Module MATH4341: Spatio-Temporal Statistics

Department: Mathematical Sciences

MATH4341: Spatio-Temporal Statistics

Prerequisites

• Advanced Statistical Modelling (MATH3411) OR Bayesian Computation and Modelling (MATH3421)

Corequisites

• None

Excluded Combination of Modules

• None

Aims

• To introduce the theory, computation and practice of the statistical analysis of problems involving aspects of space and time.

Content

• Spatial statistics: introduction to regionalized statistical concepts (variable, stationarity, random functions, variograms); geostatistics, (co-)kriging; spatial models on lattices; aerial data analysis; point pattern data analysis; and computations with INLA.
• Temporal statistics and time series: components and properties of time series; local and moving-average methods; ARMA models; spectral anlaysis; forecasting and inference; state-space models.

Learning Outcomes

• Be able to identify, explain, and theoritise spatial and temporal dependencies in statistical problems.
• Be able to explain, apply, and generalize appropriate statistical methodology to address spatio-temporal problems.
• Be able to design, explain, interpret, and extend statistical models appropriate for spatial and/or temporal data sets, as well as make inferences and draw conclusions from the analysis of such models.
• Have a coherent understanding of the theory, computation and application of the mathematics underlying the introduced statistical models and methods.
• Be able to use appropriate software to facilitate spatio-temporal statistical analysis.

• Students will have specialised statistical skills in the following areas which can be used with minimal guidance: modelling, use of software, analysis of datasets on different domains (space/time).

• Students will have advanced skills in the following areas: statistical modelling, problem formulation and solution, critical and analytical thinking.

Modes of Teaching, Learning and Assessment and how these contribute to the learning outcomes of the module

• Lectures demonstrate what is required to be learned and the application of the theory to practical examples.
• Computer practicals consolidate the studied material, explore theoretical ideas in practice, enhance practical understanding, and develop practical data analysis skills.
• Assignments for self-study develop problem-solving skills and enable students to test and develop their knowledge and understanding.
• Formative assessments provide feedback to guide students in the correct development of their knowledge and skills in preparation for the summative assessment.
• The end-of-year examination assesses the knowledge acquired and the ability to solve predictable and unpredictable problems.

Teaching Methods and Learning Hours

Summative Assessment

Formative Assessment:

Eight assignments to be submitted.

■ Attendance at all activities marked with this symbol will be monitored. Students who fail to attend these activities, or to complete the summative or formative assessment specified above, will be subject to the procedures defined in the University's General Regulation V, and may be required to leave the University