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1

If all spatial references are the same, build a few models to append a few of the feature classes at a time. Make another model that runs the smaller models. Run the model overnight. I've had terrible luck running into memory issues with large data loads operations, this method works best for me.


1

I suspect that your google SRID may be incorrect. I believe that google geocode results use SRID 4326 (WGS 84). I believe the google uses 3857 for the map display, but outputs the data as 4326.


3

You have to reproject one layer to another name and the CRS of the other before you can merge them. Merging only works if both files look well-placed with On-the-fly-reprojectionturned OFF.


1

The method to do this would be: Union county layer with jurisdiction (this will take care of those jurisdictions that overlap multiple counties) Convert those unioned jurisdiction features to a centroid layer, use the Feature to Point tool. Spatial join county layer with centroid layer Export table to xls or csv


0

Since you're exporting to spreadsheet anyway, I'd convert the Jurisdiction dataset to centroids and then spatial join to the County polys. Each point would collect the county name that it fell within. Export the centroids to spreadsheet.


3

Here you go. A couple of utility functions and then the meat in one function (and no for loops :)) islines <- function(g1, g2){ ## return TRUE if geometries intersect as lines, not points inherits(gIntersection(g1,g2),"SpatialLines") } sections <- function(sl){ ## union and merge and disaggregate to make a ## set of non-overlapping ...



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