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So many different solutions to last week's Challenge have been posted here!
This week's Challenge riffs off a process typically used in spatial analysis with raster inputs: interpolation. In this Challenge, you are provided with two inputs: a polygon representing the island of Maui (hey, there's still snow in the forecast here in CO...can you blame me for picking a beachy location?) and a table of values representing the elevation measurements for 500 m x 500 m grid cells (much like a Digital Elevation Model). However, some grid cells contain a value of 0. We'll use some spatial tools to interpolate, or estimate, the values of the cells containing 0 from a "nearest neighborhood" or surrounding cell values.
First, build a 500 m x 500 m grid around the island of Maui. Then, interpolate the missing value using the average of the known measurements from the surrounding cells, or "neighborhood". Use a neighborhood of the 8 nearest surrounding cells in a unique cardinal direction (see example below; a neighborhood of a cell containing a 0 is outlined in blue. In this example, the new interpolated value of the center cell would be 61.5). Should a missing value be located on the edge, use only the nearest cells in a unique cardinal direction, even if 8 values are not used for the calculation.
Hint: Grid cell Grd37_68 is column 37, row 68. The Grid tool starts with column 0.