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.

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

Maximum residue limit (MRL) standard which specifies the highest level of every pesticide residue in different agricultural products plays a critical role in food safety. However, such standards which related to the characteristics of pesticides and the classification of agricultural products which organized into a hierarchical structure are complex and vary widely across different regions or countries. So it is a big challenge to compare multi-regional MRL standard data comprehensively. In this paper, we present a multi-comparable visual analytic approach for complex hierarchical data and a visual analytics system (McVA) to support multiple comparison and evaluation of MRL standard. With a cooperative multi-view visual design, our proposed approach links the hierarchies of MRL datasets and provides the capacity for comparison at different levels and dimensions. We also introduce a metric model for evaluating the completeness and strictness of MRL standards quantitatively. The case study of real problems and the positive feedback from domain experts demonstrate the effectiveness of this approach.

A Multi-comparable visual analytic approach for complex hierarchical data

Maximum residue limit (MRL) standard which specifies the highest level of every pesticide residue in different agricultural products plays a critical role in the food safety. However, such standards which relate to the classification of agricultural products and the characteristics of pesticides are complex and vary widely across different regions or countries. In this work, we present an interactive visual analytics system (McVA) to support multiple comparison and evaluation of MRL data standards. With a cooperative multi-view visual design, our proposed system links the hierarchies of MRL datasets and provides the capacity for comparison at different levels and dimensions. We also introduce a metric model for comprehensive evaluation of the completeness and strictness of an MRL, so that the MRL data with hierarchical structure and multidimensional attributes in the system can be visualized and compared more efficiently. We demonstrate the effectiveness of the approach by a case study of a real problem and positive feedback collected from domain experts.

/pdf/mcva-a-multi-comparison-visual-analysis-system-for-maximum-46xsmb3778.pdf

McVA: A Multi-comparison Visual Analysis System for Maximum Residue Limit Standard in Food Safety

In order to satisfy the requirement of visual analysis for multi-type of hierarchical datasets and their relationships in some fields, such as food safety, a hybrid layout algorithm was proposed for Double Interrelated Tree(DIT) based on node-link and sunburst A node-link tree based on force-directed algorithm and a space-filling tree based on sunburst were used to visualize two types of hierarchical dataset respectively, and tree nodes having association relationships together were connected by a line to form a double interrelated tree In order to reduce visual clutter and edge crossings, a layout optimization algorithm DRA(Detour Route Algorithm) based on path around-on was proposed The optimized double interrelated tree was applied into the pesticide residue detection dataset The layout result can clearly show regional hierarchical information, pesticides' classification information, and relationships between them

Hybrid Layout Algorithm for Double Interrelated Tree

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Hybrid Layout Algorithm for Double Interrelated Tree