Course Modules 

Module 1: Foundations of Spatial Data Science and GeoAI

• Concepts of GeoAI, spatial machine learning, and intelligent mapping

• Overview of Python and R environments for geospatial analytics

• Installing core libraries and IDEs (JupyterLab, RStudio)

• Types of geospatial data: raster, vector, time-series

• Exercise: Setting up your GeoAI toolkit in Python & R

Module 2: Spatial Data Wrangling and Visualization

• Reading and manipulating spatial datasets with geopandas and sf

• Coordinate systems, projections, and transformations

• Joining spatial and attribute data

• Map creation and spatial visualizations with matplotlib, ggplot2, and leaflet

• Lab: Create an interactive choropleth map using R and Python

Module 3: Exploratory Spatial Data Analysis (ESDA)

• Spatial autocorrelation (Moran’s I, Geary’s C)

• Global vs. local spatial patterns

• Hotspot and cluster detection

• Exercise: Hotspot detection using real infrastructure or urban growth data

Module 4: Introduction to Machine Learning for Spatial Data

• Supervised vs unsupervised learning

• Data preprocessing, feature selection, normalization

• Applying scikit-learn and caret to geospatial datasets

• Model training: decision trees, random forest, KNN

• Hands-On: Land cover classification using remote sensing input

Module 5: Raster Data Processing and Terrain Modeling

• Handling rasters with rasterio (Python) and terra (R)

• Zonal statistics, NDVI calculation, and DEM analysis

• Raster classification and reclassification

• Exercise: Classify NDVI zones and terrain exposure from satellite data

Module 6: Predictive Modeling and Time-Series Forecasting

• Regression modeling (linear, logistic, spatial lag/error models)

• Time-series models for rainfall, temperature, traffic, etc.

• Validation, cross-validation, and RMSE/MAE

• Case Study: Predicting flood risk or urban expansion using spatial regression

Module 7: Deep Learning for GeoAI (Optional Advanced)

• Basics of CNNs and AI in geospatial modeling

• Using PyTorch or TensorFlow with spatial data

• Image segmentation and feature detection

• Lab: Land use classification from high-res imagery (pre-trained models)

Module 8: Spatial Clustering and Unsupervised Learning

• Clustering algorithms: K-Means, DBSCAN, hierarchical

• Spatial pattern detection (e.g., disease, infrastructure gaps)

• Clustering geotagged data for hotspot prioritization

• Exercise: Cluster analysis of health facility distribution

Module 9: Interactive Maps and Geo-Dashboards

• Generating interactive maps with folium, shiny, streamlit

• Exporting results to web maps or APIs

• Integrating model outputs with QGIS or web dashboards

• Capstone: Create a spatial intelligence dashboard for a development use case

Module 10: Project Development and Course Wrap-Up

• Final group projects 

• Presentation of models and interpretation

• Peer feedback and expert review

• Recap of tools, documentation, and continuous learning paths

• Certification and close-out discussion