Comparison of spatial interpolation methods to create high-resolution poverty maps for low- and middle-income countries.

ABSTRACT: High-resolution poverty maps are important tools for promoting equitable and sustainable development. In settings without data at every location, we can use spatial interpolation (SI) to create such maps using sample-based surveys and additional covariates. In the model-based geostatistics (MBG) framework for SI, it is typically assumed that the similarity of two areas is inversely related to their distance between one another. Applications of spline interpolation take a contrasting approach that an area's absolute location and its characteristics are more important for prediction than distance to/characteristics of other locations. This study compares prediction accuracy of the MBG approach with spline interpolation as part of a generalized additive model (GAM) for four low- and middle-in

SUBMITTER: Wong KLM 

PROVIDER: S-EPMC6228471 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature