Course Information
Registration period
Year-round Recruitment
Course Level
Study Mode
Duration
2 Hour(s) 6 Minute(s)
Language
English
Taught by
Dr. Omar AlThuwaynee
Rating
1 views
Course Overview
Comprehensive (Step-by-Step) Procedure From Prediction to ROC Validation of Maps using Logistic Regression In GIS and R
In the this course, i have shared complete process (A to Z ) based on my published articles, about how to evaluate and compare the results of applying the multivariate logistic regression method in Hazard prediction mapping using GIS and R environment.
Since last decade, geographic information system (GIS) has been facilitated the development of new machine learning, data-driven, and empirical methods that reduce generalization errors. Moreover, it gives new dimensions for the integrated research field.
STAY FOCUSED: Logistic regression (binary classification, whether dependent factor will occur (Y) in a particular places, or not) used for fitting a regression curve, and it is a special case of linear regression when the output variable is categorical, where we are using a log of odds as the dependent variable.
Why logistic regression is special? It takes a linear combination of features and applies a nonlinear function (sigmoid) to it, so it’s a tiny instance of the neural network!
In the current course, I used experimental data that consist of : Independent factor Y (Landslide training data locations) 75 observations; Dependent factors X (Elevation, slope, NDVI, Curvature, and landcover)
I will explain the spatial correlation between; prediction factors, and the dependent factor. Also, how to find the autocorrelations between; the prediction factors, by considering their prediction importance or contribution. Finally, I will Produce susceptibility map using; R studio and ESRI ArcGIS only. Model prediction validation will be measured by most common statistical method of Area under (AUC) the ROC curve.
At the the end of this course, you will be efficiently able to process, predict and validate any sort of data related to natural sciences hazard research, using advanced Logistic regression analysis capability.
Keywords: R studio, GIS, Logistic regression, Mapping, Prediction
Course Content
- 7 section(s)
- 25 lecture(s)
- Section 1 Introduction
- Section 2 Prepare dichotomous binary (1,0) training data in ArcGIS
- Section 3 Settings and Packages preparation in R Studio
- Section 4 Data Visualization and preparation in R studio
- Section 5 Data conversion and resampling in R Studio
- Section 6 Run multivariate Logistic Regression in R
- Section 7 ROC and Model Validation
What You’ll Learn
- Comprehensive understanding of Prediction Mapping Science and Tools in GIS, Validation using AUC of ROC Results of applying the multivariate logistic regression For Prediction Map, R-Code Script provided, My continuous support, taking your hand step-by-step to develop high quality prediction maps using real data
Skills covered in this course
Reviews
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IIvan F. Gatchik
No comments
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BBinh Duong
This is the best course that I have ever learned. I have done my work with great support from this course. Thank you so much the author!
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RRiecka Dewi Syafutri
Alhamdulillah, membantu dalam pengerjaan tesis saya
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AAli Rajeh Alaizari
The course is very interesting and applied. When I'm a beginner in R I was really afraid, But by following Dr. Omar AlThuwaynee step by step, I benefited a lot from it, and my fear is gone now. I am really very happy with this course. I made great progress through it. Thank you very much to the great teacher Additionally, I recommend this course to who is interested in prediction mapping using logistic regression. I really want all the courses that explained by Dr. Omar AlThuwaynee Thanks a lot, Ali!
