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. WorldPop develops methods for the integration of geolocated cluster sample data from household surveys with geospatial covariates in Bayesian geostatistical modelling frameworks to map key development and health indicators at high spatial resolution. These include indicators relating to poverty, literacy, sanitation, maternal and newborn health, contraceptive use and vaccination coverage, among others. Details of methods used and outputs can be found in Utazi et al 1, Utazi et al 2, Steele et al, Bosco et al, Ruktanonchai et al and Tatem et al.