This representation is commonly used to display rates, such as densities, market penetration, percentage of voters, etc.
The input data is split up into classes by classification.
Each surface of the working map is filled in accordance with the colors of its class. The palette contains as many colors as classes.
Numeric data.
This window allows to define the parameters of the classification.
The first step is to choose the classes count. It can be a number between 1 and 512. Be careful: For a good color representation, it is better to choose a number below 12. Two methods exist to find the optimal classes count:
Brooks and Carruthers: Classes count = Real (5*log10(N) + 0.5)
Huntsberger : Classes count = Real (3.3*log10(N) + 1.5)
Where N = data count
The next step is to choose the classification method:
The classification is made by following a Gauss distribution. The classes are centered on zero. That allows to represent negative and positive classes by hot and cold colors.
This type of quantification is not suitable for relative data (percentages).
The classes are calculated to contain the same number of elements.
Classes could have a different count of elements depending on the repartition.
Exemple: input data: 1;4;4;4;4;10 => Classes count=4 => 1.5 elements by class
Results:
Class1 = [1;4[ containing 2 elements
Class2 = [4;4[ containing 1 element
Class3 = [4;10[ containing 2 elements
Class4 = [10;10[ containing 1 element
Comment: Class1 contains 1 and 4 in spite of the limits.
The interval in which the data values have to be found is spread evenly throughout the various classes.
Jenks type quantification is based on the notion of variance, i.e. the dispersion of the data input values around the average. Its purpose is to maximize
Classes limits are indicated by the user.
Some indicators are calculated to optimize the choice of the classification method.
Disparity of a Class = Absolute_value (width / mean) - (width / middle)
The closer to 0 the Jenks factor is, the better it is.
Distance1 = Σ (distance between values and class mean)
Distance2 = Σ (distance between values and general mean)
Tai factor = 1 - Distance1 / Distance2
The closer to 1 the TAI factor is, the better it is.
Show if classes are similar or different
D = Σ (distance(class mean ; general mean)² * elements count)
Between variance = D / (classes count - 1)
The higher the within variance is, the more different classes will be. Good for a discretisation.
Show if classes are homogeneous or mixed
D = Σ (distance(values ; class mean)² )
Within variance = D / (data count - classes count)
The smaller the within variance is, the more homogeneous classes will be. Good for a discretisation.
This dialog box allows to choose colors for each class of the classification. Pre-defined palettes can be chosen (Spring, Autumn, Grey, etc).
When "Automatic Palette" is not selected you can change manually the color of each class by clicking on its color zone.
This dialog box defines the view parameters of the filling caption.
You can choose to see or not the caption on your map with the box show the caption.
If show border is selected a rectangle is drawn around the caption.
You can modify the caption title and the font.
At first the caption size is automatic. If the box Automatic size is not selected you can choose the caption size.
Number precision allows to choose the number of numeral before and after comma.
You can choose the text form [x1;x2], from x1 to x2, x1-x2 or inset.
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