A choropleth map (Greek choros-space & pleth-value) is a thematic map in which areas are shaded or patterned in proportion to the measurement of the statistical variable being displayed on the map, such as population density or per-capita income. Each color is associated with an attribute value. This value is typically quantitative.
The choropleth map provides an easy way to visualize how a measurement varies across a geographic area or it shows the level of variability within a region.
Choropleth maps are based on statistical data aggregated over previously defined regions (such as counties). Boundaries are not based off of the data value. In contrast, area-class and isarithmic map's region boundaries are defined by data patterns. Thus, where defined regions are important to a discussion (as in an election map divided by electoral regions), choropleth maps are preferred. When real-world patterns may not conform to the regions discussed because of issues like ecological fallacy and the modifiable areal unit problem (MAUP), other techniques are preferable .
For example, a map showing population may spread the color symbol representing Canada's population over its entire expanse, while most of the population lies along the coasts and southern border of the country. Unfortunately, choropleth maps are frequently used in inappropriate applications due to the abundance of choropleth data and the ease of design using Geographic Information Systems.
The dasymetric technique can be thought of as a compromise approach in many situations. Broadly speaking, choropleth maps represent two types of data: Spatially Extensive or Spatially Intensive. Spatially Extensive data are things like populations. The population of the UK might be 60 million, but it would not be accurate to cut the UK into two halves of equal area and say that the population of each half is 30 million. Spatially Intensive data are things like rates, densities and proportions. These can be thought of conceptually as field data that is averaged over an area.
When the different areas being represented in a choropleth map are not all the same size, it is best to use density, ratios, or percentages rather than absolute values. For example, a map representing the overall population of the United States would look quite different than a map showing population density in the United States. In the first map, states with large populations, such as California, Texas, and New York, could be represented in a darker shade to indicate high population. However, when shown in the population density map, these states would not necessarily be shaded as darkly, because they are rather large states, allowing for smaller population per square mile. In the population density map, a much smaller state such as Connecticut could be shown in the darkest shade, because it is a small state, with less square miles for the population to fit within. Connecticut does not have a higher population than Texas or California, but has a much smaller land area, so the population density is much greater .
When producing a choropleth map the map maker must choose an appropriate graded color series or shades of grey to show least intense to most intense, a light to dark color pattern, to represent different classes of data being mapped. This helps group the data in organized ratio values. It is not a good idea to use the colors of the rainbow in a choropleth map because each color has a different intensity and meaning and might get confusing as to what the data is showing the map user. Rainbow color schemes works better for nominal data . The ColorBrewer, created by Cynthia Brewer of Pennsylvania, is useful in formulating color swatches for choropleth maps.
The earliest known choropleth map was created in 1826 by Baron Pierre Charles Dupin .
The way data is classified and represented on a choropleth map determines how the data will be perceived and interpreted by the viewer. Which classification to use is a very important decision to make when creating a choropleth map because the different types of classification have different ways of representing, or misrepresenting the data. Some classification methods include Jenks Natural Breaks, Equal Interval, Geometric Interval, and Quantile classification.
In mapping quantitative data, color progression will be used to depict the data properly. Cartographers use many different types of color progression.
Single-Hue progression fade from a dark shade of the color to a very light shade of the same hue color used. This is a common method used to show the magnitude of the data being represented on the map.
Bi-polar progressions are normally used with two opposite hues to show a change in value from negative to positive or on either side of a central tendency, such as the mean of the variable being mapped or other significant value like room temperature. For example a typical progression when mapping temperatures is from dark blue (for cold) to dark red (for hot) with white in the middle.
Qualitative progression is often used when working with nominal, characteristic, or qualitative data. It is when the colors shown on the map seem unrelated to one another, or are arbitrarily chosen. For example, on the map of the continental United States, shown on the right, the colors are arbitrarily chosen to represent the different states.
Choropleth Map Legend.
Frequency Histogram Legend
The purpose of a frequency histogram legend in a choropleth map is to visualize statistical distribution and frequency counts in each class, in addition to serving as a key to the map .
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- Business graphs and new data visualizations (including Choropleth map) inside Excel and PowerPoint
- MapsGeek An online free application to build and share thematic maps.
- StatPlanet - Free software for making interactive choropleth maps which can be published online or viewed offline.