A compromise aspect-adaptive cylindrical projection for world maps

Abstract

There are two problems with current cylindrical projections for world maps. First, existing cylindrical map projections have a static height-to-width aspect ratio and do not automatically adjust their aspect ratio in order to optimally use available canvas space. Second, many of the commonly used cylindrical compromise projections show areas and shapes at higher latitudes with considerable distortion. This article introduces a new compromise cylindrical map projection that adjusts the distribution of parallels to the aspect ratio of a canvas. The goal of designing this projection was to show land masses at central latitudes with a visually balanced appearance similar to how they appear on a globe. The projection was constructed using a visual design procedure where a series of graphically optimized projections was defined for a select number of aspect ratios. The visually designed projections were approximated by polynomial expressions that define a cylindrical projection for any height-to-width ratio between 0.3:1 and 1:1. The resulting equations for converting spherical to Cartesian coordinates require a small number of coefficients and are fast to execute. The presented aspect-adaptive cylindrical projection is well suited for digital maps embedded in web pages with responsive web design, as well as GIS applications where the size of the map canvas is unknown a priori. We highlight the projection with a height-to-width ratio of 0.6:1, which we call the Compact Miller projection because it is inspired by the Miller Cylindrical projection. Unlike the Miller Cylindrical projection, the Compact Miller projection has a smaller height-to-width ratio and shows the world with less areal distortion at higher latitudes. A user study with 448 participants verified that the Compact Miller – together with the Plate Carrée projection – is the most preferred cylindrical compromise projection.

Keywords:

• aspect-adaptive cylindrical projection
• Compact Miller projection
• Miller projection
• adaptive composite map projections
• Mercator
• Flex Projector

Acknowledgements

The support of Esri is greatly acknowledged, including valuable discussions with David Burrows, Scott Morehouse, Dawn Wright and others. The authors also thank Brooke E. Marston, Oregon State University, for editing the text of this article, and Christine M. Escher and Eugene Zhang, both from Oregon State University, for their help in finding polynomial equations. The authors also thank the anonymous reviewers for their valuable comments.