Evaluation of Data Transformations and Validation of a Spatial Model for Spatial Dependency of Trialeurodes vaporariorum Populations in a Cherry Tomato Greenhouse

Park, Jung-Joon; Shin, Key Il; Cho, Ki Jong

doi:10.1016/s1226-8615(08)60229-0

Cited by 10 publications

(13 citation statements)

References 20 publications

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“…These methods can produce more stable modeling of variograms and reduce the effects of hotspots (Fu et al 2010). This study compared the logarithmic, Box-Cox, and normal score on variogram modeling and kriging interpolation and found that the Box-Cox and normal score were better than the logarithmic and the rank order transformation, which generated more robust empirical variograms and improved the accuracy of interpolation (Park et al, 2004;Wu et al, 2006;Wu et al, 2011). Although the normal score and Box-Cox transformations may not always produce normally distributed data, the Johnson transformation can transform data to an approximate normal distribution, and this method has been used in a variety of research fields (Heie et al 2010).…”

Section: Discussionmentioning

confidence: 96%

Accuracy and uncertainty analysis of soil Bbf spatial distribution estimation at a coking plant-contaminated site based on normalization geostatistical technologies

Niu

Zhang

et al. 2015

Environ Sci Pollut Res

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Data distribution is usually skewed severely by the presence of hot spots in contaminated sites. This causes difficulties for accurate geostatistical data transformation. Three types of typical normal distribution transformation methods termed the normal score, Johnson, and Box-Cox transformations were applied to compare the effects of spatial interpolation with normal distribution transformation data of benzo(b)fluoranthene in a large-scale coking plant-contaminated site in north China. Three normal transformation methods decreased the skewness and kurtosis of the benzo(b)fluoranthene, and all the transformed data passed the Kolmogorov-Smirnov test threshold. Cross validation showed that Johnson ordinary kriging has a minimum root-mean-square error of 1.17 and a mean error of 0.19, which was more accurate than the other two models. The area with fewer sampling points and that with high levels of contamination showed the largest prediction standard errors based on the Johnson ordinary kriging prediction map. We introduce an ideal normal transformation method prior to geostatistical estimation for severely skewed data, which enhances the reliability of risk estimation and improves the accuracy for determination of remediation boundaries.

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Section: Discussionmentioning

confidence: 96%

Accuracy and uncertainty analysis of soil Bbf spatial distribution estimation at a coking plant-contaminated site based on normalization geostatistical technologies

Niu

Zhang

et al. 2015

Environ Sci Pollut Res

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“…Park et al (2004) demonstrated that natural logarithmic (k = 0) and fourth root transformations (k = 1/4) with the actual value of +0.5 were better than any other transformations for aggregated insect pest populations. Thus, we applied these two data transformations to the raw data sets before the outlier analysis was conducted.…”

Section: Data Transformationsmentioning

confidence: 94%

“…Park et al (2004) reported that no data transformations for analyzing the geostatistics were satisfactory for correcting data sets when the empty grids (zero counts) in the sample data consisted of >20% of the total samples.…”

Section: Data Description and Outlier Cleaningmentioning

confidence: 99%

“…The spatial dependency of insect pests should be taken into consideration when ecologically based pest management programs are implemented because most insect pests are observed at specific points in their habitats and appear to be concentrated within a given spatial distribution (Southwood 1978;Binns and Nyrop 1992;Park et al 2004). To implement strategies to control insects, it is often necessary to characterize whether the insect species is equally abundant throughout the entire sampling space and whether its abundance at sampling locations is positively associated with the occurrence of certain spatially aggregated environmental conditions (Nansen et al 2003).…”

Section: Introductionmentioning

confidence: 99%

“…Box-Cox data transformations have been widely accepted as effective data transformation methods, conferring normality on skewed data (Box and Cox 1964). Park et al (2004) evaluated mathematical restricted variogram models using greenhouse whitefly data after conducting various data transformations.…”

Section: Introductionmentioning

confidence: 99%

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Detecting and cleaning outliers for robust estimation of variogram models in insect count data

Park

Shin

Lee

et al. 2011

Ecological Research

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Outlier detection and cleaning procedures were evaluated to estimate mathematical restricted variogram models with discrete insect population count data. Because variogram modeling is significantly affected by outliers, methods to detect and clean outliers from data sets are critical for proper variogram modeling. In this study, we examined spatial data in the form of discrete measurements of insect counts on a rectangular grid. Two well-known insect pest population data were analyzed; one data set was the western flower thrips, Frankliniella occidentalis (Pergande) on greenhouse cucumbers and the other was the greenhouse whitefly, Trialeurodes vaporariorum (Westwood) on greenhouse cherry tomatoes. A spatial additive outlier model was constructed to detect outliers in both the isolated and patchy spatial distributions of outliers, and the outliers were cleaned with the neighboring median cleaner. To analyze the effect of outliers, we compared the relative nugget effects of data cleaned of outliers and data still containing outliers after transformation. In addition, the correlation coefficients between the actual and predicted values were compared using the leave-one-out crossvalidation method with data cleaned of outliers and non-cleaned data after unbiased back transformation. The outlier detection and cleaning procedure improved geostatistical analysis, particularly by reducing the nugget effect, which greatly impacts the prediction variance of kriging. Consequently, the outlier detection and cleaning procedures used here improved the results of geostatistical analysis with highly skewed and extremely fluctuating data, such as insect counts.

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Host preferences and biotic potential of Trialeurodes vaporariorum and Bemisia tabaci (Hemiptera: Aleyrodidae) in tomato and pepper

Lorenzo¹,

Grille²,

Basso³

et al. 2016

Arthropod-Plant Interactions

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Evaluation of Data Transformations and Validation of a Spatial Model for Spatial Dependency of Trialeurodes vaporariorum Populations in a Cherry Tomato Greenhouse

Cited by 10 publications

References 20 publications

Accuracy and uncertainty analysis of soil Bbf spatial distribution estimation at a coking plant-contaminated site based on normalization geostatistical technologies

Accuracy and uncertainty analysis of soil Bbf spatial distribution estimation at a coking plant-contaminated site based on normalization geostatistical technologies

Detecting and cleaning outliers for robust estimation of variogram models in insect count data

Host preferences and biotic potential of Trialeurodes vaporariorum and Bemisia tabaci (Hemiptera: Aleyrodidae) in tomato and pepper

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