This dataset features three gridded population dadasets of Germany on a 10m grid. The units are people per grid cell.

Datasets

DE_POP_VOLADJ16: This dataset was produced by disaggregating national census counts to 10m grid cells based on a weighted dasymetric mapping approach. A building density, building height and building type dataset were used as underlying covariates, with an adjusted volume for multi-family residential buildings.

DE_POP_TDBP: This dataset is considered a best product, based on a dasymetric mapping approach that disaggregated municipal census counts to 10m grid cells using the same three underyling covariate layers.

DE_POP_BU: This dataset is based on a bottom-up gridded population estimate. A building density, building height and building type layer were used to compute a living floor area dataset in a 10m grid. Using federal statistics on the average living floor are per capita, this bottom-up estimate was created.

Please refer to the related publication for details.

Temporal extent

The building density layer is based on Sentinel-2 time series data from 2018 and Sentinel-1 time series data from 2017 (doi: http://doi.org/10.1594/PANGAEA.920894)

The building height layer is representative for ca. 2015 (doi: 10.5281/zenodo.4066295)

The building types layer is based on Sentinel-2 time series data from 2018 and Sentinel-1 time series data from 2017 (doi: 10.5281/zenodo.4601219)

The underlying census data is from 2018.

Data format

The data come in tiles of 30x30km (see shapefile). The projection is EPSG:3035. The images are compressed GeoTiff files (.tif). There is a mosaic in GDAL Virtual format (.vrt), which can readily be opened in most Geographic Information Systems.

Further information

For further information, please see the publication or contact Franz Schug (franz.schug@geo.hu-berlin.de). A web-visualization of this dataset is available here.

Publication

Schug, F., Frantz, D., van der Linden, S., & Hostert, P. (2021). Gridded population mapping for Germany based on building density, height and type from Earth Observation data using census disaggregation and bottom-up estimates. PLOS ONE. DOI: 10.1371/journal.pone.0249044

Acknowledgements

Census data were provided by the German Federal Statistical Offices.

Funding This dataset was produced with funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (MAT_STOCKS, grant agreement No 741950).

Census data reveals that population density varies noticeably from area to area. Small area census data do a better job depicting where the crowded neighborhoods are. In this map, the yellow areas of highest density range from 30,000 to 150,000 persons per square kilometer. In those areas, if the people were spread out evenly across the area, there would be just 4 to 9 meters between them. Very high density areas exceed 7,000 persons per square kilometer. High density areas exceed 5,200 persons per square kilometer. The last categories break at 3,330 persons per square kilometer, and 1,500 persons per square kilometer.This dataset is comprised of multiple sources. All of the demographic data are from Michael Bauer Research with the exception of the following countries:Australia: Esri Australia and MapData ServicesCanada: Esri Canada and EnvironicsFrance: Esri FranceGermany: Esri Germany and NexigaIndia: Esri India and IndicusJapan: Esri JapanSouth Korea: Esri Korea and OPENmateSpain: Esri España and AISUnited States: Esri Demographics

This dataset features a map of building types for Germany on a 10m grid based on Sentinel-1A/B and Sentinel-2A/B time series. A random forest classification was used to map the predominant type of buildings within a pixel. We distinguish single-family residential buildings, multi-family residential buildings, commercial and industrial buildings and lightweight structures. Building types were predicted for all pixels where building density > 25 %. Please refer to the publication for details.

Temporal extent

Sentinel-2 time series data are from 2018. Sentinel-1 time series data are from 2017.

Data format

The data come in tiles of 30x30km (see shapefile). The projection is EPSG:3035. The images are compressed GeoTiff files (*.tif). Metadata are located within the Tiff, partly in the FORCE domain. There is a mosaic in GDAL Virtual format (*.vrt), which can readily be opened in most Geographic Information Systems. Building type values are categorical, according to the following scheme:

0 - No building

1 - Commercial and industrial buildings

2 - Single-family residential buildings

3 - Lightweight structures

4 - Multi-family residential buildings

Further information

For further information, please see the publication or contact Franz Schug (franz.schug@geo.hu-berlin.de).
A web-visualization of this dataset is available here.

Publication

Schug, F., Frantz, D., van der Linden, S., & Hostert, P. (2021). Gridded population mapping for Germany based on building density, height and type from Earth Observation data using census disaggregation and bottom-up estimates. PLOS ONE. DOI: 10.1371/journal.pone.0249044

Acknowledgements

The dataset was generated by FORCE v. 3.1 (paper, code), which is freely available software under the terms of the GNU General Public License v. >= 3. Sentinel imagery were obtained from the European Space Agency and the European Commission.

Funding
This dataset was produced with funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (MAT_STOCKS, grant agreement No 741950).

A total of 54 Geotiffs in EPSG:4326 (can easily be opened with GIS software such as ArcGIS or QGIS) is provided . These maps are the results of 18 scenarios (S01-S18) proposed to evaluate technical requirements of electricity self-sufficient single family houses in low population density areas in Germany and the Czech Republic. The non-data values inside of the territory of the countries correspond either to pixels with no population or population beyond 1,500 inhabitants per square kilometre (The classification was made using population data from the LUISA project of the Joint Research Centre of the European Commission). The file names can be interpreted in the same way as the following example: S01_Battery_min_cost_no_sc.tif where S01 is the scenario number (01 to 18 are possible), Battery is the type of technology presented in the map (there are also optimally tilted photovoltaic panels named "PV1" and photovoltaic panels with 70° inclination named "PV2"), “min” stands for minimizing and the following word stands for the minimization objective. In this case with “cost” the objective of the scenario is to minimize cost (“battery” for battery size and “pv” for photovoltaic size are also possible). Additionally, there is “no_sc” for case studies that do not consider snow cover and "sc" in case snow cover is considered. Finally some of the files include a year at the end of the file name. This stands for the year of the irradiation and temperature data sets that were used to run the scenario. All files without a year correspond to scenarios calculated with average weather data (Average hours calculated from two decades of data from the COSMO-REA6 regional reanalysis).

Average number of heatwave days per year versus socio economic data - base scenario (1986-2005)

Heat stress exposure maps for the city of Berlin representing the average number of heatwave days per year versus socio economic data per statistical unit. The average number of heatwave days per year has been modelled over the reference period 1986-2005 using the present land use / cover situation for the city.

Exposure mapping variable include the following:

Total population 2013

Population density inhabitants per hectare 2013

Number of inhabitants aged 0 to 17 years 2013

Number of inhabitants aged 18 to 65 years 2013

Number of inhabitants aged +65 years 2013

Number of schools 2014

Number of childcare centers 2014

Number of hospitals 2014

Number of elderly stay facilities 2014

Synthetic populations for regions of the World (SPW) | Germany

Dataset information

A synthetic population of a region as provided here, captures the people of the region with selected demographic attributes, their organization into households, their assigned activities for a day, the locations where the activities take place and thus where interactions among population members happen (e.g., spread of epidemics).

License

CC-BY-4.0

Acknowledgment

This project was supported by the National Science Foundation under the NSF RAPID: COVID-19 Response Support: Building Synthetic Multi-scale Networks (PI: Madhav Marathe, Co-PIs: Henning Mortveit, Srinivasan Venkatramanan; Fund Number: OAC-2027541).

Contact information

Henning.Mortveit@virginia.edu

Identifiers

Statistics

Sources

Files description

Base data files (deu_data_v_0_9.zip)

Derived data files

Validation and measures files

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