# SherbendGeneralizer

Uses the Sherbend algorithm to simplify lines by reducing unnecessary details based on the analysis of the line’s bends.

Sherbend is a constraint-based algorithm that preserves the spatial relationship of the lines and points in the input data.  The Sherbend algorithm iteratively generalizes the bends in a line by using the Diameter parameter to select bends for generalization. The generalization process may eliminate, reduce, or combine bends, while resolving conflicts.

The strategy for generalizing bends in a line is as follows:

• Calculate the area of a reference circle whose diameter is specified by the Diameter parameter.
• For each line, determine the locations of the bends.
• For each bend, calculate its perimeter. Next, construct a circle whose circumference is equal to that perimeter. Finally, determine the adjusted area of the bend, which is 75% of the area of that circle.
• For each bend, generalize the bend if its area is below the area of the reference circle and spatial constraints are met.
• Repeat the above steps until there are no more bends to generalize.

The aim of line generalization is to reduce the details on a line for representation at a smaller scale. While the well-known Douglas-Peucker algorithm is good at reducing the number of points in a line, it is not so good at removing unnecessary details in a line. The Generalizer transformer contains a selection of algorithms under its parameters including the Douglas-Peucker algorithm.

In comparison, the Sherbend algorithm is well suited for the generalization of natural features (contours, lakes, rivers, wooded areas, etc.) because it preserves the general shape of the line. Moreover, if spatial constraints are enabled, the spatial relationship between the input entities are preserved. The Douglas-Peucker algorithm with a small tolerance is often used before or after Sherbend to further reduce the number of points to further fulfill the goals of generalization.

### Performance and Usage Notes

• The Sherbend algorithm iteratively detects and generalizes bends, and then detects and resolves spatial conflicts. The generalized lines from one iteration are passed to the next iteration until the lines cannot be generalized further. Due to this iterative process, the algorithm is time-intensive, which is a tradeoff to improved accuracy and quality of generalization.
• Constraint checking is a highly time-intensive operation. Use constraints only as necessary.
• To generalize each feature independently, consider using the Generalizer transformer.

## Examples

In this example, a bend is reduced (green = input, red = output):

In this example, a bend is eliminated:

In this example, three bends are combined into one:

The following diagram illustrates the generalization process on a single line in a real-world dataset:

This example illustrates the generalization process on a set of contours:

## Editing Transformer Parameters

Using a set of menu options, transformer parameters can be assigned by referencing other elements in the workspace. More advanced functions, such as an advanced editor and an arithmetic editor, are also available in some transformers. To access a menu of these options, click beside the applicable parameter. For more information, see Transformer Parameter Menu Options.

### Defining Values

There are several ways to define a value for use in a Transformer. The simplest is to simply type in a value or string, which can include functions of various types such as attribute references, math and string functions, and workspace parameters. There are a number of tools and shortcuts that can assist in constructing values, generally available from the drop-down context menu adjacent to the value field.

### Dialog Options - Tables

Transformers with table-style parameters have additional tools for populating and manipulating values.

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