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Issue 883: Update FEMA risk index measure (#884)

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* ETL updated

* Adding three fields to score
This commit is contained in:
Lucas Merrill Brown 2021-11-13 11:32:15 -05:00 • committed by GitHub
parent 05ebf9b48c
commit 21834b4a91
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8 changed files with 114 additions and 82 deletions
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3
data/data-pipeline/data_pipeline/etl/score/etl_score.py
View file

@ -313,6 +313,9 @@ class ScoreETL(ExtractTransformLoad):
field_names.UNEMPLOYMENT_FIELD,
field_names.HT_INDEX_FIELD,
field_names.MEDIAN_HOUSE_VALUE_FIELD,
field_names.EXPECTED_BUILDING_LOSS_RATE_FIELD_NAME,
field_names.EXPECTED_AGRICULTURE_LOSS_RATE_FIELD_NAME,
field_names.EXPECTED_POPULATION_LOSS_RATE_FIELD_NAME,
]
non_numeric_columns = [

92
data/data-pipeline/data_pipeline/etl/sources/national_risk_index/etl.py
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@ -1,3 +1,8 @@
# Note: I'm not sure why pylint is so upset with the particular dataframe `df_nri`,
# but it may be a known bug. https://github.com/PyCQA/pylint/issues/1498
# pylint: disable=unsubscriptable-object
# pylint: disable=unsupported-assignment-operation
import pandas as pd
from data_pipeline.etl.base import ExtractTransformLoad
@ -21,19 +26,37 @@ class NationalRiskIndexETL(ExtractTransformLoad):
self.RISK_INDEX_EXPECTED_ANNUAL_LOSS_SCORE_INPUT_FIELD_NAME = (
"EAL_SCORE"
)
self.RISK_INDEX_EXPECTED_ANNUAL_LOSS_SCORE_FIELD_NAME = (
"FEMA Risk Index Expected Annual Loss Score"
)
self.EXPECTED_ANNUAL_LOSS_RATE = (
"FEMA Risk Index Expected Annual Loss Rate"
self.EXPECTED_ANNUAL_LOSS_BUILDING_VALUE_INPUT_FIELD_NAME = "EAL_VALB"
self.EXPECTED_ANNUAL_LOSS_AGRICULTURAL_VALUE_INPUT_FIELD_NAME = (
"EAL_VALA"
)
self.EXPECTED_ANNUAL_LOSS_POPULATION_VALUE_INPUT_FIELD_NAME = "EAL_VALP"
self.AGRICULTURAL_VALUE_INPUT_FIELD_NAME = "AGRIVALUE"
self.POPULATION_INPUT_FIELD_NAME = "POPULATION"
self.BUILDING_VALUE_INPUT_FIELD_NAME = "BUILDVALUE"
self.EXPECTED_BUILDING_LOSS_RATE_FIELD_NAME = (
"Expected building loss rate (Natural Hazards Risk Index)"
)
self.EXPECTED_AGRICULTURE_LOSS_RATE_FIELD_NAME = (
"Expected agricultural loss rate (Natural Hazards Risk Index)"
)
self.EXPECTED_POPULATION_LOSS_RATE_FIELD_NAME = (
"Expected population loss rate (Natural Hazards Risk Index)"
)
# Note: also need to edit transform step to add fields to output.
self.COLUMNS_TO_KEEP = [
self.GEOID_FIELD_NAME,
self.RISK_INDEX_EXPECTED_ANNUAL_LOSS_SCORE_FIELD_NAME,
self.EXPECTED_ANNUAL_LOSS_RATE,
self.EXPECTED_POPULATION_LOSS_RATE_FIELD_NAME,
self.EXPECTED_AGRICULTURE_LOSS_RATE_FIELD_NAME,
self.EXPECTED_BUILDING_LOSS_RATE_FIELD_NAME,
]
self.df: pd.DataFrame
@ -77,56 +100,35 @@ class NationalRiskIndexETL(ExtractTransformLoad):
inplace=True,
)
# Calculate a risk score that does not include FEMA's measure of community vulnerability.
disaster_categories = [
"AVLN", # Avalanche
"CFLD", # Coastal Flooding
"CWAV", # Cold Wave
"DRGT", # Drought
"ERQK", # Earthquake
"HAIL", # Hail
"HWAV", # Heat Wave
"HRCN", # Hurricane
"ISTM", # Ice Storm
"LNDS", # Landslide
"LTNG", # Lightning
"RFLD", # Riverine Flooding
"SWND", # Strong Wind
"TRND", # Tornado
"TSUN", # Tsunami
"VLCN", # Volcanic Activity
"WFIR", # Wildfire
"WNTW", # Winter Weather
]
# Population EAL Rate = Eal Valp / Population
df_nri[self.EXPECTED_POPULATION_LOSS_RATE_FIELD_NAME] = (
df_nri[self.EXPECTED_ANNUAL_LOSS_POPULATION_VALUE_INPUT_FIELD_NAME]
/ df_nri[self.POPULATION_INPUT_FIELD_NAME]
)
# Note: I'm not sure why pylint is so upset with this particular dataframe,
# but it may be a known bug. https://github.com/PyCQA/pylint/issues/1498
for category in disaster_categories:
df_nri[ # pylint: disable=unsupported-assignment-operation
f"{category}"
] = (
df_nri[ # pylint: disable=unsubscriptable-object
f"{category}_EALT"
] # Expected Annual Loss - Total
/ df_nri[ # pylint: disable=unsubscriptable-object
f"{category}_EXPT"
]
)
df_nri[ # pylint: disable=unsupported-assignment-operation
self.EXPECTED_ANNUAL_LOSS_RATE
] = df_nri[ # pylint: disable=unsubscriptable-object
disaster_categories
].sum(
axis=1
# Agriculture EAL Rate = Eal Vala / Agrivalue
df_nri[self.EXPECTED_AGRICULTURE_LOSS_RATE_FIELD_NAME] = (
df_nri[
self.EXPECTED_ANNUAL_LOSS_AGRICULTURAL_VALUE_INPUT_FIELD_NAME
]
/ df_nri[self.AGRICULTURAL_VALUE_INPUT_FIELD_NAME]
)
# divide EAL_VALB (Expected Annual Loss - Building Value) by BUILDVALUE (Building Value ($)).
df_nri[self.EXPECTED_BUILDING_LOSS_RATE_FIELD_NAME] = (
df_nri[self.EXPECTED_ANNUAL_LOSS_BUILDING_VALUE_INPUT_FIELD_NAME]
/ df_nri[self.BUILDING_VALUE_INPUT_FIELD_NAME]
)
# Reduce columns.
# Note: normally we wait until writing to CSV for this step, but since the file is so huge,
# move this up here for performance reasons.
df_nri = df_nri[ # pylint: disable=unsubscriptable-object
df_nri = df_nri[
[
self.RISK_INDEX_EXPECTED_ANNUAL_LOSS_SCORE_FIELD_NAME,
self.EXPECTED_ANNUAL_LOSS_RATE,
self.EXPECTED_POPULATION_LOSS_RATE_FIELD_NAME,
self.EXPECTED_AGRICULTURE_LOSS_RATE_FIELD_NAME,
self.EXPECTED_BUILDING_LOSS_RATE_FIELD_NAME,
TRACT_COL,
]
]

9
data/data-pipeline/data_pipeline/score/field_names.py
View file

@ -60,6 +60,15 @@ FEMA_RISK_FIELD = "FEMA Risk Index Expected Annual Loss Score"
FEMA_EXPECTED_ANNUAL_LOSS_RATE_FIELD = (
"FEMA Risk Index Expected Annual Loss Rate"
)
EXPECTED_BUILDING_LOSS_RATE_FIELD_NAME = (
"Expected building loss rate (Natural Hazards Risk Index)"
)
EXPECTED_AGRICULTURE_LOSS_RATE_FIELD_NAME = (
"Expected agricultural loss rate (Natural Hazards Risk Index)"
)
EXPECTED_POPULATION_LOSS_RATE_FIELD_NAME = (
"Expected population loss rate (Natural Hazards Risk Index)"
)
# Environment
DIESEL_FIELD = "Diesel particulate matter"

32
data/data-pipeline/data_pipeline/score/score_l.py
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@ -64,19 +64,37 @@ class ScoreL(Score):
# Low income: In 60th percentile or above for percent of block group population
# of households where household income is less than or equal to twice the federal
# poverty level. Source: Census's American Community Survey]
climate_criteria = (
(
self.df[
field_names.EXPECTED_BUILDING_LOSS_RATE_FIELD_NAME
+ field_names.PERCENTILE_FIELD_SUFFIX
]
> self.ENVIRONMENTAL_BURDEN_THRESHOLD
)
| (
self.df[
field_names.EXPECTED_AGRICULTURE_LOSS_RATE_FIELD_NAME
+ field_names.PERCENTILE_FIELD_SUFFIX
]
> self.ENVIRONMENTAL_BURDEN_THRESHOLD
)
| (
self.df[
field_names.EXPECTED_POPULATION_LOSS_RATE_FIELD_NAME
+ field_names.PERCENTILE_FIELD_SUFFIX
]
> self.ENVIRONMENTAL_BURDEN_THRESHOLD
)
)
return (
self.df[
field_names.POVERTY_LESS_THAN_200_FPL_FIELD
+ field_names.PERCENTILE_FIELD_SUFFIX
]
> self.LOW_INCOME_THRESHOLD
) & (
self.df[
field_names.FEMA_EXPECTED_ANNUAL_LOSS_RATE_FIELD
+ field_names.PERCENTILE_FIELD_SUFFIX
]
> self.ENVIRONMENTAL_BURDEN_THRESHOLD
)
) & climate_criteria
def _energy_factor(self) -> bool:
# In Xth percentile or above for DOE’s energy cost burden score (Source: LEAD Score)

12
data/data-pipeline/data_pipeline/tests/sources/national_risk_index/data/input.csv
View file

@ -1,6 +1,6 @@
TRACT,TRACTFIPS,RISK_SCORE,RISK_RATNG,RISK_NPCTL,EAL_SCORE,AVLN_EALT,CFLD_EALT,CWAV_EALT,DRGT_EALT,ERQK_EALT,HAIL_EALT,HWAV_EALT,HRCN_EALT,ISTM_EALT,LNDS_EALT,LTNG_EALT,RFLD_EALT,SWND_EALT,TRND_EALT,TSUN_EALT,VLCN_EALT,WFIR_EALT,WNTW_EALT,AVLN_EXPT,CFLD_EXPT,CWAV_EXPT,DRGT_EXPT,ERQK_EXPT,HAIL_EXPT,HWAV_EXPT,HRCN_EXPT,ISTM_EXPT,LNDS_EXPT,LTNG_EXPT,RFLD_EXPT,SWND_EXPT,TRND_EXPT,TSUN_EXPT,VLCN_EXPT,WFIR_EXPT,WNTW_EXPT
40300,05007040300,10.492015,Very Low,15.3494,11.5,12.5,13.5,14.5,15.5,16.5,17.5,18.5,19.5,20.5,21.5,22.5,23.5,24.5,25.5,26.5,27.5,28.5,29.5,30.5,31.5,32.5,33.5,34.5,35.5,36.5,37.5,38.5,39.5,40.5,41.5,42.5,43.5,44.5,45.5,46.5,47.5
20100,05001020100,14.705854,Relatively Low,36.725828,12.5,13.5,14.5,15.5,16.5,17.5,18.5,19.5,20.5,21.5,22.5,23.5,24.5,25.5,26.5,27.5,28.5,29.5,30.5,31.5,32.5,33.5,34.5,35.5,36.5,37.5,38.5,39.5,40.5,41.5,42.5,43.5,44.5,45.5,46.5,47.5,48.5
40500,15007040500,10.234981,Very Low,13.997993,13.5,14.5,15.5,16.5,17.5,18.5,19.5,20.5,21.5,22.5,23.5,24.5,25.5,26.5,27.5,28.5,29.5,30.5,31.5,32.5,33.5,34.5,35.5,36.5,37.5,38.5,39.5,40.5,41.5,42.5,43.5,44.5,45.5,46.5,47.5,48.5,49.5
21010,15001021010,21.537231,Relatively Moderate,59.488033,14.5,15.5,16.5,17.5,18.5,19.5,20.5,21.5,22.5,23.5,24.5,25.5,26.5,27.5,28.5,29.5,30.5,31.5,32.5,33.5,34.5,35.5,36.5,37.5,38.5,39.5,40.5,41.5,42.5,43.5,44.5,45.5,46.5,47.5,48.5,49.5,50.5
21101,15001021101,19.434585,Relatively Low,53.392265,15.5,16.5,17.5,18.5,19.5,20.5,21.5,22.5,23.5,24.5,25.5,26.5,27.5,28.5,29.5,30.5,31.5,32.5,33.5,34.5,35.5,36.5,37.5,38.5,39.5,40.5,41.5,42.5,43.5,44.5,45.5,46.5,47.5,48.5,49.5,50.5,51.5
TRACT,TRACTFIPS,RISK_SCORE,RISK_RATNG,RISK_NPCTL,EAL_SCORE,AVLN_EALT,CFLD_EALT,CWAV_EALT,DRGT_EALT,ERQK_EALT,HAIL_EALT,HWAV_EALT,HRCN_EALT,ISTM_EALT,LNDS_EALT,LTNG_EALT,RFLD_EALT,SWND_EALT,TRND_EALT,TSUN_EALT,VLCN_EALT,WFIR_EALT,WNTW_EALT,AVLN_EXPT,CFLD_EXPT,CWAV_EXPT,DRGT_EXPT,ERQK_EXPT,HAIL_EXPT,HWAV_EXPT,HRCN_EXPT,ISTM_EXPT,LNDS_EXPT,LTNG_EXPT,RFLD_EXPT,SWND_EXPT,TRND_EXPT,TSUN_EXPT,VLCN_EXPT,WFIR_EXPT,WNTW_EXPT,EAL_VALA,EAL_VALP,EAL_VALB,AGRIVALUE,POPULATION,BUILDVALUE
40300,05007040300,10.492015,Very Low,15.3494,11.5,12.5,13.5,14.5,15.5,16.5,17.5,18.5,19.5,20.5,21.5,22.5,23.5,24.5,25.5,26.5,27.5,28.5,29.5,30.5,31.5,32.5,33.5,34.5,35.5,36.5,37.5,38.5,39.5,40.5,41.5,42.5,43.5,44.5,45.5,46.5,47.5,48.5,49.5,50.5,234.7446176,204.8883901,126.4079101
20100,05001020100,14.705854,Relatively Low,36.725828,12.5,13.5,14.5,15.5,16.5,17.5,18.5,19.5,20.5,21.5,22.5,23.5,24.5,25.5,26.5,27.5,28.5,29.5,30.5,31.5,32.5,33.5,34.5,35.5,36.5,37.5,38.5,39.5,40.5,41.5,42.5,43.5,44.5,45.5,46.5,47.5,48.5,49.5,50.5,51.5,238.9921867,179.4960371,96.24552261
40500,15007040500,10.234981,Very Low,13.997993,13.5,14.5,15.5,16.5,17.5,18.5,19.5,20.5,21.5,22.5,23.5,24.5,25.5,26.5,27.5,28.5,29.5,30.5,31.5,32.5,33.5,34.5,35.5,36.5,37.5,38.5,39.5,40.5,41.5,42.5,43.5,44.5,45.5,46.5,47.5,48.5,49.5,50.5,51.5,52.5,187.5793934,183.4527834,106.4706219
21010,15001021010,21.537231,Relatively Moderate,59.488033,14.5,15.5,16.5,17.5,18.5,19.5,20.5,21.5,22.5,23.5,24.5,25.5,26.5,27.5,28.5,29.5,30.5,31.5,32.5,33.5,34.5,35.5,36.5,37.5,38.5,39.5,40.5,41.5,42.5,43.5,44.5,45.5,46.5,47.5,48.5,49.5,50.5,51.5,52.5,53.5,142.7041082,236.9465219,175.3803106
21101,15001021101,19.434585,Relatively Low,53.392265,15.5,16.5,17.5,18.5,19.5,20.5,21.5,22.5,23.5,24.5,25.5,26.5,27.5,28.5,29.5,30.5,31.5,32.5,33.5,34.5,35.5,36.5,37.5,38.5,39.5,40.5,41.5,42.5,43.5,44.5,45.5,46.5,47.5,48.5,49.5,50.5,51.5,52.5,53.5,54.5,257.1497377,66.41934096,177.9963115

1 TRACT TRACTFIPS RISK_SCORE RISK_RATNG RISK_NPCTL EAL_SCORE AVLN_EALT CFLD_EALT CWAV_EALT DRGT_EALT ERQK_EALT HAIL_EALT HWAV_EALT HRCN_EALT ISTM_EALT LNDS_EALT LTNG_EALT RFLD_EALT SWND_EALT TRND_EALT TSUN_EALT VLCN_EALT WFIR_EALT WNTW_EALT AVLN_EXPT CFLD_EXPT CWAV_EXPT DRGT_EXPT ERQK_EXPT HAIL_EXPT HWAV_EXPT HRCN_EXPT ISTM_EXPT LNDS_EXPT LTNG_EXPT RFLD_EXPT SWND_EXPT TRND_EXPT TSUN_EXPT VLCN_EXPT WFIR_EXPT WNTW_EXPT EAL_VALA EAL_VALP EAL_VALB AGRIVALUE POPULATION BUILDVALUE
2 40300 05007040300 10.492015 Very Low 15.3494 11.5 12.5 13.5 14.5 15.5 16.5 17.5 18.5 19.5 20.5 21.5 22.5 23.5 24.5 25.5 26.5 27.5 28.5 29.5 30.5 31.5 32.5 33.5 34.5 35.5 36.5 37.5 38.5 39.5 40.5 41.5 42.5 43.5 44.5 45.5 46.5 47.5 48.5 49.5 50.5 234.7446176 204.8883901 126.4079101
3 20100 05001020100 14.705854 Relatively Low 36.725828 12.5 13.5 14.5 15.5 16.5 17.5 18.5 19.5 20.5 21.5 22.5 23.5 24.5 25.5 26.5 27.5 28.5 29.5 30.5 31.5 32.5 33.5 34.5 35.5 36.5 37.5 38.5 39.5 40.5 41.5 42.5 43.5 44.5 45.5 46.5 47.5 48.5 49.5 50.5 51.5 238.9921867 179.4960371 96.24552261
4 40500 15007040500 10.234981 Very Low 13.997993 13.5 14.5 15.5 16.5 17.5 18.5 19.5 20.5 21.5 22.5 23.5 24.5 25.5 26.5 27.5 28.5 29.5 30.5 31.5 32.5 33.5 34.5 35.5 36.5 37.5 38.5 39.5 40.5 41.5 42.5 43.5 44.5 45.5 46.5 47.5 48.5 49.5 50.5 51.5 52.5 187.5793934 183.4527834 106.4706219
5 21010 15001021010 21.537231 Relatively Moderate 59.488033 14.5 15.5 16.5 17.5 18.5 19.5 20.5 21.5 22.5 23.5 24.5 25.5 26.5 27.5 28.5 29.5 30.5 31.5 32.5 33.5 34.5 35.5 36.5 37.5 38.5 39.5 40.5 41.5 42.5 43.5 44.5 45.5 46.5 47.5 48.5 49.5 50.5 51.5 52.5 53.5 142.7041082 236.9465219 175.3803106
6 21101 15001021101 19.434585 Relatively Low 53.392265 15.5 16.5 17.5 18.5 19.5 20.5 21.5 22.5 23.5 24.5 25.5 26.5 27.5 28.5 29.5 30.5 31.5 32.5 33.5 34.5 35.5 36.5 37.5 38.5 39.5 40.5 41.5 42.5 43.5 44.5 45.5 46.5 47.5 48.5 49.5 50.5 51.5 52.5 53.5 54.5 257.1497377 66.41934096 177.9963115

22
data/data-pipeline/data_pipeline/tests/sources/national_risk_index/data/output.csv
View file

@ -1,11 +1,11 @@
GEOID10,FEMA Risk Index Expected Annual Loss Score,FEMA Risk Index Expected Annual Loss Rate
050070403001,11.5,9.540442348853764
050070403002,11.5,9.540442348853764
050010201001,12.5,9.759472262661436
050010201002,12.5,9.759472262661436
150070405001,13.5,9.967264470453644
150070405002,13.5,9.967264470453644
150010210101,14.5,10.16467498073544
150010210102,14.5,10.16467498073544
150010211011,15.5,10.352473850464468
150010211012,15.5,10.352473850464468
GEOID10,FEMA Risk Index Expected Annual Loss Score,Expected population loss rate (Natural Hazards Risk Index),Expected agricultural loss rate (Natural Hazards Risk Index),Expected building loss rate (Natural Hazards Risk Index)
050070403001,11.5,0.2415949482342093,0.2066075060457531,0.3995003157638629
050070403002,11.5,0.2415949482342093,0.2066075060457531,0.3995003157638629
050010201001,12.5,0.2813432586919213,0.2071197417936341,0.5350898265541664
050010201002,12.5,0.2813432586919213,0.2071197417936341,0.5350898265541664
150070405001,13.5,0.2807261849372409,0.2692193373944453,0.4930937667416781
150070405002,13.5,0.2807261849372409,0.2692193373944453,0.4930937667416781
150010210101,14.5,0.221568983494752,0.3608865970965789,0.3050513470809191
150010210102,14.5,0.221568983494752,0.3608865970965789,0.3050513470809191
150010211011,15.5,0.8054882693313613,0.2041612037778874,0.306186120042156
150010211012,15.5,0.8054882693313613,0.2041612037778874,0.306186120042156

1 GEOID10 FEMA Risk Index Expected Annual Loss Score FEMA Risk Index Expected Annual Loss Rate Expected population loss rate (Natural Hazards Risk Index) Expected agricultural loss rate (Natural Hazards Risk Index) Expected building loss rate (Natural Hazards Risk Index)
2 050070403001 11.5 9.540442348853764 0.2415949482342093 0.2066075060457531 0.3995003157638629
3 050070403002 11.5 9.540442348853764 0.2415949482342093 0.2066075060457531 0.3995003157638629
4 050010201001 12.5 9.759472262661436 0.2813432586919213 0.2071197417936341 0.5350898265541664
5 050010201002 12.5 9.759472262661436 0.2813432586919213 0.2071197417936341 0.5350898265541664
6 150070405001 13.5 9.967264470453644 0.2807261849372409 0.2692193373944453 0.4930937667416781
7 150070405002 13.5 9.967264470453644 0.2807261849372409 0.2692193373944453 0.4930937667416781
8 150010210101 14.5 10.16467498073544 0.221568983494752 0.3608865970965789 0.3050513470809191
9 150010210102 14.5 10.16467498073544 0.221568983494752 0.3608865970965789 0.3050513470809191
10 150010211011 15.5 10.352473850464468 0.8054882693313613 0.2041612037778874 0.306186120042156
11 150010211012 15.5 10.352473850464468 0.8054882693313613 0.2041612037778874 0.306186120042156

22
data/data-pipeline/data_pipeline/tests/sources/national_risk_index/data/transform.csv
View file

@ -1,11 +1,11 @@
GEOID10,GEOID10_TRACT,FEMA Risk Index Expected Annual Loss Score,FEMA Risk Index Expected Annual Loss Rate
050070403001,05007040300,11.5,9.540442348853764
050070403002,05007040300,11.5,9.540442348853764
050010201001,05001020100,12.5,9.759472262661436
050010201002,05001020100,12.5,9.759472262661436
150070405001,15007040500,13.5,9.967264470453644
150070405002,15007040500,13.5,9.967264470453644
150010210101,15001021010,14.5,10.164674980735441
150010210102,15001021010,14.5,10.164674980735441
150010211011,15001021101,15.5,10.352473850464467
150010211012,15001021101,15.5,10.352473850464467
GEOID10,GEOID10_TRACT,FEMA Risk Index Expected Annual Loss Score,Expected population loss rate (Natural Hazards Risk Index),Expected agricultural loss rate (Natural Hazards Risk Index),Expected building loss rate (Natural Hazards Risk Index)
050070403001,05007040300,11.5,0.24159494823420938,0.2066075060457531,0.39950031576386297
050070403002,05007040300,11.5,0.24159494823420938,0.2066075060457531,0.39950031576386297
050010201001,05001020100,12.5,0.2813432586919213,0.20711974179363413,0.5350898265541664
050010201002,05001020100,12.5,0.2813432586919213,0.20711974179363413,0.5350898265541664
150070405001,15007040500,13.5,0.28072618493724094,0.26921933739444537,0.4930937667416781
150070405002,15007040500,13.5,0.28072618493724094,0.26921933739444537,0.4930937667416781
150010210101,15001021010,14.5,0.22156898349475204,0.3608865970965789,0.30505134708091913
150010210102,15001021010,14.5,0.22156898349475204,0.3608865970965789,0.30505134708091913
150010211011,15001021101,15.5,0.8054882693313613,0.20416120377788743,0.30618612004215606
150010211012,15001021101,15.5,0.8054882693313613,0.20416120377788743,0.30618612004215606

1 GEOID10 GEOID10_TRACT FEMA Risk Index Expected Annual Loss Score FEMA Risk Index Expected Annual Loss Rate Expected population loss rate (Natural Hazards Risk Index) Expected agricultural loss rate (Natural Hazards Risk Index) Expected building loss rate (Natural Hazards Risk Index)
2 050070403001 05007040300 11.5 9.540442348853764 0.24159494823420938 0.2066075060457531 0.39950031576386297
3 050070403002 05007040300 11.5 9.540442348853764 0.24159494823420938 0.2066075060457531 0.39950031576386297
4 050010201001 05001020100 12.5 9.759472262661436 0.2813432586919213 0.20711974179363413 0.5350898265541664
5 050010201002 05001020100 12.5 9.759472262661436 0.2813432586919213 0.20711974179363413 0.5350898265541664
6 150070405001 15007040500 13.5 9.967264470453644 0.28072618493724094 0.26921933739444537 0.4930937667416781
7 150070405002 15007040500 13.5 9.967264470453644 0.28072618493724094 0.26921933739444537 0.4930937667416781
8 150010210101 15001021010 14.5 10.164674980735441 0.22156898349475204 0.3608865970965789 0.30505134708091913
9 150010210102 15001021010 14.5 10.164674980735441 0.22156898349475204 0.3608865970965789 0.30505134708091913
10 150010211011 15001021101 15.5 10.352473850464467 0.8054882693313613 0.20416120377788743 0.30618612004215606
11 150010211012 15001021101 15.5 10.352473850464467 0.8054882693313613 0.20416120377788743 0.30618612004215606

4
data/data-pipeline/data_pipeline/tests/sources/national_risk_index/test_etl.py
View file

@ -63,7 +63,7 @@ class TestNationalRiskIndexETL:
etl.transform()
# validation
assert etl.df.shape == (10, 4)
assert etl.df.shape == (10, 6)
pd.testing.assert_frame_equal(etl.df, expected)
def test_load(self, mock_etl):
@ -93,5 +93,5 @@ class TestNationalRiskIndexETL:
# validation
assert output_path.exists()
assert output.shape == (10, 3)
assert output.shape == (10, 5)
pd.testing.assert_frame_equal(output, expected)