ArcGIS R Interpolation – Spatio-Temporal Interpolation in R or ArcGIS?

arcgis-desktopinterpolationrspatial statisticstime series

I'm trying to calculate average rainfall value from a number of points using Inverse Weighted Distance tool in ArcGIS 9.3.

My problem is that: each point has it own time series, therefore the interpolation process should be able to carry out for all years (kind of iteration so to speak).

Following is a sample attribute table:

ID X Y Name Rain1990 Rain1991 Rain1992 Rain1993 .... Rain2010

1 xx1 yy1 AA 1210 1189 1863 1269 ......  
2 xx2 yy2 BB 1492 1502 2187 1923 ......
......

Could anybody show me how to do that?

Edit 1: I finally did this by using C++ code which required ArcGIS mask grid, data files & locations of all the points.

Edit 2: I recently used R to do this interpolation task. You can use either hydroTSM, gstat or spacetime packages. Few example links below:

http://spatial-analyst.net/wiki/index.php?title=Spatial_interpolation_exercises_%28NL%29

http://www.geostat-course.org/Topic_Bivand_2012

Edit 3: Added a working example below for future readers

Best Answer

I solved this by inserting a "Feature Selection" iterator into a model. (In the ModelBuilder Window, under Insert->Iterators menu.)

Use your time field as your "group by" variable. By doing this, the model will iterate once for each time in your feature class.

Then attach your preferred interpolation tool (spline, IDW, whatever) to the feature output from the iterator. Run the model, go on vacation for a few weeks, and when you come back, you will have as many grids as you have time points in the feature class.

Note that this solution assumes you have discrete time sampling points with a date or numeric field that indicates a single time point for each record in your feature set. If you are using the "begin time" and "end time" format, it might not be so straight forward.

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