2024/25 Taught Postgraduate Module Catalogue
GEOG5009M Visualisation for Geographic Data Science
15 creditsClass Size: 40
Module manager: Dr Roger Beecham
Email: r.j.beecham@leeds.ac.uk
Taught: 1 May to 31 July View Timetable
Year running 2024/25
This module is not approved as an Elective
Module summary
In this module students will learn modern techniques for processing and wrangling data, design and critique statistical graphics that expose structure in geographic data and develop expertise in communicating statistical effects under uncertainty. All data analysis activity will be performed within the R statistical programming environment.Objectives
This module seeks to equip students with the technical and critical-reasoning skills to explore, analyse and communicate structure in geographic datasets using visualization. It introduces key theory from research in cognitive science, computer science and GIS and demonstrates how this can be applied in a data analysis. Emphasis is placed on modern datasets and data analysis environments: students will develop an understanding of functional programming concepts, computational statistics and reproducible methods.Learning outcomes
On successful completion of the module students will have demonstrated the following subject learning outcomes:
1. Describe and understand processes for analysing geographic datasets from a range of sources
2. Design statistical graphics that expose structure in geographic data and that are underpinned by established principles in information visualization and cartography
3. Use modern data science and visualization frameworks to produce coherent data analyses
4. Apply modern statistical techniques for analysing, representing and communicating data and model uncertainty
Skills Learning Outcomes
On successful completion of the module students will have demonstrated the following skills learning outcomes:
5. Academic. Through assessed work, students will present outputs in a concise and evidence-based way, with integrity.
6. Digital. Students will use digital technology to collaborate with each other and to communicate their work in an engaging way.
7. Work-ready. Using open and widely-used tools for computational analysis, students will develop technical and IT skills valued in the workplace.
8. Technical. Through the use of open software and programming environments for modern computational analysis.
9. Enterprise. Through the module content and assessed activity, students will reason over quantitative evidence, making decisions under uncertainty.
Syllabus
Details of the syllabus will be provided on the Minerva organisation (or equivalent) for the module.
Teaching methods
| Delivery type | Number | Length hours | Student hours |
| Discussion forum | 8 | 2.00 | 16.00 |
| Individual Support | 8 | 1.00 | 8.00 |
| Independent online learning hours | 48.00 | ||
| Private study hours | 78.00 | ||
| Total Contact hours | 24.00 | ||
| Total hours (100hr per 10 credits) | 150.00 | ||
Opportunities for Formative Feedback
Formative feedback will be provided during practical activities where students will be encouraged to post outputs to devoted unit-by-unit discussion boards. This will allow for peer critique in addition to staff comments. Note that the outputs requested here will differ from those required as part of the summative assessments.The module leader will also be on hand to provide support (email / Teams / discussion board / etc) during the teaching weeks, in advance of assessment. Whilst not directly formative assessment, this will ensure that the students receive feedback / support on matters of need.
Methods of assessment
Coursework
| Assessment type | Notes | % of formal assessment |
| Assignment | Coursework | 30.00 |
| Assignment | Coursework | 70.00 |
| Total percentage (Assessment Coursework) | 100.00 | |
Normally resits will be assessed by the same methodology as the first attempt, unless otherwise stated
Reading list
The reading list is available from the Library websiteLast updated: 29/04/2024 16:14:37
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