Correlated Spatiotemporal Data Modeling Using Generalized Additive Mixed Model and Bivariate Smoothing Techniques
Science Journal of Applied Mathematics and Statistics
Volume 6, Issue 2, April 2018, Pages: 49-57
Received: Apr. 4, 2018; Accepted: Apr. 28, 2018; Published: May 22, 2018
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Sabyasachi Mukherjee, Department of Mathematics, NSHM Knowledge Campus, Durgapur, India
Tapan Kumar Garai, Department of Agriculture, Government of West Bengal, Medinipur, India
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Background: The present article tries to analyze a correlated spatiotemporal data using an advance regression modeling techniques. Spatiotemporal data contains the information of both space and time simultaneously. Naturally, it is very much complicated and not easy to model. This article focuses on some modeling techniques to analyze a correlated spatiotemporal agricultural dataset. This dataset contains information of soil parameters for five years across the twenty six different locations with their geographical status in term of longitude and latitude. Soil pH and fertility index are the two major limiting factors in agriculture. These two parameters are governed by many other factors viz. fertilizer use, cropping intensity, soil type, geographical location, soil health management etc. Objective: The present study has been set up to explore whether there is any spatial gradient in the average pH levels across the geographical locations while fertility index and cropping intensity are acting as possible confounder. Methods: Soil pH is the response variable which varies with respect to time and space generally has a correlated structure. Besides this, some random effects component with fixed effects having a nonlinear association with the response is observed here. Generalized additive mixed model (GAMM) regression and Bivariate Smoothing techniques have been exercised to arrive at a meaningful conclusion. Conclusions: It is found that the pH value varies with change in latitude. Besides this, year, fertility index of available potassium and phosphate are also significant cofactors of this study. Final model has been selected through minimum AIC value (204.9) and model checking plots.
Spatiotemporal Data, Spatial Gradient, PH, Fertility Index, Cropping Intensity, GAMM, Bivariate Smoothing
To cite this article
Sabyasachi Mukherjee, Tapan Kumar Garai, Correlated Spatiotemporal Data Modeling Using Generalized Additive Mixed Model and Bivariate Smoothing Techniques, Science Journal of Applied Mathematics and Statistics. Vol. 6, No. 2, 2018, pp. 49-57. doi: 10.11648/j.sjams.20180602.11
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This article is an open access article distributed under the Creative Commons Attribution License ( which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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