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A kernel is a defined neighborhood (area) of interest around a point or area object. Mathematically, as in the classic example of kernel density estimation (KDE), it is defined by a kernel function that has two characteristics. First, each kernel function specifies a range over which it is to be evaluated, using either simple connection, distance, or a count of cells (pixels) in a raster. In KDE, this range is referred to as the bandwidth of the function. Choice of bandwidth is similar to the choice of bin width when compiling a histogram, with large values resulting in smooth density estimate surfaces, and vice versa. Second, it provides some weighting that specifies the effect of distance on the calculation to be performed.

Kernel Density Function

In point ...

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