Choropleth map

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The US Presidential Election of 2004, visualised using a choropleth map

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.

Contents

[edit] Overview

Choropleth maps are based on statistical data aggregated over previously defined regions (such as counties). The boundaries are not based off of the data value. In contrast, area-class and isarithmic maps, 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), choropleths are preferred. Where real-world patterns may not conform to the regions discussed, issues such as the ecological fallacy and the modifiable areal unit problem (MAUP) can lead to major misinterpretations, and other techniques are preferable [1].

For example, a map showing population may spread the colour symbol for Canada's population over its entire expanse, while most of the population lies along the coasts and southern border. 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 choropleths 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 of the UK 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 [2].

When producing a choropleth map the map maker must choose appropriate graded color series or shades of grey to show least intensity to most intense, a light to dark color pattern, to represent different classes of data being mapped. This helps to group the data in organized ratio values. It is not a good idea to use colors of the rainbow in a choropleth map because each color has a different intensity and meaning, thus it might get confusing as to what the data is showing to the map user. Rainbow color schemes works better for nominal data [3]. 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 [4].

[edit] Data Classification

Choroplath maps showing the difference in representation, of the same data, based on data classification used.

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.


[edit] Color Progression

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

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 she the magnitude of the data being represented on the map.


Bi-polar color progression

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.


An example of qualitative colors

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.

[edit] Area Symbols

This choropleth map uses randomly placed dots within pre-defined areas.
This dot density map uses strategically placed dots and is NOT a choropleth map.
Choropleth maps can use random dots as an area symbol. They key difference between a choropleth map that does this and a dot-density map is the dispersion and purpose of the dots. In a choropleth map, the dots are used to represent a quantity that is assumed to be constant throughout that polygon, and are randomly placed throughout the polygon. This means that the individual placement of a dot in a choropleth map is not significant to the meaning of the map. In a dot density map, the dots are used to show the geographic distribution and density of specific phenomenon within the polygon--each dot's placement within the polygon is significant [5].

[edit] Choropleth Map Legend

The legend for a choropleth map is extremely important. Without a legend, all classifications on a choropleth map are pointless and have no meaning. All colors and symbols on the map will equal a value, which value is defined and explained in the map legend. The different values are represented in boxes within the legend and are usually listed vertically, but can be listed horizontally if the map is much wider than it is tall [6].

[edit] 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 [7].



[edit] See also

[edit] References

  1. T. Slocum, R. McMaster, F. Kessler, H. Howard (2009). Thematic Cartography and Geovisualization, Third Edn, pages 85-86. Pearson Prentice Hall: Upper Saddle River, NJ.
  2. Tyner, Judith A.(2010), Principles of Map Design, The Guilford Press, New York, NY.
  3. “Choropleth Maps.” Illinois State http://my.ilstu.edu/~jrcarter/Geo204/Choro/Tom/. 22 Oct 2012.
  4. Michael Friendly (2008). "Milestones in the history of thematic cartography, statistical graphics, and data visualization".
  5. Kimerling, A Jon. "Dot maps vs. choropleth maps with random dot area symbols." ArcGIS Resources. 18 April, 2008. Web . 04 November 2013.
  6. Borden D. Dent, Jeffrey S.Torfuson, and Thomas W. Holder. Cartography: Thematic Map Design Sixth EditionHigher Education 2009.
  7. Kumar, N., Frequency Histogram Legend in the Choropleth Map: A Substitute to Traditional Legends. Cartography and Geographic Information Science. March, 2013

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