Abstract
Improved understanding of geographical variation and inequity in health status, wealth and access to resources within countries is increasingly being recognized as central to meeting development goals. Development and health indicators assessed at national or subnational scale can often conceal important inequities, with the rural poor often least well represented. The ability to target limited resources is fundamental, especially in an international context where funding for health and development comes under pressure. This has recently prompted the exploration of the potential of spatial interpolation methods based on geolocated clusters from national household survey data for the high-resolution mapping of features such as population age structures, vaccination coverage and access to sanitation. It remains unclear, however, how predictable these different factors are across different settings, variables and between demographic groups. Here we test the accuracy of spatial interpolation methods in producing gender-disaggregated high-resolution maps of the rates of literacy, stunting and the use of modern contraceptive methods from a combination of geolocated demographic and health surveys cluster data and geospatial covariates. Bayesian geostatistical and machine learning modelling methods were tested across four low-income countries and varying gridded environmental and socio-economic covariate datasets to build 1×1 km spatial resolution maps with uncertainty estimates. Results show the potential of the approach in producing high-resolution maps of key gender-disaggregated socio-economic indicators, with explained variance through cross-validation being as high as 74–75% for female literacy in Nigeria and Kenya, and in the 50–70% range for many other variables. However, substantial variations by both country and variable were seen, with many variables showing poor mapping accuracies in the range of 2–30% explained variance using both geostatistical and machine learning approaches. The analyses offer a robust basis for the construction of timely maps with levels of detail that support geographically stratified decision-making and the monitoring of progress towards development goals. However, the great variability in results between countries and variables highlights the challenges in applying these interpolation methods universally across multiple countries, and the importance of validation and quantifying uncertainty if this is undertaken.
Highlights
The UN sustainable development goals (SDGs), an intergovernmental set of 17 aspirational goals and 169 targets to be achieved by 2030 [1], were launched in 2015
A model with given root mean square error (RMSE) and mean absolute error (MAE) may locally display errors both negative and positive so as for the overall bias to be mitigated by the compensating local under/over-estimations. Another model with the same RMSE and MAE may instead systematically underestimate the modelled quantity. In order for these modelling situations to be better discriminated, we introduced a new parameter for calculating the general bias of the models (equation (2.2)): general bias jobs À predj sobs where sobs and obs are the standard deviation and mean of observed data and pred is the mean of predicted values
The results highlight that relatively accurate high-resolution maps of key genderdisaggregated socio-economic indicators can be produced, with explained variance through validation being as high as 74 –75% for female literacy in Nigeria and Kenya, and in the 50 –70% range for many other variables
Summary
The UN sustainable development goals (SDGs), an intergovernmental set of 17 aspirational goals and 169 targets to be achieved by 2030 [1], were launched in 2015. A particular focus across the goals and targets is achievement ‘everywhere’, ensuring that no one gets left behind and that progress is monitored at subnational levels to avoid national-level statistics masking local heterogeneities This requires consistent, comparable evaluation and monitoring of key SDG indicators at high levels of subnational detail across the 2015–2030 period of the goals. The increasing focus on subnational assessments for the SDGs, as well as for efficient targeting of resources, and the improvement in accuracy for health and development metrics has prompted an emphasis on subnational data collection and the continued development of mapping approaches Principal among these approaches is small area estimation [2,3,4] whereby survey data on the variable of interest mapped at coarse spatial scales are integrated with census data at fine spatial scales to infer fine resolution mapping of key development metrics. With national population censuses undertaken typically only every 10 years, and sometimes longer in many low-income countries [7], this makes the application of such small area estimation approaches challenging for the ongoing monitoring of SDG indicators
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