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Figure5 compares the asymptotic MSE performance between the spatial interpolation and the space-time interpolation.
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The interplay between the time advancement scheme and the spatial interpolation is studied in detail and it is shown how the order of the trajectory solver is directly affected by the order of the spline interpolation.
Median differences in exposure between the building-matching geocodes and the spatial interpolation geocodes were –0.07 μg/m (NavTEQ), –0.06 (Google Maps), and –0.15 μg/m (Multimap) for NO2 and –0.02, –0.02, and –0.06 μg/m for PM10, respectively (see Supplemental Material, Table S1).
Median distances between coordinates estimated using the building-matching method and the spatial interpolation techniques (NavTEQ, Google Maps, and Multimap) were 27.9 m, 26.4 m, and 35.6 m, respectively.
Kriging was used as the spatial interpolation technique to examine how the different BTEX species were spatially distributed based on the sampling density.
The corresponding values using the spatial interpolation model were 81%and73%3%.
Cross-validation criteria were used to assess the quality of spatial model made from the spatial interpolation.
Then, the best-fit model was used for the spatial interpolation of the groundwater levels.
The asymptotic MSE of the spatial interpolation is given in the following proposition.
The spatial interpolation was applied by using the PGA value of each observation points.
For the spatial interpolation of environmental variables in routine and emergency situations, computation time and interpolation accuracy are important criteria to evaluate and compare algorithms.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com