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...
Saved in:
| Main Author: | |
|---|---|
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Boca Raton ; London ; New York
CRC Press, Taylor & Francis Group
2022
|
| Edition: | First edition |
| Series: | A Chapman and Hall book
|
| Subjects: | |
| Online Access: | UBT01 Volltext |
| Summary: | 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. |
| Physical Description: | 1 Online-Ressource (xix, 383 Seiten) |
| ISBN: | 9781003091776 9781000542639 9781000542608 |
| DOI: | 10.1201/9781003091776 |
Staff View
MARC
| LEADER | 00000nmm a2200000 c 4500 | ||
|---|---|---|---|
| 001 | BV047859983 | ||
| 003 | DE-604 | ||
| 005 | 20220304 | ||
| 007 | cr|uuu---uuuuu | ||
| 008 | 220302s2022 |||| o||u| ||||||eng d | ||
| 020 | |a 9781003091776 |c Online |9 9781003091776 | ||
| 020 | |a 9781000542639 |9 9781000542639 | ||
| 020 | |a 9781000542608 |9 9781000542608 | ||
| 024 | 7 | |a 10.1201/9781003091776 |2 doi | |
| 035 | |a (OCoLC)1302313190 | ||
| 035 | |a (DE-599)BVBBV047859983 | ||
| 040 | |a DE-604 |b ger |e rda | ||
| 041 | 0 | |a eng | |
| 049 | |a DE-703 | ||
| 084 | |a RB 10196 |0 (DE-625)142220:12653 |2 rvk | ||
| 084 | |a RB 10208 |0 (DE-625)142220:12658 |2 rvk | ||
| 100 | 1 | |a Li, Jin |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Spatial Predictive Modelling with R |c Jin Li |
| 250 | |a First edition | ||
| 264 | 1 | |a Boca Raton ; London ; New York |b CRC Press, Taylor & Francis Group |c 2022 | |
| 300 | |a 1 Online-Ressource (xix, 383 Seiten) | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b c |2 rdamedia | ||
| 338 | |b cr |2 rdacarrier | ||
| 490 | 0 | |a A Chapman and Hall book | |
| 505 | 8 | |a 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 | |
| 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. | |
| 650 | 0 | 7 | |a Prognoseverfahren |0 (DE-588)4358095-6 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Raumstruktur |0 (DE-588)4130125-0 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a R |g Programm |0 (DE-588)4705956-4 |2 gnd |9 rswk-swf |
| 653 | 0 | |a Computer simulation | |
| 653 | 0 | |a R (Computer program language) | |
| 653 | 0 | |a MATHEMATICS / Probability & Statistics / Regression Analysis | |
| 653 | 0 | |a Computer simulation | |
| 653 | 0 | |a R (Computer program language) | |
| 653 | 6 | |a Electronic books | |
| 653 | 6 | |a Electronic books | |
| 689 | 0 | 0 | |a Raumstruktur |0 (DE-588)4130125-0 |D s |
| 689 | 0 | 1 | |a Prognoseverfahren |0 (DE-588)4358095-6 |D s |
| 689 | 0 | 2 | |a R |g Programm |0 (DE-588)4705956-4 |D s |
| 689 | 0 | |5 DE-604 | |
| 776 | 0 | 8 | |i Erscheint auch als |n Druck-Ausgabe, Hardcover |z 9780367550547 |
| 776 | 0 | 8 | |i Erscheint auch als |n Druck-Ausgabe, Paperback |z 9780367550561 |
| 856 | 4 | 0 | |u https://doi.org/10.1201/9781003091776 |x Verlag |z URL des Erstveröffentlichers |3 Volltext |
| 912 | |a ZDB-7-TFC | ||
| 999 | |a oai:aleph.bib-bvb.de:BVB01-033242622 | ||
| 966 | e | |u https://doi.org/10.1201/9781003091776 |l UBT01 |p ZDB-7-TFC |q UBT_Einzelkauf_2022 |x Verlag |3 Volltext | |
Record in the Search Index
| _version_ | 1804183431003766784 |
|---|---|
| adam_txt | |
| any_adam_object | |
| any_adam_object_boolean | |
| author | Li, Jin |
| author_facet | Li, Jin |
| author_role | aut |
| author_sort | Li, Jin |
| author_variant | j l jl |
| building | Verbundindex |
| bvnumber | BV047859983 |
| classification_rvk | RB 10196 RB 10208 |
| collection | ZDB-7-TFC |
| 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 |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>04665nmm a2200589 c 4500</leader><controlfield tag="001">BV047859983</controlfield><controlfield tag="003">DE-604</controlfield><controlfield tag="005">20220304 </controlfield><controlfield tag="007">cr|uuu---uuuuu</controlfield><controlfield tag="008">220302s2022 |||| o||u| ||||||eng d</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9781003091776</subfield><subfield code="c">Online</subfield><subfield code="9">9781003091776</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9781000542639</subfield><subfield code="9">9781000542639</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9781000542608</subfield><subfield code="9">9781000542608</subfield></datafield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1201/9781003091776</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)1302313190</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)BVBBV047859983</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-604</subfield><subfield code="b">ger</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1="0" ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-703</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">RB 10196</subfield><subfield code="0">(DE-625)142220:12653</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">RB 10208</subfield><subfield code="0">(DE-625)142220:12658</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Li, Jin</subfield><subfield code="e">Verfasser</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Spatial Predictive Modelling with R</subfield><subfield code="c">Jin Li</subfield></datafield><datafield tag="250" ind1=" " ind2=" "><subfield code="a">First edition</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Boca Raton ; London ; New York</subfield><subfield code="b">CRC Press, Taylor & Francis Group</subfield><subfield code="c">2022</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 Online-Ressource (xix, 383 Seiten)</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="490" ind1="0" ind2=" "><subfield code="a">A Chapman and Hall book</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">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</subfield></datafield><datafield tag="520" ind1="3" ind2=" "><subfield code="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.</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Prognoseverfahren</subfield><subfield code="0">(DE-588)4358095-6</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Raumstruktur</subfield><subfield code="0">(DE-588)4130125-0</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">R</subfield><subfield code="g">Programm</subfield><subfield code="0">(DE-588)4705956-4</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Computer simulation</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">R (Computer program language)</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">MATHEMATICS / Probability & Statistics / Regression Analysis</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Computer simulation</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">R (Computer program language)</subfield></datafield><datafield tag="653" ind1=" " ind2="6"><subfield code="a">Electronic books</subfield></datafield><datafield tag="653" ind1=" " ind2="6"><subfield code="a">Electronic books</subfield></datafield><datafield tag="689" ind1="0" ind2="0"><subfield code="a">Raumstruktur</subfield><subfield code="0">(DE-588)4130125-0</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="1"><subfield code="a">Prognoseverfahren</subfield><subfield code="0">(DE-588)4358095-6</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="2"><subfield code="a">R</subfield><subfield code="g">Programm</subfield><subfield code="0">(DE-588)4705956-4</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2=" "><subfield code="5">DE-604</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield code="i">Erscheint auch als</subfield><subfield code="n">Druck-Ausgabe, Hardcover</subfield><subfield code="z">9780367550547</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield code="i">Erscheint auch als</subfield><subfield code="n">Druck-Ausgabe, Paperback</subfield><subfield code="z">9780367550561</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1201/9781003091776</subfield><subfield code="x">Verlag</subfield><subfield code="z">URL des Erstveröffentlichers</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-7-TFC</subfield></datafield><datafield tag="999" ind1=" " ind2=" "><subfield code="a">oai:aleph.bib-bvb.de:BVB01-033242622</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://doi.org/10.1201/9781003091776</subfield><subfield code="l">UBT01</subfield><subfield code="p">ZDB-7-TFC</subfield><subfield code="q">UBT_Einzelkauf_2022</subfield><subfield code="x">Verlag</subfield><subfield code="3">Volltext</subfield></datafield></record></collection> |
| 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 |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-033242622 |
| oclc_num | 1302313190 |
| open_access_boolean | |
| owner | DE-703 |
| owner_facet | DE-703 |
| physical | 1 Online-Ressource (xix, 383 Seiten) |
| psigel | ZDB-7-TFC ZDB-7-TFC UBT_Einzelkauf_2022 |
| publishDate | 2022 |
| publishDateSearch | 2022 |
| publishDateSort | 2022 |
| publisher | CRC Press, Taylor & Francis Group |
| record_format | marc |
| series2 | A Chapman and Hall book |
| 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 |