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 cartography and data visualisation, showing how this can be used to examine geographical data sets using both visual and statistical methods of data exploration. Visual analytics is a methodology that brings 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. It introduces a set of quantitative data analysis techniques explored through a set of practicals. 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: Ideation & geovisualisation paradigms (William Mackaness)
Week 2: Cartography & the notion of design (William Mackaness)
Week 3: Machine Learning and Self Organising Maps (William Mackaness)
Week 4: Visualisation Methodologies ¿ cartograms; Graph Theory: modelling flow, association & connectivity (William Mackaness)
Week 5: Longitudinal analysis Using space and time to identify causal relationships (Chris Dibben)
Week 6: Point data smoothing and Kernel Density estimation (William Mackaness)
Week 7: Spatial Interaction Modelling (Zhiqiang Feng)
Week 8: Geographically weighted regression (Zhiqiang Feng)
Week 9: Augmented Reality in Mobile Contexts (William Mackaness)
Week 10: Mapping big data and Volunteered Geographic Information (William Mackaness)
Week 11: Big Data & Smart Worlds ¿ future perspectives (William Mackaness)
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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 2022/23, Available to all students (SV1)
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Quota: 40 |
| 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) |
100% Coursework:
Essay (20%).
Submission due: Thursday, Week 5.
Project pairs: Creation of a Story Map using ESRI software (30%).
Submission due: Thursday, Week 7.
Analysis of Edinburgh census data using machine learning techniques and self organising maps (50%).
Submission due: Thursday, Week 9.
|
| 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
- Will have developed their transferable skills through development of team based problem solving.
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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.
Bailey, T.C. and Gatrell, A.C. (1995). Interactive spatial data analysis.
Chainey, S and Radcliffe, J (2000) GIS and Crime Mapping. Wiley.
Dykes, J., MacEachren, A.M., and Kraak, M.J. (Eds.). 2004Exploring Geovisualization , Amsterdam: Elsevier Science
Fotheringham, S. Brunsdon, C and Charlton, M (2000) Quantitative Geography: perspectives on spatial data analysis. Sage.
MacEachren, A.M. 2004. Geovisualization for knowledge construction and decision support. IEEE computer graphics and applications, 24(1), pp.13-17.
O'Sullivan, D. and D. J. Unwin (2003 or 2010) Geographic Information Analysis. Wiley, New York.
Visser H and T. de Nijs, 2006. The Map Comparison Kit. Environmental Modelling & Software 21, 346-358.
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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 William MacKaness
Tel: (0131 6)50 8163
Email: |
Course secretary | Ms Heather Penman
Tel: (0131 6)50
Email: |
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