# Cartographic Treemaps for Visualization of Public Healthcare Data
Tong et al. present a novel hybrid visual layout called cartographic treemap. By combining the space-filling advantages of treemaps for the display of hierarchical, multivariate data together with geo-spatial information, cartographic treemaps allow exploration, analysis and comparison of complex healthcare data . The authors work with 2 domain experts in health science throughout the development of cartographic treemap.
Figure 1 shows an overview of a cartographic treemap visualizing data of multiple diagnosis collected by 209 clinical commissioning groups (CCG) in the UK. Each node represents a CCG, the size of the node is proportional to the population and the color represents the disease.
Interactive users options are provided to adjust node size, rate of geo-spatial error, screen-space occupance, change color schemes, zoom and pan.
A standard UK map only uses 18% of the screen space, to improve the space efficiency, a region center point is calculated for each CCG and it's used as a starting point for the cartographic layout. The treemap layout is then calibrated and visualized. Overlap of nodes is avoided using the fast node overlap removal algorithm by Dwyer et al. . Bederson et al.  present the Strip treemap algorithm, which is used for ordering treemaps that represent CCGS.
- Data source: Public Health England
- Size: unknown, patients from 209 NHS CCGs
- Spatial dimensionality: 2D
- Temporal dimensionality: static
- Type: multivariate
- Libraries used: C++
- Cartographic treemap
- Geospatial Visualization of EHR
- Medical focus: discuss with Bob
Tong, C., Roberts, R., Laramee, R. S., Berridge, D., & Thayer, D. (2017). Cartographic Treemaps for Visualization of Public Healthcare Data. Computer Graphics & Visual Computing. Retrieved from https://core.ac.uk/download/pdf/132203033.pdf%0Apapers3://publication/doi/10.2312/cgvc.20171276 ↩︎
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