"""National hazard exposure maps
"""
import os
import sys
from collections import OrderedDict
import geopandas as gpd
import pandas as pd
import numpy as np
import cartopy.crs as ccrs
import cartopy.io.shapereader as shpreader
import matplotlib.pyplot as plt
from shapely.geometry import LineString
from vtra.utils import *
[docs]def main():
config = load_config()
modes = ['road', 'rail']
hazard_cols = ['hazard_type','climate_scenario','year']
plot_set = [
{
'hazard': 'landslide',
'color': ['#fdd0a2', '#fdae6b', '#fd8d3c', '#e6550d', '#a63603','#d9d9d9'],
'name': 'Landslide'
},
{
'hazard': 'flashflood',
'color': ['#dadaeb', '#bcbddc', '#9e9ac8', '#756bb1', '#54278f','#d9d9d9'],
'name':'Flashflood'
},
{
'hazard': 'flooding',
'color': ['#c6dbef', '#9ecae1', '#6baed6', '#3182bd', '#08519c','#d9d9d9'],
'name': 'Fluvial flooding'
},
{
'hazard': 'typhoon flooding',
'color': ['#c7e9c0', '#a1d99b', '#74c476', '#31a354', '#006d2c','#d9d9d9'],
'name': 'Typhoon flooding'
}
]
national_pth = os.path.join(config['paths']['output'],
'network_stats',
'national_scale_hazards_stats.xlsx')
commune_shp = os.path.join(config['paths']['data'], 'Vietnam_boundaries',
'who_boundaries', 'who_districts.shp')
labels = ['0 to 10', '10 to 20', '20 to 30', '30 to 40', '40 to 100', 'No value']
change_colors = ['#1a9850','#66bd63','#a6d96a','#d9ef8b','#fee08b','#fdae61','#f46d43','#d73027','#d9d9d9']
change_labels = ['< -40','-40 to -20','-20 to -10','-10 to 0','0 to 10','10 to 20','20 to 40','> 40','No change/value']
change_ranges = [(-1e10,-40),(-40,-20),(-20,-10),(-10,0),(0.001,10),(10,20),(20,40),(40,1e10)]
for mode in modes:
region_file = gpd.read_file(commune_shp,encoding='utf-8')
region_file.columns = map(str.lower, region_file.columns)
region_file.rename(columns={'district_i':'district_id'},inplace=True)
all_edge_fail_scenarios = pd.read_excel(national_pth,sheet_name=mode)
all_edge_fail_scenarios = all_edge_fail_scenarios[hazard_cols + ['district_id','probability','length','total_length']]
all_edge_fail_scenarios = all_edge_fail_scenarios.groupby(hazard_cols + ['district_id','probability'])['length','total_length'].sum().reset_index()
all_edge_fail_scenarios['percentage'] = 100.0*all_edge_fail_scenarios['length']/all_edge_fail_scenarios['total_length']
all_edge_fail_scenarios = all_edge_fail_scenarios.groupby(hazard_cols + ['district_id'])['percentage'].max().reset_index()
# Climate change effects
all_edge_fail_scenarios = all_edge_fail_scenarios.set_index(['hazard_type','district_id'])
scenarios = list(set(all_edge_fail_scenarios.index.values.tolist()))
change_tup = []
for sc in scenarios:
perc = all_edge_fail_scenarios.loc[[sc], 'percentage'].values.tolist()
yrs = all_edge_fail_scenarios.loc[[sc], 'year'].values.tolist()
cl = all_edge_fail_scenarios.loc[[sc], 'climate_scenario'].values.tolist()
if 2016 not in yrs:
change_tup += list(zip([sc[0]]*len(cl),[sc[1]]*len(cl),cl,yrs,[1e9]*len(cl)))
elif len(cl) > 1:
vals = list(zip(cl,perc,yrs))
vals = sorted(vals, key=lambda pair: pair[-1])
change = np.array([p for (c,p,y) in vals[1:]]) - vals[0][1]
cl = [c for (c,p,y) in vals[1:]]
yrs = [y for (c,p,y) in vals[1:]]
change_tup += list(zip([sc[0]]*len(cl),[sc[1]]*len(cl),cl,yrs,change))
change_df = pd.DataFrame(change_tup,columns=['hazard_type','district_id','climate_scenario','year','change'])
change_df.to_csv(os.path.join(config['paths']['output'],
'hazard_scenarios',
'{}_climate_change.csv'.format(mode)
), index=False
)
# Change effects
change_df = change_df.set_index(hazard_cols)
scenarios = list(set(change_df.index.values.tolist()))
for sc in scenarios:
hazard_type = sc[0]
climate_scenario = sc[1]
year = sc[2]
percentage = change_df.loc[[sc], 'change'].values.tolist()
communes = change_df.loc[[sc], 'district_id'].values.tolist()
communes_df = pd.DataFrame(list(zip(communes,percentage)),columns=['district_id','change'])
commune_vals = pd.merge(region_file,communes_df,how='left',on=['district_id']).fillna(0)
del percentage,communes,communes_df
ax = get_axes()
plot_basemap(ax, config['paths']['data'],highlight_region=[])
scale_bar(ax, location=(0.8, 0.05))
plot_basemap_labels(ax, config['paths']['data'],plot_international_left=False)
proj = ccrs.PlateCarree()
name = [c['name'] for c in plot_set if c['hazard'] == hazard_type][0]
for iter_,record in commune_vals.iterrows():
geom = record.geometry
region_val = record.change
if region_val:
cl = [c for c in range(len((change_ranges))) if region_val >= change_ranges[c][0] and region_val < change_ranges[c][1]]
if cl:
c = cl[0]
ax.add_geometries([geom], crs=proj, edgecolor='#ffffff',
facecolor=change_colors[c], label=change_labels[c])
else:
ax.add_geometries([geom], crs=proj, edgecolor='#ffffff',
facecolor=change_colors[-1], label=change_labels[-1])
# Legend
legend_handles = []
for c in range(len(change_colors)):
legend_handles.append(mpatches.Patch(color=change_colors[c], zorder=11,label=change_labels[c]))
ax.legend(
handles=legend_handles,
title='Percentage change in exposure',
loc='center left',
fancybox=True,
framealpha=1.0
)
if climate_scenario == 'none':
climate_scenario = 'current'
else:
climate_scenario = climate_scenario.upper()
plt.title('Percentage change for {} {} {}'.format(name,climate_scenario,year), fontsize=14)
output_file = os.path.join(config['paths']['figures'],
'{}-{}-{}-{}-exposure-change-percentage.png'.format(mode.replace(' ',''),name,climate_scenario.replace('.',''),year))
save_fig(output_file)
plt.close()
# Absolute effects
all_edge_fail_scenarios = all_edge_fail_scenarios.reset_index()
all_edge_fail_scenarios = all_edge_fail_scenarios.set_index(hazard_cols)
scenarios = list(set(all_edge_fail_scenarios.index.values.tolist()))
for sc in scenarios:
hazard_type = sc[0]
climate_scenario = sc[1]
year = sc[2]
percentage = all_edge_fail_scenarios.loc[[sc], 'percentage'].values.tolist()
communes = all_edge_fail_scenarios.loc[[sc], 'district_id'].values.tolist()
communes_df = pd.DataFrame(list(zip(communes,percentage)),columns=['district_id','percentage'])
commune_vals = pd.merge(region_file,communes_df,how='left',on=['district_id']).fillna(0)
del percentage,communes,communes_df
ax = get_axes()
plot_basemap(ax, config['paths']['data'],highlight_region=[])
scale_bar(ax, location=(0.8, 0.05))
plot_basemap_labels(ax, config['paths']['data'],plot_international_left=False)
proj = ccrs.PlateCarree()
colors = [c['color'] for c in plot_set if c['hazard'] == hazard_type][0]
name = [c['name'] for c in plot_set if c['hazard'] == hazard_type][0]
for iter_,record in commune_vals.iterrows():
geom = record.geometry
region_val = record.percentage
if region_val:
if region_val > 0 and region_val <= 10:
ax.add_geometries([geom], crs=proj, edgecolor='#ffffff',
facecolor=colors[0], label=labels[0])
elif region_val > 10 and region_val <= 20:
ax.add_geometries([geom], crs=proj, edgecolor='#ffffff',
facecolor=colors[1], label=labels[1])
if region_val > 20 and region_val <= 30:
ax.add_geometries([geom], crs=proj, edgecolor='#ffffff',
facecolor=colors[2], label=labels[2])
elif region_val > 30 and region_val <= 40:
ax.add_geometries([geom], crs=proj, edgecolor='#ffffff',
facecolor=colors[3], label=labels[3])
elif region_val > 40 and region_val <= 100:
ax.add_geometries([geom], crs=proj, edgecolor='#ffffff',
facecolor=colors[4], label=labels[4])
else:
ax.add_geometries([geom], crs=proj, edgecolor='#ffffff',
facecolor=colors[5], label=labels[5])
# Legend
legend_handles = []
for c in range(len(colors)):
legend_handles.append(mpatches.Patch(color=colors[c], label=labels[c]))
ax.legend(
handles=legend_handles,
title='Percentage exposure',
loc='center left',
fancybox=True,
framealpha=1.0
)
if climate_scenario == 'none':
climate_scenario = 'current'
else:
climate_scenario = climate_scenario.upper()
plt.title('Percentage exposure for {} {} {}'.format(name,climate_scenario,year), fontsize=10)
output_file = os.path.join(config['paths']['figures'],
'{}-{}-{}-{}-exposure-percentage.png'.format(mode.replace(' ',''),name,climate_scenario.replace('.',''),year))
save_fig(output_file)
plt.close()
if __name__ == '__main__':
main()