e92558716e
Adjustments made to scripts to read correct settings. Documentation of scenario options improved.
104 lines
3.1 KiB
Python
104 lines
3.1 KiB
Python
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# Build mapping between grid cells and population (total, urban, rural)
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import atlite
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import pandas as pd
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import xarray as xr
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from vresutils import shapes as vshapes
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import geopandas as gpd
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if 'snakemake' not in globals():
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from vresutils import Dict
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import yaml
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snakemake = Dict()
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with open('config.yaml') as f:
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snakemake.config = yaml.load(f)
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snakemake.input = Dict()
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snakemake.output = Dict()
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snakemake.input["urban_percent"] = "data/urban_percent.csv"
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cutout = atlite.Cutout(snakemake.config['atlite']['cutout_name'],
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cutout_dir=snakemake.config['atlite']['cutout_dir'])
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grid_cells = cutout.grid_cells()
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#nuts3 has columns country, gdp, pop, geometry
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#population is given in dimensions of 1e3=k
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nuts3 = gpd.read_file(snakemake.input.nuts3_shapes).set_index('index')
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# Indicator matrix NUTS3 -> grid cells
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I = atlite.cutout.compute_indicatormatrix(nuts3.geometry, grid_cells)
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# Indicator matrix grid_cells -> NUTS3; inprinciple Iinv*I is identity
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# but imprecisions mean not perfect
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Iinv = cutout.indicatormatrix(nuts3.geometry)
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countries = nuts3.country.value_counts().index.sort_values()
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urban_fraction = pd.read_csv(snakemake.input.urban_percent,
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header=None,index_col=0,squeeze=True)/100.
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#fill missing Balkans values
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missing = ["AL","ME","MK"]
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reference = ["RS","BA"]
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urban_fraction = urban_fraction.reindex(urban_fraction.index|missing)
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urban_fraction.loc[missing] = urban_fraction[reference].mean()
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#population in each grid cell
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pop_cells = pd.Series(I.dot(nuts3['pop']))
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#in km^2
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cell_areas = pd.Series(cutout.grid_cells()).map(vshapes.area)/1e6
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#pop per km^2
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density_cells = pop_cells/cell_areas
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#rural or urban population in grid cell
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pop_rural = pd.Series(0.,density_cells.index)
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pop_urban = pd.Series(0.,density_cells.index)
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for ct in countries:
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print(ct,urban_fraction[ct])
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indicator_nuts3_ct = pd.Series(0.,nuts3.index)
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indicator_nuts3_ct[nuts3.index[nuts3.country==ct]] = 1.
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indicator_cells_ct = pd.Series(Iinv.T.dot(indicator_nuts3_ct))
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density_cells_ct = indicator_cells_ct*density_cells
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pop_cells_ct = indicator_cells_ct*pop_cells
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#correct for imprecision of Iinv*I
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pop_ct = nuts3['pop'][indicator_nuts3_ct.index[indicator_nuts3_ct == 1.]].sum()
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pop_cells_ct = pop_cells_ct*pop_ct/pop_cells_ct.sum()
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# The first low density grid cells to reach rural fraction are rural
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index_from_low_d_to_high_d = density_cells_ct.sort_values().index
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pop_ct_rural_b = pop_cells_ct[index_from_low_d_to_high_d].cumsum()/pop_cells_ct.sum() < (1-urban_fraction[ct])
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pop_ct_urban_b = ~pop_ct_rural_b
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pop_ct_rural_b[indicator_cells_ct==0.] = False
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pop_ct_urban_b[indicator_cells_ct==0.] = False
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pop_rural += pop_cells_ct.where(pop_ct_rural_b,0.)
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pop_urban += pop_cells_ct.where(pop_ct_urban_b,0.)
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pop_cells = {"total" : pop_cells}
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pop_cells["rural"] = pop_rural
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pop_cells["urban"] = pop_urban
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for key in pop_cells.keys():
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layout = xr.DataArray(pop_cells[key].values.reshape(cutout.shape),
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[('y', cutout.coords['y']), ('x', cutout.coords['x'])])
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layout.to_netcdf(snakemake.output["pop_layout_"+key])
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