Dataset Information

Projecting 1 km-grid population distributions from 2020 to 2100 globally under shared socioeconomic pathways.

ABSTRACT: Spatially explicit population grid can play an important role in climate change, resource management, sustainable development and other fields. Several gridded datasets already exist, but global data, especially high-resolution data on future populations are largely lacking. Based on the WorldPop dataset, we present a global gridded population dataset covering 248 countries or areas at 30 arc-seconds (approximately 1 km) spatial resolution with 5-year intervals for the period 2020-2100 by implementing Random Forest (RF) algorithm. Our dataset is quantitatively consistent with the Shared Socioeconomic Pathways' (SSPs) national population. The spatially explicit population dataset we predicted in this research is validated by comparing it with the WorldPop dataset both at the sub-national and grid level. 3569 provinces (almost all provinces on the globe) and more than 480 thousand grids are taken into verification, and the results show that our dataset can serve as an input for predictive research in various fields.

SUBMITTER: Wang X

PROVIDER: S-EPMC9466344 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature

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Projecting 1 km-grid population distributions from 2020 to 2100 globally under shared socioeconomic pathways.

Wang Xinyu X Meng Xiangfeng X Long Ying Y

Scientific data 20220912 1

Spatially explicit population grid can play an important role in climate change, resource management, sustainable development and other fields. Several gridded datasets already exist, but global data, especially high-resolution data on future populations are largely lacking. Based on the WorldPop dataset, we present a global gridded population dataset covering 248 countries or areas at 30 arc-seconds (approximately 1 km) spatial resolution with 5-year intervals for the period 2020-2100 by implem ...[more]

PMID: 36097271

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Project description:The Shared Socioeconomic Pathways (SSPs) are the new set of alternative futures of societal development that inform global and regional climate change research. They have the potential to foster the integration of socioeconomic scenarios within assessments of future climate-related health impacts. To date, such assessments have primarily superimposed climate scenarios on current socioeconomic conditions only. Until now, the few assessments of future health risks that employed the SSPs have focused on future human exposure-i.e., mainly future population patterns-, neglecting future human vulnerability. This paper first explores the research gaps-mainly linked to the paucity of available projections-that explain such a lack of consideration of human vulnerability under the SSPs. It then highlights the need for projections of socioeconomic variables covering the wide range of determinants of human vulnerability, available at relevant spatial and temporal scales, and accounting for local specificities through sectoral and regional extended versions of the global SSPs. Finally, this paper presents two innovative methods of obtaining and computing such socioeconomic projections under the SSPs-namely the scenario matching approach and an approach based on experts' elicitation and correlation analyses-and applies them to the case of Europe. They offer a variety of possibilities for practical application, producing projections at sub-national level of various drivers of human vulnerability such as demographic and social characteristics, urbanization, state of the environment, infrastructure, health status, and living arrangements. Both the innovative approaches presented in this paper and existing methods-such as the spatial disaggregation of existing projections and the use of sectoral models-show great potential to enhance the availability of relevant projections of determinants of human vulnerability. Assessments of future climate-related health impacts should thus rely on these methods to account for future human vulnerability-under varying levels of socioeconomic development-and to explore its influence on future health risks under different degrees of climate change.

Dataset Information

Projecting 1 km-grid population distributions from 2020 to 2100 globally under shared socioeconomic pathways.

Publications

Projecting 1 km-grid population distributions from 2020 to 2100 globally under shared socioeconomic pathways.

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