Postgraduate Course: Visual Analytics (PGGE11239)
|School||School of Geosciences
||College||College of Science and Engineering
|Credit level (Normal year taken)||SCQF Level 11 (Postgraduate)
||Availability||Available to all students
|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 bothThis 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.
Week 1: Ideation & geovisualisation paradigms (William Mackaness)
Week 2: Visual cognition and spatial narratives (William Mackaness)
Week 3: Machine Learning and Self Organising Maps (William Mackaness)
Week 4: Interactivity and Exploratory Data Analysis (William Mackaness)
Week 5: Understanding social networks through Graph Theory (William Mackaness)
Week 6: Cluster Analysis, Hot Spots and Outliers (William Mackaness)
Week 7: Spatial Interaction Modelling (Zhiqiang Feng)
Week 8: Geographically weighted regression (Zhiqiang Feng)
Week 9: Mapping big data and Volunteered Geographic Information (William Mackaness)
Week 10: Modelling flow and geographical association (William Mackaness)
Entry Requirements (not applicable to Visiting Students)
||Other requirements|| None
Information for Visiting Students
|High Demand Course?
Course Delivery Information
|Academic year 2022/23, Available to all students (SV1)
|Learning and Teaching activities (Further Info)
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
|Assessment (Further Info)
|Additional Information (Assessment)
Written Exam 0%
Practical Exam 0%
Essay (20%). Submission due: Thursday, Week 4.
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 11.
||Formative feedback on presentation of the info graphics exercise, and feedback on first draft of final report on Crime data analysis.
|No Exam Information
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.
|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.
|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.
|Course organiser||Dr William MacKaness
Tel: (0131 6)50 8163
|Course secretary||Miss Niamh Bajai
Tel: (0131 6)50 8105