Effect of spatial data resolution on uncertainty. (January 2015)

• Record Type: Journal Article
• Authors: Pogson, Mark; Smith, Pete
• Is Part Of: Environmental modelling & software. Volume 63(2015:Jan.)
• Page Start: 87
• Page End: 96
• Publication Date: 2015-01
• Subjects: Uncertainty -- Raster grid size -- Aggregation -- Resolution -- Interpolation -- Spatial autocorrelation; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2014.09.021
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: The effect that the resolution of spatial data has on uncertainty is important to many areas of research. In order to understand this better, the effect of changing resolution is considered for a range of data. An estimate is presented for how the average uncertainty of each grid value varies with grid size, which is shown to be in good agreement with observed uncertainties. The effect of bilinear interpolation is also investigated and is observed to provide no reduction in uncertainty relative to uninterpolated data. Finally, the effects of combining aggregated spatial data are found to obey standard properties of error propagation, which means that the presented estimate of uncertainty can be used to estimate resolution-related uncertainty in spatial model results, relative to the input data. The study quantitatively demonstrates the important role of the spatial autocorrelation of data in uncertainties associated with the resolution of spatial data. Highlights: The resolution of spatial data affects uncertainty in all kinds of spatial models. We formulate and verify a general estimate of uncertainty due to reduced resolution. Spatial autocorrelation plays a key role in uncertainty associated with resolution. Interpolation appears ineffective to increase the resolution of spatial data. Uncertainty in combined spatial data is predicted by standard propagation of error.