Study information

Data Science and Statistical Modelling in Space and Time - 2024 entry

MODULE TITLEData Science and Statistical Modelling in Space and Time CREDIT VALUE15
MODULE CODEMTHM505 MODULE CONVENERProf Peter Challenor (Coordinator), Dr Hossein Mohammadi (Coordinator)
DURATION: TERM 1 2 3
DURATION: WEEKS 6 (October start) / 0 (January start) 0 (October start) / 5 (January start)
Number of Students Taking Module (anticipated) 50
DESCRIPTION - summary of the module content

In this module you will learn about modelling data that is collected over space and time. Advances in statistical and computing methodology together with the increasing availability of data recorded at very high spatial and temporal resolution has led to great advances in temporal, spatial and, more recently, spatio–temporal methods. In this module you will have the opportunity to explore the theoretical aspects of these methods and will learn how to implement them to explore and understand patterns in space and time within data from real-world examples.

 

AIMS - intentions of the module

In many applications of Data Science and Statistics, data are measured, or collected, over space and time. In such cases, methods that assume that data are independent may not be suitable, and more sophisticated modelling approaches are required. The aim of this model is to introduce the concepts behind dependencies in time and space and to learn methods for time series modelling and spatial analyses. The module will cover both theoretical and practical aspects of modelling data over space and time, with examples from computer modelling, the environment and health.

 

INTENDED LEARNING OUTCOMES (ILOs) (see assessment section below for how ILOs will be assessed)

On successful completion of this module you should be able to:

Module Specific Skills and Knowledge

1. Model correlated data structures in continuous space and time co-ordinates;
2. Explain what a Gaussian process is and how it can be used to model spatially correlated data in 1, 2 or many dimensions;
3. Describe the difference between space and time in modelling and create models using both ARIMA and state space modelling of time series;
4. Demonstrate an understanding of spatio-temporal modelling;
5. Use appropriate software and a suitable computer language for modelling correlated data in space, time and both together;

Discipline Specific Skills and Knowledge

6. Apply the theory of statistical modelling of spatially and temporally correlated data and analyse the resulting models;

Personal and Key Transferable / Employment Skills and Knowledge

7. Utilise advanced data analysis skills and be able to communicate associated reasoning and interpretations effectively in writing;
8. Use relevant computer software competently;
9. Utilise learning resources appropriately.
SYLLABUS PLAN - summary of the structure and academic content of the module

- Dependent data; distance and correlation, stationarity, the Gaussian process; covariance functions; nuggets, sampling from Gaussian processes;

- Types of covariance function, Bochner’s theorem; separability; fitting Gaussian processes; examples;

- Kriging; variograms and covariance functions; time and space; ARIMA models; state space models; dynamic linear models;

- Spatio-temporal models, hierarchical modelling.

 

LEARNING AND TEACHING
LEARNING ACTIVITIES AND TEACHING METHODS (given in hours of study time)
Scheduled Learning & Teaching Activities 30 Guided Independent Study 118 Placement / Study Abroad
DETAILS OF LEARNING ACTIVITIES AND TEACHING METHODS
Category Hours of study time Description
Scheduled Learning and Teaching Activities 20 Lectures
Scheduled Learning and Teaching Activities 10 Hands-on practical sessions
Guided Independent Learning 118 Coursework, background reading, preparation for contact time, preparation for assessments

 

ASSESSMENT
FORMATIVE ASSESSMENT - for feedback and development purposes; does not count towards module grade
Form of Assessment Size of Assessment (e.g. duration/length) ILOs Assessed Feedback Method

Online quizzes

 4 x 30 minutes All Electronic

 

SUMMATIVE ASSESSMENT (% of credit)
Coursework 100 Written Exams 0 Practical Exams
DETAILS OF SUMMATIVE ASSESSMENT
Form of Assessment % of Credit Size of Assessment (e.g. duration/length) ILOs Assessed Feedback Method
Coursework – practical modelling exercises and theoretical problems 80 20 Hours All Written and oral
Class test 20 2 Hours All Electronic, oral

 

DETAILS OF RE-ASSESSMENT (where required by referral or deferral)
Original Form of Assessment Form of Re-assessment ILOs Re-assessed Time Scale for Re-assessment

Coursework – modelling exercises and theoretical problems

Coursework – modelling exercises and theoretical problems (80%)

 All August Ref/Def Period
Class Test Class Test (20%) All August Ref/Def Period
       

 

RE-ASSESSMENT NOTES

Reassessment will be by coursework in the failed or deferred element only. For deferred candidates, the module mark will be uncapped. For referred candidates, the module mark will be capped at 50%

RESOURCES
INDICATIVE LEARNING RESOURCES - The following list is offered as an indication of the type & level of
information that you are expected to consult. Further guidance will be provided by the Module Convener

Reading list for this module:

Type Author Title Edition Publisher Year ISBN
Set Cressie, N. Statistics for Spatial Data Wiley 1991 000-0-471-84336-9
Set Shumway, R H, Stoffer, D S Time series analysis and its applications Springer 2015 978-1-4419-7865-3
CREDIT VALUE 15 ECTS VALUE
PRE-REQUISITE MODULES MTHM501, MTHM502
CO-REQUISITE MODULES
NQF LEVEL (FHEQ) AVAILABLE AS DISTANCE LEARNING No
ORIGIN DATE Tuesday 12th March 2024 LAST REVISION DATE Tuesday 12th March 2024
KEY WORDS SEARCH None Defined

Please note that all modules are subject to change, please get in touch if you have any questions about this module.