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: âSystematically introducing major components of SPM process. âNovel hybrid methods (228 hybrids plus numerous variants) of modern statistical methods or machine learning methods with mathematical and/or univariate geostatistical methods. âNovel predictive accuracy-based variable selection techniques for spatial predictive methods. âPredictive accuracy-based parameter/model optimization. âReproducible examples for SPM of various data types in R. This book provides guidelines recommendations and reproducible examples for developing optimal predictive models by considering various components and associated factors for quality-improved spatial predictions. It provides valuable tools for researchers modelers and university students not only in SPM field but also in other predictive modeling fields. Dr Li has produced over 100 various publications in spatial predictive modelling statistical computing ecological and environmental modelling and ecology developed a number of hybrid methods for SPM and published four R packages for variable selections as well as SPM. . Language: English