def get_distritos():
distritos = loadShapeFile('data/distritos/DISTRITOS_20200101_MADRID.shp')
pob_file = pd.read_excel('data/distritos/dist_pob.xls',
header=4,
usecols=['Distrito',
'Total']).rename(columns={
'Total': 'Inhabitants',
'Distrito': 'Name'
})
pob_file['Name'] = pob_file['Name'].str.capitalize()
distritos = distritos.join(pob_file)
return distritos
def get_barrios():
barrios = loadShapeFile('data/barrios/barrios.shp')
names_b = []
for nom in barrios['DESBDT']:
name = nom.split(' ', 1)[-1]
names_b.append(name)
barrios['Name'] = np.array(names_b)
pob_file = pd.read_excel(
'data/barrios/barrios_pob.xls', header=4,
usecols=['Total']).rename(columns={'Total': 'Inhabitants'})
barrios = barrios.join(pob_file)
barrios = barrios.drop(['DESBDT'], axis=1)
return barrios
def get_secciones_censales():
census = pd.read_csv('data/CensoMadrid_2019.csv', sep=';',
header=0).rename(columns={
'Total': 'Inhabitants',
'Edad (grupos quinquenales)': 'Edad'
})
census = census.loc[census['Sexo'] == 'Ambos Sexos'].loc[
census['Sección'] != 'TOTAL'].loc[census['Edad'] == 'Total']
census = census.set_index('Sección')
census['Inhabitants'] = pd.to_numeric(census['Inhabitants'].str.replace(
'.', ''))
# Combine the census population data with the ShapeFile of seccionesCensales
seccionesCensales_shp = loadShapeFile(
'data/secciones_censales/SECC_CE_20200101_MADRID.shp')
census = census['Inhabitants']
census = seccionesCensales_shp.join(census, on='CUSEC')
return census
def get_distritos(census):
distritos = loadShapeFile('data/distritos/DISTRITOS_20200101_MADRID.shp')
dist_names = [
'Centro', 'Arganzuela', 'Retiro', 'Salamanca', 'Chamartín', 'Tetuán',
'Chamberi', 'Fuencarral-El Pardo', 'Moncloa-Aravaca', 'Latina',
'Carabanchel', 'Usera', 'Puente de Vallecas', 'Moratalaz',
'Ciudad Lineal', 'Hortaleza', 'Villaverde', 'Villa de Vallecas',
'Vicálvaro', 'San-Blas Canillejas', 'Barajas'
]
dist_inhabitants = [0] * 21
# Use the inhabitants from the census areas and their CDIS value to get inhabitants in districts
for i in range(len(census.index)):
dist_inhabitants[int(census['CDIS'][i]) -
1] += census['Inhabitants'][i]
distritos['Name'] = dist_names
distritos['Inhabitants'] = dist_inhabitants
return distritos
def get_secciones_censales():
columns = pd.read_excel(
'data/secciones_censales/2801_CensoMadrid2019.xls').loc[5]
census = pd.read_excel('data/secciones_censales/2801_CensoMadrid2019.xls',
names=columns,
header=8,
index_col=0)
census.index = census.index.str.strip()
census = census.iloc[:4417]
census = census.groupby(census.index).sum()
# Combine the census population data with the ShapeFile of seccionesCensales
seccionesCensales_shp = loadShapeFile(
'data/secciones_censales/SECC_CE_20200101_MADRID.shp')
census_inhabitants = census.rename({'Total': 'Inhabitants'},
axis=1)['Inhabitants']
census_inhabitants = seccionesCensales_shp.join(census_inhabitants,
on='CUSEC')
return census_inhabitants
type=str,
default='data/sentinel/sent_B08.jp2',
help=
"String containing the location to the sentinel2 image in the B08 band."
)
args = parser.parse_args()
return args
if __name__ == '__main__':
args = arg_parse()
green_build_join(args.all_spaces, args.merge_green_spaces,
args.merge_build_spaces)
buildSpaces = loadShapeFile('data/buildSpaces.shp')
greenSpaces = loadShapeFile('data/greenSpaces.shp')
if args.green_proximity:
greenSpaces = greenProxInd(greenSpaces)
greenSpaces.to_file('data/greenSpaces.shp', driver='ESRI Shapefile')
if args.get_census:
print('Starting calculations for censal areas')
census = get_secciones_censales()
census = indexes_calc(census, greenSpaces, buildSpaces)
census = proxToSection(census, greenSpaces)
NDVI_img(args.sent_B04, args.sent_B08, args.NDVI_img_out)
census = calc_NDVI_section(census, args.NDVI_img_out)
# Save results into a new ShapeFile
census = geopandas.GeoDataFrame(census, geometry='geometry')
ndviImage = rasterio.open(img_out,'w',
driver='Gtiff',
width=band4.width,
height = band4.height,
count=1, crs=band4.crs,
transform=band4.transform,
dtype='float64')
ndviImage.write(ndvi,1)
ndviImage.close()
if __name__ == '__main__':
# Load ShapeFiles of green areas and censal areas
columns = pd.read_excel('data/secciones_censales/2801_CensoMadrid2019.xls').loc[5]
census = pd.read_excel('data/secciones_censales/2801_CensoMadrid2019.xls', names=columns, header=8, index_col=0)
census.index = census.index.str.strip()
census = census.iloc[:4417]
census = census.groupby(census.index).sum()
# Combine the census population data with the ShapeFile of seccionesCensales
seccionesCensales_shp = loadShapeFile('data/secciones_censales/SECC_CE_20200101_MADRID.shp')
section = census.rename({'Total': 'Inhabitants'}, axis=1)['Inhabitants']
section = seccionesCensales_shp.join(section, on='CUSEC')
img_NDVI = 'out/ndviImage.tiff'
b4 = 'data/sentinel/sent_B04.jp2'
b8 = 'data/sentinel/sent_B08.jp2'
NDVI_img(b4, b8, img_NDVI)
calc_NDVI_section(section, img_NDVI)
total = 0
num = 0
greenInSection = [
greenSpaces['proxIndex'][pos]
for pos in idx.intersection(section.bounds)
]
for ind in greenInSection:
total += ind
num += 1
try:
prox = total / num
except ZeroDivisionError:
prox = 0
prox_avg.append(prox)
sectionAreas['prox_avg'] = np.array(prox_avg)
return sectionAreas
if __name__ == '__main__':
# Load ShapeFiles of green areas and section areas
greenSpaces = loadShapeFile('data/greenSpaces.shp')
sectionAreas = loadShapeFile('out/indexes.shp')
tic = process_time()
sect = proxToSection(sectionAreas, greenSpaces)
toc = process_time()
print('Time:' + str(toc - tic))
#greenSpaces.to_file('data/greenSpaces.shp', driver='ESRI Shapefile')
def green_build_join(all_spaces, merge_green_spaces, merge_build_spaces):
if all_spaces:
nums = []
buildSpaces = pd.DataFrame([])
greenSpaces = pd.DataFrame([])
for i in range(21):
if i < 9:
nums.append('D0' + str(i + 1))
else:
nums.append('D' + str(i + 1))
print(
'Creating build spaces and green spaces from all the census areas')
for folder in nums:
is_green = 1
is_build = 1
const = []
greens = []
try:
buildingsUnderConstruction_shp = loadShapeFile(
'data/carto2/' + folder +
'/03_EDIFICIO_EN_CONSTRUCCION_P.shp')
const.append(buildingsUnderConstruction_shp)
except:
is_build = 0
try:
buildings_shp = loadShapeFile('data/carto2/' + folder +
'/03_EDIFICIO_P.shp')
const.append(buildings_shp)
except:
is_build = 0
try:
undefinedBuildings_shp = loadShapeFile(
'data/carto2/' + folder + '/03_EDIFICIO_INDEFINIDO_P.shp')
const.append(undefinedBuildings_shp)
except:
is_build = 0
if buildSpaces.empty:
buildSpaces = pd.concat([
buildings_shp, buildingsUnderConstruction_shp,
undefinedBuildings_shp
])
else:
if is_build:
buildSpaces = pd.concat([
buildSpaces, buildings_shp,
buildingsUnderConstruction_shp, undefinedBuildings_shp
])
else:
for shp in const:
buildSpaces = pd.concat([buildSpaces, shp])
try:
urbanGardens_shp = loadShapeFile('data/carto2/' + folder +
'/11_HUERTO_URBANO_P.shp')
greens.append(urbanGardens_shp)
except:
is_green = 0
try:
gardenAreas_shp = loadShapeFile('data/carto2/' + folder +
'/11_ZONA_AJARDINADA_P.shp')
greens.append(gardenAreas_shp)
except:
is_green = 0
try:
gardenInPatios_shp = loadShapeFile(
'data/carto2/' + folder +
'/11_ZONA_AJARDINADA_SOBRE_PATIO_P.shp')
greens.append(gardenInPatios_shp)
except:
is_green = 0
if greenSpaces.empty:
greenSpaces = pd.concat([gardenAreas_shp, gardenInPatios_shp])
else:
if is_green:
greenSpaces = pd.concat([
greenSpaces, gardenAreas_shp, urbanGardens_shp,
gardenInPatios_shp
])
else:
for shp in greens:
greenSpaces = pd.concat([greenSpaces, shp])
buildSpaces.to_file('data/buildSpaces.shp', driver='ESRI Shapefile')
greenSpaces.to_file('data/greenSpaces.shp', driver='ESRI Shapefile')
else:
if merge_green_spaces:
# Load shapefiles of green and constructed areas
urbanGardens_shp = loadShapeFile(
'data/Carto_1000/11_HUERTO_URBANO_P.shp')
gardenAreas_shp = loadShapeFile(
'data/Carto_1000/11_ZONA_AJARDINADA_P.shp')
gardenInPatios_shp = loadShapeFile(
'data/Carto_1000/11_ZONA_AJARDINADA_SOBRE_PATIO_P.shp')
greenSpaces = pd.concat(
[urbanGardens_shp, gardenAreas_shp, gardenInPatios_shp])
greenSpaces.to_file('data/greenSpaces.shp',
driver='ESRI Shapefile')
if merge_build_spaces:
buildings_shp = loadShapeFile(
'data/Carto_1000/03_EDIFICIO_EN_CONSTRUCCION_P.shp')
buildingsUnderConstruction_shp = loadShapeFile(
'data/Carto_1000/03_EDIFICIO_INDEFINIDO_P.shp')
undefinedBuildings_shp = loadShapeFile(
'data/Carto_1000/03_EDIFICIO_P.shp')
buildSpaces = pd.concat([
buildings_shp, buildingsUnderConstruction_shp,
undefinedBuildings_shp
])
buildSpaces.to_file('data/buildSpaces.shp',
driver='ESRI Shapefile')