Temporal Group Visualization Method for Pesticide Residue Detection Data

Abstract

Abstract: To show and compare more statistics in pesticide residue detection data, a temporal grouping visualization method was proposed. The method divided data set according to the hierarchy of data meaning. The stacked statistical likely figure was designed to watch data and its trending over time. The multidimensional data objects were grouped in the two-dimensional plane space layout. The scattered bubble charts was introduced to express the classification of agricultural products. Stacked bar graph was adopted to show the sequential sampled attribute data. Multi-fold rose diagram was designed to display comparably the pesticide detection result and its temporal attribute. Experimental results and expert evaluations denote that the proposed method can realize data classification and comparison of multiple attribute display and time change. Data contrast and the trend over time can be realized in addition.

Key words: data visualization, pesticide residues data, temporal data, data comparison

CLC Number:

TP391

Chen Hongqian, Fang Yi, Yang Qianyu, Li Hui, Chen Yi. Temporal Group Visualization Method for Pesticide Residue Detection Data[J]. Journal of System Simulation, 2016, 28(10): 2510-2519.

References 13

[1]	Keim D A, Kriegel H P.Visualization Techniques for Mining Large Databases: A Comparison[J]. IEEE Transactions on Knowledge & Data Engineering:(S1041-4347), 1996, 8(6): 923-938.
[2]	任磊, 杜一, 马帅, 等. 大数据可视分析综述[J]. 软件学报, 2014(9): 1909-1936.
[3]	Ahlberg C, Shneiderman B.Visual Information Seeking: Tight Coupling of Dynamic Query Filters with Starfield Displays[C]// Proceedings, Conference on Human Factors in Computing Systems, CHI 1994, Boston, Massachusetts, USA, April 24-28, 1994. 1994: 7-13.
[4]	Ward M O.XmdvTool: integrating multiple methods for visualizing multivariate data [C]// Conference on Visualization. USA: IEEE Computer Society Press, 1994: 326-333.
[5]	Tatu A, Albuquerque G, Eisemann M, et al.Automated Analytical Methods to Support Visual Exploration of High-Dimensional Data[J]. IEEE Transactions on Visualization & Computer Graphics (S1077-2626), 2011, 17(5): 584-597.
[6]	Elmqvist N, Dragicevic P, Fekete J D.Rolling the dice: multidimensional visual exploration using scatterplot matrix navigation.[J]. IEEE Transactions on Visualization & Computer Graphics (S1077-2626), 2008, 14(6): 1141-1148.
[7]	Aigner W, Miksch S, Müller W, et al.Visual methods for analyzing time-oriented data.[J]. IEEE Transactions on Visualization & Computer Graphics (S1077-2626), 2008, 14(1): 47-60.
[8]	Havre S, Hetzler E, Whitney P, et al.ThemeRiver: visualizing thematic changes in large document collections[J]. IEEE Transactions on Visualization & Computer Graphics (S1077-2626), 2010, 8(1): 9-20.
[9]	Carlis J V.Interactive visualization of serial periodic data[C]// ACM Symposium on User Interface Software & Technology. USA: ACM, 2000: 29-38.
[10]	Plaisant C, Milash B, Rose A, et al.LifeLines: visualizing personal histories[C]// Readings in information visualization. USA: Morgan Kaufmann Publishers Inc., 1999: 221-227.
[11]	Xie C, Chen W, Huang X, et al.VAET: A Visual Analytics Approach for E-Transactions Time-Series.[J]. IEEE Transactions on Visualization & Computer Graphics (S1077-2626), 2014, 20(12): 1743-1752.
[12]	Albo Y, Lanir J, Bak P, et al.Off the Radar: Comparative Evaluation of Radial Visualization Solutions for Composite Indicators.[J]. IEEE Transactions on Visualization & Computer Graphics (S1077-2626), 2016, 22(1): 569-578.
[13]	Bach B, Shi C, Heulot N, et al.Time Curves: Folding Time to Visualize Patterns of Temporal Evolution in Data.[J]. IEEE Tran Sactions on Visualization & Computer Graphics (S1077-2626), 2015, 22(1): 1-10.