Best Practices For Topological Editing To Remove Gaps Between Adjacent Polygons
Identifying Polygon Gaps
Finding small sliver polygons between adjacent polygons is the first step in identifying gaps in polygon continuity. Configuring topology rules to flag sliver polygons under a minimum area threshold is an automated way to highlight potential gaps. Visual inspection of polygons at different map scales also reveals larger gaps not caught by automated rules.
Running comprehensive geometry validation on the polygon dataset is another diagnostic method for catching gaps between polygons, as well as other spatial anomalies. The validation process will flag undershoots, overshoots, and null geometries which may indicate gaps.
Finding sliver polygons using topology rules
A sliver polygon is a small, narrow polygon sliver wedged between larger adjacent polygons that should share a common border. Setting up a topology rule to define a minimum polygon area will enable automated flagging of sliver polygons below that area tolerance.
For example, a minimum polygon area rule of 1 square meter would highlight all polygons smaller than 1 sq m. Visually inspecting these undersized polygons reveals cases of sliver polygons caught between properly defined larger polygons where a gap exists.
Visual inspection to spot gaps
Visual inspection of polygons at varying map scales takes advantage of the human eye’s ability to spot gaps that automated rules may miss. Zooming in on the borders between adjacent polygons makes smaller gaps more apparent. Using shaded relief or imagery backgrounds provides additional context for spotting gaps between polygons.
Draping polygons over hillshaded terrain often highlights cases of adjacent polygons not properly lined up with the terrain surface. Background imagery also clearly shows gaps where polygons do not fully delineate adjacent forest stands, agricultural fields or other features the polygons aim to define.
Running diagnostics on geometry validation
Geometry diagnostics analyze polygon geometries to flag topological anomalies like undershoots, overshoots and null geometries which may indicate gaps. Undershoots are vertices extending past an adjacent polygon border while overshoots do not reach a common border.
Both cases can indicate gaps between neighboring polygons. Null geometries are polygons with collapsed or missing coordinates also indicative of gaps between properly defined polygons.
Automated geometry validation processes quickly analyze thousands of polygon geometries to find potential gaps. Targeted editing then resolves the flagged topological anomalies by closing gaps and realigning polygon borders.
Closing Gaps Between Polygons
Manually closing gaps between adjacent polygons involves snapping vertex edits along shared borders or trimming/extending polygon edges to meet at a common boundary. Batch processing scripts or edit operations like dissolve can automate gap closing across many polygons with shared borders.
Snapping vertices to shared borders
Snapping adjacent polygon vertices uses vertex editing to align borders along a shared edge. With topology editing enabled, manually dragging vertex edits enables on-the-fly snapping to the closest topological point or edge.
Snapping to the shared edge pulls the vertices into alignment, closing small gaps created by coordinate imprecision or user editing error. SUCCESSive snapped vertices close the gap and realign the two polygons along their true shared boundary.
Extending/trimming edges to meet
Trimming or extending shared polygon segment edges provides another approach to closing gaps between polygons. Trimming one of the segments to the endpoint of the neighboring segment removes overlap and connects the segments.
When gaps exists instead, extending the segments to meet in the gap allows finer control over the intersection point placement to properly delineate the border.
This trim/extend process applies only to closing gaps that lie approximately along the natural neighboring boundary. Larger gaps or cases requiring realignment benefit more from other approaches.
Dissolving common borders
Dissolving connects neighboring polygons along common boundaries by merging polygons with the same attribute. Dissolving adjoining parcels owned by the same entity or forest stands of the same species are examples.
For gaps caused by imprecise delineation of such common boundaries, dissolve combines the polygons by removing those internal borders as part of the aggregation process.
This automated process closes small gaps in the dissolved boundary in the process of removing it by redefining the neighboring polygons to share that now common outline.
Automating Gap Detection and Repair
Custom script tools can batch process pipelines to automate detecting, highlighting and fixing topology gaps across the full dataset. This leverages the computational power of GIS for efficient automated analysis and editing.
Configuring topology rules to flag gaps
Customizing topology rules provides the configurability to automatically detect gap conditions based on the specific geometry situation. Defining valid polygons, minimum areas, and maximum gaps tailored to the layer detects where unconstitutional gaps exist.
The automated topology system then highlights or selects all polygon geometries violating the rules. Exporting that subset streamlines manual review and editing focus only on actual gap occurrences rather than inspecting all polygons.
Running batch processing to fix gaps
Batch processing tools can automate the gap closing edit operations across an entire polygon dataset. After custom rule-based topology has identified offending polygons, scripted processes trim, extend, reshape or merge to resolve gaps.
For simple gaps along shared edges, smoothly extending, reshaping or merging along the border can close gaps across hundreds of complex polygons faster than manual techniques.
Fixing gaps on feature import
Validating geometry on import provides another chance to fix gaps entering a dataset before propagating downstream. Customized checks tailored to source data specifics on import highlight issues.
Gap checks could validate polygon cluster density, flag extended undershoots or overlaps, and look for other evidence of potential gaps in the incoming polygons. Automated and assisted edits when loading data then address these before introduction.
Verifying Gap Repair
After efforts to identify and repair gaps between adjacent polygons, additional validation checks verify the process fully closed gaps without introducing any new artifacts. This confirmation allows confident downstream usage with gap-free continuity between connected polygons.
Validating fixed geometries
As gaps get introduced, identifying and editing problematic polygons can itself also unintentionally distort geometries in undesired ways. Re-running validation on updated polygons provides quality assurance those fixes operated properly.
Ideally scripts combine identification steps directly with repair procedures then follow up with self-validation to confirm edits resolved the right gaps without side effects.
Visual QC of polygon continuity
Visual inspection remains the gold standard for verification, even with automated assistance. CAD-like onscreen overlays superimpose shaded relief, imagery or custom hillshades to confirm manual repairs align accurately with true ground conditions.
Zooming in on former gap locations validates vertex merges, edge extensions, and dissolve alignments now meet cleanly without gaps or overlaps between adjoining polygons.
Analysis of topology rule errors
As automated processes flag topology rule violations to identify gap geometry defects, running topology analysis after gap closing provides quantified metrics on the reduction in error count.
Seeing specific rule violations such as undershoots, null geometries and minimum area failures reduced to zero confirms the automated processes resolved those gap-related issues across the dataset.
Best Practices Summary
Addressing discontinuities and gaps within a polygon dataset requires first identifying potential problem locations then editing to close and reconnect their shared boundaries. Automating portions of this workflow enables efficient analysis and repairs.
Set up topology rules for polygons
Customized topology rules specifically tailored to catch types of gaps or misalignments common in the source dataset ensures automated flagging locates just the right violations.
Inspect new data for gaps
Both visual inspection techniques and automated diagnostics should validate incoming polygon data on insertion to catch gaps on entry before impacting downstream processes.
Automate gap fixing where possible
Scripted processes scale consistency gap detection and repair operations across entire datasets for efficient hands-off fixing of common gap issues.
Confirm gap closure with diagnostics
Final validation reporting based on topology analysis paired with visual inspection provides reliable confirmation that automated gap repair operations succeeded in closing gaps for continuous polygons.
