# 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

- 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