Spatial Predictive Modelling with R:
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 prediction...
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| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Boca Raton ; London ; New York
CRC Press, Taylor & Francis Group
2022
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| Ausgabe: | First edition |
| Schriftenreihe: | A Chapman and Hall book
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| Schlagworte: | |
| Online-Zugang: | UBT01 URL des Erstveröffentlichers |
| Zusammenfassung: | 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: ⁰́ØSystematically introducing major components of SPM process.⁰́ØNovel hybrid methods (228 hybrids plus numerous variants) of modern statistical methods or machine learning methods with mathematical and/or univariate geostatistical methods.⁰́ØNovel predictive accuracy-based variable selection techniques for spatial predictive methods.⁰́ØPredictive accuracy-based parameter/model optimization.⁰́ØReproducible 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. |
| Beschreibung: | 1 Online-Ressource (xix, 383 Seiten) |
| ISBN: | 9781003091776 9781000542639 9781000542608 |
| DOI: | 10.1201/9781003091776 |
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| 520 | 3 | |a 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: ⁰́ØSystematically introducing major components of SPM process.⁰́ØNovel hybrid methods (228 hybrids plus numerous variants) of modern statistical methods or machine learning methods with mathematical and/or univariate geostatistical methods.⁰́ØNovel predictive accuracy-based variable selection techniques for spatial predictive methods.⁰́ØPredictive accuracy-based parameter/model optimization.⁰́ØReproducible 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. | |
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Datensatz im Suchindex
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| author | Li, Jin |
| author_facet | Li, Jin |
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| author_sort | Li, Jin |
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| contents | 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 |
| ctrlnum | (OCoLC)1302313190 (DE-599)BVBBV047859983 |
| discipline | Geographie |
| discipline_str_mv | Geographie |
| doi_str_mv | 10.1201/9781003091776 |
| edition | First edition |
| format | Electronic eBook |
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| id | DE-604.BV047859983 |
| illustrated | Not Illustrated |
| index_date | 2024-07-03T19:17:28Z |
| indexdate | 2024-07-10T09:23:20Z |
| institution | BVB |
| isbn | 9781003091776 9781000542639 9781000542608 |
| language | English |
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| publisher | CRC Press, Taylor & Francis Group |
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| spelling | Li, Jin Verfasser aut Spatial Predictive Modelling with R Jin Li First edition Boca Raton ; London ; New York CRC Press, Taylor & Francis Group 2022 1 Online-Ressource (xix, 383 Seiten) txt rdacontent c rdamedia cr rdacarrier A Chapman and Hall book 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 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: ⁰́ØSystematically introducing major components of SPM process.⁰́ØNovel hybrid methods (228 hybrids plus numerous variants) of modern statistical methods or machine learning methods with mathematical and/or univariate geostatistical methods.⁰́ØNovel predictive accuracy-based variable selection techniques for spatial predictive methods.⁰́ØPredictive accuracy-based parameter/model optimization.⁰́ØReproducible 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. Prognoseverfahren (DE-588)4358095-6 gnd rswk-swf Raumstruktur (DE-588)4130125-0 gnd rswk-swf R Programm (DE-588)4705956-4 gnd rswk-swf Computer simulation R (Computer program language) MATHEMATICS / Probability & Statistics / Regression Analysis Electronic books Raumstruktur (DE-588)4130125-0 s Prognoseverfahren (DE-588)4358095-6 s R Programm (DE-588)4705956-4 s DE-604 Erscheint auch als Druck-Ausgabe, Hardcover 9780367550547 Erscheint auch als Druck-Ausgabe, Paperback 9780367550561 https://doi.org/10.1201/9781003091776 Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | Li, Jin Spatial Predictive Modelling with R 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 Prognoseverfahren (DE-588)4358095-6 gnd Raumstruktur (DE-588)4130125-0 gnd R Programm (DE-588)4705956-4 gnd |
| subject_GND | (DE-588)4358095-6 (DE-588)4130125-0 (DE-588)4705956-4 |
| title | Spatial Predictive Modelling with R |
| title_auth | Spatial Predictive Modelling with R |
| title_exact_search | Spatial Predictive Modelling with R |
| title_exact_search_txtP | Spatial Predictive Modelling with R |
| title_full | Spatial Predictive Modelling with R Jin Li |
| title_fullStr | Spatial Predictive Modelling with R Jin Li |
| title_full_unstemmed | Spatial Predictive Modelling with R Jin Li |
| title_short | Spatial Predictive Modelling with R |
| title_sort | spatial predictive modelling with r |
| topic | Prognoseverfahren (DE-588)4358095-6 gnd Raumstruktur (DE-588)4130125-0 gnd R Programm (DE-588)4705956-4 gnd |
| topic_facet | Prognoseverfahren Raumstruktur R Programm |
| url | https://doi.org/10.1201/9781003091776 |
| work_keys_str_mv | AT lijin spatialpredictivemodellingwithr |