"""Road network risks and adaptation maps
"""
import os
import sys
from collections import OrderedDict
import ast
import numpy as np
import geopandas as gpd
import pandas as pd
import cartopy.crs as ccrs
import cartopy.io.shapereader as shpreader
import matplotlib.pyplot as plt
from shapely.geometry import LineString
from atra.utils import *
[docs]def main():
config = load_config()
data_path = config['paths']['data']
output_path = config['paths']['output']
hazard_cols = ['climate_scenario','year']
duration = 30
hazard_set = [
{
'hazard': 'fluvial flooding',
'name': 'Fluvial flooding'
},
{
'hazard': 'pluvial flooding',
'name': 'Pluvial flooding'
}
]
change_colors = ['#1a9850','#66bd63','#a6d96a','#d9ef8b','#fee08b','#fdae61','#f46d43','#d73027','#969696']
change_labels = ['< -100','-100 to -50','-50 to -10','-10 to 0','0 to 10','10 to 50','50 to 100',' > 100','No change/value']
change_ranges = [(-1e10,-100),(-100,-50),(-50,-10),(-10,0),(0.001,10),(10,50),(50,100),(100,1e10)]
risk_set = [
{
'column': 'max_eael',
'title': 'Max EAEL',
'legend_label': "EAEL (million USD)",
'divisor': 1000000,
'significance': 0
},
{
'column': 'ead',
'title': 'EAD',
'legend_label': "EAD (million USD)",
'divisor': 1000000,
'significance': 0
},
{
'column': 'max_risk',
'title': 'Total Risk',
'legend_label': "Risk (million USD)",
'divisor': 1000000,
'significance': 0
},
]
road_file = gpd.read_file(os.path.join(data_path, 'network',
'road_edges.shp'),encoding='utf-8')
road_file = road_file[road_file['road_type'] == 'national']
region_file = gpd.read_file(os.path.join(data_path, 'network',
'bridge_edges.shp'),encoding='utf-8')
fail_scenarios = pd.read_csv(os.path.join(output_path,
'risk_results',
'bridge_combined_climate_risks.csv'))
fail_scenarios['max_eael'] = duration*fail_scenarios['max_eael_per_day']
fail_scenarios['max_risk'] = fail_scenarios['max_eael'] + fail_scenarios['ead']
# all_edge_fail_scenarios = fail_scenarios[fail_scenarios['max_eael'] > 0]
all_edge_fail_scenarios = fail_scenarios
# Climate change effects
all_edge_fail_scenarios = all_edge_fail_scenarios.set_index(['bridge_id'])
scenarios = list(set(all_edge_fail_scenarios.index.values.tolist()))
change_tup = []
for sc in scenarios:
eael = all_edge_fail_scenarios.loc[[sc], 'max_risk'].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:
for e in range(len(eael)):
if eael[e] > 0:
# change_tup += list(zip([sc[0]]*len(cl),[sc[1]]*len(cl),cl,yrs,[0]*len(cl),eael,[1e9]*len(cl)))
change_tup += [(sc,cl[e],yrs[e],0,eael[e],1e9)]
elif len(yrs) > 1:
vals = list(zip(cl,eael,yrs))
vals = sorted(vals, key=lambda pair: pair[-1])
change = 100.0*(np.array([p for (c,p,y) in vals[1:]]) - vals[0][1])/vals[0][1]
cl = [c for (c,p,y) in vals[1:]]
yrs = [y for (c,p,y) in vals[1:]]
fut = [p for (c,p,y) in vals[1:]]
change_tup += list(zip([sc]*len(cl),cl,yrs,[vals[0][1]]*len(cl),fut,change))
change_df = pd.DataFrame(change_tup,columns=['bridge_id','climate_scenario','year','current','future','change']).fillna(np.inf)
change_df = change_df[change_df['change'] != np.inf]
change_df.to_csv(os.path.join(output_path,
'network_stats',
'national_bridge_{}_days_risks_climate_change_combined.csv'.format(duration)
), index=False
)
# Change effects
change_df = change_df.set_index(hazard_cols)
scenarios = list(set(change_df.index.values.tolist()))
for sc in scenarios:
climate_scenario = sc[0]
year = sc[1]
percentage = change_df.loc[[sc], 'change'].values.tolist()
edges = change_df.loc[[sc], 'bridge_id'].values.tolist()
edges_df = pd.DataFrame(list(zip(edges,percentage)),columns=['bridge_id','change'])
edges_vals = pd.merge(region_file,edges_df,how='left',on=['bridge_id']).fillna(0)
del percentage,edges,edges_df
proj_lat_lon = ccrs.PlateCarree()
ax = get_axes()
plot_basemap(ax, data_path)
scale_bar(ax, location=(0.8, 0.05))
plot_basemap_labels(ax, data_path, include_regions=True)
ax.add_geometries(
list(road_file.geometry),
crs=proj_lat_lon,
linewidth=1.0,
edgecolor='#969696',
facecolor='none',
zorder=5
)
# name = [c['name'] for c in hazard_set if c['hazard'] == hazard_type][0]
for record in edges_vals.itertuples():
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_lat_lon,linewidth=1.5,edgecolor=change_colors[c],facecolor='none',zorder=8)
ax.add_geometries([geom.buffer(0.1)],crs=proj_lat_lon,linewidth=0,facecolor=change_colors[c],edgecolor='none',zorder=8)
else:
# ax.add_geometries([geom], crs=proj_lat_lon, linewidth=1.5,edgecolor=change_colors[-1],facecolor='none',zorder=7)
ax.add_geometries([geom.buffer(0.1)], crs=proj_lat_lon, linewidth=0,facecolor=change_colors[-1],edgecolor='none',zorder=7)
# Legend
legend_handles = []
for c in range(len(change_colors)):
legend_handles.append(mpatches.Patch(color=change_colors[c], label=change_labels[c]))
ax.legend(
handles=legend_handles,
title='Percentage change in Risks',
loc=(0.55,0.2),
fancybox=True,
framealpha=1.0
)
if climate_scenario == 'none':
climate_scenario = 'current'
else:
climate_scenario = climate_scenario.upper()
title = 'Percentage change in Flood Risks for {} {}'.format(climate_scenario.replace('_',' ').title(),year)
print(" * Plotting {}".format(title))
plt.title(title, fontsize=10)
output_file = os.path.join(config['paths']['figures'],
'national-bridges-{}-{}-{}-days-risks-change-percentage.png'.format(climate_scenario.replace('-',' ').title(),
year,duration))
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:
climate_scenario = sc[0]
if climate_scenario == 'none':
climate_scenario = 'baseline'
else:
climate_scenario = climate_scenario.upper()
year = sc[1]
max_risk = all_edge_fail_scenarios.loc[[sc], 'max_risk'].values.tolist()
max_eael = all_edge_fail_scenarios.loc[[sc], 'max_eael'].values.tolist()
ead = all_edge_fail_scenarios.loc[[sc], 'ead'].values.tolist()
edges = all_edge_fail_scenarios.loc[[sc], 'bridge_id'].values.tolist()
edges_df = pd.DataFrame(list(zip(edges,max_risk,max_eael,ead)),columns=['bridge_id','max_risk','max_eael','ead'])
edges_vals = pd.merge(region_file,edges_df,how='left',on=['bridge_id']).fillna(0)
del edges_df
for c in range(len(risk_set)):
proj_lat_lon = ccrs.PlateCarree()
ax = get_axes()
plot_basemap(ax, data_path)
scale_bar(ax, location=(0.8, 0.05))
plot_basemap_labels(ax, data_path, include_regions=True)
ax.add_geometries(
list(road_file.geometry),
crs=proj_lat_lon,
linewidth=1.0,
edgecolor='#969696',
facecolor='none',
zorder=5
)
# generate weight bins
column = risk_set[c]['column']
weights = [record[column] for iter_, record in edges_vals.iterrows()]
max_weight = max(weights)
width_by_range = generate_weight_bins(weights, width_step=0.04, n_steps=5)
road_geoms_by_category = {
'national': [],
'none':[]
}
for iter_,record in edges_vals.iterrows():
geom = record.geometry
val = getattr(record,column)
if val > 0:
cat = 'national'
else:
cat = 'none'
buffered_geom = None
for (nmin, nmax), width in width_by_range.items():
if nmin <= val and val < nmax:
buffered_geom = geom.buffer(width)
if buffered_geom is not None:
road_geoms_by_category[cat].append(buffered_geom)
else:
print("Feature was outside range to plot", iter_)
styles = OrderedDict([
('national', Style(color='#e41a1c', zindex=9, label='National')), # red
('none', Style(color='#969696', zindex=6, label='No hazard exposure/effect'))
])
for cat, geoms in road_geoms_by_category.items():
cat_style = styles[cat]
ax.add_geometries(
geoms,
crs=proj_lat_lon,
linewidth=0,
facecolor=cat_style.color,
edgecolor='none',
zorder=cat_style.zindex
)
# name = [h['name'] for h in hazard_set if h['hazard'] == hazard_type][0]
x_l = -62.4
x_r = x_l + 0.4
base_y = -42.1
y_step = 0.8
y_text_nudge = 0.2
x_text_nudge = 0.2
ax.text(
x_l,
base_y + y_step - y_text_nudge,
risk_set[c]['legend_label'],
horizontalalignment='left',
transform=proj_lat_lon,
size=10)
divisor = risk_set[c]['divisor']
significance_ndigits = risk_set[c]['significance']
max_sig = []
for (i, ((nmin, nmax), line_style)) in enumerate(width_by_range.items()):
if round(nmin/divisor, significance_ndigits) < round(nmax/divisor, significance_ndigits):
max_sig.append(significance_ndigits)
elif round(nmin/divisor, significance_ndigits+1) < round(nmax/divisor, significance_ndigits+1):
max_sig.append(significance_ndigits+1)
elif round(nmin/divisor, significance_ndigits+2) < round(nmax/divisor, significance_ndigits+2):
max_sig.append(significance_ndigits+2)
else:
max_sig.append(significance_ndigits+3)
significance_ndigits = max(max_sig)
for (i, ((nmin, nmax), width)) in enumerate(width_by_range.items()):
y = base_y - (i*y_step)
line = LineString([(x_l, y), (x_r, y)]).buffer(width)
ax.add_geometries(
[line],
crs=proj_lat_lon,
linewidth=0,
edgecolor='#000000',
facecolor='#000000',
zorder=2)
if nmin == max_weight:
value_template = '>{:.' + str(significance_ndigits) + 'f}'
label = value_template.format(
round(max_weight/divisor, significance_ndigits))
else:
value_template = '{:.' + str(significance_ndigits) + \
'f}-{:.' + str(significance_ndigits) + 'f}'
label = value_template.format(
round(nmin/divisor, significance_ndigits), round(nmax/divisor, significance_ndigits))
ax.text(
x_r + x_text_nudge,
y - y_text_nudge,
label,
horizontalalignment='left',
transform=proj_lat_lon,
size=10)
if climate_scenario == 'none':
climate_scenario = 'Current'
title = 'Bridges ({}) {} {}'.format(risk_set[c]['title'],climate_scenario.replace('_',' ').title(),year)
print ('* Plotting ',title)
plt.title(title, fontsize=12)
legend_from_style_spec(ax, styles,loc='lower left')
# output
output_file = os.path.join(
config['paths']['figures'],
'national-bridges-{}-{}-{}-{}-days.png'.format(climate_scenario.replace('.',''),
year,
risk_set[c]['column'],duration))
save_fig(output_file)
plt.close()
if __name__ == '__main__':
main()