Image 1 of 1: ‘Two bar charts of 2007 life expectancy for Mexico, the United States, Cuba and Canada. The left chart's axis starts at 70 years, so Canada's bar looks about three times taller than Mexico's. The right chart's axis starts at 0, and all four bars look nearly the same height, between 76 and 81 years.’
The same life expectancy data with a truncated
axis (left) and a full axis (right)
Image 1 of 1: ‘Map of the contiguous United States with counties in light grey, except 208 counties shown in magenta where unemployment was 8% or higher in 2016. The title reads: 208 U.S. counties had unemployment of 8% or more in 2016. Highlighted counties cluster in central California, the Southwest and Navajo Nation area, Appalachian Kentucky, the Mississippi Delta and Alabama's Black Belt.’
A map with one clear message: counties with
unemployment of 8% or more in 2016
Figure 2
Image 1 of 1: ‘A deliberately poorly designed map of 2016 county unemployment. Counties are colored with a rainbow palette in 12 classes, mostly dark blue with scattered cyan, yellow and red counties. There is no legend and no meaningful title (only 'map1'), thick black county borders, a bright yellow background, and leftover axis labels reading Easting and Northing in metres.’
A deliberately poor map of the same unemployment
data
Figure 3
Image 1 of 1: ‘Two county maps of the contiguous United States side by side. Left: 2016 unemployment rate in five orange classes, with the darkest counties in the Pacific Northwest, California, the Southwest, Appalachia and the Deep South. Right: 2012 population density in five blue classes, dark in the East, the Great Lakes, California and Florida, and very light across most of the Great Plains and Mountain West.’
Unemployment rate and population density side by
side
Image 1 of 1: ‘A county map of 2016 unemployment rates in the contiguous United States, shaded in five blue classes, inside a black frame. Red labels with arrows identify each layout element: the title at the top, the mapped area, the legend at lower right, the north arrow, the scale bar at lower left, the data source and projection line at the bottom, and the neat line, which is the frame around the layout.’
The elements of a map layout, labeled on a map
of county unemployment
Figure 2
Image 1 of 1: ‘Three maps of the contiguous United States. Left, sequential: county unemployment rates in five shades of blue from very light to dark. Middle, diverging: each county's unemployment rate compared with the national county median of 5.0 percent, with dark and light blue below the median, grey within half a point, and light and dark red above. Right, categorical: states colored by Census region in four distinct colors, green for Northeast, orange for Midwest, purple-blue for South and pink for West.’
Sequential, diverging and categorical color
schemes on three U.S. maps
Figure 3
Image 1 of 1: ‘Four world maps in different projections, with Greenland and the Democratic Republic of the Congo colored orange; the two countries have about the same land area. In Plate Carree, Greenland looks larger than the Congo. In Mercator, Greenland looks many times larger than the Congo and the far north is greatly stretched. In Equal Earth and Mollweide, both of which preserve area, Greenland and the Congo appear about the same size, and the continents' outlines curve toward the edges.’
The world in four projections, with Greenland
and the Democratic Republic of the Congo highlighted
Figure 4
Image 1 of 1: ‘Choropleth map of the contiguous United States with states shaded in five green classes by 2012 population density. The darkest states, with more than 284 people per square mile, are in the Northeast and Florida. The lightest, with fewer than 38 people per square mile, are in the Great Plains and Mountain West.’
Choropleth map of population density by
state
Figure 5
Image 1 of 1: ‘Map of the contiguous United States with blue circles sized by population for the 60 largest cities. New York has by far the largest circle, followed by Los Angeles and Chicago. A legend shows circle sizes for 500,000, 2 million and 8 million people.’
Proportional symbol map of the 60 largest U.S.
cities
Figure 6
Image 1 of 1: ‘Map of Indiana's 92 counties with blue dots, each representing 5,000 people, placed randomly within each county. Dots are densest around Indianapolis in the center, the Chicago suburbs in the northwest corner, Fort Wayne in the northeast and South Bend in the north, and sparse across rural counties.’
Dot density map of Indiana population
Figure 7
Image 1 of 1: ‘Non-contiguous cartogram of the contiguous United States. Grey outlines show each state's true size. Inside each outline, the state is redrawn smaller in proportion to its population density and shaded purple by total population. Dense eastern states such as New Jersey stay close to full size, while western states such as Wyoming and Montana shrink to tiny shapes.’
Non-contiguous cartogram of U.S. states scaled
by population density
Figure 8
Image 1 of 1: ‘Map of Indiana counties shaded in five orange classes by 2016 unemployment rate, with grey circles sized by population on top. The largest circle is Marion County (Indianapolis). Several northwestern counties near Chicago have both large circles and dark orange shading.’
Multivariate map of Indiana unemployment and
population
Figure 9
Image 1 of 1: ‘Two county maps of the contiguous United States. Left: number of Hispanic residents, where almost every county is in the lightest class and only a few large counties stand out, such as Los Angeles, Harris County (Houston), Cook County (Chicago) and Miami-Dade. Right: Hispanic residents as a percent of county population, showing broad regions with high shares across South and West Texas, New Mexico, Southern California and Arizona, along with scattered counties in the Great Plains, Washington and Florida.’
Hispanic population as a count and as a percent
of county population
Figure 10
Image 1 of 1: ‘Six maps of Indiana counties shaded by population density, one for each classification method. Equal interval: almost every county is in the lightest class, with only Marion County (Indianapolis) dark. Quantile: counties are spread evenly across five classes, so many rural counties look dark. Natural breaks: a few urban counties stand out in darker classes, most counties are light. Standard deviation: 85 of 92 counties fall within one standard deviation of the mean, with only a few above. Pretty breaks: only three classes with round numbers, and nearly every county in the first. Geometric interval: a gradual spread of shades that separates rural counties from each other and from urban counties.’
The same Indiana population density data
classified six ways
Figure 11
Image 1 of 1: ‘Three histograms of Indiana county population density, each with red vertical lines marking class breaks. Most counties have fewer than 200 people per square mile, with a long tail to one county above 2,000. Equal interval breaks are spaced evenly from about 480 to 1,860, leaving almost all counties below the first break. Quantile breaks are clustered between about 54 and 224, within the main body of the data. Natural breaks fall at about 139, 300, 582 and 989.’
Histogram of Indiana county population density
with class breaks for three methods
Image 1 of 1: ‘Screenshot of the QGIS Layer Properties for the Tracts layer on the Symbology tab with the Rule-based renderer. Six rules are listed with their colors: five yellow to dark red classes whose filters compare pct_no_vehicle with the class limits, such as pct_no_vehicle less than or equal to 2.37, and a sixth hatched gray rule labeled No households with the filter pct_no_vehicle IS NULL.’
Rule-based symbology converted from Graduated,
with a rule for no data.
Figure 2
Image 1 of 1: ‘Screenshot of the QGIS Layer Properties for the Parks layer on the Labels tab with Rule-based Labeling. One rule is listed: it is ticked, its filter reads area greater than 300000, and its text is the name field.’
Rule-based labeling for parks over 30
hectares.
Figure 3
Image 1 of 1: ‘Screenshot of the QGIS Print Layout window. A Letter page holds the title Car-free households cluster near downtown Lafayette and campus, the main map of Lafayette tracts in yellow to dark red with green parks and a dashed city boundary, a small locator map of Indiana in the upper right with Tippecanoe County in red, a north arrow at the upper left, a legend at the lower left, a scale bar of 0 to 2 km at the lower right and a source line below the map. The Items panel lists the source label, picture, scale bar, legend, title, Map 2 and Map 1; Item Properties for Map 1 show Lock layers ticked.’
The print layout with the main map, the locator
inset and the map items.
Figure 4
Image 1 of 1: ‘Finished map titled Car-free households cluster near downtown Lafayette and campus. Census tracts of Lafayette and West Lafayette are shaded yellow to dark red by the share of households without a vehicle, darkest near downtown Lafayette and the Purdue campus, with one hatched gray tract with no households. Green parks are drawn on top, six of them labeled with white halos, and a dashed line marks the city limits. A locator map of Indiana in the upper right shows Tippecanoe County in red. A legend at the lower left lists 0 to 2, 2 to 9, 9 to 15, 15 to 26 and 26 to 41 percent, no households, parks and city limits. A north arrow, a 0 to 2 km scale bar and a source line complete the layout.’
Image 1 of 1: ‘Four maps of Indiana counties shaded by 2016 unemployment rate in five classes. Top left: a red-yellow-green palette as seen with typical color vision, where green means low and red means high. Top right: the same map with simulated deuteranopia; the greens and reds both turn into similar olive and mustard tones, so the lowest and highest classes are hard to tell apart. Bottom left: the viridis palette, from yellow for low to dark purple for high. Bottom right: viridis with simulated deuteranopia; colors shift to yellow, tan, grey and navy, but the classes still run clearly from light to dark.’
The same Indiana unemployment map in a red-green
palette and in viridis, with simulated deuteranopia
