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4.65 out of 5
4.65
69 reviews on Udemy

Prediction Maps & Validation using Logistic Regression & ROC

Comprehensive (Step-by-Step) Procedure From Prediction to ROC Validation of Maps using Logistic Regression In GIS and R
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

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

Introduction

1
Introduction to Logistic Regression

Prepare dichotomous binary (1,0) training data in ArcGIS

1
Create dichotomous binary (1,0) training data
2
Merge binary dichotomous (0,1) training data

Settings and Packages preparation in R Studio

1
Working environment setting in R studio
2
Install multiple required packages
3
Export raster factors from ArcGIS into R studio

Data Visualization and preparation in R studio

1
Load and Plot raster factors in R
2
More about raster visualization and export in R
3
Classify Raster Factors In R
4
Raster breaks and color bars in R
5
Crop a Specific Raster Area In R

Data conversion and resampling in R Studio

1
Conversion of Dependent and Independent Factors for LR Model in R
2
Categorical Independent Factors for LR Model
3
Preparation of Categorical Independent Factor for LR Model
4
Data Resampling in R

Run multivariate Logistic Regression in R

1
Stacking Dependent and Independent Rasters in R
2
Remove No Data (NA) and Produce Data Frame Table
3
Run Logistic Regression Function
4
Run ANOVA and McFadden R squared Tests
5
Confusion Matrix of Prediction Results in R

ROC and Model Validation

1
Calculate and Plot AUC of ROC Curve for Validation Assessment
2
Graphing a Probability Curve S-Shape with Multiple Predictors
3
Exercise No. 1
4
Produce Prediction Index Map Using LR coefficients in R Studio
5
Produce Prediction Index Map Using LR coefficients in ArcGIS
You can view and review the lecture materials indefinitely, like an on-demand channel.
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Includes

2 hours on-demand video
1 article
Certificate of Completion

About

AulaGEO is a Ge-engineering specialized academy.

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