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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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