According to the latest revision of the United Nations (UN), World Population Prospects, the world’s population is projected to grow from 7.7 billion in 2019 to 10.9 billion in 2100 [1
]. Considered part of the four global demographic “megatrends”, population growth next to population ageing, migration and urbanization, is an important indicator for economic, social and environmental development [2
]. For this reason, accurate knowledge of the size, location, and distribution of the human population is fundamental for successfully achieving a sustainable future. An effective monitoring of global population change, allows implementing efficient government policies to allocate financial resources, plan interventions and quantify populations at risk.
To this end, since the late 1980s considerable efforts have been taken to produce global or continental scale, high resolution gridded population maps describing the spatial distribution of human population [3
]. Over the last 20 years, the ongoing improvement in the availability and spatial detail of census population data, the better quality and spatial resolution of remote sensing data, the development of sophisticated geospatial analysis methods and the statistical refinement of modelling techniques, have been leveraged to produce more accurate datasets that capture the changes in magnitude, composition and distribution of human population over time [4
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
], the LandScan Global Population database [8
], the Global Human Settlement Layer-Population grid (GHS-POP) [10
], the WorldPop datasets [12
] 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
], epidemiological modelling and disease burden estimation [21
], interconnectivity and accessibility analyses [24
], deriving past and future population estimates [15
], disaster management [29
] and human settlement characterisation [32
] among others.
The modelling techniques of these population distribution datasets are based on a common methodology which consists of the disaggregation of census data from administrative units (polygons or source zones) into smaller areal units of fixed spatial resolution (grid cells or target zones) [3
]. Population disaggregation is accomplished using two areal interpolation methods: areal-weighting and dasymetric mapping. With areal-weighting interpolation, a grid of fixed spatial resolution is intersected with the census polygons and each grid cell is assigned a portion of the total population based solely on the proportion of the area of the administrative unit that falls within each grid cell [9
]. Thus far, the GPWv4 is the only dataset produced based on areal-weighting interpolation, while the rest of the population datasets employ a dasymetric mapping approach. This method seeks to improve the distribution of population through the incorporation of one or multiple geospatial covariates or categorical ancillary datasets that influence the variations in the densities and distribution of population within the administrative units [33
The most commonly employed geospatial covariates include: land cover and land use types, intensity of nightlights, climatic factors, human settlements, urban/rural extents, water features, road networks and topographic elevation and slope. In this regard, LandScan and WorldPop population grids use multiple best-available local or global covariates that are statistically assessed to produce a weighted layer that is used as input in the dasymetric modelling method [8
]. Here, the resulting population grids show an asymmetrical distribution of population counts per administrative unit, in which each grid cell is assigned a portion of the population depending on the individual calculated weights [34
]. While sophisticated, this technique presents a number of limitations and disadvantages. For example, the assignment of relative weights to each individual covariate layer is subjective and based on local relationships [35
]. In other words, the model is country-specific limiting the direct transferability of the model to global scales [8
]. Moreover, temporal agreement between all covariate layers and population census data is difficult to achieve, restricting the production of a globally consistent population dataset. Finally, the use of multiple covariate layers reduces the applicability of the final population grids, as explained by Balk et al. [3
In this framework, however, it has been demonstrated that not all the commonly used geospatial covariates are equally important for population disaggregation. According to the research presented by Nieves et al. [4
], geographical data pertaining to the built-up environment and urban extents are the two most important covariates for predicting population densities and are significantly more important than other covariates at both regional and global scales. In this respect, the GHS-POP and HRSL population grids are processed using a binary-dasymetric mapping approach, restricting the distribution of population only to those grid cells identified as human settlements. The GHS-POP uses the Global Human Settlement Layer built-up grids (GHSL-BUILT) [37
], while the HRSL uses a binary mask of areas identified as human-made buildings extracted from very high-resolution satellite imagery. While this modelling approach is less complex and allows global transferability [33
], the population mapping accuracies of these products largely depend on the complete identification of building structures and are affected by omission and commission errors [13
In this context, the German Aerospace Center (DLR) has developed dedicated global layers and related analysis tools that describe the built-up environment and its characteristics with high accuracy and high spatial resolution. The first includes the Global Urban Footprint (GUF) dataset, which was released in 2016 [38
]. The GUF was produced based on an operational framework that automatically processed and analysed over 180,000 TerraSAR-X/TanDEM-X radar images collected during 2011–2013. It provides a global human settlement map at 12 m resolution [39
], which up to now, has been employed by more than 500 institutions for a broad scope of applications [40
], including studies focused on population disaggregation [12
]. Currently, the DLR is working on a suite of follow-on products—the World Settlement Footprint (WSF)—with an extended semantic depth, based on the joint analysis of Landsat 8 and Sentinel 1 optical and radar imagery [43
]. The first two releases of this new suite will include: (i) a binary settlement mask named WSF-2015 outlining settlements globally at 10-m resolution; and (ii) the experimental WSF-2015-Density layer. which estimates the percent of impervious surface for the pixels labelled as settlement in the WSF-2015 [43
Impervious surfaces are primarily associated with streets, sidewalks and building structures. They can be defined as surfaces consisting of materials such as asphalt, concrete or stone that seal the soil surface, eliminating water infiltration [44
]. Impervious surfaces extracted using different remote sensing methodologies have been examined in a small number of population distribution studies [45
]. In these studies, the authors have demonstrated that impervious surfaces are highly correlated to population counts, making them good predictors of population distribution. Nevertheless, these studies have only focused on limited areas, thus leading to results and methodologies that are not globally transferable. In the same way, in producing population distribution maps based on settlement extent products, Reed et al. [34
] showed that an initial version of the WSF-2015 layer was capable of producing population distribution maps with predictive accuracies higher than the GHSL layer and relatively close to the HRSL layer, employing different population distribution methods. However, while currently the HRSL layer is available only for a limited number of countries, the novel WSF-2015 and the experimental WSF-2015-Density layers have the potential to become the ideal covariates to support population disaggregation methods and to produce global population distribution datasets with improved accuracy and higher spatial resolution than those currently available.
Following this premise, the main goal of this research was to examine the suitability of the WSF-2015 and the—thus far experimental—WSF-2015-Density layers as input covariates for the development of a new global population distribution dataset. Population distribution maps were produced using a dasymetric mapping approach in combination with the finest population census data available at global scale at the time of writing. We specifically focused on the systematic cross-comparison between the performance of the binary and the impervious layer, to investigate if quality and accuracy improvements in population disaggregation can be achieved with the WSF-2015-Density layer, compared to the already established binary approach that has been employed by other population datasets and their baseline settlement layers.
Through a comprehensive quantitative assessment, we evaluated the mapping performance of each covariate layer, addressing the influence of: (i) the spatial resolution of the input census data; (ii) the quality of the input covariate layers; and (iii) the spatial distribution of the built-up environment, on the final results.
The corresponding analyses were conducted for nine representative countries of different size and different levels of urbanisation and population aggregation.
In the above sections, we present a set of comprehensive analyses to compare the relative accuracies of population distribution maps produced using the WSF-2015 and the experimental WSF-2015-Density layers. The first analysis consisted of an overall accuracy assessment carried at the validation unit level, where metrics such as MAE, MAPE, RMSE and R2
) were used to evaluate maps produced using three spatial aggregation levels of the administrative units (Table 2
). The results presented in Table 6
show that, for all countries and both covariate layers, the highest accuracy values were reported for population maps produced using the finest input census units (Analysis I, Table 2
), with accuracies decreasing from one level of spatial aggregation to the next. These results are directly in line with previous findings [17
], and confirm the premise that higher accuracies in population mapping can be achieved with improvements in the resolution of the input census data. In the same way, from a comparative point of view, the overall accuracy results showed that, for the majority of the countries, except Cambodia and Malawi, the WSF-2015-Density layer performed better than the WSF-2015.
When interpreting and comparing the overall accuracy results between countries and between covariate layers, there are, however, a set of considerations that need to be taken into account. First, it is important to understand, that regardless of the input covariate layer used for population disaggregation, high accuracies can be reached, when the number and ASR of the of the administrative units used for validation are similar to those of the administrative units used as input data (Table 2
). This can be seen, for example, by examining the results of Analysis I for Côte d’Ivoire, Myanmar and Vietnam (Table 6
). The fact that these countries reported relatively good accuracy results is more likely to be due to the small difference between the number of administrative units used as input and validation units (407, 225 and 625, respectively) and the small ratio between their ASR (2.16, 2.11 and 3.30, respectively). These results are linked to the scale effect of the modifiable areal unit problem (MAUP), where the correlation between variables increases as the areal unit size becomes similar [59
A second consideration to keep in mind is to avoid the use of the R2
metric as unique statistical indicator to report the accuracy of population distribution models. Previous research has demonstrated that the lack of variability in the data influences the coefficient of determination [60
]. For example, for England, where significantly low R2
values were obtained in comparison to the MAE, MAPE and RMSE metrics, these can be related to the fact that the original census data reports similar population counts for a large number of the administrate units used for validation. This can be seen in the boxplots of Figure 8
, where the reported actual population counts of the validation units of England are constrained within a small range of values. This small variability in the data, according to Goodwin et al. [60
], results in a poor correlation between the estimated population counts and actual population counts as exemplified in the scatter plots of Figure 9
. Here, it is possible to observe an amorphous or non-structured appearance of the data points for England in comparison to France, which results in a poor correlation, signalised by the almost horizontal trend-line.
The aforementioned findings indicate that the use of single statistics metrics can be misleading and that population distribution maps can report high accuracy results independently of the quality of the underlying covariate layers used for population disaggregation. Therefore, it is important to emphasise, not only that full dissemination of the data used for modelling and validation is essential when reporting accuracy results [3
], but also that, to evaluate the real effectiveness of the covariate layers, it is necessary to undertake more in-depth analyses using complementary metrics.
In this research, with the use of the REE statistical metric (Equation (5)), it was possible to evaluate the amount and distribution of error generated by each covariate layer (Figure 3
and Table 7
), and identify the areas where large errors of underestimation and large errors overestimation can be expected (Figure 4
). Our results show that both layers perform similarly, distributing approximately the same percentage of each country’s total population with the same REE values. For all countries, the largest percentage of the population has been estimated with errors ranging from −25% to 25%, which in previous research has been considered as “accurately estimated” [54
]. Nevertheless, only in Côte d’ Ivoire, Germany, England and Myanmar this represent more than 50% of the total population, which indicates that, for the rest of the countries, a significant percentage of the total population was distributed with larger errors of underestimation and errors overestimation.
We attribute these errors to the quality (completeness) of the covariate layers and to the fact that they do not take into account information on the land or building use. On the one hand, our findings indicate that errors of underestimation are reported in validation units where not enough settlement pixels have been found for population disaggregation. These errors increase as the ratio between the actual population and the number of settlement pixels increases (Figure 4
a). This means, for example, that, in countries where a large percentage of the population and validation units were “greatly underestimated” (Table 7
and Figure 4
b), such as France, Cambodia, Mexico and Malawi, this can be explained by the large amount of validation units where zero or very few settlement pixels have been identified (Figure 10
). Therefore, despite the fact that the thematic accuracy of the WSF-2015 layer clearly outperforms any of the currently existing global human settlements masks [43
], it is clear the data still show limitations with respect to a complete detection of all building structures. This can be explained by the spatial resolution of the Sentinel-1 and Landsat imagery used as input data, which restricts the identification of building structures, especially in regions where the settlement pattern is characterised by wide-spread single houses or very small hamlets.
On the other hand, errors of overestimation are reported in validation units where a large number of settlement pixels have been reported in comparison to the amount of actual population, and that they increase as the ration between these two parameters decreases (Figure 4
a). After a visual analysis of VHR satellite imagery, we found that large errors of overestimation are mainly reported in validation units where seaports and industrial complexes exist. Figure 11
shows an example of the population distribution results for an input unit in England with this particular built-up environment. The red line represents the geographical boundary of the input unit used for population disaggregation and the blue lines represent the geographical boundaries of the validation units. Here, it is possible to observe industrial areas in the southern parts of the input unit. These areas capture a large amount of the population counts comparable to high-density residential areas, reporting large errors of overestimation in the validation units. In the selected validation unit, for example the WSF-2015-Density layer reported a higher REE (186.56%) in comparison to the WSF-2015 (154.49%). This does not mean, however, that in every validation unit where this built-up environment exists the binary layer will perform better than the impervious layer. Depending on the extent and geographical boundaries of the input units, industrial or port areas can be mixed with residential areas, influencing the performance of each layer. More detailed information on and discussion of this aspect is provided at the end of this section in the context of the SSC index.
Similar accuracy limitations have been reported in the production of the GHS-POP and the HRSL population datasets [10
]. Even when several local studies have demonstrated that information on the building use has the potential to improve population distribution results [61
], this remains a major source of limitation in the production of global population datasets, as it is not possible to derive detailed semantic information on the building use through remote sensing methodologies. Population datasets such as LandScan and WorldPop integrate land use and land cover covariates to improve their results; however, as mentioned above, this introduces global transferability limitations and applicability restrictions.
For this reason, in this study, we began to analyse the relationship between the inherent characteristics of the underlying built-up environment and the performance of each covariate layer, as an alternative approach that could be used to minimise the errors introduced by the quality and lack of functional characterisation of the input covariate layers. Here, we introduced the SSC index as a globally transferable metric to categorise the input units in terms of the size and coverage of the underlying settlement objects. Our results clearly indicate that WSF-2015-Density layer distributes population with higher accuracies in regions with high SSC index values, reaching improvements up to ~30% over the WSF-2015 layer (Table 8
). For regions with low and medium SSC index values, the performance of each covariate layer varies from country to country. Figure 12
shows the distribution of the SSC index values and the mean SSC index value for the “low” and “medium” SSC classes for each country.
Focusing on the distribution of the “low” SSC class, countries where the WSF-2015 reported in average less RMSE values are also the countries where more than half of the input units reported SSC index values lower than 0.40. In other words, the SSC index values fell below the mean of the “low” SSC class that ranges from >0 to 1. For the “medium” SSC class, the distribution of the SSC index values among countries is relatively similar. The mixture of medium to highly populated cities and rural areas within these input units represent challenging modelling regions where further analyses are required to identify the particular circumstances where one layer outperforms the other.
Nevertheless, it is important to notice that the WSF-2015-Density layer performed better in all three classes of the SSC index for countries such as Germany, France and England, hence suggesting that the overall performance is largely driven by an accurate identification of building structures within the rural areas of each country. In this context, it is expected that limitations derived from the current underestimation of smaller settlements and isolated buildings can be overcome by the future integration of Sentinel-2 data in the production of future WSF datasets, due to its increased spatial resolution [40
As a final note, it is important to mention that, even when the population distribution maps presented in this research have been produced using the most frequently employed population census data, the difficulties in the acquisition of the finest census data, the challenges in integrating census data with spatial boundaries and the uncertainties of population estimates based on statistical projections, are additional sources of errors and uncertainty limiting the accuracy of the population distribution models. Therefore, as stated by Doxsey-Whitfield et al. [6
], acquiring up-to-date global population census data at the highest spatial detail possible should remain a priority for improving global population mapping.
The presented study focused on the cross-comparison of population distribution maps produced using the WSF-2015 and the experimental WSF-2015-Density layers. The main objective was to investigate if higher accuracies in population distribution mapping can be achieved using additional information on the build-up environment, such as the percentage of impervious surface, in comparison to the already established binary approach employed by other population datasets and their baseline settlement layers.
The results of the quantitative assessment showed that the overall accuracies between both covariate layers are comparably similar, with the best accuracy results reported for population distribution maps produced using the finest input census data. Our results indicate that, while both layers distribute the largest percentage of each country’s total population with estimation errors ranging from −25% to 25%, remaining limitations derived from: (i) the incomplete identification of settlement pixels; and (ii) the lack of information on the building use, still introduce large errors of underestimation and errors overestimation in a considerable percentage of the population.
Notwithstanding these limitations, from a comparative point of view, our results have shown that population distribution maps produced on basis of the WSF-2015-Density layer provide a more realistic representation of the spatial distribution of the population, as the heterogeneous allocation of population counts prevents the appearance of artificial patterns between neighbouring administrative census units. Furthermore, it has been demonstrated that the WSF-2015-Density layer produces higher accuracies in high-density built-up environments and is capable to improve the estimation accuracies of the WSF-2015 layer up to ~30%, especially in those countries where a good percentage of building structures have been identified within the rural areas. The fact that the WSF-2015-Density layer is derived from remote sensing approaches that do not require a priori knowledge of the land cover makes it a strong suitable proxy capable to improve global population distribution methodologies, and, as it is not based on local relationships, it has no applicability restrictions in comparison to other existing products. Moreover, it provides global coverage and can be straightforward updated allowing time agreement with census population data, enabling the production of a consistent global population distribution dataset with higher accuracy and spatial resolution than those currently available.
One of the strengths of our study is the implementation of the SSC index, used to investigate the correlation between the built-up environment and the performance of each covariate layer. Our results suggest that higher accuracies in population disaggregation could be achieved with the correct preselection of the input covariate at the input unit level; however, to implement this preselection, additional research is still necessary, as the SSC index cannot provide a complete distinction between the covariate layers in areas with middle SSC index values.
However, in the light of these highly promising results, future research will focus on the validation and open release of the WSF-2015-Density layer, expanding the accuracy assessment of population mapping to other regions of the world, with special focus on arid and semi-arid areas, and comparing the results against other existing global population distribution datasets. Within this outlook, deeper research on the SSC index will also be included, to develop a methodology that can help minimise the inherent distribution errors derived from the quality and functional characterisation of the input covariates, as well as in the production of a new global population dataset.