Cartography Checklists

Last updated on 2026-10-17 | Edit this page

Estimated time: 75 minutes

Overview

Questions

  • What should I decide before I start making a map?
  • How do audience, message and medium shape a map’s design?
  • How can a map mislead, even when the data are correct?
  • When do I need more data, or a better understanding of my data, before mapping?

Objectives

  • Identify the audience, purpose and medium of a map before designing it
  • Choose the data attributes that support a single clear message
  • Recognize design choices that confuse or mislead readers
  • Decide whether additional data or research is needed
  • Use a checklist to review a map before sharing it

Why plan before you map?

Maps are persuasive. People tend to trust them, and a map can be read in seconds. That makes a well designed map a powerful way to share research, and a poorly designed one a powerful way to mislead.

Most map problems are decided before any software is opened: who the map is for, what it should say, and which data can support that message. This episode is a set of questions to ask, in order, every time you make a map. The next episode covers the design tools (color, classification, projections) you use to answer them.

Good cartography rests on a few core design principles:

  • Legibility: everything the reader needs can be read easily.
  • Visual contrast: important features stand out from less important ones.
  • Figure and ground: the data (figure) separates clearly from the background (ground), such as a basemap.
  • Hierarchy: the most important element is noticed first, the least important last.
  • Balance: the elements are arranged without clutter or large empty areas.

1. Know your audience

The audience shapes every other decision.

Ask yourself:

  • Are they researchers, policymakers, community members or students?
  • How familiar are they with maps and with your topic?
  • How much detail can they use?
Audience Design implications
General public Few classes, plain language labels, a title that states the conclusion, minimal jargon
Policymakers One clear message, the places they are responsible for clearly marked, sources stated
Researchers More detail, precise class breaks, projection and methods stated, uncertainty shown
Callout

Key idea

A map of the same data for scientists and for the public should not look the same.

2. Define your message

Every map should answer one clear question. If you cannot state the message in one sentence, the reader will not be able to find it either.

Ask yourself:

  • What is the single most important takeaway?
  • Am I showing a pattern, a comparison, or a change over time?

Avoid:

  • Showing many variables at once on one map
  • Leaving the reader to work out the point alone

A useful habit is to write the message as the map’s title. The map below states its conclusion in the title, shows only one distinction (8% or more versus below 8%), and greys out everything else so the highlighted counties stand out.

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

3. Choose the right data attributes

Not all data belongs on your map. Select the variables that support your message and that make sense spatially.

Ask yourself:

  • Which variable answers my question?
  • Do I need a supporting variable, such as population, to interpret it?
  • What type of geometry do I have: points, lines or polygons?
  • Is my variable a count (number of people) or a rate (percent, per 1,000, per square mile)? Counts on area maps usually need to be converted to rates; see the next episode.

Matching data to visual variables:

Data Visual variable Example
Ordered quantities (low to high) Lightness of one color Unemployment rate
Magnitudes at locations Symbol size City population
Categories with no order Different hues or shapes Land use type
Challenge

Challenge 1: Which variable?

You have county data on average temperature, total precipitation and elevation. Your goal is a map of drought risk. Which variable would you prioritize, and what else might you need?

Precipitation is the most direct indicator, because drought is a shortage of water. Even better is precipitation compared with normal (for example, percent of the long-term average), since 20 inches a year is normal in some places and a severe drought in others. Temperature is a useful supporting variable because heat increases evaporation. Elevation alone says little about drought.

4. Consider how readers will perceive the map

Maps are not neutral. Design choices change what readers believe.

Ask yourself:

  • Do my colors carry meanings I do not intend (for example, red for “danger”)?
  • Does my classification make some places look worse or better than they are?
  • Can the main pattern be seen at a glance?
  • Can someone with color vision deficiency read it? (See the Accessibility episode.)

Darker or more saturated colors are read as “more”. Keep that order consistent with the data: dark for high values, light for low values.

The map below uses the same unemployment data as the map in section 2, and is very hard to read:

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

What goes wrong here:

  • A rainbow palette has no natural order, so readers cannot tell which colors mean high or low.
  • Twelve classes are more than anyone can match to a legend, and there is no legend anyway.
  • The title (“map1”) says nothing; the axis labels are leftover software defaults.
  • Heavy black borders and a bright background compete with the data (poor figure and ground).
  • Equal interval classes put almost every county in the first few classes, so most of the map is one dark blue.

Best practice: make the data the figure and everything else the ground. Use a light, muted basemap or none at all, thin light boundaries, and a sequential palette with five to seven classes.

5. Choose the right medium

Where the map will be seen affects its design.

Medium Design implications
Web map Interactive, zoomable; simplify the basemap; add halos to labels so they read over any background
Print Static, high resolution; check how it looks in black and white; print a test copy
Slides Simple and bold; large text; one message per slide; viewed from a distance
Social media Small screens; very few classes; title and source inside the image

Ask yourself: Will readers zoom in? Will it be printed in greyscale? How large will it appear?

6. Will your map inform decisions?

Some maps are exploratory; others guide real actions such as where to open a clinic or which neighborhoods receive funding.

Decision making maps should:

  • Be as accurate as the data allow
  • Show uncertainty where possible (for example, margins of error for American Community Survey estimates, or hatching for unreliable values)
  • Avoid simplifications that change the conclusion

Examples include flood risk maps used by planners and disease maps used during outbreaks.

Callout

Important

If your map influences decisions, accuracy and clarity matter more than visual appeal.

7. Understand what your audience already knows

Ask yourself:

  • Do readers understand the variable (for example, what “median household income” means)?
  • Do units, time periods or scales need explaining?
  • Would an annotation help (“highest rate in the state”)?

Tips:

  • Include a legend with units and a descriptive title.
  • Use plain language where possible.
  • Give context: time period, geography and data source.
  • Include the essential map elements: a scale bar when distance matters, a north arrow when orientation is not obvious, and a source line.

Aim for maximum information with minimum effort: the reader should get the main idea quickly, without struggling.

8. Do you need more data?

Incomplete data can lead to misleading maps.

Ask yourself:

  • Are there variables that change how this one should be read?
  • Is the data recent enough?
  • Is the spatial resolution right for the question (state, county, tract)?

For example, a map of unemployment rates can look dramatic in sparsely populated counties where a change of a few hundred jobs moves the rate a lot. Showing population next to it puts the pattern in context:

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

The darkest fifth of counties on the left (unemployment of 6.5% or more) are, on average, less densely populated than other counties and together are home to only about 1 in 10 people in the contiguous United States. Meanwhile, the 41 counties with more than a million residents had a median rate of 4.5%, slightly below the median for all counties (5.0%). Neither map alone tells the whole story. Rates and counts answer different questions: “where is unemployment common?” versus “where do most unemployed people live?”

9. Do you understand your data?

Before mapping, make sure you understand your dataset.

Ask yourself:

  • What exactly does each variable measure, and how was it collected?
  • Are there known biases, gaps or margins of error?
  • Have you explored the data with summary statistics and quick plots?

If not:

  • Do some exploratory analysis (histograms, maps of each variable)
  • Read the metadata and documentation
  • Ask someone who knows the data
  • For advanced work, run a sensitivity check: does the pattern change if you change the classification, the year or the geographic unit?

Cartography checklist

Before sharing a map, check that:

Challenge

Challenge 2: Review a map

Apply the checklist to the poorly designed map in section 4. Which items fail?

  • Clear message: fails. The title is “map1” and nothing says what is shown.
  • Misleading interpretations: fails. The rainbow palette and equal interval classes hide the pattern and make a few counties look extreme.
  • Units, time period, source: fail. There is no legend, year or source.
  • Color vision deficiency: fails. Rainbow palettes contain red and green pairs that many readers cannot distinguish.
  • Medium: questionable. The bright background would print poorly and distract on screen.

Final thought

A good map is not only attractive. It is honest, clear and purposeful: it respects the data, serves its audience, and communicates without distortion.

Discussion
  • Think of a map you saw recently. What did it do well? What could be improved?
  • How would you present the same data differently for a different audience?
  • Share an example of a map that misled you, or one that worked brilliantly, and explain why.
Key Points
  • Decide the audience, message and medium before designing a map.
  • State the message in the title and show only the data that supports it.
  • Rates and counts answer different questions; choose deliberately.
  • Design choices such as palette and classification change what readers believe.
  • Review every map against a checklist before sharing it.