Claudio Bosco|Victor A. Alegana|Tomas J. Bird|Carla Pezzulo|Linus Bengtsson|Alessandro Sorichetta|James Steele|Garry Hornby|Corrine W. Ruktanonchai|Nick Ruktanonchai|Erik Wetter|Andrew J. Tatem
Exploring the high-resolution mapping of gender-disaggregated development indicators
University of Southampton|Flowminder Foundation|Loughborough University|University of Southampton|Flowminder Foundation|University of Southampton|Flowminder Foundation|University of Southampton|Flowminder Foundation|University of Southampton|Flowminder Foundation|Karolinska Institutet|University of Southampton|Flowminder Foundation|University of Southampton|Flowminder Foundation|University of Southampton|University of Southampton|Flowminder Foundation|University of Southampton|Flowminder Foundation|University of Southampton|Flowminder Foundation|Stockholm School of Economics|University of Southampton|Flowminder Foundation
improved understanding geographical variation inequity health status wealth access resources within countries increasingly recognized central meeting development goals development health indicators assessed national subnational scale often conceal important inequities rural poor often least well represented ability target limited resources fundamental especially international context funding health development comes pressure recently prompted exploration potential spatial interpolation methods based geolocated clusters national household survey data highresolution mapping features population age structures vaccination coverage access sanitation remains unclear however predictable different factors across different settings variables demographic groups test accuracy spatial interpolation methods producing genderdisaggregated highresolution maps rates literacy stunting use modern contraceptive methods combination geolocated demographic health surveys cluster data geospatial covariates bayesian geostatistical machine learning modelling methods tested across four lowincome countries varying gridded environmental socioeconomic covariate datasets build 11 km spatial resolution maps uncertainty estimates results show potential approach producing highresolution maps key genderdisaggregated socioeconomic indicators explained variance crossvalidation high 7475 female literacy nigeria kenya 5070 range many variables however substantial variations country variable seen many variables showing poor mapping accuracies range 230 explained variance using geostatistical machine learning approaches analyses offer robust basis construction timely maps levels detail support geographically stratified decisionmaking monitoring progress towards development goals however great variability results countries variables highlights challenges applying interpolation methods universally across multiple countries importance validation quantifying uncertainty undertaken