Geospatial Analysis With R

Beginner to Advance

What you'll learn

To learn the basics of R Programming

To carry out the geospatial analysis in R programming

To perform geospatial operations using Vector and Raster Data

To do the GIS based research using R programming

To create the static, interactive and animated map in R

Requirements

Basic concept on any Programming Language

Basic knowledge about Geographic Information System and Remote Sensing

Description

In the rapidly evolving data-driven world, geospatial analysis has become a vital tool for understanding spatial relationships, uncovering patterns, and making informed decisions. The "Geospatial Analysis with R" Udemy course is an immersive and practical journey designed to equip learners with the skills and knowledge required to harness the power of R for geospatial data analysis and visualization. Whether you are a seasoned data analyst or a novice with a passion for geography, this course will take you on a comprehensive exploration of geospatial data, from acquisition and preprocessing to advanced analysis techniques and web mapping applications.1. The Foundation of Geospatial Analysis: The course begins with an introduction to geospatial data and its significance in various domains. Learners will grasp the fundamentals of spatial data types, coordinate reference systems, and spatial projections, setting the stage for a deeper understanding of spatial analysis with R. Additionally, popular geospatial libraries such as sf, raster, sp, and leaflet will be introduced, providing learners with the necessary tools to dive into spatial data manipulation and visualization.2. Data Acquisition and Preprocessing: To embark on geospatial analysis, one must first acquire spatial data from different sources. This module guides learners through the process of acquiring data from shapefiles, GeoJSON, raster files, and other formats. Furthermore, it emphasizes the importance of data cleaning and preprocessing to ensure data accuracy and reliability. Learners will gain hands-on experience in preparing spatial data for analysis, an essential step before delving into more advanced techniques.3. Spatial Data Visualization: Visualization plays a crucial role in effectively communicating spatial information. In this module, learners will learn to create visually appealing and informative maps using R's powerful visualization package, ggplot2. The course will cover customizing maps with layers, legends, and labels, allowing learners to tell compelling stories with their geospatial data. Moreover, learners will explore the capabilities of the leaflet package to create interactive web maps, facilitating easy sharing and presentation of their analysis results.4. Spatial Analysis Techniques: Once the foundation is laid, learners will delve into fundamental spatial analysis techniques. They will learn how to perform spatial operations, including spatial joins and buffering, which are essential for combining and analyzing spatial data from various sources. Additionally, the course will cover advanced analysis techniques such as spatial interpolation and geostatistics, enabling learners to gain deeper insights into spatial patterns and relationships.5. Spatial Data Modeling: This module introduces learners to the world of spatial data modeling. They will discover geospatial regression, a powerful tool for modeling spatial relationships, allowing them to explore how geographic factors impact their data. Furthermore, learners will be introduced to machine learning techniques applied to geospatial data, equipping them with the skills to build predictive models for spatial analysis.6. Web Mapping Applications: In the modern era of data visualization and sharing, web mapping has become a valuable skill. This module will guide learners through the process of developing interactive web maps using R and the leaflet package. Learners will learn how to incorporate their spatial analysis results into web maps, enhancing data accessibility and communication.7. Geospatial Data Analysis Projects: The course concludes with real-world geospatial analysis projects, challenging learners to apply the concepts and techniques they have learned throughout the course. These projects will span diverse domains, such as environmental analysis, urban planning, and public health, providing learners with practical experience in solving spatial problems using R.By the end of "Geospatial Analysis with R," learners will have developed the expertise to work confidently with geospatial data, make data-driven decisions, and visualize geographic information in captivating ways. This course equips learners, regardless of their level of expertise, to become proficient geospatial analysts, unleashing the power of spatial data for insightful analysis and decision-making. Enroll today and embark on an exciting journey to explore the world of geospatial analysis with R!

Overview

Section 1: Introduction to Course and Tutor

Lecture 1 Introduction Session

Section 2: Basic Introduction to R Programming

Lecture 2 Introduction to R Programming and R Studio

Lecture 3 Installation of R and R Studio

Lecture 4 Overview of R Studio Interface

Lecture 5 Pushing Codes into Github Repository

Lecture 6 Variables in R

Lecture 7 Data Types in R

Lecture 8 Operators in R

Lecture 9 Decision Making in R

Lecture 10 Loops in R

Lecture 11 Functions in R

Lecture 12 Strings in R

Lecture 13 Vectors in R

Lecture 14 Lists in R

Lecture 15 Matrices in R

Lecture 16 Array in R

Lecture 17 Factors in R

Lecture 18 Dataframes in R

Lecture 19 R-Packages

Section 3: Reading the External Data Source in R

Lecture 20 Reading the CSV files

Lecture 21 Reading the Excel Files

Lecture 22 Reading the JSON Files

Section 4: Charts and Graphs with ggplot2

Lecture 23 Pie Charts

Lecture 24 Bar Charts

Lecture 25 Histograms

Lecture 26 Scatterplots

Lecture 27 Line Charts

Section 5: Introduction to sp and sf packages

Lecture 28 What and Why sp and sf packages? Differences between them.

Lecture 29 Creating a sf object

Lecture 30 Creating a sp object

Lecture 31 Load and Display Data with sf

Lecture 32 Load and Display Data with rgdal

Lecture 33 Creating a spatial object from latitude and longitude

Lecture 34 Reprojection or Projection Transformation of sf object

Lecture 35 Reprojection or Projection Transformation of sp object

Section 6: Vector Data Operations

Lecture 36 Creating Point Data

Lecture 37 Creating Line Data

Lecture 38 Creating Polygon Data

Lecture 39 Loading External Shapefile, Viewing Metadata and Plotting

Lecture 40 Joining the Attribute Data

Lecture 41 Attribute Queries

Lecture 42 Geoprocessing in R

Section 7: Raster Data Operations

Lecture 43 Introduction to Raster and Raster Package

Lecture 44 Creating Raster Objects from Scratch

Lecture 45 Reading the Raster Layers from the File

Lecture 46 Raster Stack vs Raster Brick

Lecture 47 Stacking Raster Layers from File

Lecture 48 Raster Algebra

Lecture 49 Modifying a Raster Object (crop, overlay, calc)

Lecture 50 Plotting Raster Layer and Raster Stack

Lecture 51 Cell Level Functions, Accessing Cell Values

Lecture 52 Resampling a raster file

Lecture 53 Reprojecting a raster file

Section 8: Map Making with tmap

Lecture 54 Plotting Shape with tmap

Lecture 55 Plotting Multiple Shapes and Layers

Lecture 56 Adding Compass

Lecture 57 Adding Grid Lines or Graticules

Lecture 58 Adding Scalebar

Lecture 59 Adding Legend

Lecture 60 Working with Bounding Box

Lecture 61 Layouting Map

Lecture 62 Creating a Facet Map

Lecture 63 Making an Animated Map

Lecture 64 Making Interactive Map with tmap

Section 9: Interactive map with Leaflet

Lecture 65 Basic to Leaflet

Lecture 66 Adding Shapefiles in Leaflet

Lecture 67 Adding Raster Image in Leaflet

Lecture 68 Adding Legends in a Leaflet Map

Lecture 69 Showing and Hiding Layers

Lecture 70 Creating Choropleth Map in R using Leaflet

Section 10: Remote Sensing Analysis

Lecture 71 Introduction

Lecture 72 Finding the Image Properties, Image Information and Statistics

Lecture 73 Raster Stacking and Getting True Color Image

Lecture 74 Raster Stacking using Filenames

Lecture 75 Plotting Single Band Images

Lecture 76 Subsetting and Renaming Bands

Lecture 77 Spatial Subsetting or Cropping

Lecture 78 Relation between Bands

Lecture 79 Extracting Pixel Values

Lecture 80 Spectral Profiles

Lecture 81 Finding Vegetation Indices

Lecture 82 Generating Histograms

Lecture 83 Thresholding Raster Values

Lecture 84 Unsupervised Classification

Lecture 85 Supervised Classification

Lecture 86 Digital Elevation Model and its Derivatives

Section 11: Creating Web Apps with Shiny

Lecture 87 Getting Started

Lecture 88 Basic Example of Shiny App

Lecture 89 UI Input Widgets

Lecture 90 UI Output

Lecture 91 Knowing about the Server

Lecture 92 Reactive Programming

Lecture 93 Event Reactive Programming

Lecture 94 Layout

Lecture 95 Simple Geospatial App with Shiny

Lecture 96 Deploying Web App in Shinyapps.io

Section 12: Project Works

Lecture 97 Cropland Suitability Analysis Using Weighted Overlay in R

Lecture 98 Estimating the Land Surface Temperature using Landsat 8

Lecture 99 Watershed Delineation in R

Those who are interested in Geospatial Analysis using programming language.