Prediction Maps & Validation using Logistic Regression & ROC

Prediction Maps & Validation using Logistic Regression & ROC
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Created by:Dr. Omar AlThuwaynee
Comprehensive (Step-by-Step) Procedure From Prediction to ROC Validation of Maps using Logistic Regression In GIS and R
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
No statistical background needed
Basics background about ArcGIS software and R
Interest in GIS prediction maps using real life Data
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
Who this course is for:
Students and researchers of Natural hazards, environmental Science, Ecology, and Natural Sciences
Students and researchers interested in using GIS for producing hazard susceptible maps.
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