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li j spatial predictive modelling r 2022

Type:: E-books
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Textbook in PDF format Spatial predictive modeling (SPM) is an emerging discipline in applied sciences, playing a key role in the generation of spatial predictions in various disciplines. SPM refers to preparing relevant data, developing optimal predictive models based on point data, and then generating spatial predictions. This book aims to systematically introduce the entire process of SPM as a discipline. The process contains data acquisition, spatial predictive methods and variable selection, parameter optimization, accuracy assessment, and the generation and visualization of spatial predictions, where spatial predictive methods are from geostatistics, modern statistics, and machine learning. The key features of this book are Systematically introducing major components of SPM process. Novel hybrid methods (228 hybrids plus numerous variants) of modern statistical methods or machine learning methods with mathematical and/or univariate geostatistical methods. Novel predictive accuracy-based variable selection techniques for spatial predictive methods. Predictive accuracy-based parameter/model optimization. Reproducible examples for SPM of various data types in R. Data acquisition,data quality control, and spatial reference systems Predictive variables and exploratory analysis Model evaluation and validation Mathematical spatial interpolation methods Univariate geostatistical methods Multivariate geostatistical methods Modern statistical methods Tree-based machine learning methods Support vector machines Hybrids of modern statistical methods with geostatistical methods Hybrids of machine learning methods with geostatistical methods Applications and comparisons of spatial predictive methods Appendix

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Li J. Spatial Predictive Modelling with R 2022.pdf 75.5 MB

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li j spatial predictive modelling r 2022

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