GV2MU-Managing Uncertainty

Module Provider: Geography and Environmental Science
Number of credits: 10 [5 ECTS credits]
Level:5
Terms in which taught: Autumn term module
Pre-requisites:
Non-modular pre-requisites:
Co-requisites:
Modules excluded:
Current from: 2023/4

Module Convenor: Dr Liz Stephens
Email: elisabeth.stephens@reading.ac.uk

Type of module:

Summary module description:

This module will equip students with the skills to interpret, critique and discuss quantitative geographical approaches and data, as well as developing an understanding of the different sources of uncertainty and the implications for communicating findings and decision-making. The module will use seminars and computer-based lab practicals to enable students to engage with research datasets that reflect contemporary geographical challenges such as forecasting of natural hazards, understanding climate change, and predicting socio-demographic changes.

Aims:

The aim of this module is to develop a student’s ability to interpret, critique and discuss quantitative geographical approaches and data. Students will be taught numerical analysis skills in ‘R’ through which they can visualise, interpret and interrogate geographical datasets related to predicting natural hazards, understanding climate change and understanding socio-demographic data.Ìý

Assessable learning outcomes:

By the end of this module, students should be able to:Ìý

• Recognise and describe different sources of uncertainty


• Visualise data in a number of different ways in ‘R’


• Calculate summary statistics in ‘R’


• Critically appraise quantitative geographical research

Additional outcomes:

This module will provide the opportunity to develop the following transferable skills:

• Teamwork


• Data handling


• Data presentation


• Written presentation


• Critical thinkingÌý

Outline content:

Introductory lectures will lead into computer-lab based practicals through which students will engage with issues such as identifying trends in data, addressing uncertainties in sample size and measurement uncertainty, extrapolation and correlation versus causation.

Brief description of teaching and learning methods:

Introductory lectures for each topic, followed by hr guided computer-lab practicals to address a topic / key issue each week. A one-hour introduction to the project will be provided as well as lectures on writing an executive summary / communicating to stakeholders.ÌýÌý