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

Abstract

Low- and middle-income country cities face unprecedented urbanization and growth in slums. Gridded population data (e.g., ~100 × 100 m) derived from demographic and spatial data are a promising source of population estimates, but face limitations in slums due to the dynamic nature of this population as well as modelling assumptions. In this study, we compared field-referenced boundaries and population counts from Slum Dwellers International in Lagos (Nigeria), Port Harcourt (Nigeria), and Nairobi (Kenya) with nine gridded population datasets to assess their statistical accuracy in slums. We found that all gridded population estimates vastly underestimated population in slums (RMSE: 4958 to 14,422, Bias: −2853 to −7638), with the most accurate dataset (HRSL) estimating just 39 per cent of slum residents. Using a modelled map of all slums in Lagos to compare gridded population datasets in terms of SDG 11.1.1 (percent of population living in deprived areas), all gridded population datasets estimated this indicator at just 1–3 per cent compared to 56 per cent using UN-Habitat’s approach. We outline steps that might improve that accuracy of each gridded population dataset in deprived urban areas. While gridded population estimates are not yet sufficiently accurate to estimate SDG 11.1.1, we are optimistic that some could be used in the future following updates to their modelling approaches.

Improving the Accuracy of Gridded Population Estimates in Cities and Slums to Monitor SDG 11: Evidence from a Simulation Study in Namibia

“…Nine multi-country gridded population datasets are available in LMICs: LandScan [9,10], WorldPop-Global-Unconstrained (hereafter WPG-Unconstrained) [11,12], WorldPop-Global-Constrained (hereafter WPG-Constrained) [13], HRSL [14], GRID3 [15,16], GHS-POP [17,18], WPE [19], GPW [20,21], and WorldPop-Peanut Butter [22] (Table 1). While more detailed summaries of these datasets are available elsewhere [6,7,23], we provide a visual glossary of terms to understand their key characteristics (Figure 1). Top-down gridded population datasets are derived by disaggregating census or other complete population counts to grid cells (i.e., GHS-POP, GPW, HRSL, LandScan, WPG-Constrained, WPG-Unconstrained, and WPE), while bottom-up estimates are derived from microcensus population counts or assumptions about the average population per building (i.e., GRID3, WP-Peanut Butter).…”

“…Previous work evaluated the accuracy of the above nine gridded population datasets in Nigerian and Kenyan slums as compared to mapped and field referenced data by Slum Dwellers International (SDI) [7]. Findings from that study highlight that the most accurate gridded population datasets only predicted 39% of field-referenced slum residents [7]. There were potentially two reasons for these systematic underestimates of slum dwellers across datasets.…”

“…First, the implicit assumption in all topdown gridded population models is that average population densities in larger administrative units represent population densities in much smaller grid cells [24]. Second, most gridded products assessed in that study did not include an auxiliary dataset, such as building density, which correlates with variation in urban population density at the scale of the gridded output [7]. The exception was WPG-Constrained which uses cell-level building footprint metrics (e.g.…”

Earth Observations and Statistics: Unlocking Sociodemographic Knowledge through the Power of Satellite Images

“…Data innovations are key in support of evidence-based decision-making and to monitor the implementation of policies [10][11][12].…”

Comparison of Twelve Machine Learning Regression Methods for Spatial Decomposition of Demographic Data Using Multisource Geospatial Data: An Experiment in Guangzhou City, China

“…The typical global gridded population datasets mainly include the Gridded Population of the World (GPW) [19], the Global Rural-Urban Mapping Project (GRUMP) [2], LandScan Global [20], Global Human Settlement Population Grid datasets (GHS-POP) [21], and Worldpop [22]. However, most of these datasets are designed for extensive coverage and free accessibility for the undeveloped regions in the world; their accuracy on a local scale, such as that of a city or subdistrict, still need to be analyzed [23,24].…”