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Suppose your regression model is of type Y = b0 + b1*X + e. Interpolate the explanatory variable (X) to the area of interest (AOI). This means to fill in the empty 100m X 100m grid. Which method/tool to pick up for this depends on the analysis. Use tool raster calculator to create a new raster with values for the response variable Y in the AOI based on the ...


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The saga algorithm is a raster creation tool, so the result is a raster. you can use polygonize with the result to get a vector layer. the edges are not intepolated due to the distribution of your points. see en.wikipedia.org/wiki/Bicubic_interpolation for more information


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It is a bad idea to measure distances in lat-long, as a degree of longitude is not the same ground length (in meters) as a degree of latitude. The linestring has 3 points that are not aligned. The first (top) half of the linestring vary a bit in longitude and a lot in latitude. The second (bottom) half of the linestring vary much more in longitude than in ...


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You can replace the values using where(). // Replace masked pixels by the mean of the previous and next months // (otherwise, how to deal with the first images??) var replacedVals = composites.map(function(image){ var currentDate = ee.Date(image.get('system:time_start')); var meanImage = composites.filterDate( currentDate.advance(-2, '...


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Thanks for the data sample to look at. It is hard for me to have confidence in my answer because I don't know anything about your data. When looking at it, it appears PDOP is the meaningful variable? Or maybe Altitude? The others are a timestamp, lat, long, and variables which do not change (Cobalt-60, Celsium-137, Eu-152). So I ran the IDW tool using PDOP ...


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