Visualizing Geographic Information: VisualPoints vs CartoDraw

Abstract

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.

Evaluating Cartogram Effectiveness

“…Among deformation cartograms the most popular variant is the ones generated by the diffusion-based algorithms of Gastner and Newman [21], which we use in our evaluation. Others of this type include the rubbermap cartograms by Tobler [49], contiguous area cartograms by Dougenik et al [20], CartoDraw by Keim et al [28], constraintbased continuous area cartograms by House and Kocmoud [25], and medial-axis-based cartograms by Keim et al [30].…”

A Shape Grammar for Developing Glyph‐based Visualizations

“…Much of this work proposes new techniques for two and 3D visualizations. Examples of 2D visualizations include extended ‘focus+context’[FS04] and distorted 2D maps [KNPS03]. Three‐dimensional visualization of geo‐spatial data has recently attracted much attention in research [DMK05, WKD*05].…”

Why Maps Matter in GIScience

“…However, some of the interesting alternatives require complex computation and data handling, and are not available in off-the-self software. Examples of these maps are cartograms, schematic networks and three-dimensional models (Avelar, 2002;Keim, et al, 2003;Krisp and Ahonen-Rainio, 2003). To be successful, these alternatives should be used in conjunction to the familiar map view.…”

“…With the tremendous increase in available data the use of maps alone is often no longer sufficient. Graphic techniques to reduce the dimensionality of the data, as they are common in information visualization are needed (Keim et al, 2003(Keim et al, , 2005. These graphics, such as parallel co-ordinate plots, scatter plots or even selforganizing maps should also be linked to the map view.…”