GIS-106

Introdução

Sobre o curso

• Informações sobre o Curso

  Referências (Leituras)

• An Introduction to R for Spatial Analysis and Mapping
• Applied Spatial Data Analysis with R
• Geocomputation with R - free eBook
• R for Data Science - Introdução ao R

  Duração

• 20 horas

  Conteúdo Programático

  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
    2. Data and Plots
  • The basic ingredients of R: variables and assignment
  • Data types and Data classes
  • Plots
  • Reading, writing, loading and saving data
    3. Handling Spatial Data in R
  • Introduction: GISTools
  • Mapping spatial objects
  • Mapping spatial data attributes
  • Simple descriptive statistical analyses
    4. Programming in R
  • Building blocks for Programs
  • Writing Functions
  • Writing Functions for Spatial Data
    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
    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
    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
    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
    9. R and Internet Data
  • Direct Access to Data
  • Using RCurl
  • Working with APIs
  • Using Specific Packages
  • Web Scraping