, C.; McIlwraith, D.; Chen, T.; Yang, S.; Guo, Y.; Wu, F. Deep sequence learning with auxiliary information for traffic prediction. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 537-546, 2018.
DOI
[198]
Xie, X.; Wang, J. C.; Liang, H. Y.; Deng, D. Z.; Cheng, S. B.; Zhang, H.; Chen, W.; Wu, Y. PassVizor: Toward better understanding of the dynamics of soccer passes. IEEE Transactions on Visualization and Computer Graphics Vol. 27, No. 2, 1322-1331, 2021.
DOI Google Scholar
[199]
Wu, Y. C.; Xie, X.; Wang, J. C.; Deng, D. Z.; Liang, H. Y.; Zhang, H.; Cheng, S.; Chen, W. ForVizor: Visualizing spatio-temporal team formations in soccer. IEEE Transactions on Visualization and Computer Graphics Vol. 25, No. 1, 65-75, 2019.
DOI Google Scholar
[200]
Hu, K.; Gaikwad, S. N. S.; Hulsebos, M.; Bakker, M. A.; Zgraggen, E.; Hidalgo, C.; Kraska, T.; Li, G.; Satyanarayan, A.; Demiralp, Ç. VizNet: Towards a large-scale visualization learning and benchmarking repository. In: Proceedings of the CHI Conference on Human Factors in Computing Systems, Paper No. 662, 2019.
DOI
[201]
Deng, D. Z.; Wu, Y. H.; Shu, X. H.; Wu, J.; Xu, M. Y.; Fu, S. W.; Cui, W.; Wu, Y. VisImages: A corpus of visualizations in the images of visualization publications. arXiv preprint arXiv: 2007.04584, 2021.
Google Scholar
[202]
Wu, A. Y.; Wang, Y.; Shu, X. H.; Moritz, D.; Cui, W. W.; Zhang, H. D.; Zhang, D.; Qu, H. AI4VIS: Survey on artificial intelligence approaches for data visualization. IEEE Transactions on Visualization and Computer Graphics , 2021.
DOI Google Scholar
[203]
Wang, Q. W.; Chen, Z. T.; Wang, Y.; Qu, H. M. A survey on ML4VIS: Applying Machine Learning advances to data visualization. IEEE Transactions on Visualization and Computer Graphics , 2021.
DOI Google Scholar
[204]
Yuan, J.; Chen, C. J.; Yang, W. K.; Liu, M. C.; Xia, J. Z.; Liu, S. X. A survey of visual analytics techniques for machine learning. Computional Visual Media Vol. 7, No. 1, 3-36, 2021.
DOI Google Scholar
[205]
Lv, P.; Wei, H.; Gu, T. X.; Zhang, Y. Z.; Jiang, X. H.; Zhou, B.; Xu, M. Trajectory distributions: A new description of movement for trajectory prediction. Computional Visual Media Vol. 8, No. 2, 213-224, 2022.
DOI Google Scholar
[206]
Liang, Y.; Ouyang, K.; Sun, J.; Wang, Y.; Zhang, J.; Zheng, Y.; Rosenblum, D. S.; Zimmermann, R. Fine-grained urban ow prediction. In: Proceedings of the Web Conference, 1833-1845, 2021.
DOI
[207]
Liang, Y.; Ouyang, K.; Jing, L.; Ruan, S.; Liu, Y.; Zhang, J.; Rosenblum, D. S.; Zheng, Y. UrbanFM: Inferring fine-grained urban flows. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 3132-3142, 2019.
DOI
[208]
Chu, X. T.; Xie, X.; Ye, S. N.; Lu, H. L.; Xiao, H. G.; Yuan, Z. Q.; Chen, Z.; Zhang, H.; Wu, Y. TIVEE: Visual exploration and explanation of badminton tactics in immersive visualizations. IEEE Transactions on Visualization and Computer Graphics Vol. 28, No. 1, 118-128, 2022.
DOI Google Scholar
[209]
Ye, S. N.; Chen, Z. T.; Chu, X. T.; Wang, Y. F.; Fu, S. W.; Shen, L. J.; Zhou, K.; Wu, Y. ShuttleSpace: Exploring and analyzing movement trajectory in immersive visualization. IEEE Transactions on Visualization and Computer Graphics Vol. 27, No. 2, 860-869, 2021.
DOI Google Scholar
[210]
Chen, Z. T.; Su, Y. J.; Wang, Y. F.; Wang, Q. W.; Qu, H. M.; Wu, Y. C. MARVisT: Authoring glyph-based visualization in mobile augmented reality. IEEE Transactions on Visualization and Computer Graphics Vol. 26, No. 8, 2645-2658, 2020.
DOI Google Scholar
[211]
Hurter, C.; Riche, N. H.; Drucker, S. M.; Cordeil, M.; Alligier, R.; Vuillemot, R. FiberClay: Sculpting three dimensional trajectories to reveal structural insights. IEEE Transactions on Visualization and Computer Graphics Vol. 25, No. 1, 704-714, 2019.
DOI Google Scholar
[212]
Su, C. Y.; Yang, C.; Chen, Y. H.; Wang, F. P.; Wang, F.; Wu, Y. D.; Zhang, X. Natural multimodal interaction in immersive flow visualization. Visual Informatics Vol. 5, No. 4, 56-66, 2021.
DOI Google Scholar
[213]
Schwab, M.; Saffo, D.; Zhang, Y. X.; Sinha, S.; Nita-Rotaru, C.; Tompkin, J.; Dunne, C.; Borkin, M. A. VisConnect: Distributed event synchronization for collaborative visualization. IEEE Transactions on Visualization and Computer Graphics Vol. 27, No. 2, 347-357, 2021.
DOI Google Scholar
[214]
Isenberg, P.; Fisher, D.; Paul, S. A.; Morris, M. R.; Inkpen, K.; Czerwinski, M. Co-located collaborative visual analytics around a tabletop display. IEEE Transactions on Visualization and Computer Graphics Vol. 18, No. 5, 689-702, 2012.
DOI Google Scholar
[215]
Wu, A. Y.; Tong, W.; Dwyer, T.; Lee, B.; Isenberg, P.; Qu, H. M. MobileVisFixer: Tailoring web visualizations for mobile phones leveraging an explainable reinforcement learning framework. IEEE Transactions on Visualization and Computer Graphics Vol. 27, No. 2, 464-474, 2021.
DOI Google Scholar
[216]
Brehmer, M.; Lee, B.; Isenberg, P.; Choe, E. K. Visualizing ranges over time on mobile phones: A task-based crowdsourced evaluation. IEEE Transactions on Visualization and Computer Graphics Vol. 25, No. 1, 619-629, 2019.
DOI Google Scholar
[217]
Whitlock, M.; Wu, K. K.; Szafir, D. A. Designing for mobile and immersive visual analytics in the field. IEEE Transactions on Visualization and Computer Graphics Vol. 26, No. 1, 503-513, 2020.
Google Scholar
[218]
Brehmer, M.; Lee, B.; Isenberg, P.; Choe, E. K. A comparative evaluation of animation and small multiples for trend visualization on mobile phones. IEEE Transactions on Visualization and Computer Graphics Vol. 26, No. 1, 364-374, 2020.
DOI Google Scholar
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Publication history

Received: 10 December 2021
Accepted: 08 February 2022
Published: 18 October 2022
Issue date: March 2023

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© The Author(s) 2022.

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).

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