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2023/24 Taught Postgraduate Module Catalogue

GEOG5009M Visualisation for Geographic Data Science

15 creditsClass Size: 40

Module manager: Roger Beecham

Taught: 1 May to 31 July View Timetable

Year running 2023/24

Pre-requisite qualifications

Students should have completed the PGCert year of the programme before attempting this module, or be able to evidence equivalent prior learning through other educational programmes or work experience.

This module is not approved as an Elective

Module summary

This module will equip students with the technical and critical-reasoning skills to explore, analyse and communicate structure in geospatial datasets using visualization. Through the module students will learn modern techniques for processing and wrangling geospatial 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. Whilst an advantage, prior familiarity with R or programming concepts is not a requirement.


This module seeks to equip students with the technical and critical-reasoning skills to explore, analyse and communicate structure in geospatial datasets using visualization.

It introduces key theory from research in cognitive science, computer science and GIS and demonstrates how this can been 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 completion of this module, students will be able to:

1. Describe, process and combine 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


An indicative syllabus for this module is show below:

1. Introduction to visualization for geographic data science
2. Tidy spatial data and data wrangling
3. Visualization fundamentals
4. Exploratory visual data analysis
5. Exploratory visual data analysis of geospatial data
6. Uncertainty visualization
7. Storytelling in visual data analysis
8. No new content: coursework surgery

Teaching methods

Delivery typeNumberLength hoursStudent hours
Discussion forum82.0016.00
Individual Support81.008.00
Independent online learning hours48.00
Private study hours78.00
Total Contact hours24.00
Total hours (100hr per 10 credits)150.00

Private study

Private study and online learning includes directed and independent wider reading, independent practical work and researching/undertaking module assessments and related tasks.

Opportunities for Formative Feedback

Formative feedback will be provided via discussion boards where students are encouraged to post questions, comments, practical outputs etc. Note that the outputs requested here will differ from those required as part of the summative portfolio.
The module leader will also provide support via email and Skype/Collaborate Ultra during the learning stages, in advance of assessment.

Methods of assessment

Assessment typeNotes% of formal assessment
ReportProject report (2,500 word equivalent)70.00
PortfolioWeekly outputs from practicals (1,500 word equivalent)30.00
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 website

Last updated: 28/04/2023 14:56:06


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