Showing papers on "Cartogram published in 2003"
1 Jan 2003
TL;DR: This chapter focuses on the cartographic Java applet MAPresso that allows the interactive creation of choropleth and cartogram maps and the addition of the Dorling cartogram completes this special view.
Abstract: Publisher Summary This chapter focuses on the cartographic Java applet MAPresso that allows the interactive creation of choropleth and cartogram maps. It is used for the basic communication of spatial information and for analyzing data. Among various other features, the cartogram maps provide the possibility of getting unusual insights into spatial structures. The addition of the Dorling cartogram completes this special view. The programming language Java is ideal for implementing such interactive web content. The use of the Java applet model ensures usability and accessibility for a broad audience. The applet uses Arc shapefiles and data in a simple text file format. Distributed for free, the applet has integrated into several websites around the world. The major applications are for the mapping of political and demographic statistics. With more intensive collaboration, the process of developing such Java applets could be accelerated, leading to more options for interactive thematic mapping through the Web. The requirements are software components in the form of Java classes and freely available map datasets. With an increasing number of simple and robust mapping applets, and the availability of digital maps, thematic cartography can fully utilize the potential of the web to communicate spatial information to a large audience.
17 citations
22 Oct 2003
TL;DR: An efficient method called HistoScale to compute Pseudo-Cartograms is proposed, which provides an efficient and convenient approximation of cartograms, since a complete computation of cartogram computation is expensive.
Abstract: Nowadays, two types of maps, the so-called Thematic Map and Choropleth Map, are used in Cartography and GISSystems. Thematic Maps are used to emphasize the spatial distribution of one or more geographic attributes. Popular thematic maps are the Choropleth Maps (Greek: choro = area, pleth = value), in which enumeration or data collection units are shaded to represent different magnitudes of a variable. Besides, the statistical values are often encoded as colored regions on these maps. On both types of maps, high values are often concentrated in densely populated areas, and low statistical values are spread out over sparsely populated areas. These maps, therefore, tend to highlight patterns in large areas, which may, however, be of low importance. A cartogram can then be seen as a generalization of a familiar land-covering choropleth map. According to this interpretation, an arbitrary parameter vector gives the intended sizes of the cartogram’s regions, that is, a familiar land-covering choropleth map is simply a cartogram whose regions sizes proportional to the land area. In addition to the classical applications mentioned above, a key motivation for cartograms as a general information visualization technique is to have a method for trading off shape and area adjustments. PseudoCartograms provide an efficient and convenient approximation of cartograms, since a complete computation of cartograms is expensive. In this poster, we propose an efficient method called HistoScale to compute Pseudo-Cartograms.
13 citations
TL;DR: This paper compares two algorithms that solve the continuous cartogram problem based on the Gridfit technique, which uses pixel-based distortion based on a quadtree-like data structure to make continuous cartograms that strictly retain the topology of the input mesh.
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.