MarkerClustering

If you have lots and lots of markers to display on a map then often your map becomes slow or you can't see the forest for the trees because those markers are basically everywhere. A common strategy is to use cluster markers and to only show how many markers are in a given area. Javascript map components often already have support for this. Here you can read how client side clustering can be done with GoogleMaps API.

Sometimes this client side clustering is not enough since you still have to send lots and lots of data down to the client. This is when server side clustering comes into play.

This is library is for such server side clustering of map points. It's a netstandard library and can be used in all .NET Core programs and ASP.NET Core.

Original Lib

This library is inspired by Google Maps Server-side Clustering with C# , but the algorithm here is a bit simplified for performance reasons.

Installation

• The latest version of MarkerClusterung is available on NuGet.

Usage

The following code describes a typical usage in ASP.NET Core and GoogleMaps:

    // Retrieve map markers from data store
    let markers = getAllMarkers()

    // filter markers to the bounding box of your map (optional)
    let filtered =
        markers
        |> Array.filter (fun m ->
            m.Lat <= ne.Lat && m.Long >= ne.Long &&
              m.Lat >= sw.Lat && m.Long <= sw.Long)

    // Map to library's MapPoint<'a>
    let points : MapPoint<_VisibleChargingPoint_> [] =
        filtered
        |> Array.map (fun m -> { X = m.Lat; Y = m.Long; Data = m })

    // run clustering with your map's current zoom level
    let clustering = Clustering(zoomLevel)
    let result = clustering.RunClustering points

Caching

If your markers don't change that often then you may want to put the clustering in a separate task. A good strategy could be to use FaaS Azure Functions or AWS Lambda to calculate all clusters for all zoom levels. Those clusters could be stored in a distributed cache like Redis, which then allows you to query the clusters by map center and radius.