Visualizing Geographic Information: VisualPoints vs CartoDraw

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.

[1]  T. A. Keahey,et al.  Area-normalized thematic views , 1998 .

[2]  Tamara Munzner,et al.  Exploring Large Graphs in 3D Hyperbolic Space , 1998, IEEE Computer Graphics and Applications.

[3]  Kurt Mehlhorn,et al.  LEDA: a platform for combinatorial and geometric computing , 1997, CACM.

[4]  Herbert Edelsbrunner,et al.  A combinatorial approach to cartograms , 1995, SCG '95.

[5]  Jeffrey S. Torguson,et al.  Cartography , 2019, Dictionary of Geotourism.

[6]  Daniel A. Keim,et al.  The Gridfit algorithm: an efficient and effective approach to visualizing large amounts of spatial data , 1998, Proceedings Visualization '98 (Cat. No.98CB36276).

[7]  Daniel A. Keim,et al.  Efficient cartogram generation: a comparison , 2002, IEEE Symposium on Information Visualization, 2002. INFOVIS 2002..

[8]  Berthold K. P. Horn.  Robot vision , 1986, MIT electrical engineering and computer science series.

[9]  James A. Dougenik,et al.  AN ALGORITHM TO CONSTRUCT CONTINUOUS AREA CARTOGRAMS , 1985 .

[10]  Donald H. House,et al.  Continuous cartogram construction , 1998, Proceedings Visualization '98 (Cat. No.98CB36276).

[11]  S. Selvin,et al.  Transformations of maps to investigate clusters of disease. , 1988, Social science & medicine.

[12]  Hans-Peter Kriegel,et al.  Using extended feature objects for partial similarity retrieval , 1997, The VLDB Journal.

[13]  C. Cauvin,et al.  Cartographic transformations and the piezopleth maps method , 1989 .

[14]  Daniel A. Keim,et al.  CartoDraw: a fast algorithm for generating contiguous cartograms , 2004, IEEE Transactions on Visualization and Computer Graphics.

[15]  Kurt Mehlhorn,et al.  The LEDA Platform of Combinatorial and Geometric Computing , 1997, ICALP.

[16]  Donald H. House,et al.  Continuous cartogram construction , 1998 .

[17]  Vladimir S. Tikunov,et al.  A New Technique for Constructing Continuous Cartograms , 1993 .

[18]  M. Sheelagh T. Carpendale,et al.  Tardis: a visual exploration environment for landscape dynamics , 1999, Electronic Imaging.

[19]  Edward L. Robertson,et al.  Nonlinear magnification fields , 1997, Proceedings of VIZ '97: Visualization Conference, Information Visualization Symposium and Parallel Rendering Symposium.

[20]  Robert A. Hummel,et al.  Massively parallel model matching: geometric hashing on the Connection Machine , 1992, Computer.