The Simplify Line and Simplify Polygon tools simplify lines and polygons to reduce feature complexity for display at smaller scales. The tools operate by identifying and removing relatively insignificant vertices to reduce feature complexity while retaining inherent character and shape. There are different simplification algorithms, which will generate slightly different results.

## Simplification algorithms

### Retain critical points (Douglas-Peucker)

The Retain critical points (Douglas-Peucker) (algorithm='POINT_REMOVE' in Python) option provides fast, simple line simplification algorithm. It keeps critical points that depict the essential shape of a line and removes all other points. The algorithm begins by connecting the endpoints of a line with a trend line. The distance of each vertex to the trend line is measured perpendicularly. Vertices closer to the line than the tolerance are eliminated. The line is then divided by the vertex farthest from the trend line, which makes two new trend lines. The remaining vertices are measured against these lines, and the process continues until all vertices within the tolerance are eliminated. This option is based on the algorithm defined by Douglas and Peucker (1973).

Retain critical points (Douglas-Peucker) is efficient for data compression and for eliminating redundant details; however, the line that results may contain unpleasant sharp angles and spikes that reduce the cartographic quality of the line. Use Retain critical points (Douglas-Peucker) for relatively small amounts of data reduction or compression and when you don't need high cartographic quality.

### Retain critical bends (Wang-Müller)

The Retain critical bends (Wang-Müller) (algorithm='BEND_SIMPLIFY' in Python) algorithm applies shape recognition techniques that detect bends, analyze their characteristics, and eliminate insignificant ones. A linear feature can be seen as composed by a series of bends (Wang, 1996), each defined as having the same sign (positive or negative) for the inflection angles at its consecutive vertices. Several geometrical properties of each bend are compared with those of a reference half circle, the diameter of which equals the specified simplification tolerance. These measures determine whether a bend is kept or eliminated, meaning replacing the bend by its baseline (the line connecting the endpoints of the bend). The simplification takes place iteratively such that the smaller bends may "disappear" in the early rounds and, therefore, form bigger bends. The resulting line follows the main shape of the original line more faithfully and shows better cartographic quality than from Retain critical points (Douglas-Peucker). This option is based on the algorithm defined by Wang and Müller (1998).

### Retain weighted effective areas (Zhou-Jones)

The Retain weighted effective areas (Zhou-Jones) algorithm (algorithm='WEIGHTED_AREA' in Python) works by first identifying triangles of effective area for each vertex. Those triangles are then weighted by a set of metrics to compare the flatness, skewness, and convexity of each area. The weighted areas guide the removal of their corresponding vertices to simplify the line while retaining as much character as possible. This option is based on the algorithm defined by Zhou and Jones (2005).

## Maintaining topological integrity

The tools ensure that topology is maintained during processing. Topological errors will not be introduced. In cases where simplification would otherwise result in a topology violation, the tools first try to split the geometry into two parts, and simplify them independently. This is a recursive process. The sub-parts may be subsequently split again, and so on. If topology cannot be maintained using this approach, the feature is put in waiting, and revisited after neighboring features have been processed, to see if the topology can then be maintained.

Use the Input barrier layers parameter to define one or more feature classes that contain features that should not be crossed by simplified lines or polygons. Examples include lakes and rivers where simplified roads should not be, elevation spot heights or other survey markers that contour lines cannot cross, or administrative boundaries, where simplified data must remain wholly within.

## Analyzing and improving the results

The tools simplify lines and polygon outlines one at a time; the longer a line or outline runs, the more pleasing the result will be. Keep this in mind when you collect or construct the source data. Wherever possible, position endpoints of lines on long, smooth sections of lines, rather than at severely angled sections.

Topology problems in the input feature class—features that overlap other features without an intersection point—are flagged in a field in the input feature class. This field is named SimPgnFlag in the case of Simplify Polygon and SimLinFlag in the case of Simplify Line. A value of 1 in these fields indicates that a topological error was present for that feature in the input feature class.

For reference, an ID field corresponding to the object ID of the input feature is also added to the output feature class. The field is called InPoly_FID in the case of the Simplify Polygon tool, or InLine_FID in the case of the Simplify Line tool. In addition, MinSimpTol and MaxSimpTol fields are added to the output to store the tolerance that was used.

A derived point output feature class is created of point features that represent either the endpoints of lines simplified to zero-length lines in the case of Simplify Line), or polygons simplified to zero-area polygons or to a polygon smaller than the minimum area as defined by the Minimum Area parameter in the case of Simplify Polygon. A simplified polygon that has been reduced to empty geometry or to a polygon smaller than the minimum area as defined by the Minimum Area parameter.

## Working with large datasets

The topology handling of these tools require that multiple features be considered simultaneously. When you work with large datasets, you may exceed memory limitations. In this case, consider processing input data by partitions. You can do this by identifying a relevant polygon feature class in the Cartographic Partitions environment setting that covers and subdivides the input data. Portions of the data, defined by partition boundaries, will be simplified sequentially, but the output feature class will be seamless and consistent at partition edges. See Generalizing large datasets using partitions for more information.

## References

Douglas, David H. and Peucker, Thomas K. (1973) "Algorithms for the Reduction of the Number of Points Required to Represent a Digitised Line or its Caricature", The Canadian Cartographer, 10(2), pp112-122.

Wang, Zeshen and Müller, Jean-Claude (1998) "Line Generalization Based on Analysis of Shape Characteristics", Cartography and Geographic Information Systems 25(1), pp3-15.

Zhou, Sheng and Jones, Christopher B., (2005) Shape-Aware Line Generalisation with Weighted Effective Area" in Fisher, Peter F. (Ed.) Developments in Spatial Handling 11th International Symposium on Spatial Handling, pp369-80.