Investigating Spatial Error Structures in Continuous Raster Data

TL;DR

This paper explores the spatial variation of errors in continuous raster data assessments using geographically weighted diagnostics and correlation analysis to better understand local error structures.

Contribution

It introduces a moving window approach to generate geographically weighted error metrics and maps their spatial variations, enhancing error analysis in raster data.

Findings

Spatial error structures vary across the study area.

Geographically weighted diagnostics reveal local error patterns.

GW correlation offers an alternative view of local accuracy.

Abstract

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.