A combinatorial approach to cartograms
Computational Geometry: Theory and Applications
Proceedings of the conference on Visualization '98
Continuous cartogram construction
Proceedings of the conference on Visualization '98
LEDA: a platform for combinatorial and geometric computing
LEDA: a platform for combinatorial and geometric computing
Robot Vision
Exploring Large Graphs in 3D Hyperbolic Space
IEEE Computer Graphics and Applications
Using extended feature objects for partial similarity retrieval
The VLDB Journal — The International Journal on Very Large Data Bases
Nonlinear Magnification Fields
INFOVIS '97 Proceedings of the 1997 IEEE Symposium on Information Visualization (InfoVis '97)
Efficient Cartogram Generation: A Comparison
INFOVIS '02 Proceedings of the IEEE Symposium on Information Visualization (InfoVis'02)
CartoDraw: A Fast Algorithm for Generating Contiguous Cartograms
IEEE Transactions on Visualization and Computer Graphics
HistoScale: An Efficient Approach for Computing Pseudo-Cartograms
Proceedings of the 14th IEEE Visualization 2003 (VIS'03)
Dynamic visualization of spatially referenced information
ISVC'05 Proceedings of the First international conference on Advances in Visual Computing
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Cartograms are a well-known technique for showing geography-related statistical information, such as population demographics and epidemiological data. The basic idea is to distort a map by resizing its regions according to a statistical parameter, but in a way that keeps the map recognizable. In this paper, we deal with the problem of making continuous cartograms that strictly retain the topology of the input mesh. We compare two algorithms that solve the continuous cartogram problem. The first one uses an iterative relocation of vertices based on scanlines. This algorithm explicitly accounts for induced shape error. The second one is based on the Gridfit technique, which uses pixel-based distortion based on a quadtree-like data structure. The basic idea is to insert pixels, the number of which corresponds to a statistical parameter, into the data structure and distort the pixels such that every pixel obtains a unique, nonoverlapping position. Relocation of vertices of the map are positioned using the same distortion. We discuss the results obtained from both methods, compare their shape and area trade-offs as well as their efficiency, and show results from different applications.