Comparative Review Of Tools To Eliminate Sliver Polygons In Popular Gis Platforms

What are Sliver Polygons and Why Do They Matter?

Sliver polygons, also known as sliver shapes or gaps, refer to small polygon features in geospatial datasets that are often unwanted artifacts of geoprocessing operations. They are typically long and narrow slivers of space between larger, more meaningful polygon features. Common causes of sliver polygons include overlaying layers based on misaligned geometry nodes, splitting polygons with cut lines or trace lines, dissolving features, and edge-matching polygon layers with slight spatial discrepancies.

The presence of sliver polygons can negatively impact both visualization and analysis in GIS platforms. As unwanted features, they contribute to visual clutter and cartographic noise when symbolized, posing challenges for data quality and clear communication of spatial patterns. Their disproportionate length-to-area ratios can also distort analytical calculations like densities and statistical summaries across features and surfaces.

A Comparative Analysis of Tools to Fix Sliver Polygons

ArcGIS: Eliminate and Integrate Tools

ArcGIS provides two core geoprocessing tools specifically designed to mitigate sliver polygons – the Eliminate and Integrate tools. The Eliminate tool removes small sliver polygon features entirely by deleting them based on a minimum area threshold or converting them to lines or points. The Integrate tool aggregates sliver polygons with neighboring features based on shared geometry nodes and boundaries.

QGIS: v.clean.advanced with Snap Option

In QGIS, the v.clean.advanced tool in the Processing Toolbox allows fixing sliver polygons using its snap functionality. By setting an appropriate snapping distance tolerance, small slivers can be merged with adjoining polygons they overlap and share vertices with, eliminating the gaps.

Global Mapper: Auto Merge Overlapping Shapes

Global Mapper has an Auto Merge Overlapping Shapes tool that automatically merges sliver polygons smaller than a set size with adjacent polygons to improve dataset quality. Combined with other generalization tools, it provides batch processing power to fix slivers suited for large geospatial databases.

Step-by-Step Tutorials and Example Code

Walkthrough of Using Eliminate Tool in ArcGIS with Python Code

The Eliminate tool in ArcToolbox > Cartography Tools > Generalization provides options to set a minimum area threshold, with smaller slivers either removed or converted to alternative features. The following Python code executes the tool on-the-fly as part of an editing workflow:

import arcpy
input_features = "C:/data/polygons.shp" 
output_feature_class = "C:/output/cleaned.shp"
switch_to_lines_at = "1000 SquareMeters"
switch_to_points_at = "100 SquareMeters"
arcpy.EliminatePolygonPart_cartography(input_features, output_feature_class, switch_to_lines_at, switch_to_points_at)

This iterates through the polygon dataset, eliminates all slivers smaller than 100 sq. meters by converting them to points, and converts those between 100-1000 sq. meters to lines – greatly reducing noise and unwanted artifacts.

Using v.clean.advanced in QGIS Processing Toolbox with Sample Data

QGIS offers a user-friendly graphical modeler to customize the parameters of v.clean.advanced for sliver removal. Using sample land parcel data, the steps are:

  1. Select algorithm v.clean.advanced from the Processing Toolbox
  2. Check “Snap” option under Advanced Parameters
  3. Set snapping threshold such as 0.001 map units
  4. Run modeler on land parcel layer to eliminate slivers by merging them

Configuring Auto Merge Overlapping Shapes in Global Mapper XML Script

An easy way to fix slivers with the Auto Merge tool is through Global Mapper Scripting. The following script language snippet shows the key parameters:

    <AutoMergeOverlappingShapes MergeThreshold="100 sq meters"/> 
  <LoadDataset Filename="parcels.shp" />
  <Export Format="ShapeFile" Filename="cleaned_parcels.shp" ObjectType="ExportAllObjects" />

This automatically merges any sliver polygons under 100 sq. meters in area. The filtered dataset can then be exported with significantly reduced artifacts and gaps.

Choosing the Right Tool for Your Needs

With an array of options available, the optimal automated sliver removal tool depends on several factors:

  • Format – Support for different geospatial data models like shapefiles, file geodatabases, and enterprise geodatabases.
  • Performance – Processing time and resources required, which varies by dataset complexity.
  • Customization – Fine-tuning parameters for different definitions of sliver size and treatment.
  • Automation – Ability to embed into editing workflows through scripting vs manual clean-up.

For basic shapefile processing, all options have user-friendly options with decent performance. For large enterprise datasets, ArcGIS tools like Eliminate provide robust optimization tailored to multi-user databases. The choice comes down to existing platform experience and IT infrastructure considerations.

Moving Beyond Sliver Polygons

Eliminating existing sliver polygons is one reactive approach, but more fundamental solutions involve preventing their introduction in the first place. Alternative strategies include:

Topology Rules

Imposing topological constraints on geospatial databases ensures integrity during editing by validating geometry relationships. Rules like preventing gaps or overlaps can block the creation of sliver polygon artifacts.

Improved Overlay Tools

Overlay operations based on topological relationships rather than simple intersections can construct integrated outputs avoiding misaligned geometry and gaps between features.

New Data Models

Moving to spatio-temporal geospatial databases with 3D topologies allows representing map features as shared nodes and edges with integrated validation. This further overcomes limitations of the planar 2D polygon model underlying sliver issues.

As GIS platforms evolve, sliver prevention will be increasingly built-in by design rather than addressed retrospectively. But for current polygon datasets, targeted sliver fixing tools remain essential for high-quality mapping and analytics.

Leave a Reply

Your email address will not be published. Required fields are marked *