In [13]:

import geopandas as gpd
from pathlib import Path
import pandas as pd

In [2]:

city = 'Luanda'

In [3]:

# read catchment AOI
aoi = gpd.read_file('AOI/luanda_catchment_level4.shp').to_crs(epsg = 4326)

In [6]:

output_folder = Path('output')
dam_data = gpd.read_file(r"C:\Users\Owner\Documents\Career\World Bank\CRP\data\GOODD\GOOD2_dams.shp")
reservoir_data = gpd.read_file(r"C:\Users\Owner\Documents\Career\World Bank\CRP\data\GRanD\GRanD_reservoirs_v1_3.shp")

In [15]:

dam_data.columns

Out[15]:

Index(['DAM_ID', 'Count_ID', 'Latitud', 'Longitud', 'geometry'], dtype='object')

In [21]:

# filter for dams in AOI
dams_list = []

for i in range(len(aoi)):
    dams_list.append(dam_data.loc[dam_data.within(aoi.loc[i, 'geometry'])])

In [24]:

dams = pd.concat(dams_list)

In [28]:

dams.to_file(output_folder / f'{city.lower()}_dams.shp')

In [25]:

# filter for reservoirs in AOI
reservoirs_list = []

for i in range(len(aoi)):
    reservoirs_list.append(reservoir_data.loc[reservoir_data.within(aoi.loc[i, 'geometry'])])

In [27]:

reservoirs = pd.concat(reservoirs_list)

In [29]:

reservoirs.to_file(output_folder / f'{city.lower()}_reservoirs.shp')