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4

Similar to @AndreJ, but use a dynamic gnomic projection, I mean a dynamic azimuthal equidistant projection for even more accuracy. An AEQ projection centred on each point will project equal distances in all directions, such as a buffered circle. (A Mercator projection will have some distortions in north and eastern directions, since it wraps around the side ...


4

Instead of searching for the right UTM zone, you could create a custom transverse mercator projection for every point with +proj=tmerc +lat_0=.... +lon_0=... +k=1 +x_0=0 +y_0=0 +ellps=WGS84 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs Draw the circle in that projection. The projected circle vertex coordinates will always be the same, so you have to create ...


2

Michael Miles-Stimson is correct in his comment above; nearest-neighbour (NN) and majority resampling methods should only be applied to categorical data, i.e. nominal and ordinal level data. Elevation, even when it is presented as integer values (which is a practice that I wish we could make illegal and punishable by lengthy jail terms), is not categorical. ...


2

It seems that the proj.4 implementation of Wagner VII does not have a definition for the inverse projection: proj -I +proj=wag7 Rel. 4.8.0, 6 March 2012 <proj>: inverse projection not available program abnormally terminated In this case, no reprojection is possible. See also this ticket on similar problems: http://trac.osgeo.org/proj/ticket/234


1

NAD1983 is a geographic coordinate system. There are a few different ways you could go about aligning these data, but I'll give an example that I think is simple and clean. (1) Add both of your CSV tables to ArcMap and plot the points. Make sure you plot them using the correct geographic coordinate system - File1(NAD1927) and File2(NAD1983). Right click the ...


1

You can use the osr module (part of the standard GDAL install, so it should come with your Python bindings) and do something like this: import osr import gdal inDS = gdal.open(r'c:\somedirectory\myRaster.tif') inSRS_wkt = inDS.GetProjection() # gives SRS in WKT inSRS_converter = osr.SpatialReference() # makes an empty spatial ref object ...


1

Your input coordinates look as lat/long or long/lat coordinates, so you should use 4326 instead of 900913: SELECT ST_AsText(ST_Transform(ST_GeomFromText('POINT(-29.16297 -51.1795034)', 4326), 29182)); Or: SELECT ST_AsText(ST_Transform(ST_GeomFromText('POINT(-51.1795034 -29.16297)', 4326), 29182)); The input on that case should be POINT(long lat).


1

Both CRS you mention (SIRGAS-Chile / UTM zone 19S and WGS 84 / UTM zone 19S) use the same ellipsoid with towgs84 set to all zero. So they can be regarded as identical. Apart from that: A point in the map remains on its position, regardless of the CRS you are saving it with. If you use an older CRS with a certain shift to WGS84, like PSAD56 / UTM zone 19S, ...


1

The first layer is definitely not WGS84 longlat, but something projected like UTM or other systems used in Spain. The second one might be correct. I imported the extents as delimited text, setting WGS84-UTM 30N EPSG:32630 for the large ones (in green) and WGS84 EPSG:4326 for the small ones in red, On-the-fly-reprojection activated. Project CRS is set to ...


1

Below is a work around method using python and bunch of libraries such as netCDF4, numpy and shapefile. With Anaconda, these library installations are very much easy. The steps of the method are Import WRF ARW output into python, by python netCDF4 library. Query WRF ARW output variable XLAT, XLONG into numpy array of latitude and longitude using python ...



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