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Color Map Atlas for Continuously Scaled Maps

,   and  Richard Careaga
14 min read

Table of Contents

## Please note: Alaska and Hawaii are being shifted and are not to scale.

Color, Design and Communication

Graphic design standards, especially for color, are an official Good Thing. Branding, visual cueing, consistency. Map colors are hard, especially when dealing with continuous data.

An example of continuous data

The U.S. Bureau of Economic Analysis publishes quarterly statistics on gross domestic product by state.1. For the second quarter 2018, it provided the following.

(#tab:gdp_table)2018 Second Quarter GDP for the 50 States NAME GDP geometry Arizona 322,995 MULTIPOLYGON (((-1111066 -8… Arkansas 123,605 MULTIPOLYGON (((557903.1 -1… California 2,791,626 MULTIPOLYGON (((-1853480 -9… Colorado 343,316 MULTIPOLYGON (((-613452.9 -… Connecticut 264,949 MULTIPOLYGON (((2226838 519… Georgia 557,294 MULTIPOLYGON (((1379893 -98… Illinois 820,408 MULTIPOLYGON (((868942.5 -2… Indiana 348,366 MULTIPOLYGON (((1279733 -39… Louisiana 236,514 MULTIPOLYGON (((1080885 -16… Minnesota 351,868 MULTIPOLYGON (((594664.5 21… Mississippi 109,181 MULTIPOLYGON (((1052956 -15… Montana 47,040 MULTIPOLYGON (((-864991.3 5… New Mexico 93,323 MULTIPOLYGON (((-568680.6 -… North Dakota 51,972 MULTIPOLYGON (((-39585.46 1… Oklahoma 187,043 MULTIPOLYGON (((-34.95064 -… Pennsylvania 746,635 MULTIPOLYGON (((1734901 -36… Tennessee 345,710 MULTIPOLYGON (((1467705 -78… Virginia 507,407 MULTIPOLYGON (((2133159 -45… Delaware 71,164 MULTIPOLYGON (((2058738 -29… West Virginia 72,288 MULTIPOLYGON (((1827955 -42… Wisconsin 319,280 MULTIPOLYGON (((721464.2 26… Wyoming 37,644 MULTIPOLYGON (((-324546.2 -… Alabama 210,298 MULTIPOLYGON (((1145349 -15… Florida 971,078 MULTIPOLYGON (((1994020 -19… Idaho 72,065 MULTIPOLYGON (((-873311.5 1… Kansas 159,167 MULTIPOLYGON (((40850.31 -8… Maryland 396,750 MULTIPOLYGON (((2063382 -46… New Jersey 597,752 MULTIPOLYGON (((2096848 -22… North Carolina 534,880 MULTIPOLYGON (((2173840 -77… South Carolina 221,061 MULTIPOLYGON (((1607185 -91… Washington 518,701 MULTIPOLYGON (((-1642369 55… Vermont 32,490 MULTIPOLYGON (((2197053 124… Utah 163,887 MULTIPOLYGON (((-909692.9 -… Iowa 183,496 MULTIPOLYGON (((717165 -185… Kentucky 199,833 MULTIPOLYGON (((934668.1 -8… Maine 61,230 MULTIPOLYGON (((2391419 325… Massachusetts 537,860 MULTIPOLYGON (((2364278 491… Michigan 503,384 MULTIPOLYGON (((1112263 184… Missouri 303,577 MULTIPOLYGON (((936191 -904… Nebraska 118,536 MULTIPOLYGON (((-336456.4 -… Nevada 155,890 MULTIPOLYGON (((-1188405 -4… New Hampshire 80,427 MULTIPOLYGON (((2305592 212… New York 1,583,899 MULTIPOLYGON (((2160781 -11… Ohio 641,630 MULTIPOLYGON (((1425651 -22… Oregon 224,764 MULTIPOLYGON (((-1676653 30… Rhode Island 59,201 MULTIPOLYGON (((2325633 198… South Dakota 49,706 MULTIPOLYGON (((-325003.8 -… Texas 1,640,409 MULTIPOLYGON (((314225.2 -1… Alaska 51,214 MULTIPOLYGON (((-1434617 -2… Hawaii 88,593 MULTIPOLYGON (((-231495.6 -…

The two obvious things from the chart are that the numbers differ and that in general the larger states in population are also the larger states in GDP. Whenever making maps of continuous data, they will almost always turn out like this, unless there are marked regional differences and you take the default option.

Here is the GDP data, also with the default option.

Three Problems: One Major, One Important and One Nuisance

  • Major: The GDP map doesn’t tell you anything you could not have guessed from the population map
  • You can see subtle differences in shade, but there are too many, and a small portion of the population is blue colorblind.
  • Nuisance: Can’t see the state names against the dark background

The Major Problem: Counts Need Transformation

If we are looking for more interesting differences among states, we should put them on a common footing, so that population size alone doesn’t dominate the effects. For disease, as an example, cases per hundred thousand is commonly used. Here’s what the GDP data looks like on that basis.

The Important Problem

The default continuous blue scale, even with transformed data, still requires squinting to distinguish different values.

Alpha adjustment

We can tinker with that by applying an alpha (density) filter.

The signature for scale_alpha_continuous is

scale_alpha_continuous(…, range = c(0.1, 1))

The range can be narrowed

scale_alpha_continuous(range = c(0.1, 0.75))

or even further lightened

scale_alpha_continuous(range = c(0.1, 0.50))

Beyond Blue Palette Solutions

Viridis palette

Five bright (some might say garish) scales are available.

scale_fill_viridis_c(option="magma")

scale_fill_viridis_c(option="inferno")

scale_fill_viridis_c(option="plasma")

scale_fill_viridis_c(option="viridis")

scale_fill_viridis_c(option="viridis")

Fortunately, the palette can be muted with an alpha density filter

scale_fill_viridis_c(alpha = 0.50, option="cividis")

ggplot’s scale_fill_gradient can also over-ride default colors

The signature of scale_fill_gradient is

scale_fill_gradient(..., low = "#132B43", high = "#56B1F7", space = "Lab", na.value = "grey50", guide = "colourbar", aesthetics = "fill")

in the blue range. You can adjust that with the arguments to low and high. The following example uses #FBF7F6 and #A97263.

How did I come up with those particular values?

library(munsell)

show_col(seq_gradient_pal("white", mnsl("10R 4/6"))(x))(seq(0, 1, length.out = 25)))

This approach, and other do-it-yourself color palette selection approaches can become a time sink.See R color cheatsheet A better approach is to have a selection of pre-made color cards, and try them out until one of them highlights the differences you are trying to show.

RColorBrewer

This set of palettes plays well with ggplot and should answer most needs.

RColorBrewer

The three sections are for sequential (i.e., continuous), divergent (a neutral color in the center progressing toward more intense colors in both direction) or qualitative, for discrete data but coercible into continuous data.

Through ggplot’s scale_colour_distiller function, any of these can be smoothly interpolated into a six-interval scale.

All of the scales can be flipped, working from left-to-right on the palette or from right-to-left. Direction is indicated by direction = -1 or direction = 1.

Color abbreviations:

  • Yl = Yellow
  • Or = Orange
  • Rd = Red
  • Br = Brown
  • Gn = Green
  • Pu = Purple
  • Bu = Blue

There are also:

  • Reds
  • Oranges
  • Greys
  • Greens
  • Blues

The signature for scale_colour_distiller is

scale_fill_distiller(type = “seq”, palette = “YlOrRd”, direction = -1, labels = comma, guide = “colourbar”, aesthetics = “fill”)

Palette Snippets

Usage ggplot_map_object + [name of color scale]



    Alpha_default <- scale_alpha_continuous(range = c(0.1, 1))
  
    Alpha_75      <- scale_alpha_continuous(range = c(0.1, 0.75))
  
    Alpha_50      <- scale_alpha_continuous(range = c(0.1, 0.50))

    magma         <- scale_fill_viridis_c(option="magma")
  
    inferno       <- scale_fill_viridis_c(option="inferno")
  
    plasma        <- scale_fill_viridis_c(option="plasma")
  
    viridis       <- scale_fill_viridis_c(option="viridis")
  
    cividis       <- scale_fill_viridis_c(option="cividis")

    munsell_brown <- scale_fill_gradient(low = "#FBF7F6", high =  "#A97263", space = "Lab", na.value = "grey50", guide = "colourbar", aesthetics = "fill")

    # RColorBrewer

    BluesNeg1    <- scale_fill_distiller(type = "seq", palette = "Blues", direction = -1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    BluesPos1    <- scale_fill_distiller(type = "seq", palette = "Blues", direction = 1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    BuGnNeg1    <- scale_fill_distiller(type = "seq", palette = "BuGn", direction = -1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    BuGnPos1    <- scale_fill_distiller(type = "seq", palette = "BuGn", direction = 1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    BuPuNeg1    <- scale_fill_distiller(type = "seq", palette = "BuPu", direction = -1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    BuPuPos1    <- scale_fill_distiller(type = "seq", palette = "BuPu", direction = 1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    GnBuNeg1    <- scale_fill_distiller(type = "seq", palette = "GnBu", direction = -1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    GnBuPos1    <- scale_fill_distiller(type = "seq", palette = "GnBu", direction = 1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    GreensNeg1    <- scale_fill_distiller(type = "seq", palette = "Greens", direction = -1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    GreensPos1    <- scale_fill_distiller(type = "seq", palette = "Greens", direction = 1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    GreysNeg1    <- scale_fill_distiller(type = "seq", palette = "Greys", direction = -1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    GreysPos1    <- scale_fill_distiller(type = "seq", palette = "Greys", direction = 1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    OrangesNeg1    <- scale_fill_distiller(type = "seq", palette = "Oranges", direction = -1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    OrangesPos1    <- scale_fill_distiller(type = "seq", palette = "Oranges", direction = 1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    OrRdNeg1    <- scale_fill_distiller(type = "seq", palette = "OrRd", direction = -1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    OrRdPos1    <- scale_fill_distiller(type = "seq", palette = "OrRd", direction = 1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    PuBuGnNeg1    <- scale_fill_distiller(type = "seq", palette = "PuBuGn", direction = -1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    PuBuGnPos1    <- scale_fill_distiller(type = "seq", palette = "PuBuGn", direction = 1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    PuBuNeg1    <- scale_fill_distiller(type = "seq", palette = "PuBu", direction = -1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    PuBuPos1    <- scale_fill_distiller(type = "seq", palette = "PuBu", direction = 1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    PuRdNeg1    <- scale_fill_distiller(type = "seq", palette = "PuRd", direction = -1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    PuRdPos1    <- scale_fill_distiller(type = "seq", palette = "PuRd", direction = 1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    PurplesNeg1    <- scale_fill_distiller(type = "seq", palette = "Purples", direction = -1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    PurplesPos1    <- scale_fill_distiller(type = "seq", palette = "Purples", direction = 1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    RdPuNeg1    <- scale_fill_distiller(type = "seq", palette = "RdPu", direction = -1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    RdPuPos1    <- scale_fill_distiller(type = "seq", palette = "RdPu", direction = 1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    RedsNeg1    <- scale_fill_distiller(type = "seq", palette = "Reds", direction = -1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    RedsPos1    <- scale_fill_distiller(type = "seq", palette = "Reds", direction = 1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    YlGnBuNeg1    <- scale_fill_distiller(type = "seq", palette = "YlGnBu", direction = -1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    YlGnBuPos1    <- scale_fill_distiller(type = "seq", palette = "YlGnBu", direction = 1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    YlGnNeg1    <- scale_fill_distiller(type = "seq", palette = "YlGn", direction = -1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    YlGnPos1    <- scale_fill_distiller(type = "seq", palette = "YlGn", direction = 1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    YlOrBrNeg1    <- scale_fill_distiller(type = "seq", palette = "YlOrBr", direction = -1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    YlOrBrPos1    <- scale_fill_distiller(type = "seq", palette = "YlOrBr", direction = 1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    YlOrRdNeg1    <- scale_fill_distiller(type = "seq", palette = "YlOrRd", direction = -1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    YlOrRdPos1    <- scale_fill_distiller(type = "seq", palette = "YlOrRd", direction = 1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    BrBGNeg1    <- scale_fill_distiller(type = "seq", palette = "BrBG", direction = -1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    BrBGPos1    <- scale_fill_distiller(type = "seq", palette = "BrBG", direction = 1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    PiYGNeg1    <- scale_fill_distiller(type = "seq", palette = "PiYG", direction = -1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    PiYGPos1    <- scale_fill_distiller(type = "seq", palette = "PiYG", direction = 1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    PRGnNeg1    <- scale_fill_distiller(type = "seq", palette = "PRGn", direction = -1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    PRGnPos1    <- scale_fill_distiller(type = "seq", palette = "PRGn", direction = 1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    PuOrNeg1    <- scale_fill_distiller(type = "seq", palette = "PuOr", direction = -1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    PuOrPos1    <- scale_fill_distiller(type = "seq", palette = "PuOr", direction = 1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    RdBuNeg1    <- scale_fill_distiller(type = "seq", palette = "RdBu", direction = -1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    RdBuPos1    <- scale_fill_distiller(type = "seq", palette = "RdBu", direction = 1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    RdGyNeg1    <- scale_fill_distiller(type = "seq", palette = "RdGy", direction = -1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    RdGyPos1    <- scale_fill_distiller(type = "seq", palette = "RdGy", direction = 1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    RdYlBuNeg1    <- scale_fill_distiller(type = "seq", palette = "RdYlBu", direction = -1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    RdYlBuPos1    <- scale_fill_distiller(type = "seq", palette = "RdYlBu", direction = 1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    RdYlGnNeg1    <- scale_fill_distiller(type = "seq", palette = "RdYlGn", direction = -1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    RdYlGnPos1    <- scale_fill_distiller(type = "seq", palette = "RdYlGn", direction = 1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    SpectralNeg1    <- scale_fill_distiller(type = "seq", palette = "Spectral", direction = -1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    SpectralPos1    <- scale_fill_distiller(type = "seq", palette = "Spectral", direction = 1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    AccentNeg1    <- scale_fill_distiller(type = "seq", palette = "Accent", direction = -1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    AccentPos1    <- scale_fill_distiller(type = "seq", palette = "Accent", direction = 1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    Dark2Neg1    <- scale_fill_distiller(type = "seq", palette = "Dark2", direction = -1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    Dark2Pos1    <- scale_fill_distiller(type = "seq", palette = "Dark2", direction = 1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    PairedNeg1    <- scale_fill_distiller(type = "seq", palette = "Paired", direction = -1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    PairedPos1    <- scale_fill_distiller(type = "seq", palette = "Paired", direction = 1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    Pastel1Neg1    <- scale_fill_distiller(type = "seq", palette = "Pastel1", direction = -1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    Pastel1Pos1    <- scale_fill_distiller(type = "seq", palette = "Pastel1", direction = 1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    Pastel2Neg1    <- scale_fill_distiller(type = "seq", palette = "Pastel2", direction = -1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    Pastel2Pos1    <- scale_fill_distiller(type = "seq", palette = "Pastel2", direction = 1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    Set1Neg1    <- scale_fill_distiller(type = "seq", palette = "Set1", direction = -1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    Set1Pos1    <- scale_fill_distiller(type = "seq", palette = "Set1", direction = 1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    Set2Neg1    <- scale_fill_distiller(type = "seq", palette = "Set2", direction = -1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    Set2Pos1    <- scale_fill_distiller(type = "seq", palette = "Set2", direction = 1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    Set3Neg1    <- scale_fill_distiller(type = "seq", palette = "Set3", direction = -1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    Set3Pos1    <- scale_fill_distiller(type = "seq", palette = "Set3", direction = 1, labels = comma,  guide = "colourbar", aesthetics = "fill")

Credits

  1. R Development Core Team (2008). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL http://www.R-project.org.
  2. Simon Urbanek and Jeffrey Horner (2015). Cairo: R graphics device using cairo graphics library for creating high-quality bitmap (PNG, JPEG, TIFF), vector (PDF, SVG, PostScript) and display (X11 and Win32) output. R package version 1.5-9. https://CRAN.R-project.org/package=Cairo
  3. H. Wickham. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York, 2016
  4. Hao Zhu (2018). kableExtra: Construct Complex Table with ‘kable’ and Pipe Syntax. R package version 0.9.0. https://CRAN.R-project.org/package=kableExtra
  5. Yihui Xie (2018). knitr: A General-Purpose Package for Dynamic Report Generation in R. R package version 1.20. Yihui Xie (2015) Dynamic Documents with R and knitr. 2nd edition. Chapman and Hall/CRC. ISBN 978-1498716963 Yihui Xie (2014) knitr: A Comprehensive Tool for Reproducible Research in R. In Victoria 6. Stodden, Friedrich Leisch and Roger D. Peng, editors, Implementing Reproducible Computational Research. Chapman and Hall/CRC. ISBN 978-1466561595
  6. Charlotte Wickham (2018). munsell: Utilities for Using Munsell Colours. R package version 0.5.0. https://CRAN.R-project.org/package=munsell
  7. Erich Neuwirth (2014). RColorBrewer: ColorBrewer Palettes. R package version 1.1-2. https://CRAN.R-project.org/package=RColorBrewer
  8. Hadley Wickham (2018). scales: Scale Functions for Visualization. R package version 1.0.0.https://CRAN.R-project.org/package=scales
  9. Pebesma, E., 2018. Simple Features for R: Standardized Support for Spatial Vector Data. The R Journal, https://journal.r-project.org/archive/2018/RJ-2018-009/
  10. Kyle Walker (2018). tidycensus: Load US Census Boundary and Attribute Data as ‘tidyverse’ and22 ‘sf’-Ready Data Frames. R package version 0.8.1. https://CRAN.R-project.org/package=tidycensus
  11. Hadley Wickham (2017). tidyverse: Easily Install and Load the ‘Tidyverse’. R package version 1.2.1. https://CRAN.R-project.org/package=tidyverse
  1. “Source: Bureau of Economic Analysis, Gross Domestic Product By State, 2nd Quarter 2018 https://goo.gl/Jc6XyK”↩︎

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