Spatial Predictive Modeling with R

• Main Author: Li, Jin
• Format: eBook
• Language: English
• Published: United States CRC Press 2022; CRC Press LLC; Chapman & Hall
• Edition: 1
• Subjects: ENVIROnetBASE; Environmental; ENVIRONMENTALSCIENCEnetBASE; Geostatistics; Machine learning; Probabilities & applied mathematics; R (Computer program language); SCI-TECHnetBASE; Spatial analysis (Statistics); Spatio-temporal; Statistical Theory & Methods; Statistics & Computing; STATSnetBASE; STMnetBASE; Visualization; Spatio-temporal; Visualization; Spatial Predictive Modeling; Grid Data Set; Data Set; Hybrid Method; SVM Regression Model; Variable Selection Methods; Sub-data Set; Spatial Interpolation Methods; Numerous Hybrid Methods; Topographic Position Index; Predictive Error; Optimal Alpha; Environmental; Geostatistics; Stratified Random Sampling Method; Optimal Predictive Model; Average Predictive Accuracy; Predictive Accuracy; Variogram Modeling; Predictive Variables; 10-fold Cross-validation; Spatial Predictions; Machine learning; RF Tree; Anisotropy Parameters; RF Model; Search Window; Gstat Package

The book is designed to be very accessible, with a focus on methods, examples, and computing, and theoretical details kept to an absolute minimum. It could be used as a reference for researchers and practitioners in industry. It could also be used as a course text for graduate students of spatial st...

Table of Contents:

• Cover -- Half Title -- Title -- Copyright -- Contents -- Preface -- Author Bio -- 1. Data acquisition, data quality control, and spatial reference systems -- 1.1. Acquiring data for spatial predictive modeling -- 1.1.1. Non-random sampling -- 1.1.2. Unstratified random sampling -- 1.1.3. Stratified random sampling design -- 1.1.4. Stratified random sampling design with prior information -- 1.2. Data quality control -- 1.2.1. Accuracy of location information -- 1.2.2. Sampling methods -- 1.2.3. Sample duplications at the same location -- 1.2.4. Sample quality -- 1.2.5. Samples with missing values -- 1.2.6. Data accuracy -- 1.3. Spatial data types and spatial reference systems -- 1.3.1. Spatial data types -- 1.3.2. Spatial reference systems -- 2. Predictive variables and exploratory analysis -- 2.1. Principles for pre-selection of predictive variables and limitations -- 2.1.1. Principles -- 2.1.2. Availability of causal variables -- 2.1.3. Hidden predictive variables -- 2.1.4. Limitations -- 2.2. Predictive variables -- 2.2.1. Predictive variables in terrestrial environmental sciences -- 2.2.2. Predictive variables in marine environmental sciences -- 2.3. Exploratory analysis -- 2.3.1. Exploratory analysis for non-machine learning methods -- 2.3.2. Exploratory analysis for machine learning methods -- 2.3.3. Exploratory analysis for hybrid methods -- 3. Model evaluation and validation -- 3.1. Predictive errors, observational errors, and true predictive errors -- 3.1.1. Observed values and predicted values -- 3.1.2. Relationships of predictive error with observational error and true predictive error -- 3.2. Accuracy and error measures for predictive models -- 3.2.1. Accuracy and error measures for numerical data -- 3.2.2. Accuracy and error measures for categorical data -- 3.3. R functions for accuracy and error measures -- 3.3.1. Function pred.acc
• 10.6. Hybrid methods of GLM, OK, and IDW -- 10.6.1. Variable selection and parameter optimization based on predictive accuracy -- 10.6.2. Predictive accuracy -- 10.6.3. Predictions -- 10.7. Hybrid method of GLS and IDW -- 10.7.1. Variable selection and parameter optimization based on predictive accuracy -- 10.7.2. Predictive accuracy -- 10.7.3. Predictions -- 10.8. Hybrid method of GLS and OK -- 10.8.1. Variable selection and parameter optimization based on predictive accuracy -- 10.8.2. Predictive accuracy -- 10.8.3. Predictions -- 10.9. Hybrid methods of GLS, OK, and IDW -- 10.9.1. Variable selection and parameter optimization based on predictive accuracy -- 10.9.2. Predictive accuracy -- 10.9.3. Predictions -- 11. Hybrids of machine learning methods with geostatistical methods -- 11.1. Hybrid method of RF and IDW -- 11.1.1. Variable selection and parameter optimization based on predictive accuracy -- 11.1.2. Predictive accuracy -- 11.1.3. Predictions -- 11.1.4. A note on RFIDW -- 11.2. Hybrid method of RF and OK -- 11.2.1. Variable selection and parameter optimization based on predictive accuracy -- 11.2.2. Predictive accuracy -- 11.2.3. Predictions -- 11.2.4. Notes on RFOK -- 11.3. Hybrid methods of RF, OK, and IDW -- 11.3.1. Variable selection and parameter optimization based on predictive accuracy -- 11.3.2. Predictive accuracy -- 11.3.3. Predictions -- 11.3.4. A note on RFOKRFIDW -- 11.4. Hybrid method of GBM and IDW -- 11.4.1. Variable selection and parameter optimization based on predictive accuracy -- 11.4.2. Predictive accuracy -- 11.4.3. Predictions -- 11.5. Hybrid method of GBM and OK -- 11.5.1. Variable selection and parameter optimization based on predictive accuracy -- 11.5.2. Predictive accuracy -- 11.5.3. Predictions -- 11.6. Hybrid methods of GBM, OK, and IDW
• 3.3.2. Function vecv -- 3.3.3. Tovecv -- 3.4. Model validation -- 3.4.1. Validation methods -- 3.4.2. Validation functions in R -- 3.4.3. Effects of randomness associated of cross-validation methods on predictive accuracy assessments -- 3.4.4. Procedure for the assessment of the performance of predictive models -- 4. Mathematical spatial interpolation methods -- 4.1. Inverse distance weighted -- 4.1.1. Implementation of IDW in gstat -- 4.1.2. Parameter optimization for IDW -- 4.1.3. Predictive accuracy of IDW with the optimal parameters -- 4.1.4. Predictions of IDW -- 4.2. Nearest neighbors -- 4.2.1. Implementation of NN in gstat -- 4.2.2. Predictive accuracy of NN -- 4.2.3. Predictions of NN -- 4.3. K nearest neighbors -- 4.3.1. Parameter optimization for KNN -- 4.3.2. Predictive accuracy of KNN with the optimal parameter -- 4.3.3. Predictions of KNN -- 5. Univariate geostatistical methods -- 5.1. Variogram modeling -- 5.1.1. Concepts for variogram modeling -- 5.1.2. Variogram modeling and variogram model selection -- 5.2. Simple Kriging -- 5.2.1. Implementation of SK in krige -- 5.2.2. Parameter optimization for SK -- 5.2.3. Predictive accuracy of SK with the optimal parameters -- 5.2.4. SK predictions and variances -- 5.3. Ordinary kriging -- 5.3.1. Implementation of OK in gstat -- 5.3.2. Implementation of OK in krige -- 5.3.3. Parameter optimization for OK -- 5.3.4. Predictive accuracy of OK with the optimal parameters -- 5.3.5. OK predictions and variances -- 5.4. Universal kriging -- 5.4.1. Variogram modeling without anisotropy for UK -- 5.4.2. Variogram modeling with anisotropy for UK -- 5.4.3. Implementation of UK in krige with anisotropy -- 5.4.4. Parameter optimization for UK -- 5.4.5. Predictive accuracy of UK with the optimal parameters -- 5.4.6. UK predictions and variances -- 5.5. Block kriging -- 6. Multivariate geostatistical methods
• 6.1. Simple cokriging -- 6.1.1. Data normality and correlation -- 6.1.2. Parameter optimization for SCK -- 6.1.3. Predictive accuracy of SCK with the optimal parameters -- 6.1.4. SCK predictions and variances -- 6.2. Ordinary cokriging -- 6.2.1. Data requirements -- 6.2.2. Parameter optimization for OCK -- 6.2.3. Predictive accuracy of OCK with the optimal parameters -- 6.2.4. OCK predictions and variances -- 6.3. Kriging with an external drift -- 6.3.1. Application of KED -- 6.3.2. Variable selection and parameter optimization for KED -- 6.3.3. Predictive accuracy of KED -- 6.3.4. KED predictions and variances -- 7. Modern statistical methods -- 7.1. Linear models -- 7.1.1. Relationships of response variable with predictive variables -- 7.1.2. Implementation of LM in lm -- 7.1.3. Model selection based on likelihood methods -- 7.1.4. Variable selection based on predictive accuracy -- 7.1.5. Predictive accuracy -- 7.1.6. Predictions and standard errors -- 7.2. Trend surface analysis -- 7.2.1. Implementation of TSA in lm -- 7.2.2. Variable selection -- 7.2.3. Predictive accuracy -- 7.2.4. Predictions and standard errors -- 7.3. Thin plate splines -- 7.3.1. Estimation of smoothing parameter lambda -- 7.3.2. Implementation of TPS in Tps -- 7.3.3. Varible selection and parameter optimization for TPS -- 7.3.4. Predictive accuracy -- 7.3.5. Predictions -- 7.4. Generalized linear models -- 7.4.1. Implementation of GLM in glm -- 7.4.2. Implementation of GLM in glmnet -- 7.4.3. Variable selection -- 7.4.4. Parameter estimation for glmnet -- 7.4.5. Predictive accuracy -- 7.4.6. Spatial predictions and standard errors -- 7.5. Generalized least squares -- 7.5.1. Implementation of GLS in gls -- 7.5.2. Variable selection for GLS -- 7.5.3. Predictive accuracy -- 7.5.4. Predictions -- 8. Tree-based machine learning methods -- 8.1. Classification and regression trees
• 8.1.1. Implementation of CART in the function rpart -- 8.1.2. Implementation of CART in the function tree -- 8.2. Random forest -- 8.2.1. Application of RF -- 8.2.2. Variable selection for RF -- 8.2.3. Predictive accuracy of the RF models developed from variable selection methods -- 8.2.4. Comparison of variable selection methods -- 8.2.5. Predictions of RF -- 8.2.6. Notes on RF -- 8.3. Generalized boosted regression modeling -- 8.3.1. Application of GBM -- 8.3.2. Variable selection for GBM -- 8.3.3. Parameter optimization for GBM models -- 8.3.4. Predictive accuracy of GBM -- 8.3.5. Partial dependence plots for GBM -- 8.3.6. Predictions of GBM -- 9. Support vector machines -- 9.1. Application of SVM -- 9.2. Variable selection for SVM -- 9.3. Parameter optimization for SVM models -- 9.4. Predictive accuracy of SVM models -- 9.5. Predictions of SVM -- 9.6. Further modeling methods -- 10. Hybrids of modern statistical methods with geostatistical methods -- 10.1. Hybrid method of LM and IDW -- 10.1.1. Variable selection and parameter optimization based on predictive accuracy -- 10.1.2. Predictive accuracy -- 10.1.3. Predictions -- 10.2. Hybrid method of LM and OK -- 10.2.1. Variable selection and parameter optimization based on predictive accuracy -- 10.2.2. Predictive accuracy -- 10.2.3. Predictions -- 10.3. Hybrid methods of LM, OK, and IDW -- 10.3.1. Variable selection and parameter optimization based on predictive accuracy -- 10.3.2. Predictive accuracy -- 10.3.3. Predictions -- 10.4. Hybrid method of GLM and IDW -- 10.4.1. Variable selection and parameter optimization based on predictive accuracy -- 10.4.2. Predictive accuracy -- 10.4.3. Predictions -- 10.5. Hybrid method of GLM and OK -- 10.5.1. Variable selection and parameter optimization based on predictive accuracy -- 10.5.2. Predictive accuracy -- 10.5.3. Predictions
• 11.6.1. Variable selection and parameter optimization based on predictive accuracy