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updated for eal_pop_loss

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This commit is contained in:
Emma Nechamkin 2022-09-08 15:50:50 -04:00
parent e78c6d0fef
commit 57e6ba75e0
1 changed files with 9 additions and 3 deletions
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12
data/data-pipeline/data_pipeline/etl/score/etl_score.py
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@ -555,6 +555,8 @@ class ScoreETL(ExtractTransformLoad):
# #
# For *Traffic Barriers*, we want to exclude low population tracts, which may have high burden because they are # For *Traffic Barriers*, we want to exclude low population tracts, which may have high burden because they are
# low population alone. We set this low population constant in the if statement. # low population alone. We set this low population constant in the if statement.
#
# For *Population Loss*, we similarly do not want there to be non-populated tracts identified.
for numeric_column in numeric_columns: for numeric_column in numeric_columns:
drop_tracts = [] drop_tracts = []
@ -579,15 +581,19 @@ class ScoreETL(ExtractTransformLoad):
f"Dropping {len(drop_tracts)} tracts from Linguistic Isolation" f"Dropping {len(drop_tracts)} tracts from Linguistic Isolation"
) )
elif numeric_column == field_names.DOT_TRAVEL_BURDEN_FIELD: elif (numeric_column == field_names.DOT_TRAVEL_BURDEN_FIELD) or (
numeric_column
== field_names.EXPECTED_POPULATION_LOSS_RATE_FIELD
):
# Not having any people appears to be correlated with transit burden, but also doesn't represent # Not having any people appears to be correlated with transit burden, but also doesn't represent
# on the ground need. For now, we remove these tracts from the percentile calculation. (To be QAed live) # on the ground need. For now, we remove these tracts from the percentile calculation.
# We also think that low-population places should not be identified by FEMA population loss. s
low_population = 20 low_population = 20
drop_tracts = df_copy[ drop_tracts = df_copy[
df_copy[field_names.TOTAL_POP_FIELD] <= low_population df_copy[field_names.TOTAL_POP_FIELD] <= low_population
][field_names.GEOID_TRACT_FIELD].to_list() ][field_names.GEOID_TRACT_FIELD].to_list()
logger.info( logger.info(
f"Dropping {len(drop_tracts)} tracts from DOT traffic burden" f"Dropping {len(drop_tracts)} tracts from {numeric_column}"
) )
df_copy = self._add_percentiles_to_df( df_copy = self._add_percentiles_to_df(