Abstract
The knowledge of the spatial and temporal distribution of human population is vital for the study of cities, disaster risk management or planning of infrastructure. However, information on the distribution of population is often based on place-of-residence statistics from official sources, thus ignoring the changing population densities resulting from human mobility. Existing assessments of spatio-temporal population are limited in their detail and geographical coverage, and the promising mobile-phone records are hindered by issues concerning availability and consistency. Here, we present a multi-layered dasymetric approach that combines official statistics with geospatial data from emerging sources to produce and validate a European Union-wide dataset of population grids taking into account intraday and monthly population variations at 1 km2 resolution. The results reproduce and systematically quantify known insights concerning the spatio-temporal population density structure of large European cities, whose daytime population we estimate to be, on average, 1.9 times higher than night time in city centers.
Highlights
The knowledge of the spatial and temporal distribution of human population is vital for the study of cities, disaster risk management or planning of infrastructure
Dasymetric mapping can be described as a smart areal interpolation method[5] that operates by disaggregating population counts usually available per administrative units or census zones to a finer set of zones using a covariate of population distribution available at higher spatial resolution
To produce the multitemporal population grids, we downscaled monthly stocks of individual population groups at subnational level to grid-cell level using a population group-specific set of spatial covariates
Summary
The knowledge of the spatial and temporal distribution of human population is vital for the study of cities, disaster risk management or planning of infrastructure. We present a multi-layered dasymetric approach that combines official statistics with geospatial data from emerging sources to produce and validate a European Union-wide dataset of population grids taking into account intraday and monthly population variations at 1 km[2] resolution. Dasymetric mapping is often applied to generate population grids or tesselations of regular squared cells with estimates of population Such grids help mitigate the distortions associated with the Modifiable Areal Unit Problem[15] to the extent that they increase the spatial resolution, are less arbitrary, and remove the original areal heterogeneity vis-à-vis the original population enumeration zones. In a recent application in Nigeria, population was estimated independently from national census data, employing a bottom-up modeling approach combining a detailed mapping of built-up areas and a survey of local population densities[21]. For a more thorough discussion of the differences between top-down and bottom-up approaches, please refer to the paper from Wardrop et al.[20], whereas for a recent review more centered on top-down methods for large-scale applications and their fitness for use, we recommend the paper by Leyk et al.[4]
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