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risk score updated till may 2026 #3

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33,286 changes: 19,469 additions & 13,817 deletions data/MASTER_VARIABLES.csv
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33,286 changes: 19,469 additions & 13,817 deletions data/factor_scores_l1_exposure.csv
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33,286 changes: 19,469 additions & 13,817 deletions data/factor_scores_l1_flood-hazard.csv
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33,286 changes: 19,469 additions & 13,817 deletions data/factor_scores_l1_government-response.csv
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33,286 changes: 19,469 additions & 13,817 deletions data/factor_scores_l1_vulnerability.csv
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33,286 changes: 19,469 additions & 13,817 deletions data/risk_score.csv
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36,466 changes: 21,329 additions & 15,137 deletions data/risk_score_final_district.csv
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9 changes: 6 additions & 3 deletions scripts/govtresponse.py
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Original file line number Diff line number Diff line change
Expand Up @@ -12,6 +12,7 @@

import numpy as np
import pandas as pd
import os
from sklearn.preprocessing import MinMaxScaler
from tqdm import tqdm

Expand All @@ -20,14 +21,15 @@
# ---------------------------------------------------------------------------
# 1. CONFIG
# ---------------------------------------------------------------------------
DATA_DIR = Path("data")
DATA_DIR = Path(os.getcwd()) / "data"
IN_FILE = DATA_DIR / "MASTER_VARIABLES.csv"
OUT_FILE = DATA_DIR / "factor_scores_l1_government-response.csv"

# columns
GOV_RESPONSE_VARS = [
"total_tender_awarded_value",
"SDRF_sanctions_awarded_value",
"SDRF_tenders_awarded_value",
"SDMF_tenders_awarded_value",
"RIDF_tenders_awarded_value",
"Preparedness Measures_tenders_awarded_value",
"Immediate Measures_tenders_awarded_value",
Expand All @@ -36,7 +38,8 @@

MODEL_VARS = [ # used for Min–Max scaling + sum
"total_tender_awarded_value",
"SDRF_sanctions_awarded_value",
"SDMF_tenders_awarded_value",
"SDRF_tenders_awarded_value",
"Others_tenders_awarded_value",
]

Expand Down
4 changes: 2 additions & 2 deletions scripts/hazard.py
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Expand Up @@ -40,7 +40,7 @@
warnings.filterwarnings("ignore")
path = os.getcwd() #+ r"/flood-data-ecosystem-Odisha"

master_variables = pd.read_csv(path+r'/data/MASTER_VARIABLES.csv')
master_variables = pd.read_csv(path+ '/data/MASTER_VARIABLES.csv')

hazard_vars = ['sum_rain', 'Mean_Daily_Runoff','elevation_mean','distance_from_sea']#'slope_mean','distance_from_river',

Expand Down Expand Up @@ -93,6 +93,6 @@
master_variables = master_variables.merge(hazard[['timeperiod', 'object_id', 'flood-hazard']],
on = ['timeperiod', 'object_id'],how='left')
print(master_variables.columns)
master_variables.to_csv(path+r'/data/factor_scores_l1_flood-hazard.csv', index=False)
master_variables.to_csv(path+ '/data/factor_scores_l1_flood-hazard.csv', index=False)

# Normalize data using MinMaxScaler
60 changes: 37 additions & 23 deletions scripts/topis_riskscore_district.py
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Expand Up @@ -46,12 +46,14 @@ def get_financial_year(timeperiod):

cumulative_vars = [
'total_tender_awarded_value',
'SDMF_tenders_awarded_value',
#'Repair and Restoration_tenders_awarded_value',
'Preparedness Measures_tenders_awarded_value',
'Immediate Measures_tenders_awarded_value',
'RIDF_tenders_awarded_value',
'Others_tenders_awarded_value',
#'relief_and_mitigation_sanction_value',
'SDRF_sanctions_awarded_value',
'SDRF_tenders_awarded_value',
]

for var in cumulative_vars:
Expand Down Expand Up @@ -87,10 +89,10 @@ def get_financial_year(timeperiod):

topsis.columns = [col.lower().replace('_', '-').replace(' ', '-') for col in topsis.columns]
print(topsis.columns)
topsis.to_csv(os.getcwd()+r'/data/risk_score.csv', index=False)
topsis.to_csv(os.getcwd()+ '/data/risk_score.csv', index=False)

## DISTRICT LEVEL SCORES
dist_ids = pd.read_csv(os.getcwd()+r'/assets/district_objectid.csv')
dist_ids = pd.read_csv(os.getcwd()+ '/assets/district_objectid.csv')

compositescorelabels = ['1','2','3','4','5']

Expand Down Expand Up @@ -123,9 +125,9 @@ def get_financial_year(timeperiod):

indicators = ['total-tender-awarded-value',
#'sopd-tenders-awarded-value',
'sdrf-sanctions-awarded-value',
#'sdrf-tenders-awarded-value',
#'ridf-tenders-awarded-value',
#'sdrf-sanctions-awarded-value',
'sdrf-tenders-awarded-value',
'ridf-tenders-awarded-value',
#'ltif-tenders-awarded-value',
#'cidf-tenders-awarded-value',
'preparedness-measures-tenders-awarded-value',
Expand Down Expand Up @@ -192,17 +194,17 @@ def get_financial_year(timeperiod):
'sum-runoff',
'peak-runoff',
'distance-from-sea',
"total-no-of-death-of-humans-in-flood-and-cyclone",
"population-affected",
"cultivated-area-affected-in-hectare",
"road-length",
#"total-no-of-death-of-humans-in-flood-and-cyclone",
#"population-affected",
#"cultivated-area-affected-in-hectare",
#"road-length",


#'topsis-score',
#'risk-score',
#'exposure',
#'vulnerability',
#'government-response',
'topsis-score',
'risk-score',
'exposure',
'vulnerability',
'government-response',

]

Expand Down Expand Up @@ -237,11 +239,11 @@ def get_financial_year(timeperiod):
aggregation_rules = {
# Sum columns
'total-tender-awarded-value': 'sum',
#'ridf-tenders-awarded-value': 'sum',
'ridf-tenders-awarded-value': 'sum',
'preparedness-measures-tenders-awarded-value': 'sum',
'immediate-measures-tenders-awarded-value': 'sum',
'others-tenders-awarded-value': 'sum',
'sdrf-sanctions-awarded-value': 'sum',
'sdrf-tenders-awarded-value': 'sum',

'sum-population': 'sum',
'inundation-intensity-sum': 'sum',
Expand Down Expand Up @@ -280,14 +282,20 @@ def get_financial_year(timeperiod):

#'efficiency': 'mean',

"total-no-of-death-of-humans-in-flood-and-cyclone": 'sum',
"population-affected": 'max',
"cultivated-area-affected-in-hectare": 'sum',
"road-length": 'sum',
#"total-no-of-death-of-humans-in-flood-and-cyclone": 'sum',
#"population-affected": 'max',
#"cultivated-area-affected-in-hectare": 'sum',
#"road-length": 'sum',


# Max for hazard levels
'max-rain':'max',
'topsis-score': 'mean',
'risk-score': 'mean',
'exposure': 'mean',
'vulnerability': 'mean',
'government-response': 'mean',
'flood-hazard': 'mean',
}

rounding_rules = {
Expand All @@ -311,9 +319,15 @@ def get_financial_year(timeperiod):
'road-length':0,
'elevation-mean':0,
'slope-mean':0,
'topsis-score':2,
'flood-hazard': 0,
'risk-score': 0,
'exposure': 0,
'vulnerability': 0,
'government-response': 0,
}
#'crop-area':0,

}

dist_indicators = topsis.groupby(['district', 'timeperiod']).agg(aggregation_rules).reset_index()

Expand Down Expand Up @@ -372,4 +386,4 @@ def apply_rounding_rules(df, rounding_rules):
# dist.set_index(['object-id', 'timeperiod'])], axis=1).reset_index()

final.rename(columns={'preparedness-measures-tenders-awarded-value': 'restoration-measures-tenders-awarded-value', 'mean-sexratio':'sexratio','healthcenters':'health-centers-count'}, inplace=True)
final.to_csv(os.getcwd()+r'/data/risk_score_final_district.csv', index=False)
final.to_csv(os.getcwd()+'/data/risk_score_final_district.csv', index=False)
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