Sentence examples for spatial imputation from inspiring English sources

This may likewise occur with spatial imputation models and it is suggested that future research explore alternative imputation models for the non-living C stocks in forest ecosystems.

OHIP performs a spatial imputation on addresses that are not geocoded successfully [ 13].

One disadvantage of a multi-stage approach — for example, first fitting splines to the data for sampled profiles, then imputing new data for the target interval, before considering a spatial analysis with the imputed data — is that the imputation generally ignores any uncertainty in the imputed data, which might give misleading conclusions.

Before using the imputed constituent levels in the analysis, we confirmed that the Bayesian spatial GP modeling was appropriate for imputation via a cross-validation (CV) study (see Supplemental Material, "Cross validation study").

The objective of this paper was to provide a method for improving composition and spatial configuration in landscape level data when applying imputation schemes such as kNN.

The methodological framework comprises two steps: (i) composition at landscape level is preserved by a restricted imputation technique; and (ii) composition and spatial configuration within forest stands is improved by rearranging imputed sample-plots between stands.

A general methodological framework was presented for using imputation to provide information for missing spatial data from nongeocoded addresses from a GIS.

In this manuscript we develop and evaluate a set of imputation strategies for dealing with missing spatial information from nongeocoded addresses.

To avoid simply removing the observations with missing values and reducing the sample size to half, we adopted a Bayesian spatial GP modeling and conducted a single value imputation for the missing data separately for each constituent.

We developed an approach for evaluating and imputing field plots based on their similarity across multiple spatial environmental variates, their species composition, and their geographical distance between source and imputation to produce a map that is appropriate for initializing an FLM.

Robustness to missing data is tackled by means of simple imputation (data replacement) schemes, such as exponential forecasts and spatial interpolation.