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奥属西里西亚 1837-1910 年行政单位及其社会经济属性历史数据集。

Historical dataset of administrative units with social-economic attributes for Austrian Silesia 1837-1910.

机构信息

Jagiellonian University, Faculty of Geography and Geology, Institute of Geography and Spatial Management, Kraków, Poland.

University of Ostrava, Faculty of Science, Department of Human Geography and Regional Development, Ostrava, Czech Republic.

出版信息

Sci Data. 2020 Jun 30;7(1):208. doi: 10.1038/s41597-020-0546-z.

DOI:10.1038/s41597-020-0546-z
PMID:32606356
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7326999/
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.

摘要

为了在空间和时间上分析社会经济数据,许多学科的科学家都需要历史行政边界。在本文中,我们提供了一组历史数据,包括奥地利西里西亚(位于现代捷克和波兰的一个历史地区)的行政单位边界和示例社会经济属性。该数据集涵盖了近 700 个地籍或政治公社及其分部级别的行政单位边界,是通过对 1837-1841 年期间的历史地图(1:28800)进行手动矢量化获得的。当地级别的单位可以很容易地加入到更高一级的分区,如法院或政治区,时间范围为 1837-1910 年。然后,可以将这些数据与大约在同一时期每 10 年收集一次的统计数据相结合。在质量评估中,分析了制图数据和人口普查数据之间的关系及其可信度。目前的数据集提供了许多将广泛的历史统计数据结合在一起的可能性,以便通过高级分析更好地了解各种人口和经济过程,例如,使用 GIS。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7924/7326999/8cd3b3c010a6/41597_2020_546_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7924/7326999/9ce10497a3e2/41597_2020_546_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7924/7326999/20cd7d8d5bfd/41597_2020_546_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7924/7326999/1dd378a69e77/41597_2020_546_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7924/7326999/7cf427fe4a73/41597_2020_546_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7924/7326999/18958f37d38e/41597_2020_546_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7924/7326999/c119835dd195/41597_2020_546_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7924/7326999/8cd3b3c010a6/41597_2020_546_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7924/7326999/9ce10497a3e2/41597_2020_546_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7924/7326999/20cd7d8d5bfd/41597_2020_546_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7924/7326999/1dd378a69e77/41597_2020_546_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7924/7326999/7cf427fe4a73/41597_2020_546_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7924/7326999/18958f37d38e/41597_2020_546_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7924/7326999/c119835dd195/41597_2020_546_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7924/7326999/8cd3b3c010a6/41597_2020_546_Fig7_HTML.jpg

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