I want to union a given set of shapefiles. In the shapefile are stored polygons. The data bounding box is Germany, so the data is relatively big. I tried to use all functions implemented in QGIS. Either they failed or the processing time is just too long to get any results. I read a lot of question throughout this site and I set up a PostGIS Database.
My query is:
CREATE TABLE dlm_union_2 AS SELECT a.gid AS ID, ST_UNION(a.geom,b.geom) AS geom FROM sie01_f a, sie02_f b
I added the Selection of the a.gid since I have no objects returned when I export the table via pgSHAPELOADER.
I want to use more than two tables in one expression and is there a way to get this faster?
With only two tables my processing time is now about 800,000 ms and the progress isn't finished yet. After that I want to buffer the merged regions and would like to union it again afterwards. I have a lot of shapes that I have to union at different states of my project.
I am working on WIN 7, 4GB ram, Intel Core i5 M520. PostGIS and PostgreSQL should be up to date, because I installed the components today.
I have created a buffer on a multilinestring table. Processing time was about 300,000 ms what is actually great compared to functions implemented in QGIS. My problem is that I need to union the result for further analysis. I am using ST_UNION with a single table with polygon geometry input. Objectsize is around 300,000 polygon features. At this point my processing time is 2,800,000 ms and I don't know when it will finish.
Is there any way to get such a calculation faster?
After processing time 5,319,543 ms the query aborted with following exception. This was the try to use ST_UNION with one table including 300,000 polygons.
ERROR: GEOSUnaryUnion: std::bad_alloc ********** Fehler ********** ERROR: GEOSUnaryUnion: std::bad_alloc SQL Status:XX000
According to @Mattmakesmaps I tried the way of ST_Buffer(ST_Collect(geom),0) instead of using the UNION command. With the errors I am posted above I was not able the handle the whole extend of the data. So I split the problem into smaller ones. In my case I used the state-borders in Germany to divide my problem into 16 smaller ones. @Mattmakesmaps solution works fine here. Recently I am try to use the ST_Simplify command to make the dimension of the polygons smaller giving me a chance to process all of the data at once.