ArticleScientific data2020
Historical dataset of administrative units with social-economic attributes for Austrian Silesia 1837-1910.
Article in Scientific data, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Automatic vectorization of historical maps: A benchmark.PloS one · 2024Article
- Gridded land use data for the conterminous United States 1940-2015.Scientific data · 2022Article
- Combining Remote-Sensing-Derived Data and Historical Maps for Long-Term Back-Casting of Urban Extents.Remote sensing · 2021Article
- Fine-grained, spatiotemporal datasets measuring 200 years of land development in the United States.Earth system science data · 2021Article
- Historical dataset of administrative units with social-economic attributes for Austrian Silesia 1837-1910.Scientific data · 2020Article
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Authors and funding
4 authors.
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Abstract
Scientists from many disciplines need historical administrative boundaries in order to analyse socio-economic data in space and time. In this paper, we present a set of historical data consisting of administrative unit boundaries and exemplary socio-economic attributes for Austrian Silesia, an historical region located in modern Czechia and Poland. The dataset covers nearly 700 administrative unit boundaries on the level of cadastral or political communes and their subparts and was acquired through manual vectorisation of historical maps (1:28,800) from the period 1837-1841. The local-level units can be easily joined into higher-level divisions such as court or political districts for the period 1837-1910. The data can then be combined with statistical data collected approximately every 10 years for a similar period. Within the quality assessment, the relations between cartographic and census data and their credibility are analysed. The present dataset provides many possibilities for joining a wide range of historical statistical data to better understand various demographic and economic processes based on advanced analyses, e.g., by using GIS.
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