Introduction to Spatial Data Analysis and GIS in R from Udemy

Welcome to my Spatial Data Analysis in R course.

This course is designed for beginners and intermediate-level individuals who want to delve into the fascinating world of spatial data analysis using the R programming language. We will focus on the latest packages and practices for working with real-world spatial data.

Course Content:

Section 01: Introduction to Spatial Data

Get to know the essential fundamentals of spatial data analysis.
Learn to handle spatial data in R.

Section 02: Coordinate Reference Systems

Explore coordinate reference systems and their importance in spatial analysis.

Section 03: Geometric Operations

Learn to perform key operations with geometries, including spatial filters, spatial joins, spatial predicates, and transformations like centroids and buffers.

Section 04: Raster Data

Learn what raster data is and the most common tasks with this type of data.
This section includes a brief introduction to remote sensing.

Section 05: Maps with ggplot2

Master the creation of amazing maps using ggplot2. Unify your prior skills to effectively visualize spatial data.

Section 06: Web Maps

Take your visualizations to the next level by creating interactive web maps with mapview and leaflet extensions, including synchronized maps.

Course Details:

Duration: Approximately 14 hours and 30 minutes.
Lessons: Over 150 lessons that will guide you from basics to advanced techniques in spatial data analysis in R.
Teaching Mode:
Theory and Theory Quizzes
Guided Practices
Proposed Exercises with Solutions

This course is intensive, starting from the basics and taking you to acquire advanced skills in spatial data analysis and visualization. Upon completion, you will not only be an intermediate user but an expert with solid knowledge in the exciting field of spatial analysis.

I invite you to join me and explore the power of R in spatial data analysis.

Hope to see you in class.

What's inside

Learning objectives

Understand the difference between spatial data and non-spatial data
Read/write different spatial data formats
Extract properties from spatial data
Understand how the coordinates reference systems (crs) work

Work with spatial filters and spatial joins
Understand the spatial predicates (crosses, contains, intersects ...)
Generate maps and web maps
Work with geometry transformations (buffer, centroid, intersection, crop, ...)

Understand the difference between spatial data and non-spatial data
Read/write different spatial data formats
Extract properties from spatial data
Understand how the coordinates reference systems (crs) work
Work with spatial filters and spatial joins
Understand the spatial predicates (crosses, contains, intersects ...)
Generate maps and web maps
Work with geometry transformations (buffer, centroid, intersection, crop, ...)

Syllabus

Introduction

Introduction to the course

Environment set up (install R, Rtools and RStudio)

Rate the course

Course contents

Results of the course

Overview of course contents and packages installation

Update packages (recommended, optional)

Introduction to Spatial Data - Theory

Module introduction

Spatial Data vs Non-Spatial Data

Geometry types

Simple features

QUIZ - Spatial data theory

Introduction to Spatial Data - Practice

Data download

Exploratory Data Analysis I

Exploratory Data Analysis II

Convert data frame to spatial object

Theory - Vectorial formats

Exporting shapefiles from R

Importing shapefiles into R

Importing/Exporting from Geopackage

Creating exporatory maps

Simple features are tidy data

Spatial Properties - Bounding box

Spatial Properties - Coordinates and Attributes table

Spatial Properties - Geometry type

Spatial Properties - Coordinates Z and M

Summary of the module

CHALLENGE 01 - Introduction

Overview of Challenge 01

Solution of Challenge 01

Coordinates Reference Systems - Theory

Coordinates Reference System (CRS)

Geographic CRS vs Projected CRS

UTM Coordinates

Projections

Codifications for CRS

Codifications for geometries

Webs of interest

QUIZ - CRS theory

Coordinates Reference Systems - Practice

CRS in simple features

CRS transformation

Visualizing in different CRS

CRS transformation using proj4

CRS transformation on the fly

Assign vs Transform the CRS - Common errors

CHALLENGE 02 - CRS

Overview of Challenge 02

Solution of Challenge 02

Learn spatial predicates, filters, spatial joins, unary transformations and binary transformations

Spatial predicates

Spatial measurements

Unary transformations

Binary transformations

QUIZ - Geometry operations

Geometry Operations - Practice

Module Introduction

Working data

Predicate functions for counting

Spatial Filters I

Spatial Filters II

Spatial joins

Measures - Length

Measures - Area

Measures - Distances

Unary Transformations - Centroids

Unary Transformations - Buffer

Unary Transformations - Validate geometries

Unary Transformations - Simplify geometries

Unary Transformations - Voronoi Polygons

Unary Transformations - Delaunay Triangulation

Binary Transformations - Intersection and Crop

Binary Transformations - Difference

Binary Transformations - Symmetric Difference

Binary Transformations - Symmetric Difference with terra

Binary Transformations - Combine geometries

Binary Transformations - Union/Dissolve I

Binary Transformations - Union/Dissolve II

Summary of the Module

CHALLENGE 03 - Geometry Operations

Overview of Challenge 03

Solution of Challenge 03 - I

Solution of Challenge 03 - II

Solution of Challenge 03 - III

Introduction to Raster Data - Theory

Introduction to raster data

Raster Fundamentals

Analysis of raster data

QUIZ - Raster Data

Introduction to Raster Data - Practice

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Uses R, a language popular in statistical computing and graphics, making it valuable for learners interested in data analysis and visualization

Covers coordinate reference systems, which are essential for accurately representing and analyzing spatial data, ensuring learners grasp a fundamental concept

Includes hands-on practices and proposed exercises with solutions, which reinforces learning and allows learners to apply their knowledge

Teaches ggplot2 for creating maps, which is a powerful and flexible visualization tool widely used in the R community

Requires installing R, Rtools, and RStudio, which may pose a challenge for learners with limited technical experience or older machines

Briefly introduces remote sensing, which may not be sufficient for learners seeking in-depth knowledge of remote sensing techniques and applications

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Introduction to Spatial Data Analysis and GIS in R with these activities:

Review Basic R Syntax and Data Structures

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Reinforce your understanding of fundamental R concepts like data types, functions, and control flow. This will make it easier to grasp the spatial data analysis concepts in R.

Browse courses on R Programming

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Review R documentation on data types and structures.
Practice writing simple R scripts.
Complete online R tutorials.

Review 'Geocomputation with R'

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Deepen your understanding of spatial data analysis concepts and techniques using R. This book provides a solid foundation for the course material.

View Geocomputation with Python (Chapman & Hall/CRC... on Amazon

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Read the chapters relevant to the course syllabus.
Work through the code examples in the book.
Try applying the techniques to your own spatial datasets.

Practice Spatial Data Manipulation in R

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Sharpen your skills in manipulating spatial data using R packages like `sf` and `terra`. This will improve your ability to perform spatial analysis tasks efficiently.

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Download spatial datasets from online sources.
Practice reading, writing, and transforming spatial data.
Experiment with different spatial operations.

Four other activities

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Contribute to an Open Source Spatial R Package

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Enhance your understanding of spatial data analysis by contributing to an open-source R package. This will give you hands-on experience with real-world code and collaboration.

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Identify an open-source spatial R package on GitHub.
Explore the package's code and documentation.
Find a bug to fix or a feature to add.
Submit a pull request with your changes.

Create a Blog Post on a Spatial Analysis Technique

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Solidify your understanding of a specific spatial analysis technique by explaining it in a blog post. This will force you to think critically about the concepts and communicate them clearly.

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Choose a spatial analysis technique covered in the course.
Research the technique and its applications.
Write a clear and concise blog post explaining the technique.
Include code examples and visualizations.

Review 'Spatial Data Science with applications in R'

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Expand your knowledge of spatial data science beyond the course material. This book provides a deeper dive into advanced topics and techniques.

View Spatial Data Science on Amazon

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Read the chapters that align with your interests.
Experiment with the code examples in the book.
Consider applying the techniques to your own research projects.

Spatial Data Analysis Project: Crime Mapping

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Apply your spatial data analysis skills to a real-world problem by creating a crime map. This will allow you to integrate different concepts learned in the course and develop a practical application.

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Find a publicly available crime dataset.
Clean and prepare the data for spatial analysis.
Perform spatial analysis techniques to identify crime hotspots.
Create a map visualizing the crime patterns.

Career center

Learners who complete Introduction to Spatial Data Analysis and GIS in R will develop knowledge and skills that may be useful to these careers:

GIS Specialist

A GIS Specialist works directly with geographic information systems to manage, analyze, and display spatial data, and this course directly applies to this role. The course covers a wide range of topics including spatial data formats, coordinate reference systems, geometric operations, working with vector and raster data, and map creation using ggplot2, mapview, and leaflet. The skills learned here form an essential part of the toolkit for a GIS Specialist. This course will help people looking to enter the field develop the core competencies for managing geospatial databases, performing spatial analysis, and creating effective visualizations using modern GIS software.

See salaries and explore the career path for GIS Specialist

Geospatial Analyst

A Geospatial Analyst uses spatial data to solve problems and provide insights, and this course directly helps in that endeavor. This role requires proficiency in manipulating and analyzing geographical data and applying various spatial analysis techniques. Learning to work with coordinate reference systems, perform geometric operations, and handle both vector and raster data in this course is directly applicable to the daily tasks of a Geospatial Analyst. Furthermore, skills in data visualization in R with packages like ggplot2 and creating web maps, using mapview and leaflet, directly translate to the needs of communicating analyses and findings effectively with a variety of audiences. This course provides a solid practical foundation in the tools and techniques required for success in this role.

See salaries and explore the career path for Geospatial Analyst

Urban Planner

Urban Planners rely heavily on spatial data to make informed decisions about land use and development, and training in spatial data analysis is essential for the role. This course helps build a foundation for this career by teaching the fundamentals of handling spatial data, understanding coordinate reference systems, and performing spatial operations, all of which are crucial for urban planning tasks. The course's focus on generating maps and web maps using ggplot2, mapview, and leaflet is particularly relevant for visualizing and communicating urban data. Urban Planners can use this course to better understand spatial relationships, conduct analysis on urban growth, and create compelling visualizations that inform planning decisions.

See salaries and explore the career path for Urban Planner

Environmental Scientist

Environmental Scientists study the natural world and the impact of human activity, often relying on spatial data. This course helps develop the practical skills Environmental Scientists need to process and analyze geographic data. The course emphasis on raster data, including basic remote sensing concepts, gives Environmental Scientists tools to work with important data sources. Training in spatial filters, spatial joins, and transformations, and mastering map creation in ggplot2, mapview, and leaflet, make this course a great asset for Environmental Scientists. These skills allow for analysis of environmental changes, impacts on ecosystems, and the spatial distribution of environmental resources.

See salaries and explore the career path for Environmental Scientist

Location Intelligence Analyst

A Location Intelligence Analyst is a role that applies spatial data analysis techniques to solve business problems, and this course directly addresses the skills needed for this purpose. This career typically involves the use of geographic data to understand market patterns, optimize logistics, and improve business strategies. This course covers many relevant topics, including working with coordinate reference systems, geometric operations, and creating maps. Location Intelligence Analysts can use the R packages taught in this course such as ggplot2, mapview and leaflet to visualize data and communicate findings, which is an important aspect of the job.

See salaries and explore the career path for Location Intelligence Analyst

Remote Sensing Analyst

Remote Sensing Analysts specialize in analyzing imagery and data acquired from satellites and other remote sensing platforms, and this course provides crucial preparation. This course's inclusion of raster data handling and the introduction to remote sensing directly facilitates the analyst's work with satellite and aerial imagery. The course also introduces geometric operations, coordinate systems, and data visualization techniques which are essential for analyzing remotely sensed data effectively. A Remote Sensing Analyst would find that the skills taught in the course are foundational for further study in this field, and that the course gives them a solid start to a career in remote sensing.

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Conservationist

Conservationists work to protect and preserve natural habitats and biodiversity, using spatial data to inform their conservation efforts. This course helps build the critical skills needed to perform this type of work. Conservationists can use this course to assess habitat fragmentation, track animal movements, and map biodiversity hotspots. The course provides training in working with spatial data, performing geometric operations, and creating maps, all of which are crucial for conservation planning. Additionally, the course's emphasis on remote sensing provides skills for tracking changes in the environment over time.

See salaries and explore the career path for Conservationist

Cartographer

Cartographers are experts in mapmaking, and this course helps build important skills for this role. The course covers the use of spatial data and operations as well as mapmaking techniques using ggplot2, mapview, and leaflet. This enables aspiring cartographers to create both static and dynamic maps, which are fundamental parts of the profession. Specifically, understanding coordinate reference systems, manipulating geometries and working with spatial data in R, makes this course valuable for anyone pursuing cartography.

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Natural Resource Manager

Natural Resource Managers oversee the management and conservation of natural resources, and this course provides a strong foundation for this work. Natural resource management requires the use of spatial data to assess resource distribution, monitor changes, and plan conservation efforts. Learning to use coordinate reference systems, spatial filters, spatial joins, and working with raster data in R, are all skills that this course provides. The visualization skills learned using ggplot2, mapview and leaflet allow Natural Resource Managers to create compelling maps that aid in decision-making and resource management.

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Transportation Planner

Transportation Planners use spatial data to plan and manage transportation systems, and this course is useful in that endeavor. Understanding spatial relationships, analyzing transportation networks, and visualizing data on maps are critical to their work, and this course covers these elements well. The course provides training in handling spatial data, coordinate systems, geometric operations, and creating maps using packages like ggplot2, mapview, and leaflet. These skills are directly applicable to the daily tasks of a Transportation Planner, such as optimizing routes and analyzing traffic patterns.

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Agricultural Scientist

Agricultural Scientists use spatial data to understand crop health, land use, and environmental factors, and this course provides skills to do so. Working with raster data, understanding coordinate reference systems, and performing geometric operations using R can be particularly helpful in analyzing agricultural data. The course's introduction to remote sensing also adds to the toolkit of an Agricultural Scientist. This course helps develop analytical and visualization skills, which are valuable for mapping crop yields and monitoring environmental impacts on agriculture.

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Public Health Analyst

Public Health Analysts often use spatial data to analyze disease patterns and health disparities, and this course helps provides a foundation for that. The course's focus on handling and analyzing spatial data, working with coordinate reference systems, and performing geometric operations directly applies to this work. The course prepares learners to create maps and visualizations that are essential for communicating public health trends and findings. Because it teaches both analysis and visualization, this course will be particularly helpful for those aspiring to enter public health analytics.

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Data Scientist

Often, Data Scientists work with geographically referenced data and need to perform analyses using spatial techniques. This course may be helpful to Data Scientists who want to add spatial analysis to their skill set. Specifically, understanding spatial data formats, working with coordinate reference systems, performing geometric operations, and creating maps can be beneficial for those who need to incorporate spatial data into their analysis. The ability to analyze both vector and raster data effectively, and integrate these analyses with other data science techniques, make this course a valuable experience for a data scientist.

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Real Estate Analyst

Real Estate Analysts often need to work with spatial data to evaluate property values, assess market trends, and analyze the impact of location on real properties. This course may be useful to Real Estate Analysts because it teaches techniques such as handling spatial data, understanding coordinate reference systems, and performing geometric operations. These skills are beneficial when analyzing geographic data related to property sales and market analysis. Creating maps and visualizations, using ggplot2, mapview, and leaflet, can also help Real Estate Analysts present findings in an understandable way.

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Market Research Analyst

Market Research Analysts may find this course useful in their work, as spatial analysis can be used to understand customer behaviors linked to specific locations. While market research is not purely spatial, this course may be helpful for a Market Research Analyst to analyze geographic variables and trends that are important to business outcomes. By understanding coordinate systems, spatial joins, and other geometric operations, a Market Research Analyst can incorporate location data to derive insights about market trends and customer distribution, which is a skill that increases their effectiveness.

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Reading list

We've selected two books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Introduction to Spatial Data Analysis and GIS in R.

Geocomputation with Python

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Provides a comprehensive guide to using R for geocomputation. It covers a wide range of topics, including spatial data structures, spatial analysis techniques, and map making. It is particularly useful for understanding the theoretical underpinnings of the spatial packages used in the course. This book is commonly used as a textbook at academic institutions.

Geocomputation with Python (Chapman & Hall/CRC The...

Kindle Edition

$$$

Spatial Data Science

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Provides a comprehensive overview of spatial data science techniques using R. It covers advanced topics such as spatial statistics, machine learning, and network analysis. It is particularly useful for students who want to delve deeper into the theoretical foundations of spatial data science. This book is more valuable as additional reading than it is as a current reference.

Introduction to Spatial Data Analysis and GIS in R

What's inside

Learning objectives

Syllabus

Good to know

Save this course

Activities

Career center

Reading list

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