How accurate are WorldPop-Global-Unconstrained gridded population data at the cell-level?: A simulation analysis in urban Namibia

Abstract

Disaggregated population counts are needed to calculate health, economic, and development indicators in Low- and Middle-Income Countries (LMICs), especially in settings of rapid urbanisation. Censuses are often outdated and inaccurate in LMIC settings, and rarely disaggregated at fine geographic scale. Modelled gridded population datasets derived from census data have become widely used by development researchers and practitioners. These datasets are evaluated for accuracy at the spatial scale of the input data which is often much courser (e.g. administrative units) than the neighbourhood or cell-level scale of many applications. We simulate a realistic "true" 2016 population in Khomas, Namibia, a majority urban region, and introduce realistic levels of outdatedness (over 15 years) and inaccuracy in slum, non-slum, and rural areas. We aggregate these simulated realistic populations by census and administrative boundaries (to mimic census data), and generate 32 gridded population datasets that are typical of a LMIC setting using WorldPop-Global-Unconstrained gridded population approach. We evaluate the cell-level accuracy of these simulated datasets using the original "true" population as a reference. In our simulation, we found large cell-level errors, particularly in slum cells, driven by the use of average population densities in large areal units to determine cell-level population densities. Age, accuracy, and aggregation of the input data also played a role in these errors. We suggest incorporating finer-scale training data into gridded population models generally, and WorldPop-Global-Unconstrained in particular (e.g., from routine household surveys or slum community population counts), and use of new building footprint datasets as a covariate to improve cell-level accuracy. It is important to measure accuracy of gridded population datasets at spatial scales more consistent with how the data are being applied, especially if they are to be used for monitoring key development indicators at neighbourhood scales with relevance to small dense deprived areas within larger administrative units.

Evaluating the Accuracy of Gridded Population Estimates in Slums: A Case Study in Nigeria and Kenya

“…However, to explicitly assess cell-level accuracy, additional datasets with population counts in small areas should be used after modelling, for example, population enumerations taken as part of routine household surveys [24] (Table 4). Simulated household-level datasets geo-located in a real-world setting provide another approach to evaluate the general accuracy of a modelling approach [84].…”

“…While this does occur, the magnitude of the problem is minimal. In an analysis of the WorldPop-Unconstrained model in Khomas Namibia, a region characterized by vast unsettled areas and the capital city of Windhoek, more than 99 per cent of the population was allocated to cells within 300 m of populated places [84]. The reason for misallocation to cells just beyond populated places was likely a consequence of the "halo" effect due to coarse covariate data (e.g., 1 × 1 km resolution night-time lights), rather than misallocation to unsettled cells.…”