martacki / pypsa-ua_gdp_data Goto Github PK

This short script is developed as pre-processing workstep to be used in an extended setting of pypsa-eur. Additional to pypsa-eur's default country settings, it covers the Ukraine and the Republic of Moldova. Nedded to make assumptions to distribute electricity demand.

Input:

• GDP_PPP_30arcsec_v3.nc: raw dataset. Available at: [M. Kummu, M. Taka, J. H. A. Guillaume. (2020), Data from: Gridded global datasets for Gross Domestic Product and Human Development Index over 1990-2015, Dryad, Dataset. doi: https://doi.org/10.5061/dryad.dk1j0] - PLEASE DOWNLOAD YOURSELF! -
• ppp_2013_1km_Aggregated.tif: raw dataset. Available at: [WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076). doi: 10.5258/SOTON/WP00647] - PLEASE DOWNLOAD YOURSELF! -
• regions_onshore.geojson: pypsa-eur output file, available after executing the workflow in pypsa-eur/resources/regions_onshore.geojson

Output:

• ppp_2013_1km_Aggregated_and_GDP_per_capita_PPP_1990_2015_v2_mapped.csv: file that maps the pypsa-eur onshore regions in UA and MD to it's associated 0.6GDP+0.4POP value.

(previously) GDP_PPP_30arcsec_v3_mapped.csv: file that maps the pypsa-eur onshore regions in UA and MD to it's associated GDP value.

Requirements:

• python
• numpy
• xarray
• rioxarray
• geopandas
• pandas
• xagg