Module manager: Dr Paul Norman
Email: p.d.norman@leeds.ac.uk
Taught: Semester 2 (Jan to Jun) View Timetable
Year running 2024/25
| GEOG5032M | GIS Data Visualisation & Analysis |
| GEOG5042M | Geographic Data Visualisation & Analysis |
| GEOG5007M | Geodemographics and Neighbourhood Analysis |
This module is not approved as an Elective
Area characteristics about neighbourhoods have widespread use in analyses which inform policy decision making in public (local and national government), and private (commercial) organisations as well as the third sector (charities). Area characteristics are also linked to individual level data to determine, for example, variations in health and educational application. On this module, students will learn how to construct a variety of area measures and methods to incorporate and analyse. The knowledge and skills involved are highly useful in many careers due to their relevance to planning and decision-making in academic, public, private and third sector settings.
This module equips students to analyse and use area (neighbourhood) characteristics to underpin decision making (local planning, policy evaluation, marketing etc). A range of measures are introduced and applied at the small-area level (urban/health research), urban-rural classifications (environmental, planning and social science applications) and geodemographics (business and marketing). Students will consider:
1. Sources of census, administrative and commercial data for measuring area characteristics
2. How to devise small-area indicators (from a theoretical and applied perspective)
3. How to use these indicators (in application areas relevant to their programme of study)
The module is taught using supported practical activities and accompanying lectures and private study.
On successful completion of the module, you will be able to demonstrate the following learning outcomes relevant to the subject:
1. Identify and critique appropriate census, administrative, survey and commercial data sources for measuring area characteristics.
2. Apply data reduction techniques to generate indicators of area type and appreciate their applications.
3. Construct, test, evaluate and apply geodemographic classifications at the area level, and relate these to data on other topics.
4. Appreciate ethical and confidentiality constraints when working with individual and small-area data, as well as conceptual issues of scale
5. Recognise and avoid statistical biases and misinterpretation of data
Skills Learning Outcomes
On successful completion of the module, you will be able to demonstrate the following skills learning outcomes:
6. ‘Digital Skills’ for ‘Critical Evaluation’ and ‘Enterprise Skills’ for ‘Planning and Mobilising Resources’ and ‘Information Searching’ through competence in cleaning, processing and preparing data suitable for and during analysis
7. ‘Academic Skills’ for ‘Presentation Skills’ and ‘Critical Thinking’ through the ability to present and explain socio-economic data
8. ‘Digital Skills’ for ‘Critical Evaluation’ and ‘Technical Skills’ for selecting and carrying out suitable data reduction methods relating to a problem or task
9. ‘Work Ready Skills’ for ‘Problem solving & analytical skills’ and ‘Technical Skills’ through the ability to utilise data-driven findings to propose solutions and/or recommendations in response to a geodemographic problem/task
Details of the syllabus will be provided on the Minerva organisation (or equivalent) for the module.
| Delivery type | Number | Length hours | Student hours |
|---|---|---|---|
| Lecture | 5 | 1 | 5 |
| Practical | 5 | 2 | 10 |
| Private study hours | 135 | ||
| Total Contact hours | 15 | ||
| Total hours (100hr per 10 credits) | 150 | ||
Formative feedback will be provided during Q&A sessions in lectures and in one-to-one support during practical work (supported by staff and postgraduate demonstrators) giving students regular opportunities to receive feedback ahead of submitting their assignments. The module leader and teaching staff 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.
| Assessment type | Notes | % of formal assessment |
|---|---|---|
| Assignment | Coursework | 30 |
| Assignment | Coursework | 70 |
| Total percentage (Assessment Coursework) | 100 | |
Normally resits will be assessed by the same methodology as the first attempt, unless otherwise stated
The reading list is available from the Library website
Last updated: 4/29/2024
Errors, omissions, failed links etc should be notified to the Catalogue Team