Hello,

I have a dataset of current flow data (x,y,z), collected by a vessel-mounted ADCP over 11 transects, which were spaced by 300m. Normally I would use Matlab to post-process and interpolate these data. In Matlab I would apply the griddata function, which creates a uniform meshgrid (dimensions defined by myself) and applies linear interpolation, which I smooth at the end.

I would like to recreate the results in ArcGIS 10.0, but I am struggling a bit as I'm not entirely sure which interpolation to use.

Initially I thought the creation of TINs would come closest to what I've done in Matlab, but I think my data are not scattered enough (being formed by straight transect lines) and I'm ending up with a very jagged appearance and bad edge effects.

I then tried the natural neighbour interpolation and the result was closer to my Matlab results, but the surface was not very smooth. IDW gave ok results, but I would prefer the resulting surface to include the measured values. Hence I applied the Spline function, but it overestimated the values in some areas. Kriging at the end gave a reasonable good result.

I was now wondering if anyone here worked with current flow data and could give some advice on how to best interpolate these in ArcGIS. As far as I understand (please apologize if I'm wrong and correct if required) is a linear interpolation fairly simple and straight forward calculating the value of a point between two known points and thus differs from available interpolation options in ArcGIS, which are all distance weighted. Accordingly I'm hesitating to apply a distance weighted interpolation as I'm not entirely sure what the effect on my data will be.

Your help and advice will be highly appreciated!

Best regards,

Astrid

I have a dataset of current flow data (x,y,z), collected by a vessel-mounted ADCP over 11 transects, which were spaced by 300m. Normally I would use Matlab to post-process and interpolate these data. In Matlab I would apply the griddata function, which creates a uniform meshgrid (dimensions defined by myself) and applies linear interpolation, which I smooth at the end.

I would like to recreate the results in ArcGIS 10.0, but I am struggling a bit as I'm not entirely sure which interpolation to use.

Initially I thought the creation of TINs would come closest to what I've done in Matlab, but I think my data are not scattered enough (being formed by straight transect lines) and I'm ending up with a very jagged appearance and bad edge effects.

I then tried the natural neighbour interpolation and the result was closer to my Matlab results, but the surface was not very smooth. IDW gave ok results, but I would prefer the resulting surface to include the measured values. Hence I applied the Spline function, but it overestimated the values in some areas. Kriging at the end gave a reasonable good result.

I was now wondering if anyone here worked with current flow data and could give some advice on how to best interpolate these in ArcGIS. As far as I understand (please apologize if I'm wrong and correct if required) is a linear interpolation fairly simple and straight forward calculating the value of a point between two known points and thus differs from available interpolation options in ArcGIS, which are all distance weighted. Accordingly I'm hesitating to apply a distance weighted interpolation as I'm not entirely sure what the effect on my data will be.

Your help and advice will be highly appreciated!

Best regards,

Astrid

I wholeheartedly agree and think that basing your decision of an appropriate interpolation model on the error is very good statistical practice. A Kriging model is a good choice because you are directly modeling (via the semivariogram) and thus, incorporating, the spatial process into the estimate. I am curious if you are using the Kriging model in Spatial Analyst or Geostatistical Analyst? The Geostatistical Analyst software is quite impressive and if you are not using it and have access I would recommend redoing your analysis. It will not take long and you will be able to more precisely model the spatial process, error (nugget) and account for any potential anisotropy (directional variation).

Sometimes given high "small distance spatial variation" splines can have wildly divergent values but nothing like you are observing. The spline results are very suspect and sounds like a bug, which are becoming alarming common.

I am quite happy that you ferreted out the fact that sometimes people utilize a model because it is computationally easy or just because "it has always be done this way" and nobody has ever questioned it. Sometimes these methodological decisions are a legacy and quite dated.