Choropleth map facts for kids
A choropleth map is a special kind of thematic map that uses different shades of color to show information. It colors in clearly defined areas, such as countries, states, or counties. The colors change depending on numbers collected for that area, like population density or average income. The name comes from two Greek words: choros, which means "area" or "region", and plethos, which means "multitude" or "a great number".
These maps are very popular because governments often collect information by borders like towns and provinces. Mapmakers can easily turn this data into colorful maps using computer programs like a geographic information system (GIS) or simple spreadsheet tools. While other maps like a heat map or an isarithmic map draw wavy boundaries based on natural patterns, a choropleth map always sticks to official borders.
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History of Choropleth Maps
The very first choropleth map was created in 1826 by a French mathematician named Baron Pierre Charles Dupin. He made a map showing basic education in France across different official departments. Dupin used darker shades of black and gray for places with lower school attendance. These kinds of maps were called "tinted maps" in France.
Soon after Dupin's invention, other mapmakers created tinted maps to show living conditions, diseases, and city growth. The Irish government published choropleth maps in its census reports in 1841. In the 1850s, a printing method called Chromolithography allowed printers to produce vibrant colors easily. This let mapmakers use many bright colors instead of just black and white.
Even though people made these maps for over a century, they did not have a standard name. In 1938, a famous geographer named John Kirtland Wright introduced the name "choropleth map". Another scientist named Glenn Trewartha tried to call them "ratio maps" in the same year. However, Wright's term became the standard name that geographers use today.
How Choropleth Maps Work
A choropleth map brings two pieces of information together. First, it uses boundary shapes, like the borders of a state. Second, it uses statistical data, which means numbers gathered inside each border.
Geographers usually look at this process in one of two ways. In one view, the districts come first. The mapmaker starts with real places, like school districts, and gathers facts about each one. In the other view, the data comes first. The mapmaker studies a human pattern across the land and divides it into borders to make it easier to measure.
Choosing Geographic Districts
The districts on a choropleth map are usually borders decided by laws, like towns, provinces, and nations. Sometimes researchers use special statistical zones called census tracts. These borders were created for governments, not for the topic being mapped. This makes choropleth maps different from chorochromatic maps and isarithmic maps, which draw custom lines directly around the data.
Using pre-made boundaries has great advantages:
- It is much easier to collect data because census bureaus already organize facts by town or county.
- Readers instantly recognize familiar places like their home state or country.
- Leaders and lawmakers can use the maps quickly to make decisions for their specific regions.
However, using pre-made borders can also cause misunderstandings. When an entire county gets colored in one solid shade, it looks like everyone living there is identical. A large county might contain both wealthy neighborhoods and struggling areas, but the map shows only one blended average. This problem is known as the ecological fallacy.
Another issue is the modifiable areal unit problem, where changing the boundary lines completely changes how the map looks. To fix this, cartographers sometimes use smaller districts. Smaller zones reveal local details, but having too many small shapes can make the map look cluttered. Mapmakers can also try a dasymetric map, which removes empty lands like lakes and forests before shading the area.
Types of Statistical Data
The numbers shown on choropleth maps come from many fields, including science, farming, and business. A variable can fit into different levels of measurement, such as categories or actual counts. Cartographers divide these numbers into two main types, similar to ideas found in physics:
- An spatially extensive variable describes a whole district as a total amount. This is like measuring total weight or counting total items. Extensive numbers add up over space. For example, if you add up the populations of England, Scotland, Wales, and Northern Ireland, you get the total population of the United Kingdom. However, mapping total counts on a choropleth map is usually a bad idea. A huge county with a large area might look more important simply because it covers more paper. Other maps, like a proportional symbol map or a cartogram, work much better for total counts.
- A spatially intensive variable, often called a field, describes a rate or average that can exist at any single point. Examples include descriptive statistics such as averages, medians, and percentages. Intensive variables do not add up to a big sum. For example, if the United Kingdom has an average of 250 people per square kilometer, each region has a similar density. Choropleth maps work best with intensive numbers because a single color can fairly describe the whole district.
Normalizing Data for Fair Maps
Before making a choropleth map, cartographers usually perform a step called normalization. In statistics, this is related to normalization and standardization. Normalization means turning a total count into a fair rate, percentage, or proportion.
Mapmakers divide the raw total by another number to make fair comparisons:
- Density: dividing total count by land area, such as people per square mile.
- Proportion: dividing a smaller group by the whole group, such as the percentage of students who ride bicycles to school.
- Mean allocation: dividing a total amount by the number of people, such as gross domestic product divided by population.
- Rate of change: comparing numbers between two years to show growth.
If a mapmaker forgets to normalize the data, the map can mislead people. For example, a map of disease cases might simply highlight the states with the largest populations. Dividing the cases by the population reveals where the illness is truly spreading fastest. Clear and fair maps prevent confusing information from spreading during health emergencies.
Grouping Data into Classes
To make a map easy to read, cartographers must decide how to match colors to numbers. A classified map groups numbers into buckets, called classes, and gives one color to each group. An unclassed map gives every distinct number its own specific shade on a smooth spectrum.
In 1973, geographer Waldo R. Tobler showed that computers could make unclassed maps easily. He pointed out that unclassed maps preserve raw data without hiding differences. However, classified maps are still more popular today. Having only five to seven colors lowers the mental effort, or cognitive load, needed to read the map.
When grouping numbers into classes, cartographers follow rules to keep groups mutually exclusive and collectively exhaustive. This ensures that every data point belongs to exactly one group. Mapmakers use several methods to choose these boundaries:
- Exogenous rules use outside facts to pick number cutoffs. An established rule might use an official Poverty threshold or government tax brackets. An ad-hoc rule uses everyday ideas, like dividing temperatures into "freezing", "mild", and "sweltering".
- Endogenous rules look at patterns inside the dataset itself:
- Natural breaks search for gaps where numbers naturally separate into clusters. The Jenks natural breaks optimization, created by George F. Jenks, is a clever heuristic algorithm related to the k-means clustering algorithm. It groups similar numbers together automatically.
- Equal intervals use an arithmetic progression to make each group span the exact same range of numbers. A related method uses the arithmetic mean and steps outward by standard deviations.
- Quantiles divide the districts so that each color gets the exact same number of places. For example, dividing counties into four quartiles puts one-fourth of the counties into each color bucket.
- Geometric progression multiplies each step by a fixed ratio, like doubling the number for each class. This method works well when data has extreme skewness, as seen in a geometric or exponential distribution.
- Head/tail Breaks repeatedly splits numbers around their average value. It creates class counts that follow a power of two, which is very helpful for highly skewed numbers.
Choosing the Right Colors
Colors on a choropleth map must tell a clear story. If the data goes from low to high, the colors should go naturally from light to dark. This lets people spot patterns instantly without constantly checking the key. The human eye struggles to tell apart more than seven shades of a single color hue.
Mapmakers also make sure their maps work for people with color vision deficiencies. For people with forms of color blindness like dichromacy, mixing red and green can make a map impossible to read.
Cartographers usually pick from several color arrangements:
- Sequential progression: uses changes in color lightness to show amounts. A single-hue design fades from a pale tint to a dark shade of one color. A partial-spectral scheme blends neighboring colors, like pale yellow moving into deep orange and red.
- Divergent progression: connects two dark colors through a neutral white or light gray middle. It is great for showing differences from a central tendency, like freezing temperatures in blue, hot temperatures in red, and average days in white.
- Spectral progression: uses all the rainbow hues across the wheel. It helps when numbers follow a cycle, like seasons, rather than simple "more versus less" amounts.
- Qualitative progression: uses unrelated colors like green, orange, and purple. It is used for separate categories, such as showing the most popular sports or languages across regions.
Combining Topics and Reading Legends
Sometimes cartographers want to show two different topics at once. They create a special map called a bivariate choropleth map. For example, a map might use shades of blue for education levels and shades of red for household income. When both numbers are high in a county, the colors blend together to make purple.
This blending technique works best when nearby places share similar values, a property called spatial autocorrelation. If the data is too scattered, the map can look messy and hard to read.
Every choropleth map needs a clear legend filled with map symbols. The legend acts like a decoder ring. On a classified map, it shows small color boxes alongside the numbers they represent. On an unclassed map, it displays a smooth color gradient that fades between the lowest and highest values.
Some advanced maps include a small histogram bar chart in the legend. This graph shows how many districts fall into each color group, giving readers extra insight into the numbers.
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