Geospatial Programming and Spatial Machine Learning

• Credit value: 30 credits at Level 7
• Convenor and tutor: Dr Roberto Murcio
• Assessment: coding geospatial tasks in Python (50%) and designing and implementing machine learning solutions to tackle geospatial problems (50%)

In this module, you will acquire core programming concepts using Python as the main language. You will learn how to work with spatial data and create automated, reproducible geospatial data workflows. You will learn the principles of geospatial artificial intelligence (GeoAI) and how to apply machine learning concepts to geographic problems. In parallel, you will build on your cartographic skills to acquire theoretical concepts for visual analysis and spatial communication.

Indicative syllabus

• Introduction to programming
• Spatial data
• Mapping and visualisation
• Networks
• GeoAI and machine learning for spatial analysis

By the end of this module, you will have:

• acquired core concepts in computer programming (variables, functions, data and control structures)
• written code to perform introductory machine learning analyses
• applied theoretical concepts for the visual analysis and communication of spatial data
• imported, integrated, manipulated and analysed spatial data using contemporary programming tools
• worked with diverse data formats and standards.