# tong2017cartographic
# Cartographic Treemaps for Visualization of Public Healthcare Data
Concept
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 [1]. The authors work with 2 domain experts in health science throughout the development of cartographic treemap.
Implementation
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.
Related Work
The authors adhere to the definition of cartographic visualization by Cruz et al. [2] and the definition of geospatial treemap by Man et al. [3].
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. [4][5]. Bederson et al. [6] present the Strip treemap algorithm, which is used for ordering treemaps that represent CCGS.
Data Characteristics
- Data source: Public Health England
- Size: unknown, patients from 209 NHS CCGs
- Spatial dimensionality: 2D
- Temporal dimensionality: static
- Type: multivariate
- Libraries used: C++
Visualization Techniques
- Cartographic treemap
- Treemap
- Cartogram
Papers Cited
39
Years Spanned
1934-2017
Application Domain
- Geospatial Visualization of EHR
- Medical focus: discuss with Bob
Reference
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 âŠī¸
Cruz, P., Cruz, A., & Machado, P. (2015). Contiguous Animated Edge-Based Cartograms for Traffic Visualization. IEEE Computer Graphics and Applications. https://doi.org/10.1109/MCG.2015.108 âŠī¸
Mansmann, F., Keim, D. A., North, S. C., Rexroad, B., & Sheleheda, D. (2007). Visual analysis of network traffic for resource planning, interactive monitoring, and interpretation of security threats. IEEE Transactions on Visualization and Computer Graphics. https://doi.org/10.1109/TVCG.2007.70522 âŠī¸
Dwyer, T., Marriott, K., & Stuckey, P. J. (2006). Fast node overlap removal. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). https://doi.org/10.1007/11618058_15 âŠī¸
Dwyer, T., Marriott, K., & Stuckey, P. J. (2007). Fast node overlap removal - Correction. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). https://doi.org/10.1007/978-3-540-70904-6_44 âŠī¸
Bederson, B. B., Shneiderman, B., & Wattenberg, M. (2002). Ordered and quantum treemaps: Making effective use of 2D space to display hierarchies. ACM Transactions on Graphics. https://doi.org/10.1145/571647.571649 âŠī¸