Overview
Advanced Spatial Analytics and Modelling develops theoretical and computational expertise in spatial analysis, statistical modelling, and geocomputation. The course examines spatial data structures, coordinate reference systems, spatial dependence and heterogeneity, exploratory spatial data analysis, spatial autocorrelation, point-pattern analysis, geostatistics, variograms, kriging, spatial regression, generalized linear and additive spatial models, geographically weighted methods, network analysis, raster and vector modelling, cellular automata, agent-based approaches, and spatial optimization.
Students acquire, clean, visualize, model, and interpret environmental, urban, ecological, and social spatial data using contemporary GIS and programming environments. Emphasis is placed on model selection, diagnostics, validation, uncertainty propagation, scale effects, and the modifiable areal unit problem. Students produce reproducible analytical workflows and communicate results through maps, visualizations, and technically defensible narratives.
Learning Outcomes
- Evaluate spatial data structures, coordinate reference systems, scales, and representations for diverse analytical purposes.
- Select and implement appropriate spatial statistical, geostatistical, network, raster, vector, cellular automata, and agent-based models.
- Diagnose spatial dependence, heterogeneity, multicollinearity, nonstationarity, and violations of model assumptions.
- Estimate, validate, and compare spatial predictions using variograms, kriging, spatial regression, generalized linear models, additive models, and geographically weighted methods.
- Quantify and propagate uncertainty across spatial data acquisition, modelling, prediction, and interpretation.
- Analyze the effects of scale and the modifiable areal unit problem on spatial inference and decision-making.
- Construct reproducible geospatial workflows for data acquisition, cleaning, visualization, analysis, and model documentation.
- Communicate spatial model results through technically accurate maps, visualizations, statistical summaries, and evidence-based narratives.
Timetable
| Type | Length | Frequency | Period |
|---|---|---|---|
| Lecture | 2 hours | Weekly | All semester |
| Lab | 2 hours | Weekly | All semester |
| Tutorial | 1 hour | Fortnightly | All semester |
| Seminar | 1 hour | Fortnightly | All semester |
Assessment Schedule
| Type | Description | Weighting |
|---|---|---|
| Assignment | Spatial analysis exercises (4 × 5%) | 20.00% |
| Test | Practical modelling tests (2 × 7.5%) | 15.00% |
| Capstone | Reproducible spatial modelling project | 30.00% |
| Exam | Final examination | 35.00% |
Prerequisites
- Requirement Advanced competence in statistical inference, linear modelling, GIS, and programming for data analysis.
Teaching Staff & Programs
This course is delivered jointly by faculty from the participating programs listed below. In line with the Douchewater Way, the University of Sexology tailors core instruction directly to each cohort's specific discipline — adapting curriculum to program needs rather than forcing students into a one-size-fits-all model. Learn more about our approach at The Douchewater Way.