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Visual analytics of economic features for multivariate spatio-temporal GDP data

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A Correction to this article was published on 27 July 2023

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Abstract

Many prevalent visualization packages can be used to visualize the GDP data from different perspectives. However, it is difficult to integrate these visualizations and provide a comprehensive analysis to assist users get deeper insights into the various economic features of GDP data, due to its spatio-temporal and multidimensional attributes. In this paper, we propose a visualization tool for the analysis of spatio-temporal multidimensional GDP data, aiming at the combination of the extraction of economic clusters in a time period and the track of dynamic feature evolutions across time periods. MDS is first employed to reduce the multiple dimensions of GDP data, in which the attributes used to achieve similarity matrix are selected interactively by users, according to their requirements. The 2D coordinates obtained by MDS are further clustered based on a hierarchical clustering scheme, allowing the analysts to visually capture the economic features of interest in a time period. We also design a temporal visualization to visually present the dynamic changes of clusters, which largely helps users track the various evolutions of economic features. In addition, stability is defined to evaluate the disorder of clusters between adjacent time periods and used to map meaningful colors to different glyphs in the visualizations. A rich set of interactions are further provided to help users highlight and explore economic features of interest. We demonstrate the usefulness of our system in two case studies based on a real-world GDP data of China.

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Acknowledgements

The authors would like to thank the anonymous reviewers for their valuable comments. This work was supported by NFS of China Project Nos. 61303133, 61503330, the Zhejiang Provincial Natural Science Foundation Nos. LY18F020024, LQ14F020007, LY15F020014, the National Statistical Scientific Research Project No. 2015LD03, the Zhejiang Science and Technology Plan of China No. 2014C31057 and the First Class Discipline of Zhejiang-A (Zhejiang University of Finance and Economics Statistics).

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Correspondence to Weihua Su.

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Zhou, Z., Li, H., Liu, F. et al. Visual analytics of economic features for multivariate spatio-temporal GDP data. J Vis 21, 337–350 (2018). https://doi.org/10.1007/s12650-017-0449-z

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