An introduction to R for spatial analysis and mapping
Сохранить в:
| Главные авторы: | , |
|---|---|
| Формат: | |
| Язык: | английский |
| Опубликовано: |
London, United Kingdom
SAGE
2015
|
| Предметы: | |
| Online-ссылка: | Disponible Online - Cap. 5 pp 128-143-Cap. 6 pp 173-215-Cap. 8 pp 253-297. |
| Метки: |
Добавить метку
Нет меток, Требуется 1-ая метка записи!
|
Оглавление:
- Part 1: Introduction Objectives of this book Spatial Data Analysis in R Chapters and Learning Arcs The R Project for Statistical Computing Obtaining and Running the R software The R interface Other resources and accompanying website Part 2: Data and Plots The basic ingredients of R: variables and assignment Data types and Data classes Plots Reading, writing, loading and saving data Part 3: Handling Spatial Data in R Introduction: GISTools Mapping spatial objects Mapping spatial data attributes Simple descriptive statistical analyses Part 4: Programming in R Building blocks for Programs Writing Functions Writing Functions for Spatial Data Part 5: Using R as a GIS Spatial Intersection or Clip Operations Buffers Merging spatial features Point-in-polygon and Area calculations Creating distance attributes Combining spatial datasets and their attributes Converting between Raster and Vector Introduction to Raster Analysis Part 6: Point Pattern Analysis using R What is Special about Spatial? Techniques for Point Patterns Using R Further Uses of Kernal Density Estimation Second Order Analysis of Point Patterns Looking at Marked Point Patterns Interpolation of Point Patterns With Continuous Attributes The Kringing approach Part 7: Spatial Attribute Analysis With R The Pennsylvania Lung Cancer Data A Visual Exploration of Autocorrelation Moran's I: An Index of Autocorrelation Spatial Autoregression Calibrating Spatial Regression Models in R Part 8: Localised Spatial Analysis Setting Up The Data Used in This Chapter Local Indicators of Spatial Association Self Test Question Further Issues with the Above Analysis The Normality Assumption and Local Moran's-I Getis and Ord's G-statistic Geographically Weighted Approaches Part 9: R and Internet Data Direct Access to Data Using RCurl Working with APIs Using Specific Packages Web Scraping Epilogue