2023/24 Undergraduate Module Catalogue

GEOG1400 Digital Geographies

20 creditsClass Size: 250

Module manager: Alexis Comber
Email: a.comber@leeds.ac.uk

Taught: Semester 2 (Jan to Jun) View Timetable

Year running 2023/24

Module replaces

GEOG1081

This module is not approved as a discovery module

Module summary

Digital Geographies is an exciting module offering students a beginner’s guide to the important and rapidly changing world of geographical data analysis. It blends an understanding with core numerical skills and conceptual understandings of how space and spatial relationships can be represented. A combination of weekly lectures and computer practicals introduce students to basic techniques of spatial data analysis, hypothesis testing and mapping, the potential uses and applications of data from policing to planning, and the opportunities presented by the many new forms of data. Critically, students learn how to determine the reliability and statistical representativeness of data, to link different data, to interpret the results of different statistical tests, to create spatial data and to undertake spatial analysis, linking across the human (social) and physical (environmental) domains.

Objectives

The overall aims of this module are to:

- embed an understanding of geographical data (digital spatial data literacy) and data handling skills within the physical and human geography curricula

- develop students’ understanding of spatial, numerical, statistical and GIS-related theory

- enhance students’ numeric skills and increase their ability to handle, process and analysis of digital geographic data

- train students in a number of vital skills in how to perform basic data and spatial data manipulations, summaries and mapping.

- enable students to develop specific skills in using statistical software (e.g. GIS, R/RStudio, Excel) for data analysis, manipulation producing summary statistics and visualisations

- provide students with a foundational understanding of spatial and geographic data and quantitative methods to enable them to have a greater choice of optional and discovery modules at levels 2 and 3

Learning outcomes
At the end of this module students will be able:

1. to understand different sources and types of data and spatial data

2. to demonstrate an ability to investigate and summarise the statistical properties of non-spatial and spatial data

3. to produce maps, under cartographic conventions

4. to undertake statistical and spatial analyses of spatial data using statistical software and GIS

Skills outcomes
The module will be built around the learning and teaching of explicit core QAA sub-specific skills (https://www.qaa.ac.uk/docs/qaa/sbs/sbs-geography-22.pdf):

From 3.30 of the QAA

spatial awareness and observation

recognising the moral, ethical and safety issues involved in all aspects of geographical enquiry

employing a variety of social survey methods, for example, questionnaire surveys and semi-structured interviews

employing a variety of science laboratory skills and methods, for example, soil, water and sediment sample preparation and analysis

primary data generation, collection and recording, and the use of secondary data sets, both quantitative and qualitative

critically evaluating, interpreting and combining different types of geographical evidence, for example, texts, visual and material culture, archival data, maps, digitised and laboratory data

analysis and problem-solving through quantitative and qualitative methods

applying methods for the collection and analysis of spatial and environmental information, for example, by using GIS, remote sensing, statistical and mathematical modelling

preparing effective maps, diagrams and visualisations.



From 3.32 of the AQQ

understanding the appropriate and ethical use of evidence and data

numeracy and statistical literacy

abstraction and synthesis of information

taking responsibility for learning and reflection upon that learning.

From 3.33 of the AQQ

retrieval, handling and archiving of datasets

an understanding of intellectual property and data privacy

the ability to take creative approaches to problem-solving.

Syllabus

The module will have the following indicative structure:

1. Introduction to GIS and types of data / spatial data: why geography is special!

2. Introduction descriptive statistics and sources of data (social and environmental)

3. Scale, Cartography and Projections

4. Spatial analysis I, manipulating spatial data (indices, composites, spatial composites, etc)

5. Spatial analysis II, more advanced spatial analysis (distances, access, viewsheds)

Reading Week

6. Experimental design, data collection and samples, introduction to the module survey results

7. More on sampling a, bias and representativeness, new forms of data (citizen science, social media etc)

8. Hypothesis Testing I Statistical Tests of Difference and Significance

9. Hypothesis testing II: Parametric and Nonparametric tests

10. Hypothesis Testing III: Regression for inference and prediction


Leaving the final week free allows for a reading work, an additional topic, a revision session etc.

Teaching methods

Delivery type	Number	Length hours	Student hours
Drop-in Session	9	1.00	9.00
Lecture	10	2.00	20.00
Practical	9	1.00	9.00
Practical	10	2.00	20.00
Private study hours			142.00
Total Contact hours			58.00
Total hours (100hr per 10 credits)			200.00

Private study

Private Study and Independent Learning - Detail private study and independent learning outside formal classes as a guide to students about what is expected from them for the module
• c. 50 hours to additional reading to enhance their understanding of themes introduced in lectures and practicals;
• c. 32 hours to preparation for practicals and the fieldwork;
• c. 22 hours to reading and other preparation for the class test;
• c. 50 hours to reading and revision in preparation for the end-of-module project report

Opportunities for Formative Feedback

Each practical is formative and will have self-test questions with answers against which students can test their understanding and data manipulation skills: students will be able to compare their “results” and understanding with model answers. Staff will be able to monitor performance in practicals.

Additionally, each week there will be optional, pre-recorded maths support sessions, intended to help students who have not studied maths or science at A-level.

Methods of assessment

Coursework

Assessment type	Notes	% of formal assessment
Portfolio	.	50.00
In-course MCQ	.	50.00
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: 21/09/2023

Disclaimer

Browse Other Catalogues

• Undergraduate module catalogue
• Taught Postgraduate module catalogue
• Undergraduate programme catalogue
• Taught Postgraduate programme catalogue

Errors, omissions, failed links etc should be notified to the Catalogue Team.PROD