Hot answers tagged google-fusion-tables
It is trivial with ogr2ogr and SQLite SQL dialect. Next examples write 10 first lakes into one KML file and next 10 lakes into another KML file ogr2ogr -f kml batch_1.kml lakes.shp -dialect sqlite -sql "select * from lakes limit 10" ogr2ogr -f kml batch_2.kml lakes.shp -dialect sqlite -sql "select * from lakes limit 10 offset 10"
The ogr2ogr example is probably the most efficient, but if you prefer to do it without commandline you can just open the attribute table of the layer you're saving as KML, order the table on ID and starting from the top you select a number of records which you think will be within the limit (if the file is 150 megabytes, maybe select half of the records?) ...
The error message on console is: Access Not Configured. Please use Google Developers Console to activate the API for your project. At bottom of example page on openlayers.org posted in question you can see this text: View the fusiontables.js source to see how this is done. You will need to get your own apikey from Google's API Console for this to ...
From what I understand of your question, it appears that the map you're seeking to create will be fairly straightforward. If you've already calculated the ratios in question, simply add them to a table that contains geometries for European countries; the free 'tm_world_borders' table provided by CartoDB under the "Common Data" tab would I suspect work well. ...
I see that you (or someone else?) has included the google-fusion-tables tag. What you're trying to achieve is pretty straightforward with Fusion Tables. You'll need your public debt numbers per country/per time period (of course), but you'll also need KML outlines of each country to use in Fusion Tables. If you don't have the boundaries already, you can ...
While I can't test it, your VRT is different to that specified here: http://www.webrian.ch/2011/09/google-fusion-tables-in-qgis.html Try this: <OGRVRTDataSource> <OGRVRTLayer name="ps"> <SrcDataSource>GFT:refresh=<MY_VERY_LONG_TOKEN_IN_HERE></SrcDataSource> ...
I had a similar problem and used a kml parser (geoxml3) to locally parse a kml. I later ended up storing each kml file as a database table and looping through the records to plot a polygon. Loading the kml from a server was the slowest of all.
Two ways to minimize the size of each request. 1 - Make sure the polygon shapes are smoothed to the resolution. I.e. you don't need to show all the minute bends in the lake if you are viewing it at country level. So if you are going the JSON route, have different JSON files for different zoom levels. I found that having 3 different simplifications worked ...
This problem is known as Too many markers. See this article: https://developers.google.com/maps/articles/toomanymarkers for possible solutions. They list a few.
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