Choropleth map

A choropleth map uses colors.

It shows facts about a place. These maps use colors to show data. For example, they can show wealth in different lands.

One man made an early map in 1826. He showed how many people could read in France.

These maps use shapes like countries or states. The colors help us see patterns quickly. It is a smart way to learn about our world.

A choropleth map is a special kind of map.

It uses colors to show facts about a place. These maps use shapes like countries or states. They also use shapes like counties or provinces. These shapes are called districts.

Each district gets a color. The color tells us about a specific fact. For example, a map might show how much money people have.

It might also show how many people live in an area.

One of the first maps like this was made in 1826. A man named Baron Pierre Charles Dupin made it. It showed how many people in France could read.

These maps are very helpful. They help us see patterns in the world. They are easy to make with computer tools. But they can sometimes be tricky. A big district might look more important than a small one. This happens even if the small one has more people. We use a way called normalization to help fix this.

A choropleth map is a special kind of thematic map.

It uses colors to show different facts about a specific area. These colors represent a summary of information within certain shapes. We call these shapes districts or spatial units. They can be large things like countries or states. They can also be smaller areas like counties or provinces.

This type of map is very common today. Most people use them because they are easy to make with computer tools. You can use spreadsheets or special mapping software to create them quickly.

To make these maps, you need two different sets of data. First, you need spatial data to show the boundaries of the districts. Second, you need statistical data about the thing you want to study. There are two ways to think about how these parts work together. In one way, the districts themselves are the main focus. We call this the district dominant view. In the other way, the information itself is the main focus. This is called the variable dominant view.

People have been using these maps for a long time. The earliest known choropleth map was made in 1826. A man named Baron Pierre Charles Dupin created it. His map showed how much education was available in France. Soon, other maps called "tinted maps" were made to show things like crime or disease. These maps became popular as more census data was collected. In 1841, Ireland published maps in its official census reports. The name "choropleth map" was not used until 1938. A geographer named John Kirtland Wright introduced the term then.

These maps work best with a type of data called intensive variables. These are things like population density or the percentage of people in a group.

An example is a map showing the fraction of Australians who are Anglican.

It is usually a bad idea to use total counts, which are extensive variables. For instance, mapping the total population of a whole country can be misleading. A large district might look much more important than a small one. This happens even if the small district is very crowded.

To make the maps more accurate, scientists use a trick called normalization.

This helps show the real patterns without being tricked by the size of the shapes. Instead of just showing a total number, it might show a ratio. For example, it can show people per square kilometer.

This makes the map easier to understand and remember. You can see patterns in wealth, language, or even elections.

These maps help us see how the world is organized. They turn hard numbers into a picture we can see.

A choropleth map is a specific type of statistical thematic map.

It uses pseudocolor to represent an aggregate summary of a geographic characteristic. This characteristic is measured within specific spatial enumeration units. These units are often called districts. They can be large areas like countries or provinces. They can also be smaller areas like counties or census tracts. These maps are vital for visualizing how a variable changes across a geographic area. They also help show the level of variability within a specific region. Because most statistical data is already organized into known geographic units, these maps are very common.

To create a choropleth map, a cartographer must combine two distinct datasets. The first dataset is spatial data. This data provides a partition of geographic space into distinct districts. The second dataset is statistical data. This data represents a variable that has been aggregated within each district. There are two ways to view the relationship between these datasets. The first is the district dominant model. In this view, the districts are the primary focus. Various attributes are collected for each existing governmental unit. The second is the variable dominant model. Here, the focus is on the variable as a geographic phenomenon. The partitioning into districts is simply a convenient way to measure it.

Geographically, the districts used in these maps are usually pre-defined entities. These might be administrative units like states or countries. They might also be districts created specifically for statistical aggregation. Because these boundaries are set before the data is applied, they may not match the actual pattern of the variable. This is a major difference between choropleth maps and isarithmic maps. An isarithmic map uses boundaries drawn according to the pattern of the variable itself.

Using pre-defined regions has several practical advantages. It makes the data easier to compile using GIS or spreadsheets. It also makes the districts easily recognizable to the reader. This allows the information to be used for policy decisions tied to specific districts, such as election results.

Despite these advantages, choropleth maps can lead to misinterpretations. A constant color is applied to an entire district. This makes the district look homogeneous, or the same throughout. In reality, there may be significant variation inside that district. For example, a single city might contain wealthy and poor neighborhoods. However, the map might color the whole city with one "moderate" shade. This can lead to the ecological fallacy or the modifiable areal unit problem (MAUP). These issues occur when real-world patterns do not match the regional units. To reduce these errors, cartographers can use smaller districts. Smaller districts show finer variations, but they can also make a map look overly complex.

The history of these maps is quite long. The earliest known choropleth map was created in 1826 by Baron Pierre Charles Dupin. He used it to show the availability of basic education in France by department. Soon after, other "tinted maps" appeared in France. These maps visualized "moral statistics" like crime, disease, and living conditions. The popularity of these maps grew alongside national Censuses. For example, Ireland published choropleth maps in its 1841 Census reports. Color became more common after 1850 due to chromolithography. The specific term "choropleth map" was introduced in 1938 by geographer John Kirtland Wright.

Data types are very important when choosing how to map a variable. Variables can be spatially extensive or spatially intensive. An extensive variable is a global property. It applies to an entire district, such as a total population count. Mapping these is generally discouraged because it can be misleading. A large district with a high total population might look more significant than a small, crowded district.

Instead, choropleth maps are best suited for spatially intensive variables. These are also called fields or statistical surfaces. These variables represent properties that could be measured at any location, like density or proportions.

An example is the fraction of Australians identifying as Anglican.

To make intensive variables work correctly, cartographers use a technique called normalization.

Normalization derives an intensive variable from one or more extensive variables. This is often done by calculating a ratio. A common example is calculating population density by dividing total population by total area. This prevents large geographic areas from appearing more dominant simply because they are big.

Without normalization, a map might incorrectly suggest that a large, empty territory is more urbanized than a small, dense one. By using normalized data, the map provides a more accurate view of the underlying phenomenon. This allows us to see clear patterns in human and natural systems, such as wealth or agriculture.