University of Sydney Course Explorer – Visualisation

October 30, 2011 § Leave a comment

For the final project of COMP5048 Information Visualisation, we decided to visualise the various faculties, courses and subjects within the University. With a specific focus on shared subjects amongst courses and faculties.

We initially intended to go with a node-network representation, but there were simply too many nodes.

Instead, we opted to use multiple of visualisations with related datasets and interactions, as depicted in the video below:

In the future, larger amounts of time could have been devoted to exploring the representation of the datasets as nodes, however, given our time-frame, I stand by our decision to use interaction to enhance the mental model across different complementary visualisations.

The following is our report.



“Mapping Social Statistics” – Dots?

October 7, 2011 § Leave a comment

A very interesting video by Bill Rankin on the implications of mapping with dots vs. solid colours. There are social and cartographic consequences to the visual choices we make.

UOS to Course Pre-Visualization

September 28, 2011 § Leave a comment

Our COMP5048 project requires data on UOS to Course relations. Hence, some pre-visualizations were done for analytical purposes.

Data was translated from the original core data to form an adjacency list.

This data had to be reduced, eliminating repetitions (so, for one thing, it could be mapped on a spreadsheet). There are also various versions of this data, it was transformed into different formats (csv, tab-delimited-text,) at different subsets to the original (~28630 nodes), to experiment with across different visualization programs.

Spreadsheet software were used to generate some quick visualizations. Apple’s iWork Numbers was found to be too unstable for large amounts of data, with Excel surpassing it in performance.

The following scatter chart was generated to visualize similar course-to-subject structures. The x-axis is UOS_Index, the y-axis is Course_ID. (Ignore the symbols).

This same data was then fed into some of my own previous java-visualization applications. However, the full data set would run out of memory (due to inefficiencies in implementation for this specific case).

Hence, other visualization programs were experimented with.
– Ggobi, i unfortunately, I could not seem to figure out, and it seemed outdated.
– Tulip, i had heard good things about of it’s windows equivalent, however, it did not appear to that it could accept adjacency matrix’s.
– Gephi, i had also heard good things about this program and decided to try it out. It’s an amazing program (for one, allowing the import of multiple formats).

Gephi also has inbuilt layout algorithms with manipulable variables. These were very fun (and useful) to experiment with. I initially experimented with a ~10% subset of the data. A couple of results are shown below.

Algorithms such as ‘Yifan Hu Proportional’ and ‘Fruchterman Reingold’ seemed quite useful to the context. ‘Yifan Hu’s Multilevel’ and ‘Force Atlas’ also appeared interesting.

These same algorithms were then applied to the entire UOS to Course dataset.

The algorithms were stopped after an hour or so, but a clearer graph may have been established given more computing power / time to run.

The time it takes for this amount of data to reach viable visual patterns in real time is inappropriate for the project.

Alternatives include:

  • Pre-Cluster Nodes for later Visualization in real-time (Eades, et. al.)
  • Pre-Compute Locations of all Nodes
  • Ordering the rows in the excel graph examples, (like using genome / dna – like visualizations) to compare the similarity between course streams and show shared subjects could also be an alternate form of visualizing similarities in the core dataset.
  • Overall, alternative visualizations to the project.


Such data can be used as hard data to set node positions.

COMP5048 Project Initial Report

September 28, 2011 § Leave a comment

This is an initial report for a planned information visualization which will act as a group core project for COMP5048.

Our visualization system is designed to facilitate future potential University of Sydney students with exploring, and deciding upon, available courses and degrees. Through the agile design of such a system, we will conduct user studies in order to improve our system and evaluate the adoption of various themes and Natural User Interface (NUI) elements within the context of information visualization.

Artistic Visualizations and Evaluations

September 28, 2011 § Leave a comment

The visualization of data allows for it’s exploration and understanding. It communicates insight about the data to others. (See Benefits of Visualization (Card et. al.).

The Gestalt Principles are theories on visual perception which can be utilized in the effective creation of visualizations. They consist of principles of:

  • Similarity
  • Proximity
  • Continuation Principle
  • Connectedness
  • Figure-Ground Relationship
  • Closure
  • Symmetry
  • Area


However, the use of such principles in information visualization is useless without evaluating it’s effect (positive or otherwise). Such evaluation can be done through interviews, qualitative / quantitative techniques, analytical inspection (observation, heuristic evaluation), empirical evaluation forms (usability tests (e.g. ‘Think Aloud’) which are usually done in the early stages of design). When a system is near completion, controlled experiments are often done to gain quantitative results through strict procedures.


Eye tracking can also be used for studies, in one particular study, useres were presented with different stimulai;

and their eye movements were used to discern how crossings affect eye movements and performance, the impact of crossings differing with crossing angle and size of graphs, and that people have geodesic-path tendency in searching for shortest paths.


The below diagram is also an interesting representation of where the graph visualisation research community is aiming for. I think it’s great that we’re developing theories on how users’ read graphs, but believe there should always be user test – feedback loop between the making of algorithms and the theories used.

Layered Graph Drawing (The Sugiyama Method)

September 28, 2011 § Leave a comment

There are various graph drawing conventions and aesthetics. Consider a digraph, we would like:

  1. Edges roughly pointed in one direction
  2. a. Nodes evenly distributed
    b. Long edges avoided.
  3. Edge Crossing Minimized.
  4. Edges should be as straight / vertical as possible.


The Sugiyama Method aims to address these conventions, and is useful for dependency diagrams, flow diagrams, conceptual lattices and other directed graphs. Essentially, layered networks are useful in representing Dependency relations.

The Sugiyama Method:

  1. Cycle Removal
    – may temporarily Reverse some edges
    – each cycle must have at least one edge against the flow (NP Hard), requires heuristic (e.g. enhanced greedy heuristic) or Randomized Algorithms.
  2. Layering. (assigning y)
    – vertices may be introduced to split edges
  3. Node Ordering
    – the Ordering is all that matters (not co-ordinates), NP Hard.
    – Many heuristics. e.g. Layer-by-layer sweep (two-layer crossing problem), addressed by 1. Sorting, 2. Barrycentre, or 3. Median, methods.
  4. Co-Ordinate Assignment.

Slides on this topic can be found here.

18th Graph Drawing Contest

September 28, 2011 § Leave a comment

This is a report of two graph drawings that I did for the 18th Graph Drawing Contest (which was also used for COMP5048 Assignment 1).

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