High-resolution data and maps of material stock, population, and employment in Austria from 1985 to 2018.

Buildings Change aftereffect trend analysis Industrial ecology Infrastructure Landsat Socio-economic metabolism Time series analysis

Journal

Data in brief
ISSN: 2352-3409
Titre abrégé: Data Brief
Pays: Netherlands
ID NLM: 101654995

Informations de publication

Date de publication:
Apr 2023
Historique:
received: 28 11 2022
revised: 09 02 2023
accepted: 13 02 2023
entrez: 13 3 2023
pubmed: 14 3 2023
medline: 14 3 2023
Statut: epublish

Résumé

High-resolution maps of material stocks in buildings and infrastructures are of key importance for studies of societal resource use (social metabolism, circular economy, secondary resource potentials) as well as for transport studies and land system science. So far, such maps were only available for specific years but not in time series. Even for single years, data covering entire countries with high resolution, or using remote-sensing data are rare. Instead, they often have local extent (e.g., [1]), are lower resolution (e.g., [2]), or are based on other geospatial data (e.g., [3]). We here present data on the material stocks in three types of buildings (commercial and industrial, single- and multifamily houses) and three types of infrastructures (roads, railways, other infrastructures) for a 33-year time series for Austria at a spatial resolution of 30 m. The article also presents data on population and employment in Austria for the same time period, at the same spatial resolution. Data were derived with the same method applied in a recent study for Germany [4].

Identifiants

pubmed: 36909013
doi: 10.1016/j.dib.2023.108997
pii: S2352-3409(23)00115-4
pmc: PMC9999155
doi:

Types de publication

Journal Article

Langues

eng

Pagination

108997

Informations de copyright

© 2023 The Author(s).

Déclaration de conflit d'intérêts

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Références

Remote Sens Environ. 2020 Sep 1;246:111810
pubmed: 32884160
PLoS One. 2021 Mar 26;16(3):e0249044
pubmed: 33770133
Remote Sens Environ. 2021 Jan;252:112128
pubmed: 34149105
Environ Sci Technol. 2021 Mar 2;55(5):3368-3379
pubmed: 33600720
PLoS One. 2015 Feb 17;10(2):e0107042
pubmed: 25689585
J Ind Ecol. 2015 Aug;19(4):538-551
pubmed: 27524878

Auteurs

Franz Schug (F)

Geography Department, Humboldt-Universität zu Berlin, Unter den Linden 6, Berlin 10099, Germany.
Integrative Research Institute on Transformations of Human-Environment Systems, Humboldt-Universität zu Berlin, Unter den Linden 6, Berlin 10099, Germany.
SILVIS Lab, Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, 1630 Linden Dr., Madison, WI 53706, USA.

Dominik Wiedenhofer (D)

Institute of Social Ecology, University of Natural Resources and Life Sciences Vienna, Schottenfeldgasse 29, Vienna 1070, Austria.

Helmut Haberl (H)

Institute of Social Ecology, University of Natural Resources and Life Sciences Vienna, Schottenfeldgasse 29, Vienna 1070, Austria.

David Frantz (D)

Geoinformatics - Spatial Data Science, Trier University, Behringstraße 21, Trier 54296, Germany.

Doris Virág (D)

Institute of Social Ecology, University of Natural Resources and Life Sciences Vienna, Schottenfeldgasse 29, Vienna 1070, Austria.

Sebastian van der Linden (S)

Institute of Geography and Geology, University of Greifswald, Friedrich-Ludwig-Jahnstraße 18, Greifswald 17489, Germany.

Patrick Hostert (P)

Geography Department, Humboldt-Universität zu Berlin, Unter den Linden 6, Berlin 10099, Germany.
Integrative Research Institute on Transformations of Human-Environment Systems, Humboldt-Universität zu Berlin, Unter den Linden 6, Berlin 10099, Germany.

Classifications MeSH