Cluster Map
A Cluster Map groups nearby markers into clusters that break apart as you zoom in, keeping huge point datasets fast and readable. Instead of a wall of overlapping pins, viewers see clean cluster bubbles that reveal detail on demand.
Connect a Google Sheet or upload a file and Mapji handles the clustering automatically — ideal for thousands of locations.
What you can do
- Automatic clustering
- Custom cluster styling
- Handle thousands of points
How to create a cluster map
- 1
Create a Cluster map
Start a new site and choose the Cluster map type.
- 2
Add your points
Upload GeoJSON, a Shapefile, KML or a spreadsheet with lat/lng columns — or connect a Google Sheet.
- 3
Tune clusters
Set the cluster radius, the zoom where clusters break apart, and the size thresholds and their colors. Spread out overlapping points so each can be clicked, and break clusters down by category with donut charts — they come with a legend you can title.
- 4
Publish
Share your live clustered map on your own domain.
Cluster Map FAQ
How many markers can a cluster map handle?
Mapji clusters thousands of points smoothly, with a 10,000-feature limit per data source.
Can it stay in sync with a spreadsheet?
Yes. Connect a Google Sheet and the map updates automatically as the sheet changes.
Step-by-step guides
Most bad maps are the right data in the wrong form. What all ten map types are actually for, and the mismatches that make maps unreadable.
Connect a Google Sheet as a live, read-only source so your map updates itself. Column setup, the five map types that support it, and the real limits.
Turn a spreadsheet into an interactive map — plus the coordinate problems that cause nine out of ten failed imports, and what to do if you only have addresses.
Getting an embedded map to size itself correctly, stop hijacking the page scroll, and look like part of your site rather than bolted on.
Clustering keeps a dense point map readable — and donut clusters encode a second dimension. How to set both up, plus when a heatmap is the better call.
Turn location data into a clear geographic heatmap — how many points you need, why radius matters most, and the sampling caveat worth stating.