Showing papers on "Cartogram published in 1998"
18 Oct 1998
TL;DR: A new algorithm for the construction of continuous area cartograms is presented that was developed by viewing their construction as a constrained optimization problem and uses a relaxation method that exploits hierarchical resolution, constrained dynamics, and a scheme that alternates goals of achieving correct region areas and adjusting region shapes.
Abstract: Area cartograms are used for visualizing geographically distributed data by attaching measurements to regions of a map and scaling the regions such that their areas are proportional to the measured quantities. A continuous area cartogram is a cartogram that is constructed without changing the underlying map topology. We present a new algorithm for the construction of continuous area cartograms that was developed by viewing their construction as a constrained optimization problem. The algorithm uses a relaxation method that exploits hierarchical resolution, constrained dynamics, and a scheme that alternates goals of achieving correct region areas and adjusting region shapes. It is compared favorably to existing methods in its ability to preserve region shape recognition cues, while still achieving high accuracy.
73 citations
1 Jan 1998
TL;DR: A new constraint-based continuous area cartogram construction method that is unique in its ability to preserve essential cues for recognition of region shapes is presented, and results are shown to be superior in both accuracy and preservation of shape recognition cues.
Abstract: We present a new constraint-based continuous area cartogram construction method that is unique in its ability to preserve essential cues for recognition of region shapes. It automatically achieves desired region areas while maintaining correct map topology. The algorithm is compared with a number of existing methods, and results are shown to be superior in both accuracy and preservation of shape recognition cues. Through hierarchical resolution, we first perform gross adjustments upon a coarsely resampled map and later refine the map at progressively higher levels of detail.
34 citations
1 Jan 1998
TL;DR: The DEMP algorithm was applied to a data set previously analyzed with conventional techniques, and no statistically significant evidence for geographic non-uniformity of rates was found, in agreement with the original analysis performed by the California Department of Health Services.
Abstract: In studying geographic disease distributions, one normally compares rates among arbitrarily defined geographic subareas (e.g. census tracts), thereby sacrificing some of the geographic detail of the original data. The sparser the data, the larger the subareas must be in order to calculate stable rates. This dilemma is avoided with the technique of Density Equalizing Map Projections (DEMP){copyright}. Boundaries of geographic subregions are adjusted to equalize population density over the entire study area. Case locations plotted on the transformed map should have a uniform distribution if the underlying disease risk is constant. On the transformed map, the statistical analysis of the observed distribution is greatly simplified. Even for sparse distributions, the statistical significance of a supposed disease cluster can be calculated with validity. The DEMP algorithm was applied to a data set previously analyzed with conventional techniques; namely, 401 childhood cancer cases in four counties of California. The distribution of cases on the transformed map was analyzed visually and statistically. To check the validity of the method, the identical analysis was performed on 401 artificial cases randomly generated under the assumption of uniform risk. No statistically significant evidence for geographic non-uniformity of rates was found, in agreement with the original analysis performed by the California Department of Health Services.
7 citations