Extraction of patterns and knowledge from large geospatial datasets.
About this course
GEOM 5720 introduces the principles and techniques of data mining as applied to geospatial information. You will learn how to extract meaningful patterns from large and complex datasets that contain location-based attributes. The course covers classification, clustering, and regression methods tailored for spatial data.
You will work with real-world datasets to build predictive models and uncover hidden relationships. Emphasis is placed on the unique challenges of spatial autocorrelation and scale when applying standard data mining tools.
Who takes this course
Evaluation
Your final grade is based on a midterm examination (30%), a practical project with a written report (50%), and class participation (20%).
Credits: 3 · Hours: 45
Prerequisites
Learning outcomes
- Apply data mining algorithms to spatial and non-spatial datasets.
- Evaluate the accuracy and reliability of geospatial models.
- Visualize complex data patterns using advanced mapping techniques.
- Interpret results to support decision-making in environmental or urban contexts.
Evaluation
| Component | Weight (%) |
| Midterm examination | 30 |
| Practical project with report | 50 |
| Class participation | 20 |