  The objective of this study is to investigate spatial structures of error in
the assessment of continuous raster data. The use of conventional diagnostics
of error often overlooks the possible spatial variation in error because such
diagnostics report only average error or deviation between predicted and
reference values. In this respect, this work uses a moving window (kernel)
approach to generate geographically weighted (GW) versions of the mean signed
deviation, the mean absolute error and the root mean squared error and to
quantify their spatial variations. Such approach computes local error
diagnostics from data weighted by its distance to the centre of a moving kernel
and allows to map spatial surfaces of each type of error. In addition, a GW
correlation analysis between predicted and reference values provides an
alternative view of local error. Full abstract can be found in the pdf.
