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

Abstract

<jats:p>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-income countries. We also identify any potentially generalizable data characteristics influencing comparative accuracy. We found spatially scattered pockets of wealth in Malawi and Tanzania (corresponding to the major cities), and overarching spatial gradients in Kenya and Nigeria. Spline interpolation/GAM performed better than MBG for Malawi, Nigeria and Tanzania, but marginally worse in Kenya. We conclude that the spatial patterns of wealth and other covariates should be carefully accounted for when choosing the best SI approach. This is particularly pertinent as different methods capture geographical variation differently.</jats:p>

Version 3 of the Global Aridity Index and Potential Evapotranspiration Database

“…These datasets been downloaded currently in excess of 47,000 times, and applied across a wide range of disciplines, with nearly 1500 citations on topics ranging from agricultural and natural resource science, to genetics, anthropology, archaeology, conflict resolution, and climate change. It has been found useful in a wide variety of applications, particularly related, but not limited to water management 21 , 22 and crop production, but also socio-ecological and socio-economic applications related to sustainable development 23 , 24 , climate change impacts 25 , 26 , and adaptation 27 , 28 . The topics of papers citing this dataset range from global environmental stratification 29 – 31 , to human migration 32 , pastoralism and dryland environmental threats 33 , 34 , wildlife and restoration ecology 35 , fire modeling 36 , child mortality 37 , and epidemiological 38 – 40 and other human and livestock health research 41 – 45 , such as the effect of malaria control 39 , 40 , or mapping the zoonotic niche of Ebola virus disease in Africa 38 .…”

Current realities versus theoretical optima: quantifying efficiency and sociospatial equity of travel time to hospitals in low-income and middle-income countries

“…Perceived physical barriers, actual travel pattern, road conditions, time spent in transit and so on likely vary among people living in the same pixel and are unaccounted for. Other assumptions of travel friction (eg, the impact of seasonal and temporal variabilities) and wealth (eg, displacement of DHS geocodes) have been detailed elsewhere 25 26. Lastly, our definition for hospital was based on data on the type of health facility as given in the MFLs; and these hospitals may vary somewhat in capacity, quality of care and the range of health services that they provide.…”

“…To derive high-resolution poverty maps, we used model-Based Geostatistics for Kenya and a generalised additive model for each of Malawi, Nigeria and Tanzania. Our choices of modelling methods were based on a previous analysis that compared the performances of these approaches 26. Lastly, we sourced country administrative areas boundary files from the freely available Database of Global Administrative Area (www.gadm.org).…”

A Hybrid Neural Network–Particle Swarm Optimization Informed Spatial Interpolation Technique for Groundwater Quality Mapping in a Small Island Province of the Philippines

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