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Found also this http://www.rise-group.org/risem/clusterpy/index.html the tool is also partly available as a plugin in Qgis


Completely edited my previous answer. First of all, you're dealing with @52k points so whenever you're running an analysis with this amount of data, chances are QGIS looks frozen but more often than not it is still processing (can check this with Task Manager and CPU usage). To start, we need to filter out all the unnecessary points we don't want so I ...


you can use setIcon like that var img2 = "<img src='image.jpg' />"; var icon2 = L.divIcon({ html: img2, // Specify a class name we can refer to in CSS. className: 'image-icon', // Set a markers width and height. iconSize: [52, 52] }); marker.setIcon(icon2); Look at this JSFiddle


here is my simple approach: create a new map in umap: http://umap.openstreetmap.fr/en click Import Data a select all the gpx files you have and upload them into map (you can import all of them at once) enter Edit map settings > Default properties, choose opacity 0.25, weight 10. The three steps above will take 5 minutes and here is the result:


You need to do this in a two-stage process using the Vector->Analysis Tools->Mean Coordinates tool in the second step. This tool will return the mean coordinates for sets of point within a layer if they have a unique ID field. So, if you have a polygon layer which defines your areas, do a spatial join (Vector->Data Management->Join attributes ...


You could combine both layers by adding a binary column (0,1) to identify whether the building is from X or Y. From there using GeoDa you could identify local spatial auto-correlation (clustering) and determine whether it was high-low (one layer clustered around the other layer) low-high (the inverse) or high-high or low-low (self-clustering). User's guide ...


You can use Vector > Analysis Tools > Distance Matrix, and a join to achieve what you ask. I will use qgis sample data airport's layer to exemplify. This is a small dataset so I'm not sure how it will go with a 275000 points shapefile. 1. Create a distance matrix using your layer as both destination and target. Don't forget to tick "Use only the nearest ...


Switch out the user name. That should do it. // add cartodb layer with one sublayer cartodb.createLayer(map, { user_name: 'your_user_name'


Bottom up clustering solution from Get a single cluster from cloud of points with maximum diameter in postgis which involves no dynamic queries. CREATE TYPE pt AS ( gid character varying(32), the_geom geometry(Point)) and a type with cluster id CREATE TYPE clustered_pt AS ( gid character varying(32), the_geom geometry(Point) ...


I've written a bottom-up hierarchical clustering algorithm, it has extra parameters that might not be useful to other users, but those should be easy to remove in implementation. First, create a new type to have point ids and geometries. CREATE TYPE pt AS ( gid character varying(32), the_geom geometry(Point)) and a type with cluster id CREATE TYPE ...

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