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Journal of Visualization

, Volume 22, Issue 6, pp 1257–1280 | Cite as

Visual analysis of impact factors of forest pests and diseases

  • Bo Yang
  • Weiqun CaoEmail author
  • Chengming Tian
Regular Paper
  • 21 Downloads

Abstract

In the field of forest pest and disease research, researchers have combined the experience and data accumulated over many years and conducted long-term and systematic observations of the research object; they have used regression analysis to determine the factors that affect the occurrence of pests and diseases. This traditional approach is time-consuming and highly dependent on expert experience. In this paper, we propose a multicombination multivariable linear regression model to quantitatively describe the multiple linear combinations of relationship between multiple independent variables and a single dependent variable. Based on this model and a data flow model combined with statistical principles and visualization techniques, we propose a multicombination multivariable linear regression visual analysis method to assist researchers in quickly assessing the correlations between the disease indexes of forest diseases and pests and the factors that may affect the pest and disease occurrences. Based on this approach, a multicombination multivariable linear regression visual analysis system was designed and implemented, and the cases of a given forest pest and disease data set were analyzed. It is shown that the multicombination multivariable linear regression visual analysis method can effectively assist researchers in quickly understanding pest and disease data, determining impact factors, and finding relevant laws.

Graphic abstract

Keywords

Forest pests and diseases Multicombination multivariable Linear regression analysis Visual analysis 

Notes

Acknowledgements

The authors would like to thank the forest protection experts from Shanxi Provincial Bureau of Forestry Pest Control and Quarantine for providing valuable feedback and suggestions for this project. The authors wish to thank the reviewers for their comments. This work is supported by the Fundamental Research Funds for the Central Universities (2015ZCQ-XX).

References

  1. Arbuckle JL (2010) IBM SPSS Amos 19 user’s guide. Amos Development Corporation, CrawfordvilleGoogle Scholar
  2. Becker RA, Cleveland WS (1987) Brushing scatterplots. Technometrics 29(2):127–142.  https://doi.org/10.1080/00401706.1987.10488204 MathSciNetCrossRefGoogle Scholar
  3. Benesty J, Chen J, Huang Y, Cohen I (2009) Pearson Correlation Coefficient, pp 1–4. Springer, Berlin.  https://doi.org/10.1007/978-3-642-00296-0_5 Google Scholar
  4. Carr DB, Littlefield RJ, Nicholson WL, Littlefield JS (1987) Scatterplot matrix techniques for large n. J Am Stat Assoc 82(398):424–436.  https://doi.org/10.1080/01621459.1987.10478445 MathSciNetCrossRefGoogle Scholar
  5. Cody R (2007) Learning SAS by example: a programmer’s guide. SAS Institute, USAGoogle Scholar
  6. Di X, Wang H, Yao C, Wang F (1994) Study on the relationship between biomass of combustibles and forest components in the forest surface of the Greater Khingan. For Fire Prev 41(02):16–18Google Scholar
  7. Ferdosi BJ, Roerdink JB (2011)Visualizing high-dimensional structures by dimension ordering and filtering using subspace analysis. In: Computer graphics forum, vol 30, pp 1121–1130. Wiley, New York.  https://doi.org/10.1111/j.1467-8659.2011.01961.x CrossRefGoogle Scholar
  8. Giesen J, Mueller K, Schuberth E, Wang L, Zolliker P (2007) Conjoint analysis to measure the perceived quality in volume rendering. IEEE Trans Vis Comput Graph 13(6):1664–1671.  https://doi.org/10.1109/TVCG.2007.70542 CrossRefGoogle Scholar
  9. Gong D (2009) Applying multiple linear regression to set up growth model of pinus taiwanensis. Prot For Sci Technol (03), 18–20+29.  https://doi.org/10.13601/j.issn.1005-5215.2009.03.025
  10. Gong S (2005) Applied statistics. Tsinghua University Press LTD, BeijingGoogle Scholar
  11. Han J, Kamber M, Pei J (2011) Data mining: concepts and techniques. Elsevier, New YorkzbMATHGoogle Scholar
  12. Inselberg A, Dimsdale B (1990) Parallel coordinates: a tool for visualizing multi-dimensional geometry. In: Proceedings of the first IEEE conference on visualization: visualization ‘90, pp 361–378.  https://doi.org/10.1109/VISUAL.1990.146402
  13. Jia J, He X, Jin Y (2015) Statistics, 6th edn. China Renmin University Press, BeijingGoogle Scholar
  14. Journalist P (2009) The eastern calabash forest farm. Forestry of Shanxi 2009(06):48Google Scholar
  15. Li J, Gao H, Liu S, Zhou L (2014) Relationship between poplar wood and handsheet properties based on multiple linear regression model. J Northeast For Univ 42(02):91–95.  https://doi.org/10.13759/j.cnki.dlxb.2014.02.022 CrossRefGoogle Scholar
  16. Luo L, Li M (2014) Statistics, 2nd edn. Hunan University Press, BeijingGoogle Scholar
  17. Ma J, Hui W, Zhao F (2007) Arceuthobium sichuanense, a parasitic plant attacking spruce in Qinghai province. For Pest Dis 26(01):19–21.  https://doi.org/10.3969/j.issn.1671-0886.2007.01.007 CrossRefGoogle Scholar
  18. McCambridge WF, Hawksworth FG, Edminster CB, Laut JG (1982) Ponderosa Pine mortality resulting from a mountain pine beetle outbreak, vol no 235. USDA.  https://doi.org/10.5962/bhl.title.98611. https://www.biodiversitylibrary.org/item/177384
  19. Musselman LJ (1997) Dwarf mistletoes: biology, pathology, and systematics. Econ Bot 51(1):86–86.  https://doi.org/10.1007/BF02910408 CrossRefGoogle Scholar
  20. Pearson K (2006) Note on regression and inheritance in the case of two parents. Proc R Soc Lond 58:240–242Google Scholar
  21. Qin C, Zhao G, Li Z, Bo Y (2011) Influence analysis of climate change on dendroctonus valens leconte’s survival. Chin Agric Sci Bull 27(19):38–43Google Scholar
  22. Song Y, Yang A, He N (2000) Pest risk analysis of red turpentine bettle (dendroctonus valens). For Pest Dis 19(6):34–37.  https://doi.org/10.3969/j.issn.1671-0886.2000.06.014 CrossRefGoogle Scholar
  23. Wang J, Mueller K (2016) The visual causality analyst: an interactive interface for causal reasoning. IEEE Trans Vis Comput Graph 22(1):230–239.  https://doi.org/10.1109/TVCG.2015.2467931 CrossRefGoogle Scholar
  24. Wang T, Hamann A, Spittlehouse DL, Murdock TQ (2012) Climatewna—high-resolution spatial climate data for western North America. J Appl Meteorol Climatol 51(1):16–29.  https://doi.org/10.1175/JAMC-D-11-043.1 CrossRefGoogle Scholar
  25. Williamson DF, Parker RA, Kendrick JS (1989) The box plot: a simple visual method to interpret data. Ann Intern Med 110(11):916–921CrossRefGoogle Scholar
  26. Xia B (2012) Effects of dwarf mistletoe (Arceuthobium sichuanense) infection on natural spruce forest and key factors of its outbreak in Qinghai province. Dissertation. Beijing Forestry UniversityGoogle Scholar
  27. Xie H (2004) Establishment on growth model of Chinese fir and application of multilinear regression. J Fujian For Sci Technol 31(01):34–37.  https://doi.org/10.3969/j.issn.1002-7351.2004.01.009 CrossRefGoogle Scholar
  28. Zhang F, Jing T, Wang Z, Xie S, Zhang H (2011) Multiple linear regression and discriminant analysis of meteorological factors and pest population density of Cryptorrhynchus lapathi. J Anhui Agric Sci 39(15):9000–9001.  https://doi.org/10.3969/j.issn.0517-6611.2011.15.067 CrossRefGoogle Scholar
  29. Zhang Z, McDonnell KT, Zadok E, Mueller K (2015) Visual correlation analysis of numerical and categorical data on the correlation map. IEEE Trans Vis Comput Graph 21(2):289–303.  https://doi.org/10.1109/TVCG.2014.2350494 CrossRefGoogle Scholar
  30. Zhou Z, Xu Z, Tian C, Luo Y (2007) The biological characteristics and management strategy of spruce dwarf mistletoe. For Pest Dis 26(04):37–39.  https://doi.org/10.3969/j.issn.1671-0886.2007.04.014 CrossRefGoogle Scholar
  31. Zhou S, Shiqian X, Chengyi P (2008) Probability theory and statistics, 4th edn. Higher Education Press, BeijingGoogle Scholar

Copyright information

© The Visualization Society of Japan 2019

Authors and Affiliations

  1. 1.School of Information Science and TechnologyBeijing Forestry UniversityBeijingChina
  2. 2.College of ForestryBeijing Forestry UniversityBeijingChina

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