Prediction Maps & Validation using Logistic Regression & ROC
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.
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.
Logistic Regression is a method for fitting a regression curve, and part of a larger class of algorithms known as Generalized Linear Model (glm). Used to predict the probability, i.e. whether dependent factor will occur (Y) in a particular places, or not.
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