2.9 Classification
methods
Classification procedures are utilized in various map production
software to facilitate user interpretation (Longley et al. 2005). However, the
statistical algorithm used to classify a range of continuous values can
strongly influence the visual impression (Evans 1977), the analysis (Smith et
al. 2007) and consequently the conclusions of a study. Based on the way a
thematic map is created, the characteristics of the original data might be
overlooked, or there might be a risk of misjudgment about the characteristic of
the original data (Osaragi 2002). Natural breaks (Jenks), Quantile, Equal
Interval, and Standard Deviation classifications are among the most popular
used in GIS software (Osaragi 2002, Longley 2005). ESRI (1996) provides a
conceptual framework of the different classification techniques along with some
of their advantages and drawbacks.
In the quantile technique, an equal number of features is
allocated to each class. While this arrangement is suitable for linearly
distributed data it can be misleading since comparable values can be grouped in
adjacent classes or diverging values can be put in the same category. On the
other hand, the natural breaks method, by looking at big jumps between values
overcome this weakness and ensures that similar values are placed in the same
class. The equal interval scheme divides the range of values into equal-length
sub-ranges and helps determining the number of intervals into which the values
are distributed. The algorithm used in the geometric interval insures that
there is a good distribution of values in term of quantity between classes.
Likewise, this technique makes reliable the change between intervals. This
approach is deemed convenient to accommodate continuous data and can generate
cartographically comprehensive results. Finally, the Standard Deviation
method shows the extent to which an attribute's values depart from the
mean of all the values.
A study conducted by Brewer and Pickle (2002) in which they asked
the respondents to evaluate seven classification methods recognized the
quantile technique as the best for conveying patterns of mapped rates. To
investigate the characteristics of different classification algorithms, Osaragi
(2002) applied them to seven different datasets. The results suggest that the
Natural Break method can be applied to different types of data for its
relatively lower loss of information compared to the other, but it is not
suitable for data with unclear division. Osaragi recommends examining the
distribution of data before choosing a particular method. Alternatively some
cartographers suggest to generate several maps for one dataset to allow the
reader to compare them (Dramowicz and Dramowicz 2004). The present study
compares the classification performed by four of these techniques on the
spatial dataset used.
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