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spatial indexing and being able to visualize your data You're absolutely right on those two counts. I would make one more point; building the geometry object is computationally expensive, especially on large or complex data sets. Performance and scaling will suffer, because the database is having to build the geometry on-the-fly. Your query will ...


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I don't know if it will help you but i tested with ogr2ogr command line tool and this command do the job (assuming 'Polygon' is the field containing the geometry in WKT) : ogr2ogr -f "ESRI Shapefile" output.shp -dialect sqlite -sql "SELECT *, GeomFromText(Polygon) FROM input" input.csv -a_srs "WGS84" Maybe you could adapt that to the ogr2ogr python tool ? ...


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As a potential starting point for a list of differences it might help to see my new PyCRS package, where I have attempted to create a class for each crs element, parameter, and datum/ellips/proj name, along with their esri_wkt vs ogc_wkt spelling. I have also specified how I see the parsing differences in terms of the wkt structure as a whole in the ...


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You may want to write a python script to achieve this using SearchCursor and SHAPE@WKT key word. for row in arcpy.da.SearchCursor("PolygonLayer", ["SHAPE@WKT"]): print row[0] Refer ESRI complete example here http://resources.arcgis.com/en/help/main/10.1/index.html#//002z0000001t000000 import arcpy infc = arcpy.GetParameterAsText(0) # Enter for loop ...



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