Visualisation Project (40%)
Relevant learning outcomes:
5. Choose an appropriate data visualisation;
6. Implement interactive data visualisations using python, R and other tools.
Details of task:
1. Identify which findings from the Data Exploration Project you wish to
communicate and who the intended audience is. Be selective, you do not
need to and should not communicate everything you found. The intended
audience might be your classmates, general public or politicians or
whoever you like.
2. Design a narrative visualisation to communicate your findings to the
intended audience. It should allow some viewer interaction and be
designed using the five-sheet design methodology.
3. Implement your visualisation as a web-based presentation using R or D3.
4. Presentation to the tutorial class [Week 12]
Submit final report and source code [Start Exams: 4/6/18]
Report Final Product: At the start of the Exam Period you need to submit
(through Moodle) a directory containing the implementation code for your
narrative visualization together with a written report of no more than 15 pages
that contains
1. Introduction: Precise description of what message you wanted your
narrative visualisation to convey and who the intended audience is.
2. Design: Description of the visualization design process including the 5
design sheets detailing the alternatives you considered and the reasons
for choosing your final design.
3. Implementation: Description of the implementation including libraries
used and reasons for the implementation decisions for your narrative
visualisation.
4. User guide: Instructions for viewing and exploring the narrative
visualisation using a standard web browser and images showing how the
visualization works.
5. Conclusion: Summarise what you achieved and a reflection on what you
learnt in this project and what in hindsight you might have done
differently to improve the result
6. Appropriate references and bibliography
Your report should contain full details of the 5 design sheets and images of the
final product as well as pointing out any reasons why your project was difficult,
e.g. large data set, use of D3 etc..
Marking Rubric (40%)
Design [15%]
/5 Appropriate use of five design sheet methodology and evaluation of
alternatives
/10 Quality of final design: clear signposting of messages and intended narrative,
provision of appropriate context for reader, good use of colour, references to
data sources and appropriateness for intended audience
Implementation [7%]
/5 Correctness and robustness, speed, accessibility
/2 Comments and code quality
Difficulty [10%]
Degree of difficulty, e.g use of non-tabular data, large dataset, D3 programming,
sophisticated user interaction [10%]
Presentation [3%]
/1 Quality of oral presentation (confidence, speed, voice) and quality of slides
(legibility, design, images etc)
/1 Logical structure
/1 Choice of content (completeness, appropriate level, discussion of design and
implementation alternatives)
Written report [5%]
/1.5 Quality of writing, referencing, images, logical structure
/3.5 Completeness