Summary
Simplifies polygon outlines by removing relatively extraneous vertices while preserving essential shape.
Illustration
Usage
This tool employs different simplification algorithms for different purposes. To learn more about these algorithms, see How Simplify Line and Simplify Polygon work.
- The Retain critical points (Douglas-Peucker) algorithm (algorithm='POINT_REMOVE' in Python) works by identifying and removing relatively redundant vertices to simplify data for display at smaller scales. It is the fastest of the simplification algorithms in this tool. This algorithm is often used for data compression or for coarse simplification. The angularity of the resulting polygon outlines increase significantly as the tolerance increases. This algorithm is based on the Douglas-Peucker algorithm: Douglas, David and Peucker, Thomas, "Algorithms for the reduction of the number of points required to represent a digitized line or its caricature," The Canadian Cartographer. 10(2), 112–22 (1973).
- The Retain critical bends (Wang-Müller) algorithm (algorithm='BEND_SIMPLIFY' in Python) works by identifying and eliminating relatively insignificant bends to simplify data for display at smaller scales. It is typically more faithful to the input geometry than the Retain critical points (Douglas-Peucker) algorithm but can take more time to process. This algorithm is based on the algorithm defined in Wang, Zeshen and Müller, Jean-Claude, "Line Generalization Based on Analysis of Shape Characteristics," Cartography and Geographic Information Systems 25(1), 3–15 (1998).
- 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 polygon outline while retaining as much character as possible. This algorithm is based on the algorithm defined in 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, 369–80 (2005).
- The Retain effective areas (Visvalingam-Whyatt) algorithm (algorithm='EFFECTIVE_AREA' in Python) works by identifying triangles of effective area for each vertex to guide the removal of vertices to simplify the polygon outline while retaining as much character as possible. This algorithm is based on the algorithm defined in Visvalingam, M. and Whyatt, J. D., "Line Generalisation by Repeated Elimination of the Smallest Area," Cartographic Information Systems Research Group (CISRG) Discussion Paper 10, The University of Hull (1992).
The Simplification Tolerance parameter value determines the degree of simplification. The larger the tolerance, the more coarse the resulting geometry. Smaller tolerances generate geometry more faithful to the input. MinSimpTol and MaxSimpTol fields are added to the output to store the tolerance that was used.
- For the Retain critical points (Douglas-Peucker) algorithm, the tolerance is the maximum allowable perpendicular distance between each vertex and the newly created line.
- For the Retain critical bends (Wang-Müller) algorithm, the tolerance is the diameter of a circle that approximates a significant bend.
- For the Retain weighted effective areas (Zhou-Jones) algorithm, the square of the tolerance is the area of a significant triangle defined by three adjacent vertices. The further a triangle deviates from equilateral, the higher weight it is given, and the less likely it is to be removed.
- For the Retain effective areas (Visvalingam-Whyatt) algorithm, the square of the tolerance is the area of a significant triangle defined by three adjacent vertices.
Any polygons that are smaller than the Minimum Area parameter are removed from the output feature class. For a group of adjacent polygons that share common edges, the minimum area applies to the total area of the group. Use the Keep collapsed points parameter to retain a record of removed polygons as point features.
Multipart polygons are simplified as individual parts.
Use the Keep collapsed points parameter (collapsed_point_option in Python) to create an output point feature class to store points that represent any polygons that are removed because they are below the minimum area. The point output is derived; it will use the same name and location as the Output feature class parameter (out_feature_class in Python) but with a _Pnt suffix. The output polygon feature class contains all the fields present in the input feature class. The output point feature class does not contain any of these fields.
The output polygon feature class is topologically correct. Any topological errors in the input data are flagged in the output polygon feature class. The output feature class includes two additional fields, InPoly_FID and SimPgnFlag, which contain the input feature IDs and topological errors or discrepancies of the input, respectively.
SimPgnFlag field values are as follows:
- SimPgnFlag = 0 indicates that no errors are present.
- SimPgnFlag = 1 indicates a topological error is present.
- SimPgnFlag = 2 indicates features that have been split by a partition and are now below the minimum area after simplification. The flag may appear on only one part of the split feature. These features are all retained in the output feature class. This situation arises only when the Cartographic Partitions environment setting is used.
Legacy:
In previous versions of this tool, topological errors could be generated during processing. The Handling topological errors (error_option in Python) parameter is still included in the tool's syntax for compatibility in scripts and models but is now ignored and hidden from the tool's dialog box.
The SimPgnFlag field was used to flag topological errors in the output feature class that were introduced by the tool in processing. Now this field flags errors that are present in the input. Also, in the course of topology resolution, previous versions of the tool modified tolerance on a per-feature basis and stored those values in the MinSimpTol and MaxSimpTol fields. In the current implementation, the values in these fields will be the same and equal to the tolerance specified in the Simplification Tolerance parameter. Be sure to modify existing models or scripts that rely on any of these fields.
Use the Input barrier layers parameter to identify features that must not be crossed by simplified polygons. Barrier features can be points, lines, or polygons.
Domains and subtypes are copied to the output even if the Transfer field domain, subtypes and attributes rules environment is not checked.
Processing large datasets may exceed memory limitations. In this case, consider processing input data by partition by identifying a relevant polygon feature class in the Cartographic Partitions environment setting. Portions of the data defined by partition boundaries will be processed sequentially. The resulting output feature class will be seamless and consistent at partition edges. See Generalizing large datasets using partitions for more information.
Syntax
arcpy.cartography.SimplifyPolygon(in_features, out_feature_class, algorithm, tolerance, {minimum_area}, {error_option}, {collapsed_point_option}, {in_barriers})| Parameter | Explanation | Data Type |
in_features | The input polygon features to be simplified. | Feature Layer |
out_feature_class | The simplified output polygon feature class. It contains all fields present in the input feature class. The output polygon feature class is topologically correct. The tool does not introduce topology errors, but topological errors in the input data are flagged in the output polygon feature class. The output feature class includes two additional fields, InPoly_FID and SimPgnFlag, which contain the input feature IDs and the input topological errors or discrepancies, respectively. SimPgnFlag values are as follows:
| Feature Class |
algorithm | Specifies the polygon simplification algorithm.
| String |
tolerance | The tolerance determines the degree of simplification. You can choose a preferred unit; otherwise, units of the input will be used. MinSimpTol and MaxSimpTol fields are added to the output to store the tolerance that was used when processing occurred.
| Linear Unit |
minimum_area (Optional) | The minimum area for a polygon to be retained. The default value is zero, that is, to keep all polygons. You can choose a preferred unit for the specified value; otherwise, units of the input will be used. | Areal Unit |
error_option (Optional) | Legacy:This is a legacy parameter that is no longer used. It was formerly used to specify how topological errors possibly introduced during processing were handled. This parameter is still included in the tool's syntax for compatibility in scripts and model but is hidden from the tool's dialog box. | String |
collapsed_point_option (Optional) | Specifies whether to create an output point feature class to store the centers of any polygons that are removed because they are smaller than the minimum_area parameter. The point output is derived; it will be named the same as the polygon output feature class you specify in the out_feature_class parameter but with a _Pnt suffix, located in the same folder.
| Boolean |
in_barriers [in_barriers,...] (Optional) | The inputs containing features to act as barriers for simplification. Resulting simplified polygons will not touch or cross barrier features. For example, when simplifying forested areas, the resulting simplified forest polygons do not cross road features defined as barriers. | Feature Layer |
Derived Output
| Name | Explanation | Data Type |
| out_point_feature_class | When the Keep collapsed points parameter (collapsed_point_option in Python) is used, an output point feature class is created to store points that represent any polygons that are removed because they are below the minimum area | Feature Class |
Code sample
The following Python window script demonstrates how to use the SimplifyPolygon tool in immediate mode.
import arcpy
import arcpy.cartography as CA
arcpy.env.workspace = "C:/data"
CA.SimplifyPolygon("soils.shp", "C:/output/output.gdb/simplified_soils", "POINT_REMOVE", 100)The following stand-alone script demonstrates how to use the SimplifyPolygon tool.
# Name: SimplifyPolygon_Example2.py
# Description: Eliminate small islands before simplifying and smoothing lake boundaries
# Import system modules
import arcpy
import arcpy.management as DM
import arcpy.cartography as CA
# Set environment settings
arcpy.env.workspace = "C:/data/Portland.gdb/Hydrography"
# Set local variables
inLakeFeatures = "lakes"
eliminatedFeatures = "C:/data/PortlandOutput.gdb/lakes_eliminated"
simplifiedFeatures = "C:/data/PortlandOutput.gdb/lakes_simplified"
smoothedFeatures = "C:/data/PortlandOutput.gdb/lakes_smoothed"
# Eliminate small islands in lake polygons.
DM.EliminatePolygonPart(inLakeFeatures, eliminatedFeatures, 100, "OR", 0, "CONTAINED_ONLY")
# Simplify lake polygons.
CA.SimplifyPolygon(eliminatedFeatures, simplifiedFeatures, "POINT_REMOVE", 50, 200, "#", "KEEP_COLLAPSED_POINTS")
# Smooth lake polygons.
CA.SmoothPolygon(simplifiedFeatures, smoothedFeatures, "PAEK", 100, "FLAG_ERRORS")Environments
Licensing information
- Basic: No
- Standard: Yes
- Advanced: Yes