Clustering points/polygons based on proximity (within specifed distance) using QGIS?

DBScan: The Density Detective

DBScan (Density-Based Spatial Clustering of Applications with Noise) is your go-to if you want the algorithm to figure out the clusters on its own, without you telling it how many to look for. It’s like a detective that sniffs out areas where points are packed tightly together and flags lone wolves as outliers.

• How it Works: DBScan has two main knobs to tweak: a minimum distance (epsilon) and a minimum number of points. It picks a point, then checks: “Are there enough other points within this radius?” If yes, boom, a cluster is born! Then it keeps expanding that cluster, checking the neighbors of neighbors. Anything left out in the cold gets labeled as noise.
• Why it’s Awesome: DBScan is a champ at finding clusters of all sorts of weird shapes, and it doesn’t get thrown off by outliers. Plus, you don’t have to guess how many clusters there are beforehand.
• The Catch: Finding the sweet spot for epsilon and the minimum number of points can be tricky – it really depends on your data. And it can get a bit slow with huge datasets.
• QGIS in Action: Head to Processing Toolbox -> QGIS geoalgorithms -> Vector analysis tools -> DBScan clustering. Plug in your layer, set your epsilon (search radius), and the minimum points. The result? A new layer with cluster IDs. Anything with a -1 is noise.

Heatmaps: Seeing the Hotspots

Okay, a heatmap isn’t technically a clustering algorithm, but it’s a killer way to see clusters. Think of it as a visual shout-out to the areas with the most action.

• How it Works: Kernel Density Estimation (KDE) – that’s the brains behind the heatmap – calculates how dense your features are in any given area. It uses a “kernel” (fancy word for a weighting function) to give closer points more influence. The result is a color-coded map where the hot colors show the high-density zones.
• Why it’s Awesome: Heatmaps are super intuitive. You can instantly spot potential clusters just by looking at the pretty colors.
• The Catch: It doesn’t give you hard cluster boundaries. It’s more of a “look over there!” kind of thing. Plus, the way you interpret the clusters depends on the settings you choose.
• QGIS in Action: Go to Raster -> Heatmap. Pick your point layer, set the radius (search distance), and play with the color ramp to make those hotspots pop!

Voronoi Polygons: Drawing Lines in the Sand

Voronoi polygons (or Thiessen polygons, if you’re feeling fancy) are like drawing boundaries around each point, showing its “territory” – the area closest to that point. It’s not direct clustering, but it’s a neat way to visualize things.

• How it Works: Imagine each point planting a flag, and then the lines are drawn halfway between each flag. Everything inside a polygon is closer to that polygon’s point than any other.
• Why it’s Awesome: It’s a super clear way to see each point’s area of influence.
• The Catch: It doesn’t cluster based on distance, and if your points are all over the place, the polygons might not really represent meaningful clusters.
• QGIS in Action: Find it under Vector -> Geometry Tools -> Voronoi Polygons. Select your point layer, and boom, you’ve got a polygon layer showing each point’s turf.

“Join by Location (within)” + Group Stats: The Power Combo

This is where you get to be a bit more creative. It lets you find points or polygons within a certain distance and group them based on shared characteristics.

• How it Works: First, use “Join by Location (within)” to find all the features within your chosen distance of each other. This creates a new layer where those nearby features are linked. Then, use “Group Stats” to group these linked features based on something they have in common (like, say, the same type of business).
• Why it’s Awesome: It’s super flexible. You can cluster based on both proximity and what the features actually are.
• The Catch: You need to pick the right distance and a good attribute to group by.
• QGIS in Action: Use the Join by Location (within) tool found in Vector -> General Research Tools. Then, use the Group Stats tool found in Vector -> Analysis Tools.