Abstract
Knowledge about the past, current and future distribution of the human population is fundamental for tackling many global challenges. Censuses are used to collect information about population within a specified spatial unit. The spatial units are usually arbitrarily defined and their numbers, size and shape tend to change over time. These issues make comparisons between areas and countries difficult. We have in related work proposed that the shape of the lit area derived from nighttime lights, weighted by its intensity can be used to analyse characteristics of the population distribution, such as the mean centre of population. We have processed global nighttime lights data for the period 1992–2013 and derived centroids for administrative levels 0–2 of the Database of Global Administrative Areas, corresponding to nations and two levels of sub-divisions, that can be used to analyse patterns of global or local population changes. The consistency of the produced dataset was investigated and distance between true population centres and derived centres are compared using Swedish census data as a benchmark.
Highlights
Background & SummaryUneven population distribution and population change trends have important effects on society and landscapes
Current and future distribution of the human population is fundamental for tackling many global challenges such as food security, climate change adaptation, and poverty reduction
Censuses constitute the typical way of collecting information about a population within a tract or any spatial unit
Summary
Uneven population distribution and population change trends have important effects on society and landscapes. Nighttime lights imagery has emerged as a popular proxy for human activity and has been used to simulate human population distribution[2] and even to generate detailed population estimates at the pixel level[3,4,5] Such data has the potential to address some of the previously discussed limitations of census data due to its high spatial and temporal resolution and disaggregated nature. We have processed global nighttime lights data for the period 1992–2013 and derived annual centroids for administrative levels 0–2 of the GADM (Database of Global Administrative Areas), corresponding to nations and two levels of sub-divisions This data is consistent in both time and space and can be used to analyse patterns of global or local population distribution changes without many of the typical caveats associated with traditional population data. These limitations are important to consider depending on the study area and scale of analysis
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