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Issue 1827: Add demographics to tiles and download files (#1833)

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* Adding demographics for use in sidebar and download files
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Lucas Merrill Brown 2022-08-22 10:05:23 -04:00 • committed by GitHub
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13 changed files with 304 additions and 46 deletions
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6
data/data-pipeline/README.md
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@ -350,7 +350,8 @@ We have four pickle files that correspond to expected files:
To update the pickles, let's go one by one:
For the `score_transformed_expected.pkl`, put a breakpoint on [this line](https://github.com/usds/justice40-tool/blob/main/data/data-pipeline/data_pipeline/etl/score/tests/test_score_post.py#L58), before the `pdt.assert_frame_equal` and run:
For the `score_transformed_expected.pkl`, put a breakpoint on [this line]
(https://github.com/usds/justice40-tool/blob/main/data/data-pipeline/data_pipeline/etl/score/tests/test_score_post.py#L62), before the `pdt.assert_frame_equal` and run:
`pytest data_pipeline/etl/score/tests/test_score_post.py::test_transform_score`
Once on the breakpoint, capture the df to a pickle as follows:
@ -378,7 +379,8 @@ score_data_actual.to_pickle(data_path / "data_pipeline" / "etl" / "score" / "tes
Then take out the breakpoint and re-run the test: `pytest data_pipeline/etl/score/tests/test_score_post.py::test_create_score_data`
For the `tile_data_expected.pkl`, put a breakpoint on [this line](https://github.com/usds/justice40-tool/blob/main/data/data-pipeline/data_pipeline/etl/score/tests/test_score_post.py#L86), before the `pdt.assert_frame_equal` and run:
For the `tile_data_expected.pkl`, put a breakpoint on [this line](https://github
.com/usds/justice40-tool/blob/main/data/data-pipeline/data_pipeline/etl/score/tests/test_score_post.py#L90), before the `pdt.assert_frame_equal` and run:
`pytest data_pipeline/etl/score/tests/test_score_post.py::test_create_tile_data`
Once on the breakpoint, capture the df to a pickle as follows:

33
data/data-pipeline/data_pipeline/content/config/csv.yml
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@ -14,6 +14,39 @@ fields:
- score_name: State/Territory
label: State/Territory
format: string
- score_name: Percent Black or African American
label: Percent Black or African American alone
format: float
- score_name: Percent American Indian / Alaska Native
label: Percent American Indian / Alaska Native
format: float
- score_name: Percent Asian
label: Percent Asian
format: float
- score_name: Percent Native Hawaiian or Pacific
label: Percent Native Hawaiian or Pacific
format: float
- score_name: Percent two or more races
label: Percent two or more races
format: float
- score_name: Percent White
label: Percent White
format: float
- score_name: Percent Hispanic or Latino
label: Percent Hispanic or Latino
format: float
- score_name: Percent other races
label: Percent other races
format: float
- score_name: Percent age under 10
label: Percent age under 10
format: float
- score_name: Percent age 10 to 64
label: Percent age 10 to 64
format: float
- score_name: Percent age over 64
label: Percent age over 64
format: float
- score_name: Total threshold criteria exceeded
label: Total threshold criteria exceeded
format: int64

33
data/data-pipeline/data_pipeline/content/config/excel.yml
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@ -18,6 +18,39 @@ sheets:
- score_name: State/Territory
label: State/Territory
format: string
- score_name: Percent Black or African American
label: Percent Black or African American alone
format: float
- score_name: Percent American Indian / Alaska Native
label: Percent American Indian / Alaska Native
format: float
- score_name: Percent Asian
label: Percent Asian
format: float
- score_name: Percent Native Hawaiian or Pacific
label: Percent Native Hawaiian or Pacific
format: float
- score_name: Percent two or more races
label: Percent two or more races
format: float
- score_name: Percent White
label: Percent White
format: float
- score_name: Percent Hispanic or Latino
label: Percent Hispanic or Latino
format: float
- score_name: Percent other races
label: Percent other races
format: float
- score_name: Percent age under 10
label: Percent age under 10
format: float
- score_name: Percent age 10 to 64
label: Percent age 10 to 64
format: float
- score_name: Percent age over 64
label: Percent age over 64
format: float
- score_name: Total threshold criteria exceeded
label: Total threshold criteria exceeded
format: int64

2
data/data-pipeline/data_pipeline/etl/constants.py
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@ -198,10 +198,12 @@ CENSUS_INFO = {
"name": "census",
"module_dir": "census",
"class_name": "CensusETL",
"is_memory_intensive": False,
}
TRIBAL_INFO = {
"name": "tribal",
"module_dir": "tribal",
"class_name": "TribalETL",
"is_memory_intensive": False,
}

25
data/data-pipeline/data_pipeline/etl/score/constants.py
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@ -315,9 +315,20 @@ TILES_SCORE_COLUMNS = {
field_names.NON_NATURAL_LOW_INCOME_FIELD_NAME: "IS_ET",
field_names.AML_BOOLEAN: "AML_ET",
field_names.ELIGIBLE_FUDS_BINARY_FIELD_NAME: "FUDS_ET",
field_names.IMPUTED_INCOME_FLAG_FIELD_NAME: "IMP_FLG"
field_names.IMPUTED_INCOME_FLAG_FIELD_NAME: "IMP_FLG",
## FPL 200 and low higher ed for all others should no longer be M_EBSI, but rather
## FPL_200 (there is no higher ed in narwhal)
field_names.PERCENT_BLACK_FIELD_NAME: "DM_B",
field_names.PERCENT_AMERICAN_INDIAN_FIELD_NAME: "DM_AI",
field_names.PERCENT_ASIAN_FIELD_NAME: "DM_A",
field_names.PERCENT_HAWAIIAN_FIELD_NAME: "DM_HI",
field_names.PERCENT_TWO_OR_MORE_RACES_FIELD_NAME: "DM_T",
field_names.PERCENT_NON_HISPANIC_WHITE_FIELD_NAME: "DM_W",
field_names.PERCENT_HISPANIC_FIELD_NAME: "DM_H",
field_names.PERCENT_OTHER_RACE_FIELD_NAME: "DM_O",
field_names.PERCENT_AGE_UNDER_10: "AGE_10",
field_names.PERCENT_AGE_10_TO_64: "AGE_MIDDLE",
field_names.PERCENT_AGE_OVER_64: "AGE_OLD",
}
# columns to round floats to 2 decimals
@ -375,4 +386,16 @@ TILES_SCORE_FLOAT_COLUMNS = [
field_names.SCORE_N_COMMUNITIES + field_names.ADJACENCY_INDEX_SUFFIX,
field_names.TRACT_PERCENT_NON_NATURAL_FIELD_NAME
+ field_names.PERCENTILE_FIELD_SUFFIX,
# Include demographic data for sidebar -- as percents, NOT as percentiles.
field_names.PERCENT_BLACK_FIELD_NAME,
field_names.PERCENT_AMERICAN_INDIAN_FIELD_NAME,
field_names.PERCENT_ASIAN_FIELD_NAME,
field_names.PERCENT_HAWAIIAN_FIELD_NAME,
field_names.PERCENT_TWO_OR_MORE_RACES_FIELD_NAME,
field_names.PERCENT_NON_HISPANIC_WHITE_FIELD_NAME,
field_names.PERCENT_HISPANIC_FIELD_NAME,
field_names.PERCENT_OTHER_RACE_FIELD_NAME,
field_names.PERCENT_AGE_UNDER_10,
field_names.PERCENT_AGE_10_TO_64,
field_names.PERCENT_AGE_OVER_64,
]

11
data/data-pipeline/data_pipeline/etl/score/etl_score.py
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@ -461,6 +461,17 @@ class ScoreETL(ExtractTransformLoad):
field_names.FUTURE_WILDFIRE_RISK_FIELD,
field_names.TRACT_PERCENT_NON_NATURAL_FIELD_NAME,
field_names.POVERTY_LESS_THAN_200_FPL_IMPUTED_FIELD,
field_names.PERCENT_BLACK_FIELD_NAME,
field_names.PERCENT_AMERICAN_INDIAN_FIELD_NAME,
field_names.PERCENT_ASIAN_FIELD_NAME,
field_names.PERCENT_HAWAIIAN_FIELD_NAME,
field_names.PERCENT_TWO_OR_MORE_RACES_FIELD_NAME,
field_names.PERCENT_NON_HISPANIC_WHITE_FIELD_NAME,
field_names.PERCENT_HISPANIC_FIELD_NAME,
field_names.PERCENT_OTHER_RACE_FIELD_NAME,
field_names.PERCENT_AGE_UNDER_10,
field_names.PERCENT_AGE_10_TO_64,
field_names.PERCENT_AGE_OVER_64,
]
non_numeric_columns = [

6
data/data-pipeline/data_pipeline/etl/score/tests/sample_data/score_data_initial.csv
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data/data-pipeline/data_pipeline/etl/score/tests/snapshots/downloadable_data_expected.pkl
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data/data-pipeline/data_pipeline/etl/score/tests/snapshots/score_data_expected.pkl
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data/data-pipeline/data_pipeline/etl/score/tests/snapshots/score_transformed_expected.pkl
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data/data-pipeline/data_pipeline/etl/score/tests/snapshots/tile_data_expected.pkl
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211
data/data-pipeline/data_pipeline/etl/sources/census_acs/etl.py
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@ -186,19 +186,25 @@ class CensusACSETL(ExtractTransformLoad):
"B03002_003E",
"B03003_001E",
"B03003_003E",
"B02001_007E", # "Some other race alone"
]
# Name output demographics fields.
self.BLACK_FIELD_NAME = "Black or African American alone"
self.AMERICAN_INDIAN_FIELD_NAME = (
"American Indian and Alaska Native alone"
)
self.ASIAN_FIELD_NAME = "Asian alone"
self.HAWAIIAN_FIELD_NAME = "Native Hawaiian and Other Pacific alone"
self.TWO_OR_MORE_RACES_FIELD_NAME = "Two or more races"
self.NON_HISPANIC_WHITE_FIELD_NAME = "Non-Hispanic White"
self.BLACK_FIELD_NAME = "Black or African American"
self.AMERICAN_INDIAN_FIELD_NAME = "American Indian / Alaska Native"
self.ASIAN_FIELD_NAME = "Asian"
self.HAWAIIAN_FIELD_NAME = "Native Hawaiian or Pacific"
self.TWO_OR_MORE_RACES_FIELD_NAME = "two or more races"
self.NON_HISPANIC_WHITE_FIELD_NAME = "White"
self.HISPANIC_FIELD_NAME = "Hispanic or Latino"
# Note that `other` is lowercase because the whole field will show up in the download
# file as "Percent other races"
self.OTHER_RACE_FIELD_NAME = "other races"
self.TOTAL_RACE_POPULATION_FIELD_NAME = (
"Total population surveyed on racial data"
)
# Name output demographics fields.
self.RE_OUTPUT_FIELDS = [
self.BLACK_FIELD_NAME,
self.AMERICAN_INDIAN_FIELD_NAME,
@ -207,9 +213,64 @@ class CensusACSETL(ExtractTransformLoad):
self.TWO_OR_MORE_RACES_FIELD_NAME,
self.NON_HISPANIC_WHITE_FIELD_NAME,
self.HISPANIC_FIELD_NAME,
self.OTHER_RACE_FIELD_NAME,
]
self.PERCENT_PREFIX = "Percent "
self.AGE_INPUT_FIELDS = [
"B01001_001E", # Estimate!!Total:
"B01001_003E", # Estimate!!Total:!!Male:!!Under 5 years
"B01001_004E", # Estimate!!Total:!!Male:!!5 to 9 years
"B01001_005E", # Estimate!!Total:!!Male:!!10 to 14 years
"B01001_006E", # Estimate!!Total:!!Male:!!15 to 17 years
"B01001_007E", # Estimate!!Total:!!Male:!!18 and 19 years
"B01001_008E", # Estimate!!Total:!!Male:!!20 years
"B01001_009E", # Estimate!!Total:!!Male:!!21 years
"B01001_010E", # Estimate!!Total:!!Male:!!22 to 24 years
"B01001_011E", # Estimate!!Total:!!Male:!!25 to 29 years
"B01001_012E", # Estimate!!Total:!!Male:!!30 to 34 years
"B01001_013E", # Estimate!!Total:!!Male:!!35 to 39 years
"B01001_014E", # Estimate!!Total:!!Male:!!40 to 44 years
"B01001_015E", # Estimate!!Total:!!Male:!!45 to 49 years
"B01001_016E", # Estimate!!Total:!!Male:!!50 to 54 years
"B01001_017E", # Estimate!!Total:!!Male:!!55 to 59 years
"B01001_018E", # Estimate!!Total:!!Male:!!60 and 61 years
"B01001_019E", # Estimate!!Total:!!Male:!!62 to 64 years
"B01001_020E", # Estimate!!Total:!!Male:!!65 and 66 years
"B01001_021E", # Estimate!!Total:!!Male:!!67 to 69 years
"B01001_022E", # Estimate!!Total:!!Male:!!70 to 74 years
"B01001_023E", # Estimate!!Total:!!Male:!!75 to 79 years
"B01001_024E", # Estimate!!Total:!!Male:!!80 to 84 years
"B01001_025E", # Estimate!!Total:!!Male:!!85 years and over
"B01001_027E", # Estimate!!Total:!!Female:!!Under 5 years
"B01001_028E", # Estimate!!Total:!!Female:!!5 to 9 years
"B01001_029E", # Estimate!!Total:!!Female:!!10 to 14 years
"B01001_030E", # Estimate!!Total:!!Female:!!15 to 17 years
"B01001_031E", # Estimate!!Total:!!Female:!!18 and 19 years
"B01001_032E", # Estimate!!Total:!!Female:!!20 years
"B01001_033E", # Estimate!!Total:!!Female:!!21 years
"B01001_034E", # Estimate!!Total:!!Female:!!22 to 24 years
"B01001_035E", # Estimate!!Total:!!Female:!!25 to 29 years
"B01001_036E", # Estimate!!Total:!!Female:!!30 to 34 years
"B01001_037E", # Estimate!!Total:!!Female:!!35 to 39 years
"B01001_038E", # Estimate!!Total:!!Female:!!40 to 44 years
"B01001_039E", # Estimate!!Total:!!Female:!!45 to 49 years
"B01001_040E", # Estimate!!Total:!!Female:!!50 to 54 years
"B01001_041E", # Estimate!!Total:!!Female:!!55 to 59 years
"B01001_042E", # Estimate!!Total:!!Female:!!60 and 61 years
"B01001_043E", # Estimate!!Total:!!Female:!!62 to 64 years
"B01001_044E", # Estimate!!Total:!!Female:!!65 and 66 years
"B01001_045E", # Estimate!!Total:!!Female:!!67 to 69 years
"B01001_046E", # Estimate!!Total:!!Female:!!70 to 74 years
"B01001_047E", # Estimate!!Total:!!Female:!!75 to 79 years
"B01001_048E", # Estimate!!Total:!!Female:!!80 to 84 years
"B01001_049E", # Estimate!!Total:!!Female:!!85 years and over
]
self.AGE_OUTPUT_FIELDS = [
field_names.PERCENT_AGE_UNDER_10,
field_names.PERCENT_AGE_10_TO_64,
field_names.PERCENT_AGE_OVER_64,
]
self.STATE_GEOID_FIELD_NAME = "GEOID2"
@ -230,7 +291,11 @@ class CensusACSETL(ExtractTransformLoad):
field_names.IMPUTED_INCOME_FLAG_FIELD_NAME,
]
+ self.RE_OUTPUT_FIELDS
+ [self.PERCENT_PREFIX + field for field in self.RE_OUTPUT_FIELDS]
+ [
field_names.PERCENT_PREFIX + field
for field in self.RE_OUTPUT_FIELDS
]
+ self.AGE_OUTPUT_FIELDS
+ [
field_names.POVERTY_LESS_THAN_200_FPL_FIELD,
field_names.POVERTY_LESS_THAN_200_FPL_IMPUTED_FIELD,
@ -280,6 +345,7 @@ class CensusACSETL(ExtractTransformLoad):
+ self.EDUCATIONAL_FIELDS
+ self.RE_FIELDS
+ self.COLLEGE_ATTENDANCE_FIELDS
+ self.AGE_INPUT_FIELDS
)
self.df = retrieve_census_acs_data(
@ -406,37 +472,104 @@ class CensusACSETL(ExtractTransformLoad):
)
# Calculate some demographic information.
df[self.BLACK_FIELD_NAME] = df["B02001_003E"]
df[self.AMERICAN_INDIAN_FIELD_NAME] = df["B02001_004E"]
df[self.ASIAN_FIELD_NAME] = df["B02001_005E"]
df[self.HAWAIIAN_FIELD_NAME] = df["B02001_006E"]
df[self.TWO_OR_MORE_RACES_FIELD_NAME] = df["B02001_008E"]
df[self.NON_HISPANIC_WHITE_FIELD_NAME] = df["B03002_003E"]
df[self.HISPANIC_FIELD_NAME] = df["B03003_003E"]
# Calculate demographics as percent
df[self.PERCENT_PREFIX + self.BLACK_FIELD_NAME] = (
df["B02001_003E"] / df["B02001_001E"]
)
df[self.PERCENT_PREFIX + self.AMERICAN_INDIAN_FIELD_NAME] = (
df["B02001_004E"] / df["B02001_001E"]
)
df[self.PERCENT_PREFIX + self.ASIAN_FIELD_NAME] = (
df["B02001_005E"] / df["B02001_001E"]
)
df[self.PERCENT_PREFIX + self.HAWAIIAN_FIELD_NAME] = (
df["B02001_006E"] / df["B02001_001E"]
)
df[self.PERCENT_PREFIX + self.TWO_OR_MORE_RACES_FIELD_NAME] = (
df["B02001_008E"] / df["B02001_001E"]
)
df[self.PERCENT_PREFIX + self.NON_HISPANIC_WHITE_FIELD_NAME] = (
df["B03002_003E"] / df["B03002_001E"]
)
df[self.PERCENT_PREFIX + self.HISPANIC_FIELD_NAME] = (
df["B03003_003E"] / df["B03003_001E"]
df = df.rename(
columns={
"B02001_003E": self.BLACK_FIELD_NAME,
"B02001_004E": self.AMERICAN_INDIAN_FIELD_NAME,
"B02001_005E": self.ASIAN_FIELD_NAME,
"B02001_006E": self.HAWAIIAN_FIELD_NAME,
"B02001_008E": self.TWO_OR_MORE_RACES_FIELD_NAME,
"B03002_003E": self.NON_HISPANIC_WHITE_FIELD_NAME,
"B03003_003E": self.HISPANIC_FIELD_NAME,
"B02001_007E": self.OTHER_RACE_FIELD_NAME,
"B02001_001E": self.TOTAL_RACE_POPULATION_FIELD_NAME,
},
errors="raise",
)
for race_field_name in self.RE_OUTPUT_FIELDS:
df[field_names.PERCENT_PREFIX + race_field_name] = (
df[race_field_name] / df[self.TOTAL_RACE_POPULATION_FIELD_NAME]
)
# First value is the `age bucket`, and the second value is a list of all fields
# that will be summed in the calculations of the total population in that age
# bucket.
age_bucket_and_its_sum_columns = [
(
field_names.PERCENT_AGE_UNDER_10,
[
"B01001_003E", # Estimate!!Total:!!Male:!!Under 5 years
"B01001_004E", # Estimate!!Total:!!Male:!!5 to 9 years
"B01001_027E", # Estimate!!Total:!!Female:!!Under 5 years
"B01001_028E", # Estimate!!Total:!!Female:!!5 to 9 years
],
),
(
field_names.PERCENT_AGE_10_TO_64,
[
"B01001_005E", # Estimate!!Total:!!Male:!!10 to 14 years
"B01001_006E", # Estimate!!Total:!!Male:!!15 to 17 years
"B01001_007E", # Estimate!!Total:!!Male:!!18 and 19 years
"B01001_008E", # Estimate!!Total:!!Male:!!20 years
"B01001_009E", # Estimate!!Total:!!Male:!!21 years
"B01001_010E", # Estimate!!Total:!!Male:!!22 to 24 years
"B01001_011E", # Estimate!!Total:!!Male:!!25 to 29 years
"B01001_012E", # Estimate!!Total:!!Male:!!30 to 34 years
"B01001_013E", # Estimate!!Total:!!Male:!!35 to 39 years
"B01001_014E", # Estimate!!Total:!!Male:!!40 to 44 years
"B01001_015E", # Estimate!!Total:!!Male:!!45 to 49 years
"B01001_016E", # Estimate!!Total:!!Male:!!50 to 54 years
"B01001_017E", # Estimate!!Total:!!Male:!!55 to 59 years
"B01001_018E", # Estimate!!Total:!!Male:!!60 and 61 years
"B01001_019E", # Estimate!!Total:!!Male:!!62 to 64 years
"B01001_029E", # Estimate!!Total:!!Female:!!10 to 14 years
"B01001_030E", # Estimate!!Total:!!Female:!!15 to 17 years
"B01001_031E", # Estimate!!Total:!!Female:!!18 and 19 years
"B01001_032E", # Estimate!!Total:!!Female:!!20 years
"B01001_033E", # Estimate!!Total:!!Female:!!21 years
"B01001_034E", # Estimate!!Total:!!Female:!!22 to 24 years
"B01001_035E", # Estimate!!Total:!!Female:!!25 to 29 years
"B01001_036E", # Estimate!!Total:!!Female:!!30 to 34 years
"B01001_037E", # Estimate!!Total:!!Female:!!35 to 39 years
"B01001_038E", # Estimate!!Total:!!Female:!!40 to 44 years
"B01001_039E", # Estimate!!Total:!!Female:!!45 to 49 years
"B01001_040E", # Estimate!!Total:!!Female:!!50 to 54 years
"B01001_041E", # Estimate!!Total:!!Female:!!55 to 59 years
"B01001_042E", # Estimate!!Total:!!Female:!!60 and 61 years
"B01001_043E", # Estimate!!Total:!!Female:!!62 to 64 years
],
),
(
field_names.PERCENT_AGE_OVER_64,
[
"B01001_020E", # Estimate!!Total:!!Male:!!65 and 66 years
"B01001_021E", # Estimate!!Total:!!Male:!!67 to 69 years
"B01001_022E", # Estimate!!Total:!!Male:!!70 to 74 years
"B01001_023E", # Estimate!!Total:!!Male:!!75 to 79 years
"B01001_024E", # Estimate!!Total:!!Male:!!80 to 84 years
"B01001_025E", # Estimate!!Total:!!Male:!!85 years and over
"B01001_044E", # Estimate!!Total:!!Female:!!65 and 66 years
"B01001_045E", # Estimate!!Total:!!Female:!!67 to 69 years
"B01001_046E", # Estimate!!Total:!!Female:!!70 to 74 years
"B01001_047E", # Estimate!!Total:!!Female:!!75 to 79 years
"B01001_048E", # Estimate!!Total:!!Female:!!80 to 84 years
"B01001_049E", # Estimate!!Total:!!Female:!!85 years and over
],
),
]
# Calculate age groups
total_population_age_series = df["B01001_001E"]
# For each age bucket, sum the relevant columns and calculate the total
# percentage.
for age_bucket, sum_columns in age_bucket_and_its_sum_columns:
df[age_bucket] = (
df[sum_columns].sum(axis=1) / total_population_age_series
)
# Calculate college attendance and adjust low income
df[self.COLLEGE_ATTENDANCE_FIELD] = (
df[self.COLLEGE_ATTENDANCE_MALE_ENROLLED_PUBLIC]

21
data/data-pipeline/data_pipeline/score/field_names.py
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@ -99,6 +99,27 @@ LOW_MEDIAN_INCOME_AS_PERCENT_OF_AMI_FIELD = (
"Low median household income as a percent of area median income"
)
# Additional ACS demographic fields.
PERCENT_PREFIX = "Percent "
PERCENT_BLACK_FIELD_NAME = PERCENT_PREFIX + "Black or African American"
PERCENT_AMERICAN_INDIAN_FIELD_NAME = (
PERCENT_PREFIX + "American Indian / Alaska Native"
)
PERCENT_ASIAN_FIELD_NAME = PERCENT_PREFIX + "Asian"
PERCENT_HAWAIIAN_FIELD_NAME = PERCENT_PREFIX + "Native Hawaiian or Pacific"
PERCENT_TWO_OR_MORE_RACES_FIELD_NAME = PERCENT_PREFIX + "two or more races"
PERCENT_NON_HISPANIC_WHITE_FIELD_NAME = PERCENT_PREFIX + "White"
PERCENT_HISPANIC_FIELD_NAME = PERCENT_PREFIX + "Hispanic or Latino"
# Note that `other` is lowercase because the whole field will show up in the download
# file as "Percent other races"
PERCENT_OTHER_RACE_FIELD_NAME = PERCENT_PREFIX + "other races"
# Age
PERCENT_AGE_UNDER_10 = "Percent age under 10"
PERCENT_AGE_10_TO_64 = "Percent age 10 to 64"
PERCENT_AGE_OVER_64 = "Percent age over 64"
# Climate
FEMA_RISK_FIELD = "FEMA Risk Index Expected Annual Loss Score"
EXPECTED_BUILDING_LOSS_RATE_FIELD = (