Evaluating spatial mapping using interpolation techniques

Abstract: In this thesis, the inverse distance weighting, different kriging methods, ordinary least squares and two variants of the geographically weighted regression was used to evaluate the spatial mapping abilities on an observed dataset and a simulated dataset. The two datasets contain the same bioclimatic variable, near-surface air temperature, uniformly distributed over the whole world. The observed dataset is the observed temperature of a global atmospheric reanalysis produced by ECMWF and the other being simulated temperature produced by SMHI’s climate model EC-earth 3.1. The data, initially containing space-time information during the time period 1993-2010 displayed no significant temporal variation when using a spatio-temporal variogram. However, each year displayed its own variation so each year was split where the different methods were used on the observed dataset to estimate a surface for each year that was then used to make comparisons to the simulated data. CLARA clustering was done on the observed geographical dataset in the hope to force the inverse distance weighting and the kriging methods to estimate a locally varying mean. However, the variograms produced displayed an irregular trend that would lead to inaccurate kriging weights. Geometric anisotropy variogram analysis was accounted for that displayed moderate anisotropy. Results show that the geographically weighted regression family outperformed the rest of the used methods in terms of root mean squared error, mean absolute error and bias. It was able to create a surface that had a high resemblance to the observed data.