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Graph showing 10 points in each interval, which makes the intervals uneven sizes.Classes are not left empty nor do they have limited or excessive number of values. Because of the way the classes are grouped, the maps can sometimes be confusing or ‘misleading’. This happens because the values that are put into the classes can be similar to one another, or very different from others in the same class.The term quartiles is used when the attribute values are divided into four classes, quintiles for five, sextiles for six etc. Shows the Median House Value in the Contiguous United States represented by the Quantile Data Classification. AdvantagesUsing the quantile classification method gives data classes at the extremes and middle the same number of values. Each class is equally represented on the map and the classes are easy to compute.
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Quantile classification is also very useful when it comes to ordinal data.DisadvantagesWhen using quantile classification gaps can occur between the attribute values. These gaps can sometimes lead to an over-weighting of the outlier in that class division.Another disadvantage is that if the number of classes is not correctly created two areas with the same value can end up in different groups. For example, imagine you have data for the number of fast food restaurants in each county for 21 counties and you want to divide the counties into 7 groups with 3 counties in each group. If 4 counties each have exactly 10 fast food restaurants one of those counties will be classified in a different group, because there are only 3 counties per group, despite the values being the same.
Equal Interval classification
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In Equal Interval Classification each class occupies an equal interval along the number line. They are found by determining the range of the data. The range is then divided by the number of classes, which gives the common difference. The class limits are established by starting at the lowest value and adding the common difference to get the upper limits of the first class, adding the common difference to this to get the limit of the second class, until the upper limit of the data is reached.[1]
Example:
Equal interval is useful when distribution of the data has a rectangular shape in the histogram. However, in geography, equal interval is most common when the classification units are nearly equal in size.
Advantages and Disadvantages
One advantage of using equal interval classification is that the steps to compute the intervals can easily be completed using a calculator or pencil and paper. A second advantage is that when the results of this classification are projected onto a map they are easily interpreted. Another advantage is that the legend limits contain no missing values or gaps. This permits faster map interpretation, but might create confusion concerning the bounds of each class.[2]
The main disadvantage of this classification type is that it fails to consider how data are distributed along the number line. For example, the map to the right shows the percentage of total homes in Arkansas which are mobile homes. There are many areas that fall into the two lower percentages, leaving most of the state the two shades of green. If a different classification was used, the data displayed in the map could be shown more effectively.
Equal Interval Map | Legend |
References
- ↑ Tyner, Judith (2010). Principles of Map Design', page 167. The Guilford Press: New York, NY.
- ↑ Slocum, Terry A. (2009). 'Thematic Cartography and Geovisualization', page 60. Pearson Prentice Hall: Upper Saddle River, NJ.
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Notes
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Further Reading
See Also
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