# Distance in QGIS' cluster point tool: radius or diameter measurement?

I am using a point cluster tool in QGIS to consolidate data points based on their longitude and latitude. I note that in the Layer Styling panel that Distance can be set to metres at scale, map units, etc. For distance, is this a measure of radius or diameter of the circle around each cluster's centroid? My base is the OpenStreetMap, so the distance is measured in metres.

In other words, if I set distance to 100,000 metres, is this a 100km radius or 100km diameter for the points of data pulled in?

## 2 Answers

A quick empirical test reveals it is the radius.

The answer is neither. It looks like a cluster of points is first created, with its own rules. It looks like all points within the given distance of a seed point are clustered, then a weighted centroid is computed. (Depending on how this seed point is computed, we can expect two similar looking sets to have a different cluster)

If there are more than 2 points, the centroid can be further away than distance/2 from a point, so the distance is not a radius. At the same time, other points could be closer than distance/2 from the centroid while still be excluded from the cluster, so it is not a diameter either.

• Your screenshot shows the contrary. OP asks about "radius or diameter of the circle around each cluster's centroid". Points at the bottom: centroid is 51 m (102/2) m away form both points. A circle around the centroid with radius of 100 m (thus: diameter of 200 m) would clearly contain both points, thus they would be clustered - on your screenshot, they are not. If measurment is diameter, 100 m diameter means 50 m for radius. In this case, indeed the two points are not clustered as the centroid is 51 m (102/2) m away form both points, so the circle does not cover the points. Nov 22, 2022 at 21:00
• @Babel Interesting observations! It may be even more complex than that (see edits).
– JGH
Nov 22, 2022 at 22:08
• indeed very interesting! I revised my answer to match your findings. Nov 22, 2022 at 22:21

#### Short answer

See @JGH's answer for the distance. In addition, be aware: distances are in cartesian measurements of the project CRS, so the projection used heavily influences the result.

#### Details

The projection (project CRS) also plays a role. Changing the project CRS also changes the points that are clustered (empirically tested, see screenshots below).

To make things even more complex: using OpenStreetMap, you probably work in `EPSG:3857`, WebMercator. This projection heavily distorts lengths. So you will not get any meaningful "real world distances" with this projection, even when using `meters at scale`. Just to be aware of this.

Points 1&3 (distance 86.1m) are clustered, points 1&2 (distance 108 m) are not (layer and project in `EPSG:3857`):

The same settings, with only project CRS changed to `EPSG:4326`: as you see, all three points are clustered...: