Douglas Leasure

Senior Researcher / Senior Data Scientist

Doug's research spans demography, population ecology, Bayesian statistics, and GIS/remote sensing. He specialises in developing novel methods to map population sizes and demographics with high spatial resolution in data-sparse settings by supplementing traditional survey data with innovative new data sources including social media activity and space-based Earth observations. His work developing bespoke hierarchical Bayesian modelling approaches aims to account for uncertainty in demographic estimation processes to support informed decision-making for crisis response, census support, government services, and global health initiatives. To promote research with real-world impacts, Doug is committed to open science and enjoys developing web applications that translate scientific results into easy-to-navigate interactive maps and tools to facilitate uptake by stakeholders globally.


Before joining LCDS at the University of Oxford, Doug led the Spatial Statistical Population Modelling team in the WorldPop Research Group at the University of Southampton developing Bayesian statistical models and applying machine learning approaches to produce high resolution population estimates supporting initiatives of the Bill and Melinda Gates Foundation and the United Nations Population Fund. He was previously a post-doctoral research associate at the River Basin Center in the Odum School of Ecology, University of Georgia developing statistical models for NASA's Ecological Forecasting program. He completed a PhD in Biological Sciences at the University of Arkansas where he held a Doctoral Academy Fellowship and was a post-doctoral research associate for the Cooperative Fish and Wildlife Research Unit of the United States Geological Survey.



Full list of publications at Google Scholar


Leasure DR, Kashyap R, Rampazzo F, Dooley CA, Elbers B, Bondarenko M, Verhagen M, Frey A, Yan J, Akimova ET, Fatehkia M, Trigwell R, Tatem AJ, Weber I, Mills MC. 2022. Nowcasting daily population displacement in Ukraine through social media advertising data. SocArXiv.


Sanchez-Cespedes, LM, Leasure DR, Tejedor-Garavito N, Cruz GHA, Velez GAG, Mendoza AE, Salazar YAM, Esch T, Tatem AJ, Bohórquez, M. O. 2022. Social cartography and satellite-derived building coverage for post-census population estimates in difficult-to-access regions of Colombia. SocArXiv.


Boo G, Darin E, Leasure DR, Dooley CA, Chamberlain HR, Lazar AN, Tschirhart K, Sinai C, Hoff NA, Fuller T, Musene K, Batumbo A, Rimoin AW, Tatem AJ. 2022. High-resolution population estimation using household survey data and building footprints. Nature Communications 13(1):1330:


Thomson DR, Leasure DR, Bird T, Tzavidis N, Tatem AJ. 2022. How accurate are WorldPop-Global-Unconstrained gridded population data at the cell-level?: A simulation analysis in urban Namibia. PLoS ONE 17(7): e0271504.


Hemstrom W, Dauwalter D, Peacock MM, Leasure DR, Wenger S, Miller MR, Neville H. 2022. Population genomic monitoring provides insight into conservation status but not correlation with demographic estimates of extinction risk in a threatened trout. Evolutionary Applications.


Nilsen K, Tejedor-Garavito N, Leasure DR, Utazi CE, Ruktanonchai CW, Wigley AS, Dooley CA, Matthews Z, Tatem AJ. 2021. A review of geospatial methods for population estimation and their use in constructing reproductive, maternal, newborn, child and adolescent health service indicators. BMC Health Services Research 21(1): 370.


Leasure DR, Jochem WC, Weber EM, Seaman V, Tatem AJ. 2020. National population mapping from sparse survey data: A hierarchical Bayesian modeling framework to account for uncertainty. Proceedings of the National Academy of Sciences 117(39): 24173-24179.


Jochem WC, Leasure DR, Pannell O, Chamberlain HR, Jones P, Tatem AJ. 2020. Classifying settlement types from multi-scale spatial patterns of building footprints. Environment and Planning B: Urban Analytics and City Science 48(5):1161-1179.


Lloyd CT, Sturrock HJW, Leasure DR, Jochem WC, Lazar AN, Tatem AJ. 2020. Using GIS and machine learning to classify residential status of urban buildings in low and middle income settings. Remote Sensing 12(23): 3847.