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I have a large (1GB+) raster that I am using as the base for the generation of an equipotential surface. This is to assess the accuracy of DEMs derived from another source. In situ observation is impossible and the equipotential surface allows me to model in error.

I have an ArcGIS model that generates a single realization of the surface. The model is outputting beautifully and generates a 1GB raster realization. I am going to generate 100 of these surfaces to start to build a statistically significant pool of realizations (Monte Carlo simulation).

How would you go about storing the rasters? How would you visualize them to allow for the selection of the most likely realization (the one at the center of the monte carlo significance envelope)?

Here are the options I have considered and I would appreciate any input, links to papers, etc.

  1. Convert to point and store each realization as a column in the attribute table. Here I am concerned about the shapefile size limit (2GB).

  2. Convert to CSV using gdal2xyz. I tried the conversion and the output of a single raster was 12GB! This is way too inefficient.

  3. I considered using R but am not sure which package would facilitate this type of research (spatstat perhaps).

  4. Graph either the histograms or cumulative distribution functions of each raster realization for visual identification.

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As commented by @jbosq:

I've done the inverse in R, sampling from a known distribution around the values from a raster. Mine was much smaller and some of the cell values were the same so I was able to generate matrices with each unique value for cells (column) along the probabilities for those values (row) and then join those back to the original locations. Another possibility might be to discretize your results to store counts for ranges rather than unique values.

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