Effect of spatial data resolution on uncertainty
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 g...
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Published in | Environmental modelling & software : with environment data news Vol. 63; pp. 87 - 96 |
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Main Authors | , |
Format | Journal Article |
Language | English |
Published |
Elsevier Ltd
01.01.2015
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Subjects | |
Online Access | Get full text |
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Summary: | 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.
•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. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
ISSN: | 1364-8152 |
DOI: | 10.1016/j.envsoft.2014.09.021 |