Please note: there may be some adjustments to the teaching arrangements published in the course catalogue for 2020-21. Given current circumstances related to the Covid-19 pandemic it is anticipated that some usual arrangements for teaching on campus will be modified to ensure the safety and wellbeing of students and staff on campus; further adjustments may also be necessary, or beneficial, during the course of the academic year as national requirements relating to management of the pandemic are revised.

Spatial Statistics (Level M) - summer STATS5102

  • Academic Session: 2021-22
  • School: School of Mathematics and Statistics
  • Credits: 10
  • Level: Level 5 (SCQF level 11)
  • Typically Offered: Summer
  • Available to Visiting Students: No
  • Available to Erasmus Students: No

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.


20 Lectures - 10 per week taught in 10m, 2 hour blocks

4 tutorials - 2 per week each last 1 hour

2 labs - 1 per week each last 2 hours

Requirements of Entry

Some optional courses may be constrained by space and entry to these is not guaranteed unless you are in a programme for which this is a compulsory course.

Excluded Courses

STATS4075 (corresponding H level course)


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

Main Assessment In: August

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.

to use spatial modelling techniques in a research context.

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.

Critique research papers in spatial statistics.

Minimum Requirement for Award of Credits

Students must submit at least 75% by weight of the components (including examinations) of the course's summative assessment.