ArticleRemote sensing of environment2018
Census-independent population mapping in northern Nigeria.
Article in Remote sensing of environment, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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Who cites it
32 citing papers in PubMed.
- Advances in small area population estimation in the absence of national census data.Proceedings of the National Academy of Sciences of the United States of America · 2026Review
- Knowledge of cervical cancer and uptake of HPV vaccine among in-school adolescent girls in Kwara State, Nigeria.Infectious agents and cancer · 2026Article
- LandScan Global 30 Arcsecond Annual Global Gridded Population Datasets from 2000 to 2022.Scientific data · 2025Article
- A move in the right direction: Tracking the traceability of British Thoroughbreds outside of racing.PloS one · 2025Article
- Treatment-Seeking for Children with Suspected Severe Malaria Attending Community Health Workers and Primary Health Centres in Adamawa State, Nigeria.International journal of public health · 2025Article
- Global gridded multi-temporal datasets to support human population distribution modelling.Gates open research · 2025Article
- The role of connectivity on malaria dynamics across areas with contrasting control coverage in the Peruvian Amazon.PLoS neglected tropical diseases · 2024Article
- Crime, inequality and public health: a survey of emerging trends in urban data science.Frontiers in big data · 2023Review
- Article
- Fine-grained population mapping from coarse census counts and open geodata.Scientific reports · 2022Article
- Improving intelligent dasymetric mapping population density estimates at 30 m resolution for the conterminous United States by excluding uninhabited areas.Earth system science data · 2022Article
- Census-independent population estimation using representation learning.Scientific reports · 2022Article
- High-resolution population estimation using household survey data and building footprints.Nature communications · 2022Article
- The cost of illness for childhood clinical pneumonia and invasive pneumococcal disease in Nigeria.BMJ global health · 2022Article
- Article
- 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 · 2021Review
- How do social media and individual behaviors affect epidemic transmission and control?The Science of the total environment · 2021Article
- A data fusion approach to the estimation of temporary populations: An application to Australia.PloS one · 2021Article
- Article
- Measuring the contribution of built-settlement data to global population mapping.Social sciences & humanities open · 2021Article
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Authors and funding
9 authors.
Funding
Abstract
Although remote sensing has long been used to aid in the estimation of population, it has usually been in the context of spatial disaggregation of national census data, with the census counts serving both as observational data for specifying models and as constraints on model outputs. Here we present a framework for estimating populations from the bottom up, entirely independently of national census data, a critical need in areas without recent and reliable census data. To make observations of population density, we replace national census data with a microcensus, in which we enumerate population for a sample of small areas within the states of Kano and Kaduna in northern Nigeria. Using supervised texture-based classifiers with very high resolution satellite imagery, we produce a binary map of human settlement at 8-meter resolution across the two states and then a more refined classification consisting of 7 residential types and 1 non-residential type. Using the residential types and a model linking them to the population density observations, we produce population estimates across the two states in a gridded raster format, at approximately 90-meter resolution. We also demonstrate a simulation framework for capturing uncertainty and presenting estimates as prediction intervals for any region of interest of any size and composition within the study region. Used in concert with previously published demographic estimates, our population estimates allowed for predictions of the population under 5 in ten administrative wards that fit strongly with reference data collected during polio vaccination campaigns.
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