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3

As you only have 2 buffer distances, you could do this in 2 easy manual steps (if you had more buffer distances I would recommend using the code from Polygeo). Step 1: Use the "Buffer" tool to create a 6 km buffer around all your points. Make sure you set Dissolve to "None" Step 2: Use the "Multiple Ring Buffer" tool to create a 6km buffer around the ...


2

To do this I think that you will need to use ArcPy with: a Search Cursor to read the geometries of your points one at a time buffer each point geometry twice (6km and 12km) and then use the 6km buffer to erase (using difference) from the 12km buffer to create an annulus polygon. an Insert Cursor to write a new polygon feature class for your annulus ...


1

This question is similar to: Clip raster by raster with data extraction and resolution change, but coming from a different angle. However, I think the answer is likely the same. First off, choose which raster you wish to be definitive. I'll repeat my previous answer here for ease: Load required libraries: library(raster) library(rgdal) Read rasters: r1 ...


0

Creating a materialized view that represents the street segments with sidewalks can help speed up the query as long as you know the underlying tables won't be updated very frequently. Create the materialized view: CREATE MATERIALIZED VIEW transportation.streets_with_sdw AS SELECT *, CASE WHEN dynamap_id in (select distinct teleatlas_id from ...


1

You need to use Intersect Tool instead of Clip.


0

Yes. You need to resample your rasters in order for them to be the same size and have the same extent. R doesn't deal with that by itself. Given that neither of your rasters fully contain the other, you should consider creating a minimum-extent raster with your preferred resolution, and then resample and crop the others to match that.


2

My solution is a GRASS GIS v.select iteration. So I get full line-shapes for each polygon, without getting self-intersection problems.


5

You can: Load required libraries: library(raster) library(rgdal) Read rasters: r1 = raster("./dir/r1.tif") r2 = raster("./dir/r2.tif") Resample to the finer grid r.new = resample(r1, r2, "bilinear") If required (for masking), set extents to match ex = extent(r1) r2 = crop(r2, ex) Removed unrequired data r.new = mask(r.new, r2)


2

Draw the area(s) you want to hide from the image and save as vectors into shapefile or other format if you prefer. Then use the gdal_rasterize utility http://www.gdal.org/gdal_rasterize.html which burns fixed, non-transparent pixels into your image and removes permanently image data below the polygons. Here is an example. The map is a RGB tiff image with ...



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