I will advanced spatial analysis and predictive modelling

"Example of a predictive hazard model I generated using spatial data and the Logistic Regression (LoR) algorithm. This showcases my ability to process terrain data, apply statistical/machine learning models, and produce publication-ready risk maps for environmental management."

This map displays a Landslide Susceptibility Zone Analysis for a mountainous region, created using the Logistic Regression (LoR) method, a popular machine learning/statistical approach in GIS.

Key Features Shown:

• Landslide Susceptibility Levels: The map categorizes the terrain into five distinct risk zones based on color coding:
• Red (Very High) & Orange (High): Areas highly vulnerable to future landslide events, mostly concentrated along steep slopes and valleys.
• Yellow (Medium): Moderate risk zones.
• Light Green (Low) & Dark Green (Very Low): Safe, stable terrains.
• Landslide Inventory: The black dots/patches represent actual historical landslide locations, which are used to train and validate the predictive accuracy of the model.
• Cartographic Elements: Features standard map elements including a scale bar (in kilometers), orientation north arrow, and precise geographic coordinates (Latitude/Longitude grid.