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Presenting content in such a way that students develop the critical reasoning & problem-solving skills that are needed in an ever-changing technological world the authors emphasize fundamental concepts as they progress through the five divisions of physical sciences physics chemistry astronomy meteorology & geology 800 pages Shipman James T ...
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An Introduction To R For Spatial Analysis And Mapping

In an age of big data data journalism and with a wealth of quantitative information around us it is not enough for students to be taught only 100 year old statistical methods using 'out of the box' software They need to have 21st-century analytical skills too This is an excellent and student-friendly text from two of the world leaders in the teaching and development of spatial analysis It shows clearly why the open source software R is not just an alternative to commercial GIS it may actually be the better choice for mapping analysis and for replicable research Providing practical tips as well as fully working code this is a practical 'how to' guide ideal for undergraduates as well as those using R for the first time It will be required reading on my own courses- Richard Harris Professor
of Quantitative Social Science University of BristolR is a powerful open source computing tool that supports geographical analysis and mapping for the many geography and non-geography' students and researchers interested in spatial analysis and mapping This book provides an introduction to the use of R for spatial statistical analysis geocomputation and the analysis of geographical information for researchers collecting and using data with location attached largely through increased GPS functionality Brunsdon and Comber take readers from zero to hero' in spatial analysis and mapping through functions they have developed and compiled into R packages This enables practical R applications in GIS spatial analyses spatial statistics mapping and web-scraping Each chapter includesExample data and
commands for exploring itScripts and coding to exemplify specific functionalityAdvice for developing greater understanding - through functions such as locator() View() and alternative coding to achieve the same ends Self-contained exercises for students to work throughEmbedded code within the descriptive text This is a definitive 'how to' that takes students - of any discipline - from coding to actual applications and uses of R
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In an age of big data data journalism & with a wealth of quantitative information around us it is not enough for students to be taught only 100 year old statistical methods using 'out of the box' software They need to have 21st-century analytical skills too This is an excellent & student-friendly text from two of the world leaders in the teaching & development of spatial analysis It shows clearly why the open source software R is not just an alternative to commercial GIS it may actually be the better choice for mapping analysis & for replicable research Providing practical tips as well as fully working code this is a practical 'how to' guide ideal for undergraduates as well as those using R for the first time It will be required reading on my own courses- Richard Harris Professor of Quantitative Social Science University of Bristol R is a powerful open source computing tool that supports geographical analysis & mapping for the many geography & non-geography' students & researchers interested in spatial analysis & mapping This book provides an introduction to the use of R for spatial statistical analysis geocomputation & the analysis of geographical information for researchers collecting & using data with location attached largely through increased GPS functionality Brunsdon & Comber take readers from zero to hero' in spatial analysis & mapping through functions they have developed & compiled into R packages This enables practical R applications in GIS spatial analyses spatial statistics mapping & web-scraping Each chapter
Includes: Example data & commands for exploring it Scripts & coding to exemplify specific functionality Advice for developing greater understanding
- through functions such as locator () View () & alternative coding to achieve the same ends Self-contained exercises for students to work through Embedded code within the descriptive text This is a definitive 'how to' that takes students
- of any discipline
- from coding to actual applications & uses of R

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