Census-independent population mapping in northern Nigeria

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.

New Perspectives for Mapping Global Population Distribution Using World Settlement Footprint Products

“…Global or large scale gridded population datasets considered state-of-the-art in terms of open access archives of population distribution data include: the Rural-Urban Mapping Project (GRUMP) [5], the Gridded Population of the World, Version 4 (GPWv4) [6,7], the LandScan Global Population database [8,9], the Global Human Settlement Layer-Population grid (GHS-POP) [10,11], the WorldPop datasets [12][13][14][15][16] and the recently developed High Resolution Settlement Layer (HRSL) population grids [17]. Current and previous versions of these products have proved to be an important source of information and essential input for a wide range of cross-disciplinary applications including: poverty mapping [18][19][20], epidemiological modelling and disease burden estimation [21][22][23], interconnectivity and accessibility analyses [24][25][26], deriving past and future population estimates [15,27,28], disaster management [29][30][31] and human settlement characterisation [32] among others.…”

Improving Urban Population Distribution Models with Very-High Resolution Satellite Information

“…As a consequence of these rapid transformations, SSA cities are exposed to increasing urban poverty and intra-urban inequalities [2], while a large part of the urban population is extremely vulnerable to health and disaster risks. In this context, detailed population data is essential in improving evidence-based decision-making by relevant authorities and organizations [3][4][5], as well as for any application relying on a human population denominator, such as estimating the population at risk, assessing vulnerability, and deriving health or development goals indicators [6][7][8]. However, this knowledge is often very limited in SSA and population data are regularly outdated and criticized regarding their reliability [6,7].…”

Fine-Resolution Population Mapping from International Space Station Nighttime Photography and Multisource Social Sensing Data Based on Similarity Matching

“…The rapid world population growth has brought a wide range of environment issues, such as carbon emission, waste treatment, resource shortage, and land destruction [1,2]. Accurate and high-resolution population distribution information is crucial for resource allocation, land use policy, environment protection, disaster prevention, and transportation planning [3][4][5].…”