Core Spatial Data Analysis: Introductory GIS with R and QGIS

Become Proficient In Spatial Data Analysis Using R & QGIS By Working On A Real Project - Get A Job In Spatial Data!
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  • Lectures 31
  • Length 2 hours
  • Skill Level All Levels
  • Languages English
  • Includes Lifetime access
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About This Course

Published 5/2016 English

Course Description

MASTER SPATIAL DATA ANALYSIS IN R & QGIS: HANDS ON TRAINING WITH A REAL SPATIAL DATA PROJECT! 

Do you find GIS & Spatial Data books & manuals too vague, expensive & not practical and looking for a course that takes you by hand, teaches you all the concepts, and get you started on a real life project?

Or perhaps you want to save time and learn how to automate some of the most common GIS tasks?

I'm very excited you found my spatial data analysis course. My course provides a foundation to carry out PRACTICAL, real-life spatial data analysis tasks in popular and FREE software frameworks. 

My name is MINERVA SINGH and i am an Oxford University MPhil (Geography and Environment) graduate. I am currently pursuing a PhD at Cambridge University (Tropical Ecology and Conservation). I have several years of experience in analyzing real life spatial data from different sources and producing publications for international peer reviewed journals.

In this course, actual spatial data from the Tam Dao National Park in Vietnam will be used to give a practical hands-on experience of working with real life spatial data and understanding what kind of questions spatial data can help us answer. The underlying motivation for the course is to ensure you can put spatial data analysis into practice today. Start analyzing spatial data for your own projects, whatever your skill level and IMPRESS your potential employers with an actual example of your spatial data analysis abilities.

This is a core course in spatial data analysis, i.e. we will focus on learning the most important and widely encountered spatial data analysis tasks in both R and QGIS

It is a practical, hands-on course, i.e. we will spend a tiny amount of time dealing with some of the theoretical concepts related to spatial data analysis. However, majority of the course will focus on working with the spatial data from the Tam Dao National Park, Vietnam. After each video you will learn a new concept or technique which you may apply to your own projects.

TAKE ACTION TODAY! I will personally support you and ensure your experience with this course is a success.

What are the requirements?

  • An interest in working with spatial data.
  • It would be useful if students are able to (a) change working directories, (b) install packages, (c) load libraries and (d) read in CSVs in R.
  • The course will be demonstrated using a Windows PC. Mac and Linux users will have to adapt the instructions to their operating systems.
  • Optional install: QGIS (While this course will introduce using QGIS alongside R for doing spatial data analysis, it is entirely possible to master all the concepts of the course in R alone).

What am I going to get from this course?

  • You will have a greater clarity of basic spatial data concepts and data types
  • Carry out practical spatial data analysis tasks in freely available software
  • Learn about the kind of questions that are answered through spatial analysis and where to obtain free spatial data.
  • Analyze spatial data using both R and QGIS
  • Process raster and vector data in both R and QGIS
  • Show off your skills & gain experience by working on a real life conservation related spatial data analysis project
  • Start analyzing spatial data for your own projects using two powerful freeware tools
  • You'll have a copy of all the data and R scripts used in the course will be provided to students for their reference and to use in their own analysis.
  • You'll also have plenty of handy hints and tips will be provided alongside the code to prevent glitches

What is the target audience?

  • Academics
  • Researchers
  • Conservation managers
  • Anybody who works/will work with spatial data

What you get with this course?

Not for you? No problem.
30 day money back guarantee.

Forever yours.
Lifetime access.

Learn on the go.
Desktop, iOS and Android.

Get rewarded.
Certificate of completion.

Curriculum

Section 1: SECTION 1: INTRODUCTION TO SPATIAL DATA ANALYSIS
04:20

This lecture presents a brief introduction to what the course is about and the course instructor. 

Article

Link to data used in this course and code files. Brief description of the software and packages to be installed prior to staring

10:04

In this section the students are going to be introduced to spatial/geo-spatial data and how they differ from GIS. The students will then be introduced to the project they are going to work with, along with an introductions of the different questions they can answer through spatial data analysis. We will talk about the important applications of spatial data analysis, sources of free data and tools to be used in this course (R and QGIS). Briefly present the different softwares and packages that need to be installed.

06:53

This lecture will introduce some of the most common spatial data concepts that we will encounter most frequently. I will touch upon Coordinate reference systems and projections. I will also briefly introduce Raster data, vector data and point data

01:09

In this lecture we will quickly reiterate the things we have learnt in the previous lectures. By now we have installed the both QGIS, R and the basic R packages needed for spatial data analysis. We are now familiar with terms such as latitude-longitude, coordinates and know the difference between raster and vector data. We are acquainted with our project study site and some of the questions spatial data analysis can help us answer. If we are all comfortable with this, its time for a quiz and next sections

3 questions

A few questions to make sure you are up to speed on the topics covered in Section 1

Section 2: SECTION 2: WORKING WITH RASTER DATA
03:09

Briefly demonstrate how raster data (with different extensions) and of different extents can be read in to QGIS

Read in Raster Data in R
06:13
03:02

In this lecture we will see how we can convert the coordinates of raster data from UTM to Latitude-longitude and vice versa.

Modify Raster Stack in R
06:02
Plot Multiple Bands as false color composites
03:42
03:52

In this lecture we will see how to apply arithmetic operations to raster bands. We will also compute NDVI in R

Band Arithmetic in QGIS
01:53
03:04

In this lecture we will see how to convert continuous value raster data to categorical raster, i.e. assign unique categories to a given range of raster values

01:50

We will be introduced to an R package- SDMTools. The functions of this package can be used for a number of landscape ecology related computations. In this lecture, we will see how the attributes relating to different raster classes or categories maybe computed in R

02:59

In this lecture we will see how landscape level statistics such as percentage land-cover, patch related statistics can be computed in QGIS. This lecture will also introduce a very useful QGIS Plugin- Lecos or Landscape Ecology.

03:50

In this lecture we will see how the pixel size (and extent) of a given raster can be modified using another raster as a baseline

03:50

In this lecture we will see how to extract the given portion of a raster or clip a raster using polygons in R.

Clipping Rasters in QGIS
01:46
03:57

In this lecture we will how a Digital Elevation Model can be examined within R. Further we will see how to derive topographic products such as slope, aspect from elevation data

Topographic Calculations in QGIS
02:44
06:47

In this lecture, we will see how to carry out basic statistical operations on raster data in R; including extracting descreptive statistics, correlation and linear regression

02:01

We will quickly discuss the things we have learnt so far in section 2 before moving to a brief quiz on raster data analysis

3 questions

What is tthe resolution of Landsat data in metres?

Section 3: SECTION 3: WORKING WITH VECTOR DATA
02:07

In this lecture we will see how we can read in vector data using QGIS

07:59

In this lecture we will explore the different functions and packages that let us read in vector data in R

08:32

In this lecture we will explore and visulaize shapefile attributes in R

05:02

In this lecture we will explore basic map visualizations using in-built spatial data of R with our own shapefiles

05:01

In this lecture we will see how to isolate specific portions of a shapefile by sub-setting and split a shapefile using a unique attribute

03:05

In this lecture we will see how to split and merge shapefiles based on a given unique attribute in QGIS

04:35
In this lecture we will learn to calculate important attributes such as area of shapefile, length and carry out operations such as intersection of different shapefiles
01:37

In this lecture we will quickly reiterate the things we have learn in Section 3

4 questions

In this quiz, we will quickly reiterate the concepts we learnt in Section 3

Section 4: Thank You!
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Instructor Biography

Minerva Singh, Ecologist and Data Scientist From Cambridge University

Hello. I am a PhD candidate at Cambridge University specializing in Tropical Ecology. I am also a Data Scientist on the side. As a part of my research I have to carry out extensive data analysis, including spatial data analysis.or this purpose I prefer to use a combination of freeware tools- R, QGIS and Python.I do most of my spatial data analysis work using R and QGIS. Apart from being free, these are very powerful tools for data visualization, processing and analysis. I also hold an MPhil degree in Geography and Environment from Oxford University. I have honed my statistical and data analysis skills through a number of MOOCs including The Analytics Edge (R based statistics and machine learning course offered by EdX), Statistical Learning (R based Machine Learning course offered by Standford online). In addition to spatial data analysis, I am also proficient in statistical analysis, machine learning and data mining. I also enjoy gneral programming, data visualization and web development. In addition to being a scientist and number cruncher, I am an avid traveller

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