Geostatistics Course for Beginners. Creating algorithms with R for QGIS.

About Course

Course objectives

About the course, three are the words that define the Geostatistics Course for Beginners; geostatistics, R and QGIS.

The main goal of this course is to learn the basics, the fundamentals of the geostatistics; mainly, in the area of the exploratory data analysis. Depending on the successful of this part of the course, it could be extended to structural analysis and also, data estimation with Kriging/cokriging Interpolation.

The second goal of the course is to learn coding with R. This coding will be based in the creation of graph for data analysis, coding for testing data and coding for the estimation of values.

The third and last goal is to integrate R within QGIS. Therefore, the objective is to create our own algorithms with R, and use them in QGIS. In this way, we can create all the work flow from QGIS.

Course requisites

Anyone can be part of this course. After all, the level of the course is for beginners. Thus, no previous knowledge is required, as we are going to start from zero. The explanations are going to be very simple and with real examples. It is a step-by-step course, easy to follow and easy to apply what you are learning. I am going to provide everything for the course, lessons, data, software, etc.

Course achievements

Once finished the Geostatistics Course for Beginners, your QGIS will become a stronger tool for spatial data analysis; and most important, you will get a better understanding on the exploratory data analysis.

Course recommendations

It is not necessary to have any previous experience on geostatitics to take the course. However, we like to recommend to watch the previous geostatitics course. You can follow the link below:

https://giscourse.online/courses/geostatistical-analysis-course-kriging-interpolation-groundwater-flow-map/

https://youtu.be/CaPzOJk4Dd4

https://youtu.be/LaVaGGcgO0w

https://youtu.be/g-i1-pu195g

Course Content

Lesson 1.
Introduction to the course. Integrate R within QGIS.

Integrating R with QGIS.

17:27

Lesson 2
In lesson 2 you will see how to get the datasets that are going to be used in this course for the Exploratory Data Analysis.

Lesson 3.
Lesson 3 is an introduction about univariate analysis. We are going to see how the univariante graphs looks like. Also, we are going to see 5 reason related to the importance of doing Exploratory data analysis. Later, we are going to talk a little bit about the definition of univariate. At the end, we are going to create some algorithms and we are going to run them with the groundwater data set.

Lesson 4.
Lesson 4 is a continuation about univariate analysis. We are going to see the definition of a Random Variable and the types of Random Variables. Also, we are going to see examples of Discrete Random Variables and Continuous Random Variable. Later, we are going to talk about the Cumulative Distribution Function (CDF). At the end, we are going to create the algorithm for the CDF and we are going to work with a data set related to contaminated soils.

Lesson 5.
Lesson 5 is about continuous Random Variables with univariate analysis. We are going to see how to create different scripts for different types of histograms, with the Meuse Data Set. Later, we are going to talk about the Probability Density Function (PDF). At the end, we are going to create the algorithm for the PDF and present an example.

Lesson 6.
In lesson 6 we still working with univariate analysis. You will see a script to create a spreadsheet table with the basic statistics. In addition, we are going to be talking about the Median, Mean and quartiles and how to calculate them from our data set. Also, we are going to see a script to create a Box Plot and we are going to analyze this type of graph to extract all the information. We are going to talk about interquartile range (IQR), quartiles and outliers. At the end, we are going to create and script to compile a histogram with a box plot. It is a very powerful combination to extract a lot of information from the distribution of our data set.

Lesson 7.
In lesson 7 we are going to define Expected Value, Variance and Standard Deviation. All these statistics are going to help us to have a better understanding of the data distribution. Also, we are going to create a script to visualize a Q-Q plot with QGIS. At the end, we are going to analyze the data distribution with the Q-Q plot.

Lesson 8.
In this lesson we are going to see the definition of Normal distribution/Gaussian distribution, Skewness and Kurtosis. Also, we are going to see examples of data transformation. We are going to work with logarithmic, inverse and square root transformations. At the end, we are going to see a comparison between all the transformations with the raw data, in order to identify which is the best transformation and why.

Lesson 9.
In lesson 9 we are going to learn how to create a help menu for the scripts. Also, we are going to work on the normality tests. We are going to use the Shapiro – Wilk test and the Kolmogorov – Smirnov test. We are going to see how to create the scripts to test the normality and we are going to run the scripts with the groundwater data set and the soil contaminated data set. Then, we are going to support the final test results with Q-Q plots.

Student Ratings & Reviews

4.8

Total 43 Ratings

36 Ratings

7 Ratings

0 Rating

Paramesha A

2 months ago

good for learning

Ayoub

4 months ago

Perfect to the last drop

Ruxshona Haknazarova

6 months ago

Good thanks

Peter Krupenko

6 months ago

Loved it a lot. Most of the statistics content I remember from university, but I've never done anything with R in QGIS so the course will help a lot. Much appreciated!

Tiisetso Samuel Khiba

6 months ago

great

Renan Silva

8 months ago

Amazing!

Pietro Sottani

10 months ago

a Well done course!

henry gonzales

12 months ago

It was very interesting and practical