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I am posting my answer in case this could prove useful to anyone jumping here in the future. In Raster Calculator I used: SetNull("DEM", "DEM", "Value <= 5") and it did the trick.


You might try downloading the 30m SRTM for your region and deriving the contours yourself. It's available through EarthExplorer for free. Contours can be generated with the Raster > Extraction > Contour tool in QGIS.


You don't need to be updating row[1] for every feature, so cursor.updateRow(row) should be under the final else statement. Also, I'd suggest using with statements as closing is better supported: fc = r"C:\Points\Test.gdb\Points_3d" with arcpy.da.UpdateCursor (fc, ["Elevation","Slope"]) as cursor: firstRun = True for row in cursor: if ...


SetNull is one way to do this, then follow with IsNull to create a binary polygon and then Raster to Polygon (simplify). A simpler workflow would be to use Con (SA): In arcpy: outCon = Con(Raster("elevation") >= 5, 1, 0) arcpy.RasterToPolygon_conversion(outCon, "c:/output/NewLandSea.shp", "SIMPLIFY") The Con tool is available interactively using ...


Not a real solution ... but you may use r.carve first to burn the main flowline into the DEM. Then re-run r.flow on that. In order to find the main flowline, some v.net.* modules may help (shortest path perhaps). In essence, using a dual-step approach.


There are several ways to accomplish what you want. You've left out some specifics such as what extensions you have available and what license level you're using, as well as how thoroughly you want to sample the raster (ie how fine is your grid). I'll make some assumptions. The simplest, if you want a point for every cell in the DEM, is the Raster to Point ...


One of the easier ways to do this is with a spatial database such as PostGIS. Load your raster into the database using raster2pgsql (which is installed with PostGIS) and then load your rivers with shp2pgsql (also installed with PostGIS). Then, you can run a simple query that samples the elevation model at the end points of all your lines: select ...


If you would like to calculate GWR in R, you should try GWmodel. If you need to do it in Python, you can also use pygwr. GWmodel contains many geographically-weighted (GW) models including gwr (GW regression), gwpca(GW principal components analysis), gwda(GW Discriminant Analysis), gwr.generalised(Generalised GWR models, including Poisson and Binomial), ...

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