Abstract
The Global Human Settlement Population Grid (GHS-POP) the latest released global gridded population dataset based on remotely sensed data and developed by the EU Joint Research Centre, depicts the distribution and density of the total population as the number of people per grid cell. This study aims to assess the GHS-POP data accuracy based on root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) and the correlation coefficient. The study was conducted for Poland and Portugal, countries characterized by different population distribution as well as two spatial resolutions of 250 m and 1 km on the GHS-POP. The main findings show that as the size of administrative zones decreases (from NUTS (Nomenclature of Territorial Units for Statistics) to LAU (local administrative unit)) and the size of the GHS-POP increases, the difference between the population counts reported by the European Statistical Office and estimated by the GHS-POP algorithm becomes larger. At the national level, MAPE ranges from 1.8% to 4.5% for the 250 m and 1 km resolutions of GHS-POP data in Portugal and 1.5% to 1.6%, respectively in Poland. At the local level, however, the error rates range from 4.5% to 5.8% in Poland, for 250 m and 1 km, and 5.7% to 11.6% in Portugal, respectively. Moreover, the results show that for densely populated regions the GHS-POP underestimates the population number, while for thinly populated regions it overestimates. The conclusions of this study are expected to serve as a quality reference for potential users and producers of population density datasets.
Highlights
Reliable information on population numbers in a given geographical area is essential for the rational pursuit of spatial planning as well as economic and social policies
Based on the knowledge obtained from the available literature and the technical specifications of GHS-POP data, we hypothesized that the data are reliable regardless of the adopted reference unit, and that the spatial distribution of the highest overestimation and underestimation of number of people is dispersed and random
The study aims to answer the following research questions: 1. What is the accuracy of the GHS-POP? Do the results of the data accuracy assessment change depending on the reference unit size? Which administrative level presents the highest errors? The answer to this question is based on the analysis of mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE) calculated for reference units at six administrative levels
Summary
Reliable information on population numbers in a given geographical area is essential for the rational pursuit of spatial planning as well as economic and social policies. Tobler used satellite remote sensing to study urban populations and found a strong statistical correlation between the settlement radius and number of inhabitants of various cities [13]. This approach is applicable at large regional scales with low-resolution imagery, and it is based on the ‘allometric’ modeling of a direct mathematical relationship between the population of an urban area and its size. This approach was included in studies by Lo and Welch [14] for Chinese cities and Stern [15] for Sudanese villages. Reliable information on the spatial distribution of people is important for local and regional communities and could have a positive impact on their quality of life and health as well as the surrounding environment
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