def calculate_vulnerability(iso3, country_folder, country_bounds, pop_folder, pop_files):
''' The hospitalization rates listed in the vul_def dictionary (at the top of this script) are
combined to create a vulnerability layer
INPUTS
iso3 [string] - iso3 code for country of interest
country_folder [string] - path to output folder
country_bounds [geopandas dataframe] - boundary within which to extract
pop_files [list of file paths] - list of global demographic rasters which are clipped out with country_bounds
RETURNS
NA - creates a file called WP2020_vulnerability_map.tif in country_folder
'''
# clip out the temporary vulnerability metrics
out_vulnerability = os.path.join(country_folder, "WP2020_vulnerability_map.tif")
if not os.path.exists(out_vulnerability):
wp_files = []
if not os.path.exists(country_folder):
os.makedirs(country_folder)
for pFile in pop_files:
curR = rasterio.open((os.path.join(pop_folder, pFile)))
out_file = os.path.join(country_folder, pFile)
if not os.path.exists(out_file):
rMisc.clipRaster(curR, country_bounds, out_file)
wp_files.append(out_file)
#Calculate vulnerability
wp_file_objects = [vulmap.wp_demographics(os.path.join(country_folder, x)) for x in wp_files]
vul = vulmap.wp_vulnerability(wp_file_objects, vul_def)
vul.calculate_vulnerability()
vul.combine_results(out_vulnerability, '')
for f in wp_files:
os.remove(f)
def extract_data(inG, inG1, inG2, inL, inR):
country_folder = os.path.join(output_folder, iso3)
adm0_file = os.path.join(country_folder, "adm0.shp")
adm1_file = os.path.join(country_folder, "adm1.shp")
adm2_file = os.path.join(country_folder, "adm2.shp")
lc_file = os.path.join(country_folder, "LC.tif")
if not os.path.exists(country_folder):
os.makedirs(country_folder)
country_bounds = inG.loc[inG['ISO3'] == iso3].to_crs({'init': 'epsg:4326'})
if not os.path.exists(adm0_file):
country_bounds.to_file(adm0_file)
if not os.path.exists(adm1_file):
try:
country_adm1 = inG1.loc[inG1['ISO3'] == iso3].to_crs(
{'init': 'epsg:4326'})
country_adm1.to_file(adm1_file)
except:
misc.tPrint("%s Could not extract ADMIN 1" % iso3)
if not os.path.exists(adm2_file):
try:
country_adm2 = inG2.loc[inG2['ISO3'] == iso3].to_crs(
{'init': 'epsg:4326'})
country_adm2.to_file(adm2_file)
except:
misc.tPrint("%s Could not extract ADMIN 2" % iso3)
if not os.path.exists(lc_file):
rMisc.clipRaster(inL, gpd.read_file(adm0_file), lc_file)
calculate_vulnerability(iso3, country_folder, country_bounds, pop_folder,
pop_files)
misc.tPrint("***%s Calculated Vulnerability" % iso3)
try:
create_urban_data(iso3,
country_folder,
country_bounds,
inR,
calc_urban=False)
misc.tPrint("***%s Calculated Urban Extents" % iso3)
except:
misc.tPrint("%s errored on HD clusters" % iso3)
try:
create_urban_data(iso3,
country_folder,
country_bounds,
inR,
calc_urban=True,
calc_hd_urban=False)
except:
misc.tPrint("%s errored on all clusters" % iso3)
def create_urban_data(iso3,
country_folder,
country_bounds,
inR,
calc_urban=True,
calc_hd_urban=True,
verbose=False,
urb_dens=300,
urb_pop=5000,
hd_urb_dens=1500,
hd_urb_pop=50000):
''' Extract urban areas from gridded population data following the EC density methodology
INPUTS:
iso3 [string] - iso3 code for country of interest
country_folder [string] - path to output folder
country_bounds [geopandas dataframe] - boundary within which to extract
inR [rasterio object] - population datasets from which country specific pop layer is extracted
[optional] calc_urban [boolean] - whether to calculate urban (lower density) extents
[optional] calc_hd_urban [boolean] - whether to calculate hd urban (higher density) extents
[optional] urb_dens/hd_urb_dens [int] - population density threshold for calculating urban areas IN THE PER PIXEL UNITS OF inR
[optional] urb_pop/hd_urb_pop [int] - total population threshold for calculating urban areas
RETURNS:
NA - the function calculates a number of files in the folder country_folder:
-- WP_2020_1km.tif: population dataaset clipped from inR
-- urban_areas.shp: vectorization of urban areas calculation
-- urban_areas_hd.shp: vectorization of high density urban areas calculation
-- urban_fishnets/URBAN_XX.shp: a 1 km fishnet is created over the five highest population areas in urban_areas.shp
-- hd_urban_fishnets/HD_URBAN_XX.shp: a 1 km fishnet is created over the five highest population areas in hd_urban_areas.shp
'''
country_pop = os.path.join(country_folder, "WP_2020_1km.tif")
urban_pop = os.path.join(country_folder, "WP_2020_1km_urban_pop.tif")
hd_urban_pop = os.path.join(country_folder, "WP_2020_1km_hd_urban_pop.tif")
urb_bounds = os.path.join(country_folder, "urban_areas.shp")
hd_urb_bounds = os.path.join(country_folder, "urban_areas_hd.shp")
urban_fishnets = os.path.join(country_folder, "urban_fishnets")
hd_urban_fishnets = os.path.join(country_folder, "hd_urban_fishnets")
# Clip pop raster
if not os.path.exists(country_pop):
rMisc.clipRaster(inR, country_bounds, country_pop)
if not os.path.exists(urban_pop) and calc_urban:
urbanR = urban.urbanGriddedPop(country_pop)
urban_vec = urbanR.calculateUrban(densVal=urb_dens,
totalPopThresh=urb_pop,
smooth=True,
queen=False,
raster_pop=urban_pop)
urban_vec.to_file(urb_bounds)
if not os.path.exists(hd_urban_pop) and calc_hd_urban:
urbanR = urban.urbanGriddedPop(country_pop)
urban_vec = urbanR.calculateUrban(densVal=hd_urb_dens,
totalPopThresh=hd_urb_pop,
smooth=True,
queen=True,
raster_pop=urban_pop)
urban_vec.to_file(hd_urb_bounds)
#Generate Fishnets
if not os.path.exists(urban_fishnets):
os.makedirs(urban_fishnets)
if not os.path.exists(hd_urban_fishnets):
os.makedirs(hd_urban_fishnets)
if calc_urban:
create_fishnet(urb_bounds, urban_fishnets, "URBAN")
if calc_hd_urban:
create_fishnet(hd_urb_bounds, hd_urban_fishnets, "HD_URBAN")