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Sunburst with ordered nodes based on hierarchical clustering: a visual analyzing method for associated hierarchical pesticide residue data

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Abstract

According to the characteristics of pesticide residue data and analyzing requirements in food safety fields, we presented a visual analyzing method for associated hierarchical data, called sunburst with ordered nodes based on hierarchical clustering (SONHC). SONHC arranged the leaf nodes in sunburst in order using hierarchical clustering algorithm, put the associated dataset as a node in center of the sunburst, and connected it with the associated leaf nodes in sunburst using colored lines. So, it can present not only two hierarchical structures but also the relationships between them. Based on SONHC and some interaction techniques (clicking, contraction and expansion, etc) we developed an associated visual analyzing system (AVAS) for pesticide residues detection results data, which can help users to inspect the hierarchical structure of pesticide and agricultural products and to explore the associations between pesticides and agricultural products, and associations between different pesticides. The results of user experience test showed that SONHC algorithm overperforms than SA and SR algorithm in ULE and ULE’s variance. AVAS system is effective in helping users to analyze the pesticide residues data. Furthermore, SONHC algorithm can also be adopted to analyze associated hierarchical data in other fields, such as finance, insurance and e-commerce.

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Acknowledgments

This work is supported by “Twelfth Five Year Plan” National Science and Technology Support Program (No. 2012BAD29B01-2), the open funding project of State Key Laboratory of Virtual Reality Technology and Systems, Beihang University (Grant No. BUAA-VR-14KF-04), and the funding of Funding Project for Innovation on Science, Technology and Graduate Education in Institutions of Higher Learning Under the Jurisdiction of Beijing Municipality (Grand No. PXM2014_014213_000043). The authors also would like to thank the conference of China Visual Analytics 2014, which provides exchange platform.

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Authors and Affiliations

  1. School of Computer and Information Engineering, Beijing Technology and Business University (BTBU), Beijing, 100048, People’s Republic of China

    Yi Chen, Xinyue Zhang, Yuchao Feng & Hongqian Chen

  2. Key Laboratory of Machine Perception (Ministry of Education) and School of EECS, Peking University, Beijing, China

    Jie Liang

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  1. Yi Chen
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  2. Xinyue Zhang
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  3. Yuchao Feng
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  4. Jie Liang
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  5. Hongqian Chen
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Correspondence to Yi Chen.

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Chen, Y., Zhang, X., Feng, Y. et al. Sunburst with ordered nodes based on hierarchical clustering: a visual analyzing method for associated hierarchical pesticide residue data. J Vis 18, 237–254 (2015). https://doi.org/10.1007/s12650-014-0269-3

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