Removing polygon sliver other than by using graphic-to-feature

Ditching Those Pesky Slivers: Polygon Cleanup Beyond the Basics

Sliver polygons. Ugh. Every GIS pro knows the feeling. Those skinny little rascals that pop up during data processing, like unwelcome guests at a party. They’re usually the result of overlay operations gone slightly awry, digitizing hiccups, or just plain old boundary mismatches. And let’s be honest, they’re a pain. They don’t represent anything real, they bloat your file sizes, and they can seriously mess with your analysis. One way to get rid of them is the “graphic-to-feature” method, but that can feel like using a spoon to dig a swimming pool, especially with big datasets. So, what else can you do? Turns out, quite a bit! Let’s dive into some alternative techniques for banishing those slivers for good.

Why Do Slivers Happen Anyway?

Before we start swinging the ban hammer, it’s worth understanding why these slivers show up in the first place. The usual suspect? Lines that are supposed to be perfectly aligned but aren’t. Think of it like this: imagine you’re drawing two adjacent countries on a map. Their shared border should be a single, continuous line. But if you draw each country separately, freehand, chances are the lines won’t match up exactly. That tiny mismatch? That’s where slivers are born. This happens a lot during manual digitization or when you’re merging different polygon layers.

Stop ‘Em Before They Start!

Honestly, the best way to deal with slivers is to prevent them from being created in the first place. Think of it as preventative medicine for your GIS data. The key here is using snapping tools during digitization. Snapping is like having a little magnet that pulls your new points to existing vertices or edges. This makes sure everything lines up perfectly, keeping your data topologically sound. Most GIS software has snapping options you can tweak – set the “snap tolerance” (how close you need to be for it to snap) and choose which layers you want to snap to. Another lifesaver? Tracing tools. These let you follow existing lines precisely, which is perfect for creating adjacent polygons without those pesky gaps.

Your Sliver-Busting Toolkit: Time to Get to Work

Okay, so prevention didn’t quite work out. Don’t sweat it! We’ve all been there. Now it’s time to pull out the big guns and get rid of those existing slivers. There are plenty of tools and techniques at your disposal. The best one for the job depends on the type of slivers you’re dealing with, how big your dataset is, and what software you have access to.

1. Topology: Your Secret Weapon

If you’re working with a geodatabase, topology is your new best friend. It’s like having a built-in quality control system for your spatial data. Topology rules, like “Must Not Overlap” or “Must Not Have Gaps,” automatically sniff out errors, including those pesky slivers. Your GIS software will then give you options to fix these errors – merging overlaps, deleting slivers, or ignoring the ones that aren’t actually a problem. Just a heads up, though: you’ll usually need to import your shapefiles or feature classes into a geodatabase to use topology.

2. The “Eliminate” Tool: Targeted Sliver Removal

This tool is pretty straightforward: it gets rid of slivers by merging them with their neighbors. Usually, you can tell it to merge the sliver with the neighbor that has the biggest area or the longest shared border. You can even use attribute queries to target specific slivers based on their size or shape. But keep in mind, it’s not perfect. Sometimes, if a sliver is surrounded by other slivers, the tool might not be able to eliminate it.

3. Align Features: Straightening Things Out

The “Align Features” tool lets you adjust polygon boundaries to match a specific path. This is super handy when you have misalignments along shared borders. Just draw a line where you want the boundary to be, and the tool will adjust the selected feature to match it (within a tolerance, of course).

4. Integrate: Forcing Coincidence

The “Integrate” tool forces edges and vertices to be exactly the same. This can fix both slivers and gaps, which is great. But be careful! You need to choose the right “cluster tolerance.” If it’s too small, it won’t work. If it’s too big, you might accidentally merge features you didn’t mean to. Use the smallest tolerance you can get away with to avoid messing things up.

5. Good Old Manual Editing: When You Need Control

Okay, it’s not the fastest method, especially for huge datasets, but sometimes you just need to get in there and do it by hand. Using snapping and editing tools, you can reshape polygon edges and vertices to eliminate slivers. It takes time and effort, but you have total control over the process.

6. Select By Attributes: Targeting Slivers by Size

Slivers are usually pretty small, right? So, you can use “Select By Attributes” to find polygons that are smaller than a certain size. Then, you can delete them or merge them with their neighbors. Easy peasy.

7. Buffering and Dissolving: A Bit of a Hack

This is a bit of a workaround, but it can work in a pinch. Buffer your polygons slightly, and then dissolve them. This can close up small gaps and slivers. The downside? It can also change your original polygon boundaries a bit, so be careful.

8. Simplify and Generalize: A Two-Step Process

Sometimes, simplifying a polygon (reducing the number of vertices) can create slivers. Go figure! If that happens, you can use the “Generalize” tool (often found in GRASS GIS) to clean up those newly formed slivers.

Tolerances: Finding the Sweet Spot

As you’ve probably noticed, a lot of these techniques rely on “tolerances.” This is basically how much wiggle room you’re giving the software when it’s snapping, merging, or integrating features. Choosing the right tolerance is crucial. Too small, and it won’t catch the slivers. Too big, and you’ll start distorting your data. The best tolerance depends on how accurate your data is and how much detail you need to preserve.

Automation: Because Who Has Time for This?

If you’re dealing with a ton of slivers, or if you need to do this regularly, automation is your friend. You can use scripting languages like Python to chain together geoprocessing tools and create custom solutions. For example, you could write a script that automatically finds slivers based on their area and merges them with their neighbors.

Wrapping Up: Conquer Those Slivers!

Getting rid of polygon slivers is a must for clean, accurate GIS data. Sure, the “graphic-to-feature” method works, but there are so many other ways to tackle the problem. By understanding why slivers happen, knowing your tools, and choosing the right settings, you can banish those slivers and create high-quality geospatial data that you can be proud of. Now go forth and conquer those slivers!