代写COMP5048、代写Java/Python程序

日期：2023-04-07 10:02

COMP5048 Visual Analytics 2023S1 Assignment 1: Individual work Deadline: April 6 (Week 7) Thursday 23:59pm (pdf on Canvas)

Construct good visualisations of FOUR of the following data to answer the given task.

You can use any suitable layout chosen from the tools assigned to each category.

Create visualisations for each data according to the instructions given.

In your report, explain your justification for your selected visualisation and analysis

methods, then evaluate and compare the pros and cons of your visualisations.

(Note: different tools not listed can be used for analysis before visualising.)

Data: Choose one graph from each category A, B, C, D:

Category A:

For this category, visualise the data using any tool from the following: Tulip, D3

A1: Best-selling albums of 2000s

Visualise the whole tree using TWO different layouts.

Select one label from the data, e.g., could be the most popular label or your favourite, and

extract the subtree rooted at the label.

Visualise the subtree rooted at the selected label.

A2: Best-selling singles of 2000s

Visualise the whole tree using TWO different layouts.

Select one genre from the data, e.g., could be the most popular genre or your favourite, and

extract the subtree rooted at the genre.

Visualise the subtree rooted at the selected genre.

Category B:

For this category, visualise the data using any tool from the following:

yEd, Tulip, Gephi, NetworkX

B1: Composers graph

Analyse the graph to identify the top 150 composers and extract the induced subgraph

containing the top composers.

Visualise the subgraph using TWO different layouts.

Analyse the subgraph using graph analysis methods to identify the most influential

composers and display the analysis results in the visualisation.

B2: TVCG collaboration graph

Analyse the graph to identify the top 150 collaborators and extract the induced subgraph

containing the top collaborators.

Visualise the subgraph using TWO different layouts.

Analyse the subgraph using graph analysis methods to identify the most influential

collaborators and display the analysis results in the visualisation.

Category C:

For this category, visualise the data using any tool from the following: yEd, Tulip, Graphviz

C1: Movie remakes

Analyse the graph to identify the influential directors and movies.

Visualise the graph using TWO different layouts.

Display the analysis results in the visualisation.

C2: Hrafnkels Saga

Analyse the graph to identify the influential characters and relationships.

Visualise the graph using TWO different layouts.

Display the analysis results in the visualisation.

Category D:

For this category, visualise the data using any tool from the following: yEd, Tulip, Gephi, NetworkX, D3

D1: “INFECTIOUS” exhibition interaction network

Analyse the graph to identify the important people and interactions, both in each time slice

and over all time slices.

Visualise the graph, showing all time slices of the graph, using TWO different methods.

Display the analysis results in the visualisation.

D2: Workplace contacts graph

Analyse the graph to identify the important people and interactions, both in each time slice

and over all time slices.

Visualise the graph, showing all time slices of the graph, using TWO different methods.

Display the analysis results in the visualisation.

Submission: Minimum 16 - page report Minimum 4 pages per data:

1st page: 1st Visualisation

2nd page: 2nd Visualisation

3rd-4th pages: Description with the following subheadings:

Design: tools and layouts with justification (design choice)

Analysis: explain analysis methods used with justification on how they support the task

Evaluation: comparison of pros and cons between the two visualisations

Acknowledge all your sources in References

Provide code, if applicable, in Appendix

In Appendix, you can include one more visualisation for each data:

Should be substantially different using different techniques

Include description as above for each alternative visualisation

Add comparison between different visualisations of the same data

Marking Rubric: (5 marks per data; total 20 marks)

Quality of visualisation: 3 marks

Quality of analysis: 1 mark

Quality of evaluation: 1 mark