2004/01/20 by Michael T. Gastner, M. E. J. Newman · 4 citations
Biochemistry, Genetics and Molecular Biology · Computer Science · Environmental Science · Mathematics · Physics and Astronomy · #Cartography #Climate variability and models #Computer science #Construct (python library) #Data Analysis with R #Data science #Demography #Distribution (mathematics) #Econometrics #Epistemology #Genetic and phenotypic traits in livestock #Geography #Mathematics #Physics #Political science #Politics #Population #Property (philosophy) #Readability #Representation (politics) #Scale (ratio) #Sociology #Statistical physics #physics.data-an #physics.soc-ph
paper · pdf · doi:10.1073/pnas.0400280101
published as Proc. Natl. Acad. Sci. USA 101, 7499-7504 (2004) · 12 pages, 3 figures
arxiv created 2004/01/20 · openalex publication_date 2004/05/10 · arxiv updated 2009/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06
Map makers have for many years searched for a way to construct cartograms, maps in which the sizes of geographic regions such as countries or provinces appear in proportion to their population or some other analogous property. Such maps are invaluable for the representation of census results, election returns, disease incidence, and many other kinds of human data. Unfortunately, to scale regions and still have them fit together, one is normally forced to distort the regions' shapes, potentially resulting in maps that are difficult to read. Many methods for making cartograms have been proposed, some of them are extremely complex, but all suffer either from this lack of readability or from other pathologies, like overlapping regions or strong dependence on the choice of coordinate axes. Here, we present a technique based on ideas borrowed from elementary physics that suffers none of these drawbacks. Our method is conceptually simple and produces useful, elegant, and easily readable maps. We illustrate the method with applications to the results of the 2000 U.S. presidential election, lung cancer cases in the State of New York, and the geographical distribution of stories appearing in the news.