Linear algebra Basic Statistics Calculus Programming Python/R

- Equip students with the skills to perform advanced geospatial data analysis and modelling. - Provide a solid foundation in both the theory and application of statistical and machine learning techniques in spatial analysis. - Enable students to develop their own geospatial applications and conduct independent research projects.

1. Introduction to Geospatial Data and Statistical Modelling a. Introduction to geospatial data (vector, raster, sources, and software) b. Basic concepts in statistical modelling (descriptive vs. inferential statistics, introduction to R/Python) 2. Spatial Data Manipulation and Visualization a. Manipulating geospatial data (reading, writing, operations) b. Visualizing geospatial data (GIS mapping, web mapping tools) 3. Spatial Statistics Fundamentals a. Exploratory Spatial Data Analysis (ESDA) (concepts of spatial autocorrelation, visualization) b. Spatial interpolation techniques (Kriging, IDW, accuracy assessment) 4. Advanced Geospatial Modelling Techniques and Unsupervised Learning a. Spatial Regression Models and Dimensionality Reduction (Spatial Lag Model, Spatial Error Model, PCA) b. Clustering Techniques for Spatial Data Analysis (k-means clustering, Self-Organizing Maps) 5. Machine Learning Models and Special Topics in Spatial Data a. Machine Learning Models for Spatial Data (Random Forests, SVM, Neural Networks) b. Integrating Unsupervised Learning Techniques in Geospatial Analysis (PCA, k-means, SOM applications) 6. Application Development and Final Project Work a. Developing Geospatial Applications (introduction to software development with geospatial data, API usage) b. Advanced Case Studies (detailed exploration of case studies in areas like climate change, disaster management) c. Final Project Workshop (project development, implementation of learned techniques, presentations)