An Introduction to R for Spatial Analysis and Mapping: Spatial Analytics and GIS
Autor Chris Brunsdon, Lex Comberen Limba Engleză Paperback – 23 dec 2018
Revised and updated, each chapter includes:
- example data and commands to explore hands-on;
- scripts and coding to exemplify specific functionality;
- self-contained exercises for students to work through;
- embedded code within the descriptive text.
The new edition includes detailed discussion of new and emerging packages within R like sf, ggplot, tmap, making it the go to introduction for all researchers collecting and using data with location attached. This is the introduction to the use of R for spatial statistical analysis, geocomputation, and GIS for all researchers - regardless of discipline - collecting and using data with location attached.
| Toate formatele și edițiile | Preț | Express |
|---|---|---|
| Paperback (1) | 395.28 lei 22-36 zile | +32.13 lei 6-12 zile |
| SAGE Publications – 23 dec 2018 | 395.28 lei 22-36 zile | +32.13 lei 6-12 zile |
| Hardback (1) | 1052.37 lei 38-44 zile | |
| SAGE Publications – 23 dec 2018 | 1052.37 lei 38-44 zile |
Preț: 395.28 lei
Nou
Puncte Express: 593
Preț estimativ în valută:
75.65€ • 78.58$ • 62.84£
75.65€ • 78.58$ • 62.84£
Carte disponibilă
Livrare economică 13-27 ianuarie 25
Livrare express 28 decembrie 24 - 03 ianuarie 25 pentru 42.12 lei
Preluare comenzi: 021 569.72.76
Specificații
ISBN-13: 9781526428509
ISBN-10: 1526428504
Pagini: 336
Dimensiuni: 170 x 242 x 15 mm
Greutate: 0.61 kg
Ediția:Second Edition
Editura: SAGE Publications
Colecția Sage Publications Ltd
Seria Spatial Analytics and GIS
Locul publicării:London, United Kingdom
ISBN-10: 1526428504
Pagini: 336
Dimensiuni: 170 x 242 x 15 mm
Greutate: 0.61 kg
Ediția:Second Edition
Editura: SAGE Publications
Colecția Sage Publications Ltd
Seria Spatial Analytics and GIS
Locul publicării:London, United Kingdom
Recenzii
There's no better text for showing students and data analysts how to use R for spatial analysis, mapping and reproducible research. If you want to learn how to make sense of geographic data and would like the tools to do it, this is your guide.
Students and other life-long learners need flexible skills to add value to spatial data. This comprehensive, accessible and thoughtful book unlocks the spatial data value chain. It provides an essential guide to the R spatial analysis ecosystem. This excellent state-of-the-art treatment will be widely used in student classes, continuing professional development and self-tuition.
In this second edition, the authors have once again captured the state of the art in one of the most widely used approaches to spatial analysis. Spanning from the absolute beginner to more advanced concepts and underpinned by a strong ‘learn by doing’ ethos, this book is ideally suited for both students and teachers of spatial analysis using R.
A timely update to the de facto reference and textbook for anyone — geographer, planner, or (geo)data scientist — needing to undertake mapping and spatial analysis in R. Complete with self-tests and valuable insights into the transition from sp to sf, this book will help you to develop your ability to write flexible, powerful, and fast geospatial code in R.
Brunsdon and Comber’s 2nd edition of their acclaimed text book is updated with the key developments in spatial analysis and mapping in R and maintains the pedagogic style that made the original volume such an indispensable resource for teaching and research.
The future of GIS is open-source! An Introduction to R for Spatial Analysis and Mapping is an ideal introduction to spatial data analysis and mapping using the powerful open-source language R. Assuming no prior knowledge, Brunsdon and Comber get the reader up to speed quickly with clear writing, excellent pedagogic material and a keen sense of geographic applications. The second edition is timely and fresh. An Introduction to R for Spatial Analysis and Mapping should be required reading for every Geography and GIS student, as well as faculty and professionals.
While there are many books that provide an introduction to R, this is one of the few that provides both a general and an application-specific (spatial analysis) introduction and is therefore far more useful and accessible. Written by two experts in the field, it covers both the theory and practice of spatial statistical analysis and will be an important addition to the bookshelves of researchers whose spatial analysis needs have outgrown currently available GIS software.
Brunsdon and Comber have produced that rare text that is both an introduction to the field of spatial analysis and, simultaneously, to the programming language R. It has been my go-to text in teaching either subject and this new edition updates and expands an already deeply comprehensive work.
Students and other life-long learners need flexible skills to add value to spatial data. This comprehensive, accessible and thoughtful book unlocks the spatial data value chain. It provides an essential guide to the R spatial analysis ecosystem. This excellent state-of-the-art treatment will be widely used in student classes, continuing professional development and self-tuition.
In this second edition, the authors have once again captured the state of the art in one of the most widely used approaches to spatial analysis. Spanning from the absolute beginner to more advanced concepts and underpinned by a strong ‘learn by doing’ ethos, this book is ideally suited for both students and teachers of spatial analysis using R.
A timely update to the de facto reference and textbook for anyone — geographer, planner, or (geo)data scientist — needing to undertake mapping and spatial analysis in R. Complete with self-tests and valuable insights into the transition from sp to sf, this book will help you to develop your ability to write flexible, powerful, and fast geospatial code in R.
Brunsdon and Comber’s 2nd edition of their acclaimed text book is updated with the key developments in spatial analysis and mapping in R and maintains the pedagogic style that made the original volume such an indispensable resource for teaching and research.
The future of GIS is open-source! An Introduction to R for Spatial Analysis and Mapping is an ideal introduction to spatial data analysis and mapping using the powerful open-source language R. Assuming no prior knowledge, Brunsdon and Comber get the reader up to speed quickly with clear writing, excellent pedagogic material and a keen sense of geographic applications. The second edition is timely and fresh. An Introduction to R for Spatial Analysis and Mapping should be required reading for every Geography and GIS student, as well as faculty and professionals.
While there are many books that provide an introduction to R, this is one of the few that provides both a general and an application-specific (spatial analysis) introduction and is therefore far more useful and accessible. Written by two experts in the field, it covers both the theory and practice of spatial statistical analysis and will be an important addition to the bookshelves of researchers whose spatial analysis needs have outgrown currently available GIS software.
Brunsdon and Comber have produced that rare text that is both an introduction to the field of spatial analysis and, simultaneously, to the programming language R. It has been my go-to text in teaching either subject and this new edition updates and expands an already deeply comprehensive work.
Cuprins
Chapter 1 Introduction
Chapter 2 Data and Plots
Chapter 3 Handling Spatial Data
Chapter 4 Programming in R
Chapter 5 Using R as a GIS
Chapter 6 Point Pattern Analysis
Chapter 7 Spatial Attribute Analysis
Chapter 8 Localised Spatial Analysis
Chapter 9 R and Internet Data
Chapter 10 Epilogue
Chapter 2 Data and Plots
Chapter 3 Handling Spatial Data
Chapter 4 Programming in R
Chapter 5 Using R as a GIS
Chapter 6 Point Pattern Analysis
Chapter 7 Spatial Attribute Analysis
Chapter 8 Localised Spatial Analysis
Chapter 9 R and Internet Data
Chapter 10 Epilogue
Notă biografică
Descriere
This is a new edition of the accessible and student-friendly 'how to' for anyone using R for the first time, for use in spatial statistical analysis, geocomputation and digital mapping.