## Spatial Statistics STATS4075

• School: School of Mathematics and Statistics
• Credits: 10
• Level: Level 4 (SCQF level 10)
• Typically Offered: Semester 2
• Available to Visiting Students: Yes

### Short Description

This course introduces statistical approaches to modelling data that have a spatial structure. The course will focus on modelling approaches for the three main types of spatially orientated data, namely: (i) geostatistics; (ii) areal (lattice) data; and (iii) spatial point processes.

### Timetable

Lectures: 20

Tutorials: 4

Practical: 2, two-hour computer lab sessions

### Requirements of Entry

3H Inference

3H Linear models

3H Generalised linear models

3H Bayesian Statistics

STATS5012

### Assessment

90-minute, end-of-course examination (100%)

Main Assessment In: April/May

Are reassessment opportunities available for all summative assessments? Not applicable for Honours courses

Reassessments are normally available for all courses, except those which contribute to the Honours classification. For non Honours courses, students are offered reassessment in all or any of the components of assessment if the satisfactory (threshold) grade for the overall course is not achieved at the first attempt. This is normally grade D3 for undergraduate students and grade C3 for postgraduate students. Exceptionally it may not be possible to offer reassessment of some coursework items, in which case the mark achieved at the first attempt will be counted towards the final course grade. Any such exceptions for this course are described below.

### Course Aims

To introduce the three main types of spatial data and describe how:

to identify trends and spatial autocorrelation.

to model spatial autocorrelation.

to predict the spatial process at unmeasured locations.

to apply the methodology to real spatial data sets in the statistical package R.

### Intended Learning Outcomes of Course

By the end of this course students will be able to:

■ describe the differences between geostatistical data, areal unit data and point process data.

■ use descriptive techniques to determine whether spatially structured data exhibit spatial autocorrelation.

■ define the concepts of stationarity and isotropy.

■ define the nugget, range and sill parameters of a spatial autocorrelation functions.

■ derive the Kriging equations for spatial prediction.

■ define and derive the class of conditionally autoregressive models.

■ determine whether a spatial point process has any spatial structure.

■ use the statistical package R to fit appropriate spatial models to geostatistical data, areal unit data and point process data.

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