Unravelling Australian population maps - Map morphing

Primary supervisor

Sarah Goodwin

Co-supervisors

We explore novel map representations and projections.

This project seeks to explore and trial new map representations for seeing Australian population data sets more effectively.

Whilst Australia has a population of 28 million, nationally it is counted as of the least densely populated in the world due to its size and topography (large areas of semi-arid and desert geography). Yet Australia is one of the world's most urbanised countries with 89% of the population living in a handful of urban areas, mostly with 50KM of the coastline along the east and south coastline. 

These factors make Australia population statistics and other data difficult to map effectively, without the need for inset maps to see detail, or relying on interaction such as zoom. An effective alternative representation that retains the spatial information but provides a rich national overview remains difficult. Map creators often need to resort to aggregating the data heavily to present the information, but this loses fine detailed information and could hinder interpretation. 

We are keen to explore alternative designs. We aim to unravel the map from the coastline at 50KM intervals to present the population statistics in an new layout. This project is influenced by our work on map morphing and interaction to encourage more informed data interpretation and data exploration. 

 

 

Aim/outline

As a thesis student you will explore the literature, help design the new visualisation, iterate on possibilities, implement the new visualisation and evaluate it against more traditional alternatives to judge its effectiveness for reading and comprehending the data. 

This will be a demanding yet exciting project. The supervisory team will guide you through the process and we hope it will lead to continued research in this area. 

You will need to have demonstrated proficiency in programming and strong mathematical skills. Whilst there is potential for continuation within the immersive environment, we anticipate this research to be 2D map distortion. But this might depend on the candidate and their programming skills. You will work closely with geospatial and visualisation academics both at Monash and our global connections toensure the ideas are effective and research is robust. 

URLs/references

Y. Yang, T. Dwyer, K. Marriott, B. Jenny and S. Goodwin, Tilt Map: Interactive Transitions Between Choropleth Map, Prism Map and Bar Chart in Immersive Environments, in IEEE Transactions on Visualization and Computer Graphics, vol. 27, no. 12, pp. 4507-4519, 1 Dec. 2021, doi: 10.1109/TVCG.2020.3004137.

Q. W. Bouts et al., Visual Encoding of Dissimilarity Data via Topology-Preserving Map Deformation, in IEEE Transactions on Visualization and Computer Graphics, vol. 22, no. 9, pp. 2200-2213, 1 Sept. 2016, doi: 10.1109/TVCG.2015.2500225.

S. R. Kobakian (2020) New algorithms for effectively visualising Australian spatio-temporal disease data. Master of Philosophy thesis, Queensland University of Technology. 10.5204/thesis.eprints.203908

 

 

Required knowledge

Honours or Minor thesis project.

Demonstrated proficiency in programming, ideally javascript, and strong mathematical skills.

Interest in cartography and understanding of the geography of Australia ideal.