The statistics calculated for the neighborhood acts as a moving window that scans across the data and can be of two types, overlapping or nonoverlapping. Overlapping neighborhood functions are also referred to as focal functions and generally calculate a specified statistic within the neighborhood. For example, you may want to find the mean or maximum value in a 3 x 3 neighborhood. A variation of the overlapping neighborhood statistics function are the high- and low-pass filter functions to smooth and accentuate data.

The nonoverlapping neighborhood functions, or block functions, allow statistics to be calculated in a specified nonoverlapping neighborhood. The block functions are commonly used for aggregating raster data to a coarser cell size. The values assigned to the coarser cells can be based on some other calculation, such as the maximum value in the coarser cell, as opposed to using the default nearest neighbor interpolation.

In addition to the predefined statistics and filters, you can also create your own custom filters by specifying neighborhoods and weight values such as edge detection filters.

Available Neighborhood Statistics tools include:

• Majority
• Maximum
• Mean
• Median
• Minimum
• Minority
• Range
• Standard Deviation
• Sum
• Variety
• High Pass
• Low Pass
• Focal Flow