Dr Jin Li works at Data2action, Australia as a Founder. He has research experience in spatial predictive modelling, statistical computing, ecological and environmental modelling, and ecology. As a scientist, he worked in the Chinese Academy of Sciences, University of New England, CSIRO, and Geoscience Australia. He was an Associate Editor (Jul 2008-Dec 2015) and an editorial board member (Jan 2016-April 2020) of Acta Oecologica, and a Guest Academic Editor (Mar 2018) and an Academic Editor (May 2018-Apr 2020) of PLOS ONE. He has produced over 100 various publications, developed a number of hybrid methods for spatial predictive modeling, and published four R packages for variable selections and spatial predictive modelling.For further information see https://www.researchgate.net/profile/Jin-Li-74, https://scholar.google.com/citations?user=Jeot53EAAAAJ&hl=en  and https://www.linkedin.com/in/jin-li-01421a68/.

• 1. Data acquisition, data quality control and spatial reference systems2. Predictive variables and exploratory analysis3. Model evaluation and validation4. Mathematical spatial interpolation methods5. Univariate geostatistical methods6. Multivariate geostatistical methods7. Modern statistical methods8. Tree-based machine learning methods9. Support vector machine10. Hybrids of modern statistical methods with mathematical and univariate geostatistical methods11. Hybrids of machine learning methods with mathematical and univariate geostatistical methods12. Applications and comparisons of spatial predictive methodsAppendix A. Data sets used in this book

Dr Jin Li works at Data2action, Australia as a Founder. He has research experience in spatial predictive modelling, statistical computing, ecological and environmental modelling, and ecology. As a scientist, he worked in the Chinese Academy of Sciences, University of New England, CSIRO, and Geoscience Australia. He was an Associate Editor (Jul 2008-Dec 2015) and an editorial board member (Jan 2016-April 2020) of Acta Oecologica, and a Guest Academic Editor (Mar 2018) and an Academic Editor (May 2018-Apr 2020) of PLOS ONE. He has produced over 100 various publications, developed a number of hybrid methods for spatial predictive modeling, and published four R packages for variable selections and spatial predictive modelling.For further information see https://www.researchgate.net/profile/Jin-Li-74, https://scholar.google.com/citations?user=Jeot53EAAAAJ&hl=en  and https://www.linkedin.com/in/jin-li-01421a68/.

• 1. Data acquisition, data quality control and spatial reference systems2. Predictive variables and exploratory analysis3. Model evaluation and validation4. Mathematical spatial interpolation methods5. Univariate geostatistical methods6. Multivariate geostatistical methods7. Modern statistical methods8. Tree-based machine learning methods9. Support vector machine10. Hybrids of modern statistical methods with mathematical and univariate geostatistical methods11. Hybrids of machine learning methods with mathematical and univariate geostatistical methods12. Applications and comparisons of spatial predictive methodsAppendix A. Data sets used in this book

Dr Jin Li works at Data2action, Australia as a Founder. He has research experience in spatial predictive modelling, statistical computing, ecological and environmental modelling, and ecology. As a scientist, he worked in the Chinese Academy of Sciences, University of New England, CSIRO, and Geoscience Australia. He was an Associate Editor (Jul 2008-Dec 2015) and an editorial board member (Jan 2016-April 2020) of Acta Oecologica, and a Guest Academic Editor (Mar 2018) and an Academic Editor (May 2018-Apr 2020) of PLOS ONE. He has produced over 100 various publications, developed a number of hybrid methods for spatial predictive modeling, and published four R packages for variable selections and spatial predictive modelling.For further information see https://www.researchgate.net/profile/Jin-Li-74, https://scholar.google.com/citations?user=Jeot53EAAAAJ&hl=en  and https://www.linkedin.com/in/jin-li-01421a68/.

• 1. Data acquisition, data quality control and spatial reference systems2. Predictive variables and exploratory analysis3. Model evaluation and validation4. Mathematical spatial interpolation methods5. Univariate geostatistical methods6. Multivariate geostatistical methods7. Modern statistical methods8. Tree-based machine learning methods9. Support vector machine10. Hybrids of modern statistical methods with mathematical and univariate geostatistical methods11. Hybrids of machine learning methods with mathematical and univariate geostatistical methods12. Applications and comparisons of spatial predictive methodsAppendix A. Data sets used in this book