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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27 July 2023
A Correction to this paper has been published: https://doi.org/10.1007/s12650-023-00938-y
References
Andrienko G, Andrienko N, Dykes J, Fabrikant SI, Wachowicz M (2008) Geovisualization of dynamics, movement and change: key issues and developing approaches in visualization research. Inf Vis 7(3):173–180
Byron L, Wattenberg M (2008) Stacked graphs-geometry aesthetics. IEEE Trans Vis Comput Gr 14(6):1245–1252
Cui W, Liu S, Tan L, Shi C, Song Y, Gao Z, Qu H, Tong X (2011) Textflow: towards better understanding of evolving topics in text. IEEE Trans Vis Comput Gr 17(12):2412–2421
Doraiswamy H, Ferreira N, Damoulas T, Freire J, Silva C (2014) Using topological analysis to support event-guided exploration in urban data. IEEE Trans Vis Comput Gr 20(12):2634–2643
Dou W, Yu L, Wang X, Ma Z, Ribarsky W (2013) Hierarchicaltopics: visually exploring large text collections using topic hierarchies. IEEE Trans Vis Comput Gr 19(12):2002–2011
Dykes J, Mountain D (2003) Seeking structure in records of spatio-temporal behaviour: visualization issues, efforts and applications. Comput Stat Data Anal 43(4):581–603 (data visualization)
Ferreira N, Poco J, Vo H, Freire J, Silva C (2013) Visual exploration of big spatio-temporal urban data: a study of new york city taxi trips. IEEE Trans Vis Comput Gr 19(12):2149–2158
Ge Y, Xiong H, Tuzhilin A, Xiao K, Gruteser M, Pazzani M (2010) An energy-efficient mobile recommender system. In: Proceedings of the 16th ACM SIGKDD international conference on knowledge discovery and data mining, KDD ’10. ACM, New York, pp 899–908
Havre S, Hetzler E, Whitney P, Nowell L (2002) Themeriver: visualizing thematic changes in large document collections. IEEE Trans Vis Comput Gr 8(1):9–20
Huaxiong Z, Fang Y (2011) Research on regional economic and industrial structure based on dynamic shift-share analysis: an empirical analysis of six provinces in central china. In: 2011 international conference on business computing and global informatization (BCGIN), pp 62–66
Kulawiak M, Lubniewski Z, Bikonis K, Stepnowski A (2009) Geographical information system for analysis of critical infrastructures and their hazards due to terrorism, man-originated catastrophes and natural disasters for the city of gdansk. In: Information fusion and geographic, information systems, pp 251–262
Liran X, Meiying J, Yunju N, Baoqiang L (2014) Study on industrial structure evolutionary of central yunnan economic zone in china - analysis based on industrial structure conversion coefficient and gis spacial analysis method. In: 2014 7th international conference on intelligent computation technology and automation (ICICTA), pp 701–704
Liu H, Gao Y, Lu L, Liu S, Qu H, Ni L (2011) Visual analysis of route diversity. In: 2011 IEEE conference on visual analytics science and technology (VAST), pp 171–180
Liu S, Cui W, Wu Y, Liu M (2014) A survey on information visualization: recent advances and challenges. Vis Comput 30(12):1373–1393
Lu F, Cao Z (2008) Empirical analysis on the contribution of the industrial structure to China’s economic growth. In: International symposium on intelligent information technology application workshops, 2008, IITAW ’08, pp 886–889
Macqueen J (1967) Some methods for classification and analysis of multivariate observations. In: 5th Berkeley symposium on mathematical statistics and probability, pp 281–297
Mennis J (2006) Mapping the results of geographically weighted regression. Cartogr J 43(2):171–179
Pilhofer A, Gribov A, Unwin A (2012) Comparing clusterings using Bertin’s idea. IEEE Trans Vis Comput Gr 18(12):2506–2515
Reda K, Tantipathananandh C, Johnson A, Leigh J, Berger-Wolf T (2011) Visualizing the evolution of community structures in dynamic social networks. In: Proceedings of the 13th Eurographics/IEEE—VGTC conference on visualization, EuroVis’11, Chichester, UK, pp 1061–1070
Sallaberry A, Pecheur N, Bringay S, Roche M, Teisseire M (2011) SequenceViewer: visualization of genes sequences. In: MedInfo’2010: international congress on medical informatics, poster
Scholz RW, Lu Y (2014) Detection of dynamic activity patterns at a collective level from large-volume trajectory data. Int J Geogr Inf Sci 28(5):946–963
Shi C, Cui W, Liu S, Xu P, Chen W, Qu H (2012) Rankexplorer: visualization of ranking changes in large time series data. IEEE Trans Vis Comput Gr 18(12):2669–2678
Shi R, Yang M, Zhao Y, Zhou F, Huang W, Zhang S (2016) A matrix-based visualization system for network traffic forensics. IEEE Syst J 10(4):1350–1360
Sun G, Wu Y, Liu S, Peng T-Q, Zhu J, Liang R (2014) Evoriver: visual analysis of topic coopetition on social media. IEEE Trans Vis Comput Gr 20(12):1753–1762
Theus M (2002) Interactive data visualization using Mondrian. J Stat Softw 07(i11):1–9
Turkay C, Slingsby A, Hauser H, Wood J, Dykes J (2014) Attribute signatures: dynamic visual summaries for analyzing multivariate geographical data. IEEE Trans Vis Comput Gr 20(12):2033–2042
Vanier DJ (2004) Geographic information systems (gis) as an integrated decision support tool for municipal infrastructure asset management. In: Proceedings of the CIB 2004 triennial congress, pp 2–9
Vehlow C, Beck F, Auwärter P, Weiskopf D (2015) Visualizing the evolution of communities in dynamic graphs. In: Computer Graphics Forum
Xu P, Wu Y, Wei E, Peng T-Q, Liu S, Zhu JJH, Qu H (2013) Visual analysis of topic competition on social media. IEEE Trans Vis Comput Gr 19(12):2012–2021
Zhang J, Yanli E, Ma J, Zhao Y, Xu B, Sun L, Chen J, Yuan X (2014) Visual analysis of public utility service problems in a metropolis. IEEE Trans Vis Comput Gr 20(12):1843–1852
Zhao Y, Liang X, Fan X, Wang Y, Yang M, Zhou F (2014) Mvsec: multi-perspective and deductive visual analytics on heterogeneous network security data. J Vis 17(3):181–196
Zhou F, Huang W, Zhao Y, Shi Y, Liang X, Fan X (2015) Entvis: a visual analytic tool for entropy-based network traffic anomaly detection. IEEE Comput Gr Appl 35(6):42–50
Zilong J (2014) Analysis of agglomeration and equilibrium characteristics of regional economic in china with spatial visualization in science computing technique. In: 2014 sixth international conference on measuring technology and mechatronics automation (ICMTMA), pp 91–94
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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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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DOI: https://doi.org/10.1007/s12650-017-0449-z