Postgraduate Course: Visual Analytics (PGGE11239)
Course Outline
| School | School of Geosciences |
College | College of Science and Engineering |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Availability | Available to all students |
| SCQF Credits | 20 |
ECTS Credits | 10 |
| Summary | This course provides an introduction to ideas of data visualisation showing how this can be used to examine geographical data sets either by themselves or as part of a larger methodology by combining geographic and non-geographic data. Visual analytics are a series of methods that bring together ideas of visualisation, user-interaction with data, and quantitative analytical techniques with the ambition of supporting analytical reasoning of geographic data. The course builds a foundation of knowledge in digital cartography and approaches to interactive visualisation primarily using R. It introduces a set of quantitative data analysis techniques including exploratory (spatial) data analysis, machine learning, social network analysis explored through a set of practicals. The fundamental idea is the use of visualisations to enhance our understanding of model outputs as well as improving the way in which information can be communicated with others. The creation of this course is in response to technological developments and more specifically to the emerging challenge of analysing and making sense of 'big data' sets in geography. |
| Course description |
Week 1: Geo-visual analytics ¿ an introduction
Week 2: The fundamentals of graphics
Week 3: Cartographic Design ¿ making informative maps
Week 4: Interactivity and Exploratory Data Analysis
Week 5: Communicating visually
Week 6: Machine learning and using visuals to crack open the black box
Week 7: Point Data Smoothing and Hot Spots
Week 8: Spatial interaction modelling
Week 9: Geographically weighted regression
Week 10: Understanding Social Networks
|
Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
|
Co-requisites | |
| Prohibited Combinations | |
Other requirements | None |
Information for Visiting Students
| Pre-requisites | None |
| High Demand Course? |
Yes |
Course Delivery Information
|
| Academic year 2025/26, Available to all students (SV1)
|
Quota: 35 |
| Course Start |
Semester 2 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
200
(
Lecture Hours 20,
Supervised Practical/Workshop/Studio Hours 14,
Summative Assessment Hours 100,
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
62 )
|
| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
|
| Additional Information (Assessment) |
Assessment details
Written Exam 0%
Coursework 100%
Practical Exam 0%
100% Coursework:
Creation of a Story Map using ESRI software (40%). Submission due: Thursday, Week 7.
Analysis of Edinburgh census data using machine learning techniques (60%). Submission due: Thursday, Week 11.
AI-Assisted Editing: AI tools may be used for identifying ideas, planning, and improving the clarity of your writing, but not for content generation. AI use must be acknowledged in your submission.
|
| Feedback |
Formative feedback on presentation of the info graphics exercise, and feedback on first draft of final report on Crime data analysis. |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- Will have pragmatic comprehension of the principles of map design and how they can be applied in GIS contexts
- Will understand the critical role interactive visualisation plays in exploratory geospatial data analysis
- Will have a knowledge of spatial analysis techniques and the conditions under which they can be applied
- Will have a capacity to source and manage large amounts of different sorts of spatial data
- Demonstrate critical reflection when using spatial data and techniques to contribute to addressing real world problems
|
Reading List
¿ Andrienko, G., Andrienko, N., Jankowski, P, Keim, D., Kraak, M.-J., MacEachren, A.M., and Wrobel, S. 2007. Geovisual analytics for spatial decision support: Setting the research agenda. International Journal of Geographical Information Science, 21(8), pp. 839-857.
¿ Unwin (2020) Why is data visualization important? What is important in data visualization? Harvard Data Science Review, 2.1
¿ Kabacoff, R. (2021) Modern Data Visulization with R, CRC Press, Taylor and Francis https://rkabacoff.github.io/datavis/
¿ Boehmke, B., Greenwell, B., (2020) Hands-On Machine Learning with R, CRC Press, Taylor and Francies https://bradleyboehmke.github.io/HOML/
¿ Wickham et al. (2023) R for Data Science, Second Edition, O¿Reilly. https://r4ds.hadley.nz/
|
Additional Information
| Graduate Attributes and Skills |
This course will provide the students with a range of highly marketable skills and introduce them to techniques and associated software that extends beyond traditional GIS. These analytical skills relate closely to the employment opportunities identified by our Industrial External Examiner and graduate feedback. The assessment are focused around problem based learning (Hung et al 2008) and team based learning, providing students with important transferable skills. Additionally they gain skills in exploratory thinking, project work, organisation and report-writing. |
| Keywords | Not entered |
Contacts
| Course organiser | Dr Gary Watmough
Tel: (0131 6)51 4447
Email: Gary.Watmough@ed.ac.uk |
Course secretary | Mrs Katherine Ingram
Tel:
Email: Katherine.Ingram@ed.ac.uk |
|
|
University Homepage