Journal article

ConcaveCubes: Supporting Cluster-based Geographical Visualization in Large Data Scale

Mingzhao Li, Farhana Choudhury, Zhifeng Bao, Hanan Samet, Timos Sellis



In this paper we study the problem of supporting effective and scalable visualization for the rapidly increasing volumes of urban data. From an extensive literature study, we find that the existing solutions suffer from at least one of the drawbacks below: (i) loss of interesting structures/outliers due to sampling; (ii) supporting heatmaps only, which provides limited information; and (iii) no notion of real-world geography semantics (e.g., country, state, city) is captured in the visualization result as well as the underlying index. Therefore, we propose ConcaveCubes, a cluster-based data cube to support interactive visualization of large-scale multidimensional urban data. Specifically, we..

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Awarded by ARC

Awarded by National Natural Science Foundation of China (NSFC)

Awarded by National Science Foundation of the US

Funding Acknowledgements

This work was partially supported by ARC DP170102726, DP170102231, DP180102050, and National Natural Science Foundation of China (NSFC) 61728204, 91646204. Zhifeng Bao is supported by a Google Faculty Award. Hanan Samet is supported in part by the National Science Foundation of the US under grant IIS-13-20791. The authors would also like to thank the anonymous EuroVis reviewers for their valuable comments and suggestions to improve the quality of the paper.