Statistical methods for analyzing spatial variability and prediction.
About this course
GEOM 3190 introduces the statistical methods used to analyse spatial variability and make predictions across geographic space. You will learn how to model spatial correlation using variograms and apply kriging techniques to estimate values at unsampled locations. The course covers both theoretical foundations and practical applications in fields such as resource estimation, environmental monitoring, and land-use planning.
Students will work with real-world geospatial datasets to build and validate geostatistical models. Emphasis is placed on interpreting results and understanding the limitations of spatial prediction methods.
Who takes this course
GEOM 3190 is offered as an elective for students in the Geomatics Technician program. It is also relevant for students in the GIS & Remote Sensing graduate certificate program who wish to specialise in spatial data analysis.
For more information about the Civil Engineering & Geomatics department, visit the Civil Engineering & Geomatics page.
Credits: 3 · Hours: 45
Prerequisites
- GEOM 1580 Introduction to Geomatics
Learning outcomes
- Define and calculate variograms from spatial data sets.
- Apply kriging techniques to estimate values at unsampled locations.
- Assess spatial autocorrelation and variability in geospatial datasets.
- Interpret geostatistical models for resource estimation and environmental monitoring.
Evaluation
| Component | Weight (%) |
| Midterm examination | 30 |
| Laboratory assignments | 35 |
| Final project report | 35 |