"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 Bristol
R 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 includes:
Example 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.
Chris Brunsdon is Professor of Geocomputation at the National University of Ireland, Maynooth. He studied Mathematics at the University of Durham and Medical Statistics at the University of Newcastle upon Tyne, and has worked in a number of universities, holding the Chair in Human Geography at Liverpool University before taking up his current position. His research interests are in health, crime and environmental data analysis, and in the development of spatial analytical tools, including Geographically Weighted Regression approach. He also has interests in the software tools used to develop such approaches, including R. Lex Comber is a Professor of Geographical Information Sciences at the University of Leicester. After studying for a BSc in Plant and Crop Sciences at Nottingham, he did his PhD at the Macaulay Land Use Research Institute (now the Hutton Institute) and the University of Aberdeen. His research covers all areas of spatial analyses and the application and development of quantitative geographical. These have been applied across topic areas that straddle both the social and environmental and include accessibility analyses, land cover / land use monitoring and handling uncertainty in geographic information and spatial data.
Part 1: IntroductionObjectives of this bookSpatial Data Analysis in RChapters and Learning ArcsThe R Project for Statistical ComputingObtaining and Running the R softwareThe R interfaceOther resources and accompanying websitePart 2: Data and PlotsThe basic ingredients of R: variables and assignmentData types and Data classesPlotsReading, writing, loading and saving dataPart 3: Handling Spatial Data in RIntroduction: GISToolsMapping spatial objectsMapping spatial data attributesSimple descriptive statistical analysesPart 4: Programming in RBuilding blocks for ProgramsWriting FunctionsWriting Functions for Spatial DataPart 5: Using R as a GISSpatial Intersection or Clip OperationsBuffersMerging spatial featuresPoint-in-polygon and Area calculationsCreating distance attributesCombining spatial datasets and their attributesConverting between Raster and VectorIntroduction to Raster AnalysisPart 6: Point Pattern Analysis using RWhat is Special about Spatial?Techniques for Point Patterns Using RFurther Uses of Kernal Density EstimationSecond Order Analysis of Point PatternsLooking at Marked Point PatternsInterpolation of Point Patterns With Continuous AttributesThe Kringing approachPart 7: Spatial Attribute Analysis With RThe Pennsylvania Lung Cancer DataA Visual Exploration of AutocorrelationMoran's I: An Index of AutocorrelationSpatial AutoregressionCalibrating Spatial Regression Models in RPart 8: Localised Spatial AnalysisSetting Up The Data Used in This ChapterLocal Indicators of Spatial AssociationSelf Test QuestionFurther Issues with the Above AnalysisThe Normality Assumption and Local Moran's-IGetis and Ord's G-statisticGeographically Weighted ApproachesPart 9: R and Internet DataDirect Access to DataUsing RCurlWorking with APIsUsing Specific PackagesWeb ScrapingEpilogue
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- ID: 9781446272954
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