HIPGDAC-ES: historical population grid data compilation for Spain (1900-2021).

IF 6.9 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES Scientific Data Pub Date : 2025-02-16 DOI:10.1038/s41597-025-04533-8
Francisco J Goerlich
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Abstract

Historical population grids are scarce or rather non-existent. This work represents a first effort in this direction. Using historical cadastral data and homogeneous population data at municipal level, we generate, for the whole of Spain, population grids with 100 m × 100 m and 1 km × 1 km resolutions and all census years from 1900 to 2021. These grids are top-down. The methods used are like those employed in the generation of population grids for the entire globe in the last decades by combining satellite imagery with demographic information from censuses. Given the richness of cadastral information, and the possibility of going much further back in time than satellite information, we can generate much older population grids. Although far from perfect, these grids provide a better approximation to the spatial distribution of the population in those years than the simple consideration of the municipal population or the count in the settlements derived from the gazetteers associated with the censuses. The possibility of taking our estimates up to 2021, where we have a bottom-up population grid from the Spanish National Statistical Institute (INE) derived from the 2021 census allows us to validate our methods, albeit only for the most recent dates.

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HIPGDAC-ES:西班牙历史人口网格数据汇编(1900-2021)。
历史上的人口网格很少,甚至根本不存在。这项工作是在这个方向上的第一次努力。利用历史地籍数据和市级同质人口数据,我们为整个西班牙生成了100米× 100米和1公里× 1公里分辨率的人口网格,涵盖了1900年至2021年的所有人口普查年份。这些网格是自上而下的。所使用的方法类似于过去几十年通过将卫星图像与人口普查的人口信息相结合来生成全球人口网格的方法。考虑到地籍信息的丰富性,以及比卫星信息回溯时间更久远的可能性,我们可以生成更古老的人口网格。虽然远非完美,但这些网格比简单地考虑城市人口或从与人口普查有关的地名志中得出的定居点人口数量更能近似地反映那些年的人口空间分布。我们的估计可能会持续到2021年,我们有一个由西班牙国家统计研究所(INE)从2021年人口普查中得出的自下而上的人口网格,这使我们能够验证我们的方法,尽管只是最近的日期。
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来源期刊
Scientific Data
Scientific Data Social Sciences-Education
CiteScore
11.20
自引率
4.10%
发文量
689
审稿时长
16 weeks
期刊介绍: Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.
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