From 426328e378126760a61388552463183801dbc2f6 Mon Sep 17 00:00:00 2001 From: Emma Nechamkin <97977170+emma-nechamkin@users.noreply.github.com> Date: Wed, 7 Sep 2022 17:13:31 -0400 Subject: [PATCH 01/11] =?UTF-8?q?Updating=20traffic=20barriers=C2=A0to=20i?= =?UTF-8?q?nclude=20low=20pop=20threshold=20(#1889)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Changing the traffic barriers to only be included for places with recorded population --- .../data_pipeline/etl/score/etl_score.py | 17 ++++++++++++++++- 1 file changed, 16 insertions(+), 1 deletion(-) diff --git a/data/data-pipeline/data_pipeline/etl/score/etl_score.py b/data/data-pipeline/data_pipeline/etl/score/etl_score.py index 62a5006d..56682d49 100644 --- a/data/data-pipeline/data_pipeline/etl/score/etl_score.py +++ b/data/data-pipeline/data_pipeline/etl/score/etl_score.py @@ -380,7 +380,8 @@ class ScoreETL(ExtractTransformLoad): ), "Join against national tract list ADDED rows" logger.info( "Dropped %s tracts not in the 2010 tract data", - pre_join_len - census_tract_df[field_names.GEOID_TRACT_FIELD].nunique() + pre_join_len + - census_tract_df[field_names.GEOID_TRACT_FIELD].nunique(), ) # Now sanity-check the merged df. @@ -551,6 +552,9 @@ class ScoreETL(ExtractTransformLoad): # For *Non-Natural Space*, we may only want to include tracts that have at least 35 acreas, I think. This will # get rid of tracts that we think are aberrations statistically. Right now, we have left this out # pending ground-truthing. + # + # 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. for numeric_column in numeric_columns: drop_tracts = [] @@ -575,6 +579,17 @@ class ScoreETL(ExtractTransformLoad): f"Dropping {len(drop_tracts)} tracts from Linguistic Isolation" ) + elif numeric_column == field_names.DOT_TRAVEL_BURDEN_FIELD: + # 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) + low_population = 20 + drop_tracts = df_copy[ + df_copy[field_names.TOTAL_POP_FIELD] <= low_population + ][field_names.GEOID_TRACT_FIELD].to_list() + logger.info( + f"Dropping {len(drop_tracts)} tracts from DOT traffic burden" + ) + df_copy = self._add_percentiles_to_df( df=df_copy, input_column_name=numeric_column, From fb4c484e5c2bed4bd60e329092cb7c05bd9c604f Mon Sep 17 00:00:00 2001 From: Emma Nechamkin <97977170+emma-nechamkin@users.noreply.github.com> Date: Thu, 8 Sep 2022 14:55:00 -0400 Subject: [PATCH 02/11] Remove no land tracts from map (#1894) remove from map --- .../data_pipeline/etl/score/etl_score_geo.py | 14 ++++++++++++-- 1 file changed, 12 insertions(+), 2 deletions(-) diff --git a/data/data-pipeline/data_pipeline/etl/score/etl_score_geo.py b/data/data-pipeline/data_pipeline/etl/score/etl_score_geo.py index da02beef..31eacbe1 100644 --- a/data/data-pipeline/data_pipeline/etl/score/etl_score_geo.py +++ b/data/data-pipeline/data_pipeline/etl/score/etl_score_geo.py @@ -60,6 +60,7 @@ class GeoScoreETL(ExtractTransformLoad): field_names.GEOID_TRACT_FIELD ] self.GEOMETRY_FIELD_NAME = "geometry" + self.LAND_FIELD_NAME = "ALAND10" # We will adjust this upwards while there is some fractional value # in the score. This is a starting value. @@ -86,13 +87,22 @@ class GeoScoreETL(ExtractTransformLoad): ) logger.info("Reading US GeoJSON (~6 minutes)") - self.geojson_usa_df = gpd.read_file( + full_geojson_usa_df = gpd.read_file( self.CENSUS_USA_GEOJSON, dtype={self.GEOID_FIELD_NAME: "string"}, - usecols=[self.GEOID_FIELD_NAME, self.GEOMETRY_FIELD_NAME], + usecols=[ + self.GEOID_FIELD_NAME, + self.GEOMETRY_FIELD_NAME, + self.LAND_FIELD_NAME, + ], low_memory=False, ) + # We only want to keep tracts to visualize that have non-0 land + self.geojson_usa_df = full_geojson_usa_df[ + full_geojson_usa_df[self.LAND_FIELD_NAME] > 0 + ] + logger.info("Reading score CSV") self.score_usa_df = pd.read_csv( self.TILE_SCORE_CSV, From 6e9c44ea7266b055f0dac0e797449d1efbc9f6b7 Mon Sep 17 00:00:00 2001 From: Lucas Merrill Brown Date: Fri, 9 Sep 2022 20:35:01 -0400 Subject: [PATCH 03/11] Issue 1831: missing life expectancy data from Maine and Wisconsin (#1887) * Fixing missing states and adding tests for states to all classes --- data/data-pipeline/data_pipeline/config.py | 1 + data/data-pipeline/data_pipeline/etl/base.py | 40 ++++- .../data_pipeline/etl/score/constants.py | 54 +++++- .../data_pipeline/etl/score/etl_utils.py | 111 ++++++++++++ .../etl/score/tests/test_etl_utils.py | 161 +++++++++++++++++- .../etl/sources/cdc_life_expectancy/etl.py | 117 ++++++++++--- .../sources/child_opportunity_index/etl.py | 2 + .../etl/sources/dot_travel_composite/etl.py | 1 + .../data_pipeline/etl/sources/eamlis/etl.py | 17 ++ .../etl/sources/fsf_wildfire_risk/etl.py | 2 + .../etl/sources/national_risk_index/etl.py | 1 + .../sources/ncld_nature_deprived/README.md | 80 --------- .../sources/ncld_nature_deprived/__init__.py | 0 .../etl/sources/ncld_nature_deprived/etl.py | 77 --------- .../etl/sources/nlcd_nature_deprived/etl.py | 5 + .../etl/sources/us_army_fuds/etl.py | 2 + .../child_opportunity_index/test_etl.py | 2 +- .../sources/doe_energy_burden/test_etl.py | 2 +- .../tests/sources/eamlis/test_etl.py | 4 +- .../tests/sources/example/test_etl.py | 26 ++- .../tests/sources/us_army_fuds/test_etl.py | 4 +- 21 files changed, 522 insertions(+), 187 deletions(-) delete mode 100644 data/data-pipeline/data_pipeline/etl/sources/ncld_nature_deprived/README.md delete mode 100644 data/data-pipeline/data_pipeline/etl/sources/ncld_nature_deprived/__init__.py delete mode 100644 data/data-pipeline/data_pipeline/etl/sources/ncld_nature_deprived/etl.py diff --git a/data/data-pipeline/data_pipeline/config.py b/data/data-pipeline/data_pipeline/config.py index 23e550a8..5dc336c8 100644 --- a/data/data-pipeline/data_pipeline/config.py +++ b/data/data-pipeline/data_pipeline/config.py @@ -12,6 +12,7 @@ settings = Dynaconf( # set root dir settings.APP_ROOT = pathlib.Path(data_pipeline.__file__).resolve().parent +settings.DATA_PATH = settings.APP_ROOT / "data" settings.REQUESTS_DEFAULT_TIMOUT = 3600 # To set an environment use: # Linux/OSX: export ENV_FOR_DYNACONF=staging diff --git a/data/data-pipeline/data_pipeline/etl/base.py b/data/data-pipeline/data_pipeline/etl/base.py index 211fbc31..65580f9a 100644 --- a/data/data-pipeline/data_pipeline/etl/base.py +++ b/data/data-pipeline/data_pipeline/etl/base.py @@ -7,6 +7,9 @@ from typing import Optional import pandas as pd from data_pipeline.config import settings +from data_pipeline.etl.score.etl_utils import ( + compare_to_list_of_expected_state_fips_codes, +) from data_pipeline.etl.score.schemas.datasets import DatasetsConfig from data_pipeline.utils import ( load_yaml_dict_from_file, @@ -43,7 +46,7 @@ class ExtractTransformLoad: APP_ROOT: pathlib.Path = settings.APP_ROOT # Directories - DATA_PATH: pathlib.Path = APP_ROOT / "data" + DATA_PATH: pathlib.Path = settings.DATA_PATH TMP_PATH: pathlib.Path = DATA_PATH / "tmp" CONTENT_CONFIG: pathlib.Path = APP_ROOT / "content" / "config" DATASET_CONFIG_PATH: pathlib.Path = APP_ROOT / "etl" / "score" / "config" @@ -82,6 +85,23 @@ class ExtractTransformLoad: # NULL_REPRESENTATION is how nulls are represented on the input field NULL_REPRESENTATION: str = None + # Whether this ETL contains data for the continental nation (DC & the US states + # except for Alaska and Hawaii) + CONTINENTAL_US_EXPECTED_IN_DATA: bool = True + + # Whether this ETL contains data for Alaska and Hawaii + ALASKA_AND_HAWAII_EXPECTED_IN_DATA: bool = True + + # Whether this ETL contains data for Puerto Rico + PUERTO_RICO_EXPECTED_IN_DATA: bool = True + + # Whether this ETL contains data for the island areas + ISLAND_AREAS_EXPECTED_IN_DATA: bool = False + + # Whether this ETL contains known missing data for any additional + # states/territories + EXPECTED_MISSING_STATES: typing.List[str] = [] + # Thirteen digits in a census block group ID. EXPECTED_CENSUS_BLOCK_GROUPS_CHARACTER_LENGTH: int = 13 # TODO: investigate. Census says there are only 217,740 CBGs in the US. This might @@ -289,6 +309,24 @@ class ExtractTransformLoad: f"`{geo_field}`." ) + # Check whether data contains expected states + states_in_output_df = ( + self.output_df[self.GEOID_TRACT_FIELD_NAME] + .str[0:2] + .unique() + .tolist() + ) + + compare_to_list_of_expected_state_fips_codes( + actual_state_fips_codes=states_in_output_df, + continental_us_expected=self.CONTINENTAL_US_EXPECTED_IN_DATA, + alaska_and_hawaii_expected=self.ALASKA_AND_HAWAII_EXPECTED_IN_DATA, + puerto_rico_expected=self.PUERTO_RICO_EXPECTED_IN_DATA, + island_areas_expected=self.ISLAND_AREAS_EXPECTED_IN_DATA, + additional_fips_codes_not_expected=self.EXPECTED_MISSING_STATES, + dataset_name=self.NAME, + ) + def load(self, float_format=None) -> None: """Saves the transformed data. diff --git a/data/data-pipeline/data_pipeline/etl/score/constants.py b/data/data-pipeline/data_pipeline/etl/score/constants.py index f50eadaa..c112eec0 100644 --- a/data/data-pipeline/data_pipeline/etl/score/constants.py +++ b/data/data-pipeline/data_pipeline/etl/score/constants.py @@ -131,6 +131,58 @@ TILES_NATION_THRESHOLD_COUNT = 21 # 60: American Samoa, 66: Guam, 69: N. Mariana Islands, 78: US Virgin Islands TILES_ISLAND_AREA_FIPS_CODES = ["60", "66", "69", "78"] TILES_PUERTO_RICO_FIPS_CODE = ["72"] +TILES_ALASKA_AND_HAWAII_FIPS_CODE = ["02", "15"] +TILES_CONTINENTAL_US_FIPS_CODE = [ + "01", + "04", + "05", + "06", + "08", + "09", + "10", + "11", + "12", + "13", + "16", + "17", + "18", + "19", + "20", + "21", + "22", + "23", + "24", + "25", + "26", + "27", + "28", + "29", + "30", + "31", + "32", + "33", + "34", + "35", + "36", + "37", + "38", + "39", + "40", + "41", + "42", + "44", + "45", + "46", + "47", + "48", + "49", + "50", + "51", + "53", + "54", + "55", + "56", +] # Constant to reflect UI Experience version # "Nation" referring to 50 states and DC is from Census @@ -399,5 +451,5 @@ TILES_SCORE_FLOAT_COLUMNS = [ # that use null to signify missing information in a boolean field. field_names.ELIGIBLE_FUDS_BINARY_FIELD_NAME, field_names.AML_BOOLEAN, - field_names.HISTORIC_REDLINING_SCORE_EXCEEDED + field_names.HISTORIC_REDLINING_SCORE_EXCEEDED, ] diff --git a/data/data-pipeline/data_pipeline/etl/score/etl_utils.py b/data/data-pipeline/data_pipeline/etl/score/etl_utils.py index f5222620..5f7b6ecd 100644 --- a/data/data-pipeline/data_pipeline/etl/score/etl_utils.py +++ b/data/data-pipeline/data_pipeline/etl/score/etl_utils.py @@ -1,11 +1,19 @@ import os import sys +import typing from pathlib import Path from collections import namedtuple import numpy as np import pandas as pd from data_pipeline.config import settings +from data_pipeline.etl.score.constants import ( + TILES_ISLAND_AREA_FIPS_CODES, + TILES_PUERTO_RICO_FIPS_CODE, + TILES_CONTINENTAL_US_FIPS_CODE, + TILES_ALASKA_AND_HAWAII_FIPS_CODE, +) +from data_pipeline.etl.sources.census.etl_utils import get_state_fips_codes from data_pipeline.utils import ( download_file_from_url, get_module_logger, @@ -305,3 +313,106 @@ def create_codebook( return merged_codebook_df[constants.CODEBOOK_COLUMNS].rename( columns={constants.CEJST_SCORE_COLUMN_NAME: "Description"} ) + + +# pylint: disable=too-many-arguments +def compare_to_list_of_expected_state_fips_codes( + actual_state_fips_codes: typing.List[str], + continental_us_expected: bool = True, + alaska_and_hawaii_expected: bool = True, + puerto_rico_expected: bool = True, + island_areas_expected: bool = True, + additional_fips_codes_not_expected: typing.List[str] = None, + dataset_name: str = None, +) -> None: + """Check whether a list of state/territory FIPS codes match expectations. + + Args: + actual_state_fips_codes (List of str): Actual state codes observed in data + continental_us_expected (bool, optional): Do you expect the continental nation + (DC & states except for Alaska and Hawaii) to be represented in data? + alaska_and_hawaii_expected (bool, optional): Do you expect Alaska and Hawaii + to be represented in the data? Note: if only *1* of Alaska and Hawaii are + not expected to be included, do not use this argument -- instead, + use `additional_fips_codes_not_expected` for the 1 state you expected to + be missing. + puerto_rico_expected (bool, optional): Do you expect PR to be represented in data? + island_areas_expected (bool, optional): Do you expect Island Areas to be represented in + data? + additional_fips_codes_not_expected (List of str, optional): Additional state codes + not expected in the data. For example, the data may be known to be missing + data from Maine and Wisconsin. + dataset_name (str, optional): The name of the data set, used only in printing an + error message. (This is helpful for debugging during parallel etl runs.) + + Returns: + None: Does not return any values. + + Raises: + ValueError: if lists do not match expectations. + """ + # Setting default argument of [] here to avoid mutability problems. + if additional_fips_codes_not_expected is None: + additional_fips_codes_not_expected = [] + + # Cast input to a set. + actual_state_fips_codes_set = set(actual_state_fips_codes) + + # Start with the list of all FIPS codes for all states and territories. + expected_states_set = set(get_state_fips_codes(settings.DATA_PATH)) + + # If continental US is not expected to be included, remove it from the + # expected states set. + if not continental_us_expected: + expected_states_set = expected_states_set - set( + TILES_CONTINENTAL_US_FIPS_CODE + ) + + # If both Alaska and Hawaii are not expected to be included, remove them from the + # expected states set. + # Note: if only *1* of Alaska and Hawaii are not expected to be included, + # do not use this argument -- instead, use `additional_fips_codes_not_expected` + # for the 1 state you expected to be missing. + if not alaska_and_hawaii_expected: + expected_states_set = expected_states_set - set( + TILES_ALASKA_AND_HAWAII_FIPS_CODE + ) + + # If Puerto Rico is not expected to be included, remove it from the expected + # states set. + if not puerto_rico_expected: + expected_states_set = expected_states_set - set( + TILES_PUERTO_RICO_FIPS_CODE + ) + + # If island areas are not expected to be included, remove them from the expected + # states set. + if not island_areas_expected: + expected_states_set = expected_states_set - set( + TILES_ISLAND_AREA_FIPS_CODES + ) + + # If additional FIPS codes are not expected to be included, remove them from the + # expected states set. + expected_states_set = expected_states_set - set( + additional_fips_codes_not_expected + ) + + dataset_name_phrase = ( + f" for dataset `{dataset_name}`" if dataset_name is not None else "" + ) + + if expected_states_set != actual_state_fips_codes_set: + raise ValueError( + f"The states and territories in the data{dataset_name_phrase} are not " + f"as expected.\n" + "FIPS state codes expected that are not present in the data:\n" + f"{sorted(list(expected_states_set - actual_state_fips_codes_set))}\n" + "FIPS state codes in the data that were not expected:\n" + f"{sorted(list(actual_state_fips_codes_set - expected_states_set))}\n" + ) + else: + logger.info( + "Data matches expected state and territory representation" + f"{dataset_name_phrase}." + ) diff --git a/data/data-pipeline/data_pipeline/etl/score/tests/test_etl_utils.py b/data/data-pipeline/data_pipeline/etl/score/tests/test_etl_utils.py index 594f4856..ed33c63e 100644 --- a/data/data-pipeline/data_pipeline/etl/score/tests/test_etl_utils.py +++ b/data/data-pipeline/data_pipeline/etl/score/tests/test_etl_utils.py @@ -2,7 +2,10 @@ import pandas as pd import numpy as np import pytest -from data_pipeline.etl.score.etl_utils import floor_series +from data_pipeline.etl.score.etl_utils import ( + floor_series, + compare_to_list_of_expected_state_fips_codes, +) def test_floor_series(): @@ -70,3 +73,159 @@ def test_floor_series(): match="Argument series must be of type pandas series, not of type list.", ): floor_series(invalid_type, number_of_decimals=3) + + +def test_compare_to_list_of_expected_state_fips_codes(): + # Has every state/territory/DC code + fips_codes_test_1 = [ + "01", + "02", + "04", + "05", + "06", + "08", + "09", + "10", + "11", + "12", + "13", + "15", + "16", + "17", + "18", + "19", + "20", + "21", + "22", + "23", + "24", + "25", + "26", + "27", + "28", + "29", + "30", + "31", + "32", + "33", + "34", + "35", + "36", + "37", + "38", + "39", + "40", + "41", + "42", + "44", + "45", + "46", + "47", + "48", + "49", + "50", + "51", + "53", + "54", + "55", + "56", + "60", + "66", + "69", + "72", + "78", + ] + + # Should not raise any errors + compare_to_list_of_expected_state_fips_codes( + actual_state_fips_codes=fips_codes_test_1 + ) + + # Should raise error because Puerto Rico is not expected + with pytest.raises(ValueError) as exception_info: + compare_to_list_of_expected_state_fips_codes( + actual_state_fips_codes=fips_codes_test_1, + puerto_rico_expected=False, + ) + partial_expected_error_message = ( + "FIPS state codes in the data that were not expected:\n['72']\n" + ) + assert partial_expected_error_message in str(exception_info.value) + + # Should raise error because Island Areas are not expected + with pytest.raises(ValueError) as exception_info: + compare_to_list_of_expected_state_fips_codes( + actual_state_fips_codes=fips_codes_test_1, + island_areas_expected=False, + ) + partial_expected_error_message = ( + "FIPS state codes in the data that were not expected:\n" + "['60', '66', '69', '78']\n" + ) + assert partial_expected_error_message in str(exception_info.value) + + # List missing PR and Guam + fips_codes_test_2 = [x for x in fips_codes_test_1 if x not in ["66", "72"]] + + # Should raise error because all Island Areas and PR are expected + with pytest.raises(ValueError) as exception_info: + compare_to_list_of_expected_state_fips_codes( + actual_state_fips_codes=fips_codes_test_2, + ) + partial_expected_error_message = ( + "FIPS state codes expected that are not present in the data:\n" + "['66', '72']\n" + ) + assert partial_expected_error_message in str(exception_info.value) + + # Missing Maine and Wisconsin + fips_codes_test_3 = [x for x in fips_codes_test_1 if x not in ["23", "55"]] + + # Should raise error because Maine and Wisconsin are expected + with pytest.raises(ValueError) as exception_info: + compare_to_list_of_expected_state_fips_codes( + actual_state_fips_codes=fips_codes_test_3, + ) + partial_expected_error_message = ( + "FIPS state codes expected that are not present in the data:\n" + "['23', '55']\n" + ) + assert partial_expected_error_message in str(exception_info.value) + + # Should not raise error because Maine and Wisconsin are expected to be missing + compare_to_list_of_expected_state_fips_codes( + actual_state_fips_codes=fips_codes_test_3, + additional_fips_codes_not_expected=["23", "55"], + ) + + # Missing the continental & AK/HI nation + fips_codes_test_4 = [ + "60", + "66", + "69", + "72", + "78", + ] + + # Should raise error because the nation is expected + with pytest.raises(ValueError) as exception_info: + compare_to_list_of_expected_state_fips_codes( + actual_state_fips_codes=fips_codes_test_4, + ) + + partial_expected_error_message = ( + "FIPS state codes expected that are not present in the data:\n" + "['01', '02', '04', '05', '06', '08', '09', '10', '11', '12', '13', '15', '16', " + "'17', '18', '19', '20', '21', '22', '23', '24', '25', '26', '27', '28', '29', " + "'30', '31', '32', '33', '34', '35', '36', '37', '38', '39', '40', '41', '42', " + "'44', '45', '46', '47', '48', '49', '50', '51', '53', '54', '55', '56']" + ) + + assert partial_expected_error_message in str(exception_info.value) + + # Should not raise error because continental US and AK/HI is not to be missing + compare_to_list_of_expected_state_fips_codes( + actual_state_fips_codes=fips_codes_test_4, + continental_us_expected=False, + alaska_and_hawaii_expected=False, + ) diff --git a/data/data-pipeline/data_pipeline/etl/sources/cdc_life_expectancy/etl.py b/data/data-pipeline/data_pipeline/etl/sources/cdc_life_expectancy/etl.py index 2aac7412..d75ca85b 100644 --- a/data/data-pipeline/data_pipeline/etl/sources/cdc_life_expectancy/etl.py +++ b/data/data-pipeline/data_pipeline/etl/sources/cdc_life_expectancy/etl.py @@ -1,58 +1,137 @@ +import pathlib from pathlib import Path import pandas as pd -from data_pipeline.etl.base import ExtractTransformLoad +from data_pipeline.etl.base import ExtractTransformLoad, ValidGeoLevel +from data_pipeline.etl.score.etl_utils import ( + compare_to_list_of_expected_state_fips_codes, +) +from data_pipeline.score import field_names from data_pipeline.utils import get_module_logger, download_file_from_url logger = get_module_logger(__name__) class CDCLifeExpectancy(ExtractTransformLoad): + GEO_LEVEL = ValidGeoLevel.CENSUS_TRACT + PUERTO_RICO_EXPECTED_IN_DATA = False + + USA_FILE_URL: str = "https://ftp.cdc.gov/pub/Health_Statistics/NCHS/Datasets/NVSS/USALEEP/CSV/US_A.CSV" + + STATES_MISSING_FROM_USA_FILE = ["23", "55"] + + # For some reason, LEEP does not include Maine or Wisconsin in its "All of + # USA" file. Load these separately. + WISCONSIN_FILE_URL: str = "https://ftp.cdc.gov/pub/Health_Statistics/NCHS/Datasets/NVSS/USALEEP/CSV/WI_A.CSV" + MAINE_FILE_URL: str = "https://ftp.cdc.gov/pub/Health_Statistics/NCHS/Datasets/NVSS/USALEEP/CSV/ME_A.CSV" + + TRACT_INPUT_COLUMN_NAME = "Tract ID" + STATE_INPUT_COLUMN_NAME = "STATE2KX" + + raw_df: pd.DataFrame + output_df: pd.DataFrame + def __init__(self): - self.FILE_URL: str = "https://ftp.cdc.gov/pub/Health_Statistics/NCHS/Datasets/NVSS/USALEEP/CSV/US_A.CSV" self.OUTPUT_PATH: Path = ( self.DATA_PATH / "dataset" / "cdc_life_expectancy" ) - self.TRACT_INPUT_COLUMN_NAME = "Tract ID" - self.LIFE_EXPECTANCY_FIELD_NAME = "Life expectancy (years)" - # Constants for output self.COLUMNS_TO_KEEP = [ self.GEOID_TRACT_FIELD_NAME, - self.LIFE_EXPECTANCY_FIELD_NAME, + field_names.LIFE_EXPECTANCY_FIELD, ] - self.raw_df: pd.DataFrame - self.output_df: pd.DataFrame - - def extract(self) -> None: - logger.info("Starting data download.") - - download_file_name = ( - self.get_tmp_path() / "cdc_life_expectancy" / "usa.csv" - ) + def _download_and_prep_data( + self, file_url: str, download_file_name: pathlib.Path + ) -> pd.DataFrame: download_file_from_url( - file_url=self.FILE_URL, + file_url=file_url, download_file_name=download_file_name, verify=True, ) - self.raw_df = pd.read_csv( + df = pd.read_csv( filepath_or_buffer=download_file_name, dtype={ # The following need to remain as strings for all of their digits, not get converted to numbers. self.TRACT_INPUT_COLUMN_NAME: "string", + self.STATE_INPUT_COLUMN_NAME: "string", }, low_memory=False, ) + return df + + def extract(self) -> None: + logger.info("Starting data download.") + + all_usa_raw_df = self._download_and_prep_data( + file_url=self.USA_FILE_URL, + download_file_name=self.get_tmp_path() + / "cdc_life_expectancy" + / "usa.csv", + ) + + # Check which states are missing + states_in_life_expectancy_usa_file = list( + all_usa_raw_df[self.STATE_INPUT_COLUMN_NAME].unique() + ) + + # Expect that PR, Island Areas, and Maine/Wisconsin are missing + compare_to_list_of_expected_state_fips_codes( + actual_state_fips_codes=states_in_life_expectancy_usa_file, + continental_us_expected=self.CONTINENTAL_US_EXPECTED_IN_DATA, + puerto_rico_expected=self.PUERTO_RICO_EXPECTED_IN_DATA, + island_areas_expected=self.ISLAND_AREAS_EXPECTED_IN_DATA, + additional_fips_codes_not_expected=self.STATES_MISSING_FROM_USA_FILE, + ) + + logger.info("Downloading data for Maine") + maine_raw_df = self._download_and_prep_data( + file_url=self.MAINE_FILE_URL, + download_file_name=self.get_tmp_path() + / "cdc_life_expectancy" + / "maine.csv", + ) + + logger.info("Downloading data for Wisconsin") + wisconsin_raw_df = self._download_and_prep_data( + file_url=self.WISCONSIN_FILE_URL, + download_file_name=self.get_tmp_path() + / "cdc_life_expectancy" + / "wisconsin.csv", + ) + + combined_df = pd.concat( + objs=[all_usa_raw_df, maine_raw_df, wisconsin_raw_df], + ignore_index=True, + verify_integrity=True, + axis=0, + ) + + states_in_combined_df = list( + combined_df[self.STATE_INPUT_COLUMN_NAME].unique() + ) + + # Expect that PR and Island Areas are the only things now missing + compare_to_list_of_expected_state_fips_codes( + actual_state_fips_codes=states_in_combined_df, + continental_us_expected=self.CONTINENTAL_US_EXPECTED_IN_DATA, + puerto_rico_expected=self.PUERTO_RICO_EXPECTED_IN_DATA, + island_areas_expected=self.ISLAND_AREAS_EXPECTED_IN_DATA, + additional_fips_codes_not_expected=[], + ) + + # Save the updated version + self.raw_df = combined_df + def transform(self) -> None: - logger.info("Starting DOE energy burden transform.") + logger.info("Starting CDC life expectancy transform.") self.output_df = self.raw_df.rename( columns={ - "e(0)": self.LIFE_EXPECTANCY_FIELD_NAME, + "e(0)": field_names.LIFE_EXPECTANCY_FIELD, self.TRACT_INPUT_COLUMN_NAME: self.GEOID_TRACT_FIELD_NAME, } ) diff --git a/data/data-pipeline/data_pipeline/etl/sources/child_opportunity_index/etl.py b/data/data-pipeline/data_pipeline/etl/sources/child_opportunity_index/etl.py index b3e40e3a..d2b7143c 100644 --- a/data/data-pipeline/data_pipeline/etl/sources/child_opportunity_index/etl.py +++ b/data/data-pipeline/data_pipeline/etl/sources/child_opportunity_index/etl.py @@ -32,6 +32,8 @@ class ChildOpportunityIndex(ExtractTransformLoad): IMPENETRABLE_SURFACES_FIELD: str READING_FIELD: str + PUERTO_RICO_EXPECTED_IN_DATA = False + def __init__(self): self.SOURCE_URL = ( "https://data.diversitydatakids.org/datastore/zip/f16fff12-b1e5-4f60-85d3-" diff --git a/data/data-pipeline/data_pipeline/etl/sources/dot_travel_composite/etl.py b/data/data-pipeline/data_pipeline/etl/sources/dot_travel_composite/etl.py index 2a99f76f..9e9d0f3f 100644 --- a/data/data-pipeline/data_pipeline/etl/sources/dot_travel_composite/etl.py +++ b/data/data-pipeline/data_pipeline/etl/sources/dot_travel_composite/etl.py @@ -16,6 +16,7 @@ class TravelCompositeETL(ExtractTransformLoad): NAME = "travel_composite" SOURCE_URL = "https://www.transportation.gov/sites/dot.gov/files/Shapefile_and_Metadata.zip" GEO_LEVEL = ValidGeoLevel.CENSUS_TRACT + PUERTO_RICO_EXPECTED_IN_DATA = False # Output score variables (values set on datasets.yml) for linting purposes TRAVEL_BURDEN_FIELD_NAME: str diff --git a/data/data-pipeline/data_pipeline/etl/sources/eamlis/etl.py b/data/data-pipeline/data_pipeline/etl/sources/eamlis/etl.py index 0c09b711..457890db 100644 --- a/data/data-pipeline/data_pipeline/etl/sources/eamlis/etl.py +++ b/data/data-pipeline/data_pipeline/etl/sources/eamlis/etl.py @@ -20,6 +20,23 @@ class AbandonedMineETL(ExtractTransformLoad): GEO_LEVEL = ValidGeoLevel.CENSUS_TRACT AML_BOOLEAN: str + PUERTO_RICO_EXPECTED_IN_DATA = False + EXPECTED_MISSING_STATES = [ + "10", + "11", + "12", + "15", + "23", + "27", + "31", + "33", + "34", + "36", + "45", + "50", + "55", + ] + # Define these for easy code completion def __init__(self): self.SOURCE_URL = ( diff --git a/data/data-pipeline/data_pipeline/etl/sources/fsf_wildfire_risk/etl.py b/data/data-pipeline/data_pipeline/etl/sources/fsf_wildfire_risk/etl.py index 2a26370e..b623206c 100644 --- a/data/data-pipeline/data_pipeline/etl/sources/fsf_wildfire_risk/etl.py +++ b/data/data-pipeline/data_pipeline/etl/sources/fsf_wildfire_risk/etl.py @@ -16,6 +16,8 @@ class WildfireRiskETL(ExtractTransformLoad): NAME = "fsf_wildfire_risk" SOURCE_URL = settings.AWS_JUSTICE40_DATASOURCES_URL + "/fsf_fire.zip" GEO_LEVEL = ValidGeoLevel.CENSUS_TRACT + PUERTO_RICO_EXPECTED_IN_DATA = False + ALASKA_AND_HAWAII_EXPECTED_IN_DATA = False # Output score variables (values set on datasets.yml) for linting purposes COUNT_PROPERTIES: str diff --git a/data/data-pipeline/data_pipeline/etl/sources/national_risk_index/etl.py b/data/data-pipeline/data_pipeline/etl/sources/national_risk_index/etl.py index c6a312c0..57681974 100644 --- a/data/data-pipeline/data_pipeline/etl/sources/national_risk_index/etl.py +++ b/data/data-pipeline/data_pipeline/etl/sources/national_risk_index/etl.py @@ -17,6 +17,7 @@ class NationalRiskIndexETL(ExtractTransformLoad): NAME = "national_risk_index" SOURCE_URL = "https://hazards.fema.gov/nri/Content/StaticDocuments/DataDownload//NRI_Table_CensusTracts/NRI_Table_CensusTracts.zip" GEO_LEVEL = ValidGeoLevel.CENSUS_TRACT + PUERTO_RICO_EXPECTED_IN_DATA = False # Output score variables (values set on datasets.yml) for linting purposes RISK_INDEX_EXPECTED_ANNUAL_LOSS_SCORE_FIELD_NAME: str diff --git a/data/data-pipeline/data_pipeline/etl/sources/ncld_nature_deprived/README.md b/data/data-pipeline/data_pipeline/etl/sources/ncld_nature_deprived/README.md deleted file mode 100644 index d8736d54..00000000 --- a/data/data-pipeline/data_pipeline/etl/sources/ncld_nature_deprived/README.md +++ /dev/null @@ -1,80 +0,0 @@ -# Nature deprived communities data - -The following dataset was compiled by TPL using NCLD data. We define as: AREA - [CROPLAND] - [IMPERVIOUS SURFACES]. - -## Codebook -- GEOID10 – Census tract ID -- SF – State Name -- CF – County Name -- P200_PFS – Percent of individuals below 200% Federal Poverty Line (from CEJST source data). -- CA_LT20 – Percent higher ed enrollment rate is less than 20% (from CEJST source data). -- TractAcres – Acres of tract calculated from ALAND10 field (area land/meters) in 2010 census tracts. - - CAVEAT: Some census tracts in the CEJST source file extend into open water. ALAND10 area was used to constrain percent calculations (e.g. cropland area) to land only. -- AcresCrops – Acres crops calculated by summing all cells in the NLCD Cropland Data Layer crop classes. -- PctCrops – Formula: AcresCrops/TractAcres*100. -- PctImperv – Mean imperviousness for each census tract. - - CAVEAT: Where tracts extend into open water, mean imperviousness may be underestimated. -- __TO USE__ PctNatural – Formula: 100 – PctCrops – PctImperv. -- PctNat90 – Tract in or below 10th percentile for PctNatural. 1 = True, 0 = False. - - PctNatural 10th percentile = 28.6439% -- ImpOrCrop – If tract >= 90th percentile for PctImperv OR PctCrops. 1 = True, 0 = False. - - PctImperv 90th percentile = 67.4146 % - - PctCrops 90th percentile = 27.8116 % -- LowInAndEd – If tract >= 65th percentile for P200_PFS AND CA_LT20. - - P200_PFS 65th percentile = 64.0% -- NatureDep – ImpOrCrp = 1 AND LowInAndEd = 1. - -We added `GEOID10_TRACT` before converting shapefile to csv. - -## Instructions to recreate - -### Creating Impervious plus Cropland Attributes for Census Tracts - -The Cropland Data Layer and NLCD Impervious layer were too big to put on our OneDrive, but you can download them here: - CDL: https://www.nass.usda.gov/Research_and_Science/Cropland/Release/datasets/2021_30m_cdls.zip - Impervious: https://s3-us-west-2.amazonaws.com/mrlc/nlcd_2019_impervious_l48_20210604.zip - - -#### Crops - -Add an attribute called TractAcres (or similar) to the census tracts to hold a value representing acres covered by the census tract. -Calculate the TractAcres field for each census tract by using the Calculate Geometry tool (set the Property to Area (geodesic), and the Units to Acres). -From the Cropland Data Layer (CDL), extract only the pixels representing crops, using the Extract by Attributes tool in ArcGIS Spatial Analyst toolbox. -a. The attribute table tells you the names of each type of land cover. Since the CDL also contains NLCD classes and empty classes, the actual crop classes must be extracted. -From the crops-only raster extracted from the CDL, run the Reclassify tool to create a binary layer where all crops = 1, and everything else is Null. -Run the Tabulate Area tool: -a. Zone data = census tracts -b. Input raster data = the binary crops layer -c. This will produce a table with the square meters of crops in each census tract contained in an attribute called VALUE_1 -Run the Join Field tool to join the table to the census tracts, with the VALUE_1 field as the Transfer Field, to transfer the VALUE_1 field (square meters of crops) to the census tracts. -Add a field to the census tracts called AcresCrops (or similar) to hold the acreage of crops in each census tract. -Calculate the AcresCrops field by multiplying the VALUE_1 field by 0.000247105 to produce acres of crops in each census tracts. -a. You can delete the VALUE_1 field. -Add a field called PctCrops (or similar) to hold the percent of each census tract occupied by crops. -Calculate the PctCrops field by dividing the AcresCrops field by the TractAcres field, and multiply by 100 to get the percent. -Impervious - -Run the Zonal Statistics as Table tool: -a. Zone data = census tracts -b. Input raster data = impervious data raster layer -c. Statistics type = Mean -d. This will produce a table with the percent of each census tract occupied by impervious surfaces, contained in an attribute called MEAN - -Run the Join Field tool to join the table to the census tracts, with the MEAN field as the Transfer Field, to transfer the MEAN field (percent impervious) to the census tracts. - -Add a field called PctImperv (or similar) to hold the percent impervious value. - -Calculate the PctImperv field by setting it equal to the MEAN field. -a. You can delete the MEAN field. -Combine the Crops and Impervious Data - -Open the census tracts attribute table and add a field called PctNatural (or similar). Calculate this field using this equation: 100 – PctCrops – PctImperv . This produces a value that tells you the percent of each census tract covered in natural land cover. - -Define the census tracts that fall in the 90th percentile of non-natural land cover: -a. Add a field called PctNat90 (or similar) -b. Right-click on the PctNatural field, and click Sort Ascending (lowest PctNatural values on top) -c. Select the top 10 percent of rows after the sort -d. Click on Show Selected Records in the attribute table -e. Calculate the PctNat90 field for the selected records = 1 -f. Clear the selection -g. The rows that now have a value of 1 for PctNat90 are the most lacking for natural land cover, and can be symbolized accordingly in a map diff --git a/data/data-pipeline/data_pipeline/etl/sources/ncld_nature_deprived/__init__.py b/data/data-pipeline/data_pipeline/etl/sources/ncld_nature_deprived/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/data/data-pipeline/data_pipeline/etl/sources/ncld_nature_deprived/etl.py b/data/data-pipeline/data_pipeline/etl/sources/ncld_nature_deprived/etl.py deleted file mode 100644 index 14d49c52..00000000 --- a/data/data-pipeline/data_pipeline/etl/sources/ncld_nature_deprived/etl.py +++ /dev/null @@ -1,77 +0,0 @@ -# pylint: disable=unsubscriptable-object -# pylint: disable=unsupported-assignment-operation - -import pandas as pd -from data_pipeline.config import settings - -from data_pipeline.etl.base import ExtractTransformLoad, ValidGeoLevel -from data_pipeline.utils import get_module_logger - -logger = get_module_logger(__name__) - - -class NatureDeprivedETL(ExtractTransformLoad): - """ETL class for the Nature Deprived Communities dataset""" - - NAME = "ncld_nature_deprived" - SOURCE_URL = ( - settings.AWS_JUSTICE40_DATASOURCES_URL - + "/usa_conus_nat_dep__compiled_by_TPL.csv.zip" - ) - GEO_LEVEL = ValidGeoLevel.CENSUS_TRACT - - # Output score variables (values set on datasets.yml) for linting purposes - ELIGIBLE_FOR_NATURE_DEPRIVED_FIELD_NAME: str - TRACT_PERCENT_IMPERVIOUS_FIELD_NAME: str - TRACT_PERCENT_NON_NATURAL_FIELD_NAME: str - TRACT_PERCENT_CROPLAND_FIELD_NAME: str - - def __init__(self): - # define the full path for the input CSV file - self.INPUT_CSV = ( - self.get_tmp_path() / "usa_conus_nat_dep__compiled_by_TPL.csv" - ) - - # this is the main dataframe - self.df: pd.DataFrame - - # Start dataset-specific vars here - self.PERCENT_NATURAL_FIELD_NAME = "PctNatural" - self.PERCENT_IMPERVIOUS_FIELD_NAME = "PctImperv" - self.PERCENT_CROPLAND_FIELD_NAME = "PctCrops" - self.TRACT_ACRES_FIELD_NAME = "TractAcres" - # In order to ensure that tracts with very small Acreage, we want to create an eligibility criterion - # similar to agrivalue. Here, we are ensuring that a tract has at least 35 acres, or is above the 1st percentile - # for area. This does indeed remove tracts from the 90th+ percentile later on - self.TRACT_ACRES_LOWER_BOUND = 35 - - def transform(self) -> None: - """Reads the unzipped data file into memory and applies the following - transformations to prepare it for the load() method: - - - Renames columns as needed - """ - logger.info("Transforming NCLD Data") - - logger.info(self.COLUMNS_TO_KEEP) - - df_ncld: pd.DataFrame = pd.read_csv( - self.INPUT_CSV, - dtype={self.INPUT_GEOID_TRACT_FIELD_NAME: str}, - low_memory=False, - ) - - df_ncld[self.ELIGIBLE_FOR_NATURE_DEPRIVED_FIELD_NAME] = ( - df_ncld[self.TRACT_ACRES_FIELD_NAME] >= self.TRACT_ACRES_LOWER_BOUND - ) - df_ncld[self.TRACT_PERCENT_NON_NATURAL_FIELD_NAME] = ( - 1 - df_ncld[self.PERCENT_NATURAL_FIELD_NAME] - ) - - # Assign the final df to the class' output_df for the load method with rename - self.output_df = df_ncld.rename( - columns={ - self.PERCENT_IMPERVIOUS_FIELD_NAME: self.TRACT_PERCENT_IMPERVIOUS_FIELD_NAME, - self.PERCENT_CROPLAND_FIELD_NAME: self.TRACT_PERCENT_CROPLAND_FIELD_NAME, - } - ) diff --git a/data/data-pipeline/data_pipeline/etl/sources/nlcd_nature_deprived/etl.py b/data/data-pipeline/data_pipeline/etl/sources/nlcd_nature_deprived/etl.py index e9951da2..651d7f68 100644 --- a/data/data-pipeline/data_pipeline/etl/sources/nlcd_nature_deprived/etl.py +++ b/data/data-pipeline/data_pipeline/etl/sources/nlcd_nature_deprived/etl.py @@ -19,6 +19,11 @@ class NatureDeprivedETL(ExtractTransformLoad): + "/usa_conus_nat_dep__compiled_by_TPL.csv.zip" ) GEO_LEVEL = ValidGeoLevel.CENSUS_TRACT + PUERTO_RICO_EXPECTED_IN_DATA = False + ALASKA_AND_HAWAII_EXPECTED_IN_DATA = False + + # Alaska and Hawaii are missing + EXPECTED_MISSING_STATES = ["02", "15"] # Output score variables (values set on datasets.yml) for linting purposes ELIGIBLE_FOR_NATURE_DEPRIVED_FIELD_NAME: str diff --git a/data/data-pipeline/data_pipeline/etl/sources/us_army_fuds/etl.py b/data/data-pipeline/data_pipeline/etl/sources/us_army_fuds/etl.py index f35d4749..945f1039 100644 --- a/data/data-pipeline/data_pipeline/etl/sources/us_army_fuds/etl.py +++ b/data/data-pipeline/data_pipeline/etl/sources/us_army_fuds/etl.py @@ -20,6 +20,8 @@ class USArmyFUDS(ExtractTransformLoad): ELIGIBLE_FUDS_BINARY_FIELD_NAME: str GEO_LEVEL: ValidGeoLevel = ValidGeoLevel.CENSUS_TRACT + ISLAND_AREAS_EXPECTED_IN_DATA = True + def __init__(self): self.FILE_URL: str = ( "https://opendata.arcgis.com/api/v3/datasets/" diff --git a/data/data-pipeline/data_pipeline/tests/sources/child_opportunity_index/test_etl.py b/data/data-pipeline/data_pipeline/tests/sources/child_opportunity_index/test_etl.py index 7183f911..6fe2ffd3 100644 --- a/data/data-pipeline/data_pipeline/tests/sources/child_opportunity_index/test_etl.py +++ b/data/data-pipeline/data_pipeline/tests/sources/child_opportunity_index/test_etl.py @@ -59,7 +59,7 @@ class TestChildOpportunityIndexETL(TestETL): def test_get_output_file_path(self, mock_etl, mock_paths): """Tests the right file name is returned.""" - etl = self._ETL_CLASS() + etl = self._get_instance_of_etl_class() data_path, tmp_path = mock_paths output_file_path = etl._get_output_file_path() diff --git a/data/data-pipeline/data_pipeline/tests/sources/doe_energy_burden/test_etl.py b/data/data-pipeline/data_pipeline/tests/sources/doe_energy_burden/test_etl.py index efa70d57..bb24ba3e 100644 --- a/data/data-pipeline/data_pipeline/tests/sources/doe_energy_burden/test_etl.py +++ b/data/data-pipeline/data_pipeline/tests/sources/doe_energy_burden/test_etl.py @@ -51,7 +51,7 @@ class TestDOEEnergyBurdenETL(TestETL): def test_get_output_file_path(self, mock_etl, mock_paths): """Tests the right file name is returned.""" - etl = self._ETL_CLASS() + etl = self._get_instance_of_etl_class() data_path, tmp_path = mock_paths output_file_path = etl._get_output_file_path() diff --git a/data/data-pipeline/data_pipeline/tests/sources/eamlis/test_etl.py b/data/data-pipeline/data_pipeline/tests/sources/eamlis/test_etl.py index 2f85b55e..b2a5f44b 100644 --- a/data/data-pipeline/data_pipeline/tests/sources/eamlis/test_etl.py +++ b/data/data-pipeline/data_pipeline/tests/sources/eamlis/test_etl.py @@ -65,7 +65,7 @@ class TestAbandondedLandMineETL(TestETL): initiliazed correctly. """ # setup - etl = self._ETL_CLASS() + etl = self._get_instance_of_etl_class() # validation assert etl.GEOID_FIELD_NAME == "GEOID10" assert etl.GEOID_TRACT_FIELD_NAME == "GEOID10_TRACT" @@ -78,7 +78,7 @@ class TestAbandondedLandMineETL(TestETL): def test_get_output_file_path(self, mock_etl, mock_paths): """Tests the right file name is returned.""" - etl = self._ETL_CLASS() + etl = self._get_instance_of_etl_class() data_path, tmp_path = mock_paths output_file_path = etl._get_output_file_path() diff --git a/data/data-pipeline/data_pipeline/tests/sources/example/test_etl.py b/data/data-pipeline/data_pipeline/tests/sources/example/test_etl.py index e4c8305f..8855baad 100644 --- a/data/data-pipeline/data_pipeline/tests/sources/example/test_etl.py +++ b/data/data-pipeline/data_pipeline/tests/sources/example/test_etl.py @@ -11,6 +11,10 @@ import numpy as np import pandas as pd from data_pipeline.etl.base import ExtractTransformLoad, ValidGeoLevel +from data_pipeline.etl.score.constants import ( + TILES_CONTINENTAL_US_FIPS_CODE, + TILES_ALASKA_AND_HAWAII_FIPS_CODE, +) from data_pipeline.tests.sources.example.etl import ExampleETL from data_pipeline.utils import get_module_logger @@ -86,7 +90,25 @@ class TestETL: self._DATA_DIRECTORY_FOR_TEST = pathlib.Path(filename).parent / "data" def _get_instance_of_etl_class(self) -> Type[ExtractTransformLoad]: - return self._ETL_CLASS() + etl_class = self._ETL_CLASS() + + # Find out what unique state codes are present in the test fixture data. + states_expected_from_fixtures = { + x[0:2] for x in self._FIXTURES_SHARED_TRACT_IDS + } + + # Set values to match test fixtures + etl_class.EXPECTED_MISSING_STATES = [ + x + for x in TILES_CONTINENTAL_US_FIPS_CODE + + TILES_ALASKA_AND_HAWAII_FIPS_CODE + if x not in states_expected_from_fixtures + ] + etl_class.PUERTO_RICO_EXPECTED_IN_DATA = False + etl_class.ISLAND_AREAS_EXPECTED_IN_DATA = False + etl_class.ALASKA_AND_HAWAII_EXPECTED_IN_DATA = True + + return etl_class def _setup_etl_instance_and_run_extract( self, mock_etl, mock_paths @@ -119,7 +141,7 @@ class TestETL: requests_mock.get = mock.MagicMock(return_value=response_mock) # Instantiate the ETL class. - etl = self._ETL_CLASS() + etl = self._get_instance_of_etl_class() # Monkey-patch the temporary directory to the one used in the test etl.TMP_PATH = tmp_path diff --git a/data/data-pipeline/data_pipeline/tests/sources/us_army_fuds/test_etl.py b/data/data-pipeline/data_pipeline/tests/sources/us_army_fuds/test_etl.py index 5d390943..ce2b63c4 100644 --- a/data/data-pipeline/data_pipeline/tests/sources/us_army_fuds/test_etl.py +++ b/data/data-pipeline/data_pipeline/tests/sources/us_army_fuds/test_etl.py @@ -98,7 +98,7 @@ class TestUSArmyFUDSETL(TestETL): - self.OUTPUT_PATH points to the correct path in the temp directory """ # setup - etl = self._ETL_CLASS() + etl = self._get_instance_of_etl_class() # validation assert etl.GEOID_FIELD_NAME == "GEOID10" assert etl.GEOID_TRACT_FIELD_NAME == "GEOID10_TRACT" @@ -113,7 +113,7 @@ class TestUSArmyFUDSETL(TestETL): def test_get_output_file_path(self, mock_etl, mock_paths): """Tests the right file name is returned.""" - etl = self._ETL_CLASS() + etl = self._get_instance_of_etl_class() data_path, tmp_path = mock_paths output_file_path = etl._get_output_file_path() From 60164c863791af413cbc3838180f3fb7ed991d1b Mon Sep 17 00:00:00 2001 From: Emma Nechamkin <97977170+emma-nechamkin@users.noreply.github.com> Date: Mon, 12 Sep 2022 13:48:38 -0400 Subject: [PATCH 04/11] Removing low pop tracts from FEMA population loss (#1898) dropping 0 population from FEMA --- .../data_pipeline/etl/score/etl_score.py | 11 ++++++++--- 1 file changed, 8 insertions(+), 3 deletions(-) diff --git a/data/data-pipeline/data_pipeline/etl/score/etl_score.py b/data/data-pipeline/data_pipeline/etl/score/etl_score.py index 56682d49..3cdedf8d 100644 --- a/data/data-pipeline/data_pipeline/etl/score/etl_score.py +++ b/data/data-pipeline/data_pipeline/etl/score/etl_score.py @@ -579,12 +579,17 @@ class ScoreETL(ExtractTransformLoad): f"Dropping {len(drop_tracts)} tracts from Linguistic Isolation" ) - elif numeric_column == field_names.DOT_TRAVEL_BURDEN_FIELD: + elif numeric_column in [ + field_names.DOT_TRAVEL_BURDEN_FIELD, + field_names.EXPECTED_POPULATION_LOSS_RATE_FIELD, + ]: # 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.ß + # Similarly, we want to exclude low population tracts from FEMA's index low_population = 20 drop_tracts = df_copy[ - df_copy[field_names.TOTAL_POP_FIELD] <= low_population + df_copy[field_names.TOTAL_POP_FIELD].fillna(0) + <= low_population ][field_names.GEOID_TRACT_FIELD].to_list() logger.info( f"Dropping {len(drop_tracts)} tracts from DOT traffic burden" From 4d02525bb31d37a6f031485dd37f5c69b37a5155 Mon Sep 17 00:00:00 2001 From: Lucas Merrill Brown Date: Thu, 15 Sep 2022 17:46:01 -0400 Subject: [PATCH 05/11] 1831 Follow up (#1902) This code causes no functional change to the code. It does two things: 1. Uses difference instead of - to improve code style for working with sets. 2. Removes the line EXPECTED_MISSING_STATES = ["02", "15"], which is now redundant because of the line I added (in a previous pull request) of ALASKA_AND_HAWAII_EXPECTED_IN_DATA = False. --- .../data-pipeline/data_pipeline/etl/score/etl_utils.py | 10 +++++----- .../etl/sources/nlcd_nature_deprived/etl.py | 3 --- 2 files changed, 5 insertions(+), 8 deletions(-) diff --git a/data/data-pipeline/data_pipeline/etl/score/etl_utils.py b/data/data-pipeline/data_pipeline/etl/score/etl_utils.py index 5f7b6ecd..998a52ac 100644 --- a/data/data-pipeline/data_pipeline/etl/score/etl_utils.py +++ b/data/data-pipeline/data_pipeline/etl/score/etl_utils.py @@ -364,7 +364,7 @@ def compare_to_list_of_expected_state_fips_codes( # If continental US is not expected to be included, remove it from the # expected states set. if not continental_us_expected: - expected_states_set = expected_states_set - set( + expected_states_set = expected_states_set.difference( TILES_CONTINENTAL_US_FIPS_CODE ) @@ -374,27 +374,27 @@ def compare_to_list_of_expected_state_fips_codes( # do not use this argument -- instead, use `additional_fips_codes_not_expected` # for the 1 state you expected to be missing. if not alaska_and_hawaii_expected: - expected_states_set = expected_states_set - set( + expected_states_set = expected_states_set.difference( TILES_ALASKA_AND_HAWAII_FIPS_CODE ) # If Puerto Rico is not expected to be included, remove it from the expected # states set. if not puerto_rico_expected: - expected_states_set = expected_states_set - set( + expected_states_set = expected_states_set.difference( TILES_PUERTO_RICO_FIPS_CODE ) # If island areas are not expected to be included, remove them from the expected # states set. if not island_areas_expected: - expected_states_set = expected_states_set - set( + expected_states_set = expected_states_set.difference( TILES_ISLAND_AREA_FIPS_CODES ) # If additional FIPS codes are not expected to be included, remove them from the # expected states set. - expected_states_set = expected_states_set - set( + expected_states_set = expected_states_set.difference( additional_fips_codes_not_expected ) diff --git a/data/data-pipeline/data_pipeline/etl/sources/nlcd_nature_deprived/etl.py b/data/data-pipeline/data_pipeline/etl/sources/nlcd_nature_deprived/etl.py index 651d7f68..27849a82 100644 --- a/data/data-pipeline/data_pipeline/etl/sources/nlcd_nature_deprived/etl.py +++ b/data/data-pipeline/data_pipeline/etl/sources/nlcd_nature_deprived/etl.py @@ -22,9 +22,6 @@ class NatureDeprivedETL(ExtractTransformLoad): PUERTO_RICO_EXPECTED_IN_DATA = False ALASKA_AND_HAWAII_EXPECTED_IN_DATA = False - # Alaska and Hawaii are missing - EXPECTED_MISSING_STATES = ["02", "15"] - # Output score variables (values set on datasets.yml) for linting purposes ELIGIBLE_FOR_NATURE_DEPRIVED_FIELD_NAME: str TRACT_PERCENT_IMPERVIOUS_FIELD_NAME: str From 876655d2b2c565e2b042074a892dab44efef972e Mon Sep 17 00:00:00 2001 From: Matt Bowen <83967628+mattbowen-usds@users.noreply.github.com> Date: Mon, 19 Sep 2022 15:17:00 -0400 Subject: [PATCH 06/11] Add tests for all non-census sources (#1899) * Refactor CDC life-expectancy (1554) * Update to new tract list (#1554) * Adjust for tests (#1848) * Add tests for cdc_places (#1848) * Add EJScreen tests (#1848) * Add tests for HUD housing (#1848) * Add tests for GeoCorr (#1848) * Add persistent poverty tests (#1848) * Update for sources without zips, for new validation (#1848) * Update tests for new multi-CSV but (#1848) Lucas updated the CDC life expectancy data to handle a bug where two states are missing from the US Overall download. Since virtually none of our other ETL classes download multiple CSVs directly like this, it required a pretty invasive new mocking strategy. * Add basic tests for nature deprived (#1848) * Add wildfire tests (#1848) * Add flood risk tests (#1848) * Add DOT travel tests (#1848) * Add historic redlining tests (#1848) * Add tests for ME and WI (#1848) * Update now that validation exists (#1848) * Adjust for validation (#1848) * Add health insurance back to cdc places (#1848) Ooops * Update tests with new field (#1848) * Test for blank tract removal (#1848) * Add tracts for clipping behavior * Test clipping and zfill behavior (#1848) * Fix bad test assumption (#1848) * Simplify class, add test for tract padding (#1848) * Fix percentage inversion, update tests (#1848) Looking through the transformations, I noticed that we were subtracting a percentage that is usually between 0-100 from 1 instead of 100, and so were endind up with some surprising results. Confirmed with lucasmbrown-usds * Add note about first street data (#1848) --- data/data-pipeline/data_pipeline/etl/base.py | 79 +-- .../etl/score/config/datasets.yml | 16 +- .../etl/sources/cdc_life_expectancy/etl.py | 10 +- .../etl/sources/cdc_places/etl.py | 38 +- .../sources/child_opportunity_index/etl.py | 1 + .../etl/sources/doe_energy_burden/etl.py | 6 +- .../etl/sources/dot_travel_composite/etl.py | 1 + .../data_pipeline/etl/sources/eamlis/etl.py | 1 + .../data_pipeline/etl/sources/ejscreen/etl.py | 21 +- .../etl/sources/fsf_flood_risk/etl.py | 3 + .../etl/sources/fsf_wildfire_risk/etl.py | 3 + .../data_pipeline/etl/sources/geocorr/etl.py | 32 +- .../etl/sources/historic_redlining/etl.py | 46 +- .../etl/sources/hud_housing/etl.py | 33 +- .../etl/sources/national_risk_index/etl.py | 1 + .../etl/sources/nlcd_nature_deprived/etl.py | 3 +- .../etl/sources/persistent_poverty/etl.py | 20 +- .../etl/sources/us_army_fuds/etl.py | 1 + .../sources/cdc_life_expectancy/__init__.py | 0 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.../sources/nlcd_nature_deprived/__init__.py | 0 .../nlcd_nature_deprived/data/extract.csv | 16 + .../nlcd_nature_deprived/data/output.csv | 16 + .../nlcd_nature_deprived/data/transform.csv | 16 + ...usa_conus_nat_dep__compiled_by_TPL.csv.zip | Bin 0 -> 618 bytes .../sources/nlcd_nature_deprived/test_etl.py | 19 + .../sources/persistent_poverty/__init__.py | 0 .../data/LTDB_Std_All_Sample.zip | Bin 0 -> 19730 bytes .../persistent_poverty/data/extract.csv | 16 + .../persistent_poverty/data/output.csv | 16 + .../persistent_poverty/data/transform.csv | 16 + .../sources/persistent_poverty/test_etl.py | 19 + 88 files changed, 2032 insertions(+), 178 deletions(-) create mode 100644 data/data-pipeline/data_pipeline/tests/sources/cdc_life_expectancy/__init__.py create mode 100644 data/data-pipeline/data_pipeline/tests/sources/cdc_life_expectancy/data/ME_A.CSV create mode 100644 data/data-pipeline/data_pipeline/tests/sources/cdc_life_expectancy/data/US_A.CSV create mode 100644 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data/data-pipeline/data_pipeline/tests/sources/geocorr/test_etl.py create mode 100644 data/data-pipeline/data_pipeline/tests/sources/historic_redlining/__init__.py create mode 100644 data/data-pipeline/data_pipeline/tests/sources/historic_redlining/data/HRS_2010.zip create mode 100644 data/data-pipeline/data_pipeline/tests/sources/historic_redlining/data/output.csv create mode 100644 data/data-pipeline/data_pipeline/tests/sources/historic_redlining/data/transform.csv create mode 100644 data/data-pipeline/data_pipeline/tests/sources/historic_redlining/test_etl.py create mode 100644 data/data-pipeline/data_pipeline/tests/sources/hud_housing/__init__.py create mode 100644 data/data-pipeline/data_pipeline/tests/sources/hud_housing/data/2014thru2018-140-csv.zip create mode 100644 data/data-pipeline/data_pipeline/tests/sources/hud_housing/data/extract.csv create mode 100644 data/data-pipeline/data_pipeline/tests/sources/hud_housing/data/output.csv create mode 100644 data/data-pipeline/data_pipeline/tests/sources/hud_housing/data/transform.csv create mode 100644 data/data-pipeline/data_pipeline/tests/sources/hud_housing/test_etl.py create mode 100644 data/data-pipeline/data_pipeline/tests/sources/nlcd_nature_deprived/__init__.py create mode 100644 data/data-pipeline/data_pipeline/tests/sources/nlcd_nature_deprived/data/extract.csv create mode 100644 data/data-pipeline/data_pipeline/tests/sources/nlcd_nature_deprived/data/output.csv create mode 100644 data/data-pipeline/data_pipeline/tests/sources/nlcd_nature_deprived/data/transform.csv create mode 100644 data/data-pipeline/data_pipeline/tests/sources/nlcd_nature_deprived/data/usa_conus_nat_dep__compiled_by_TPL.csv.zip create mode 100644 data/data-pipeline/data_pipeline/tests/sources/nlcd_nature_deprived/test_etl.py create mode 100644 data/data-pipeline/data_pipeline/tests/sources/persistent_poverty/__init__.py create mode 100644 data/data-pipeline/data_pipeline/tests/sources/persistent_poverty/data/LTDB_Std_All_Sample.zip create mode 100644 data/data-pipeline/data_pipeline/tests/sources/persistent_poverty/data/extract.csv create mode 100644 data/data-pipeline/data_pipeline/tests/sources/persistent_poverty/data/output.csv create mode 100644 data/data-pipeline/data_pipeline/tests/sources/persistent_poverty/data/transform.csv create mode 100644 data/data-pipeline/data_pipeline/tests/sources/persistent_poverty/test_etl.py diff --git a/data/data-pipeline/data_pipeline/etl/base.py b/data/data-pipeline/data_pipeline/etl/base.py index 65580f9a..5ebc8a55 100644 --- a/data/data-pipeline/data_pipeline/etl/base.py +++ b/data/data-pipeline/data_pipeline/etl/base.py @@ -115,56 +115,59 @@ class ExtractTransformLoad: # periods. https://github.com/usds/justice40-tool/issues/964 EXPECTED_MAX_CENSUS_TRACTS: int = 74160 + # Should this dataset load its configuration from + # the YAML files? + LOAD_YAML_CONFIG: bool = False + # We use output_df as the final dataframe to use to write to the CSV # It is used on the "load" base class method output_df: pd.DataFrame = None def __init_subclass__(cls) -> None: - cls.DATASET_CONFIG = cls.yaml_config_load() + if cls.LOAD_YAML_CONFIG: + cls.DATASET_CONFIG = cls.yaml_config_load() @classmethod - def yaml_config_load(cls) -> Optional[dict]: + def yaml_config_load(cls) -> dict: """Generate config dictionary and set instance variables from YAML dataset.""" - if cls.NAME is not None: - # check if the class instance has score YAML definitions - datasets_config = load_yaml_dict_from_file( - cls.DATASET_CONFIG_PATH / "datasets.yml", - DatasetsConfig, + # check if the class instance has score YAML definitions + datasets_config = load_yaml_dict_from_file( + cls.DATASET_CONFIG_PATH / "datasets.yml", + DatasetsConfig, + ) + + # get the config for this dataset + try: + dataset_config = next( + item + for item in datasets_config.get("datasets") + if item["module_name"] == cls.NAME ) + except StopIteration: + # Note: it'd be nice to log the name of the dataframe, but that's not accessible in this scope. + logger.error( + f"Exception encountered while extracting dataset config for dataset {cls.NAME}" + ) + sys.exit() - # get the config for this dataset - try: - dataset_config = next( - item - for item in datasets_config.get("datasets") - if item["module_name"] == cls.NAME - ) - except StopIteration: - # Note: it'd be nice to log the name of the dataframe, but that's not accessible in this scope. - logger.error( - f"Exception encountered while extracting dataset config for dataset {cls.NAME}" - ) - sys.exit() - - # set some of the basic fields - if "input_geoid_tract_field_name" in dataset_config: - cls.INPUT_GEOID_TRACT_FIELD_NAME = dataset_config[ - "input_geoid_tract_field_name" - ] - - # get the columns to write on the CSV - # and set the constants - cls.COLUMNS_TO_KEEP = [ - cls.GEOID_TRACT_FIELD_NAME, # always index with geoid tract id + # set some of the basic fields + if "input_geoid_tract_field_name" in dataset_config: + cls.INPUT_GEOID_TRACT_FIELD_NAME = dataset_config[ + "input_geoid_tract_field_name" ] - for field in dataset_config["load_fields"]: - cls.COLUMNS_TO_KEEP.append(field["long_name"]) - setattr(cls, field["df_field_name"], field["long_name"]) - # set the constants for the class - setattr(cls, field["df_field_name"], field["long_name"]) - return dataset_config - return None + # get the columns to write on the CSV + # and set the constants + cls.COLUMNS_TO_KEEP = [ + cls.GEOID_TRACT_FIELD_NAME, # always index with geoid tract id + ] + for field in dataset_config["load_fields"]: + cls.COLUMNS_TO_KEEP.append(field["long_name"]) + setattr(cls, field["df_field_name"], field["long_name"]) + + # set the constants for the class + setattr(cls, field["df_field_name"], field["long_name"]) + return dataset_config # This is a classmethod so it can be used by `get_data_frame` without # needing to create an instance of the class. This is a use case in `etl_score`. diff --git a/data/data-pipeline/data_pipeline/etl/score/config/datasets.yml b/data/data-pipeline/data_pipeline/etl/score/config/datasets.yml index dc06b4f0..25ed4ccd 100644 --- a/data/data-pipeline/data_pipeline/etl/score/config/datasets.yml +++ b/data/data-pipeline/data_pipeline/etl/score/config/datasets.yml @@ -289,4 +289,18 @@ datasets: field_type: percentage include_in_tiles: true include_in_downloadable_files: true - create_percentile: true \ No newline at end of file + create_percentile: true + - long_name: "CDC Life Expeectancy" + short_name: "cdc_life_expectancy" + module_name: "cdc_life_expectancy" + input_geoid_tract_field_name: "Tract ID" + load_fields: + - short_name: "LLEF" + df_field_name: "LIFE_EXPECTANCY_FIELD_NAME" + long_name: "Life expectancy (years)" + field_type: float + include_in_tiles: false + include_in_downloadable_files: true + create_percentile: false + create_reverse_percentile: true + \ No newline at end of file diff --git a/data/data-pipeline/data_pipeline/etl/sources/cdc_life_expectancy/etl.py b/data/data-pipeline/data_pipeline/etl/sources/cdc_life_expectancy/etl.py index d75ca85b..73a4959c 100644 --- a/data/data-pipeline/data_pipeline/etl/sources/cdc_life_expectancy/etl.py +++ b/data/data-pipeline/data_pipeline/etl/sources/cdc_life_expectancy/etl.py @@ -16,7 +16,12 @@ class CDCLifeExpectancy(ExtractTransformLoad): GEO_LEVEL = ValidGeoLevel.CENSUS_TRACT PUERTO_RICO_EXPECTED_IN_DATA = False + NAME = "cdc_life_expectancy" + USA_FILE_URL: str = "https://ftp.cdc.gov/pub/Health_Statistics/NCHS/Datasets/NVSS/USALEEP/CSV/US_A.CSV" + LOAD_YAML_CONFIG: bool = False + LIFE_EXPECTANCY_FIELD_NAME = "Life expectancy (years)" + INPUT_GEOID_TRACT_FIELD_NAME = "Tract ID" STATES_MISSING_FROM_USA_FILE = ["23", "55"] @@ -69,8 +74,7 @@ class CDCLifeExpectancy(ExtractTransformLoad): all_usa_raw_df = self._download_and_prep_data( file_url=self.USA_FILE_URL, download_file_name=self.get_tmp_path() - / "cdc_life_expectancy" - / "usa.csv", + / "US_A.CSV", ) # Check which states are missing @@ -91,7 +95,6 @@ class CDCLifeExpectancy(ExtractTransformLoad): maine_raw_df = self._download_and_prep_data( file_url=self.MAINE_FILE_URL, download_file_name=self.get_tmp_path() - / "cdc_life_expectancy" / "maine.csv", ) @@ -99,7 +102,6 @@ class CDCLifeExpectancy(ExtractTransformLoad): wisconsin_raw_df = self._download_and_prep_data( file_url=self.WISCONSIN_FILE_URL, download_file_name=self.get_tmp_path() - / "cdc_life_expectancy" / "wisconsin.csv", ) diff --git a/data/data-pipeline/data_pipeline/etl/sources/cdc_places/etl.py b/data/data-pipeline/data_pipeline/etl/sources/cdc_places/etl.py index bc53758d..e35a837e 100644 --- a/data/data-pipeline/data_pipeline/etl/sources/cdc_places/etl.py +++ b/data/data-pipeline/data_pipeline/etl/sources/cdc_places/etl.py @@ -1,6 +1,7 @@ +import typing import pandas as pd -from data_pipeline.etl.base import ExtractTransformLoad +from data_pipeline.etl.base import ExtractTransformLoad, ValidGeoLevel from data_pipeline.utils import get_module_logger, download_file_from_url from data_pipeline.score import field_names @@ -8,13 +9,27 @@ logger = get_module_logger(__name__) class CDCPlacesETL(ExtractTransformLoad): + NAME = "cdc_places" + GEO_LEVEL: ValidGeoLevel = ValidGeoLevel.CENSUS_TRACT + PUERTO_RICO_EXPECTED_IN_DATA = False + + CDC_GEOID_FIELD_NAME = "LocationID" + CDC_VALUE_FIELD_NAME = "Data_Value" + CDC_MEASURE_FIELD_NAME = "Measure" + def __init__(self): self.OUTPUT_PATH = self.DATA_PATH / "dataset" / "cdc_places" self.CDC_PLACES_URL = "https://chronicdata.cdc.gov/api/views/cwsq-ngmh/rows.csv?accessType=DOWNLOAD" - self.CDC_GEOID_FIELD_NAME = "LocationID" - self.CDC_VALUE_FIELD_NAME = "Data_Value" - self.CDC_MEASURE_FIELD_NAME = "Measure" + self.COLUMNS_TO_KEEP: typing.List[str] = [ + self.GEOID_TRACT_FIELD_NAME, + field_names.DIABETES_FIELD, + field_names.ASTHMA_FIELD, + field_names.HEART_DISEASE_FIELD, + field_names.CANCER_FIELD, + field_names.HEALTH_INSURANCE_FIELD, + field_names.PHYS_HEALTH_NOT_GOOD_FIELD, + ] self.df: pd.DataFrame @@ -22,9 +37,7 @@ class CDCPlacesETL(ExtractTransformLoad): logger.info("Starting to download 520MB CDC Places file.") file_path = download_file_from_url( file_url=self.CDC_PLACES_URL, - download_file_name=self.get_tmp_path() - / "cdc_places" - / "census_tract.csv", + download_file_name=self.get_tmp_path() / "census_tract.csv", ) self.df = pd.read_csv( @@ -42,7 +55,6 @@ class CDCPlacesETL(ExtractTransformLoad): inplace=True, errors="raise", ) - # Note: Puerto Rico not included. self.df = self.df.pivot( index=self.GEOID_TRACT_FIELD_NAME, @@ -65,12 +77,4 @@ class CDCPlacesETL(ExtractTransformLoad): ) # Make the index (the census tract ID) a column, not the index. - self.df.reset_index(inplace=True) - - def load(self) -> None: - logger.info("Saving CDC Places Data") - - # mkdir census - self.OUTPUT_PATH.mkdir(parents=True, exist_ok=True) - - self.df.to_csv(path_or_buf=self.OUTPUT_PATH / "usa.csv", index=False) + self.output_df = self.df.reset_index() diff --git a/data/data-pipeline/data_pipeline/etl/sources/child_opportunity_index/etl.py b/data/data-pipeline/data_pipeline/etl/sources/child_opportunity_index/etl.py index d2b7143c..4082fdce 100644 --- a/data/data-pipeline/data_pipeline/etl/sources/child_opportunity_index/etl.py +++ b/data/data-pipeline/data_pipeline/etl/sources/child_opportunity_index/etl.py @@ -25,6 +25,7 @@ class ChildOpportunityIndex(ExtractTransformLoad): # Metadata for the baseclass NAME = "child_opportunity_index" GEO_LEVEL = ValidGeoLevel.CENSUS_TRACT + LOAD_YAML_CONFIG: bool = True # Define these for easy code completion EXTREME_HEAT_FIELD: str diff --git a/data/data-pipeline/data_pipeline/etl/sources/doe_energy_burden/etl.py b/data/data-pipeline/data_pipeline/etl/sources/doe_energy_burden/etl.py index 52e8d3f0..f50a8b4d 100644 --- a/data/data-pipeline/data_pipeline/etl/sources/doe_energy_burden/etl.py +++ b/data/data-pipeline/data_pipeline/etl/sources/doe_energy_burden/etl.py @@ -15,6 +15,7 @@ class DOEEnergyBurden(ExtractTransformLoad): + "/DOE_LEAD_AMI_TRACT_2018_ALL.csv.zip" ) GEO_LEVEL = ValidGeoLevel.CENSUS_TRACT + LOAD_YAML_CONFIG: bool = True REVISED_ENERGY_BURDEN_FIELD_NAME: str @@ -56,8 +57,3 @@ class DOEEnergyBurden(ExtractTransformLoad): ) self.output_df = output_df - - def load(self) -> None: - logger.info("Saving DOE Energy Burden CSV") - - super().load() diff --git a/data/data-pipeline/data_pipeline/etl/sources/dot_travel_composite/etl.py b/data/data-pipeline/data_pipeline/etl/sources/dot_travel_composite/etl.py index 9e9d0f3f..b683b092 100644 --- a/data/data-pipeline/data_pipeline/etl/sources/dot_travel_composite/etl.py +++ b/data/data-pipeline/data_pipeline/etl/sources/dot_travel_composite/etl.py @@ -17,6 +17,7 @@ class TravelCompositeETL(ExtractTransformLoad): SOURCE_URL = "https://www.transportation.gov/sites/dot.gov/files/Shapefile_and_Metadata.zip" GEO_LEVEL = ValidGeoLevel.CENSUS_TRACT PUERTO_RICO_EXPECTED_IN_DATA = False + LOAD_YAML_CONFIG: bool = True # Output score variables (values set on datasets.yml) for linting purposes TRAVEL_BURDEN_FIELD_NAME: str diff --git a/data/data-pipeline/data_pipeline/etl/sources/eamlis/etl.py b/data/data-pipeline/data_pipeline/etl/sources/eamlis/etl.py index 457890db..a17ac429 100644 --- a/data/data-pipeline/data_pipeline/etl/sources/eamlis/etl.py +++ b/data/data-pipeline/data_pipeline/etl/sources/eamlis/etl.py @@ -19,6 +19,7 @@ class AbandonedMineETL(ExtractTransformLoad): NAME = "eamlis" GEO_LEVEL = ValidGeoLevel.CENSUS_TRACT AML_BOOLEAN: str + LOAD_YAML_CONFIG: bool = True PUERTO_RICO_EXPECTED_IN_DATA = False EXPECTED_MISSING_STATES = [ diff --git a/data/data-pipeline/data_pipeline/etl/sources/ejscreen/etl.py b/data/data-pipeline/data_pipeline/etl/sources/ejscreen/etl.py index 0ba5db88..1c10551c 100644 --- a/data/data-pipeline/data_pipeline/etl/sources/ejscreen/etl.py +++ b/data/data-pipeline/data_pipeline/etl/sources/ejscreen/etl.py @@ -1,6 +1,6 @@ import pandas as pd -from data_pipeline.etl.base import ExtractTransformLoad +from data_pipeline.etl.base import ExtractTransformLoad, ValidGeoLevel from data_pipeline.score import field_names from data_pipeline.utils import get_module_logger @@ -10,6 +10,10 @@ logger = get_module_logger(__name__) class EJSCREENETL(ExtractTransformLoad): """Load updated EJSCREEN data.""" + NAME = "ejscreen" + GEO_LEVEL: ValidGeoLevel = ValidGeoLevel.CENSUS_TRACT + INPUT_GEOID_TRACT_FIELD_NAME: str = "ID" + def __init__(self): self.EJSCREEN_FTP_URL = "https://gaftp.epa.gov/EJSCREEN/2021/EJSCREEN_2021_USPR_Tracts.csv.zip" self.EJSCREEN_CSV = ( @@ -52,16 +56,16 @@ class EJSCREENETL(ExtractTransformLoad): logger.info("Transforming EJScreen Data") self.df = pd.read_csv( self.EJSCREEN_CSV, - dtype={"ID": str}, + dtype={self.INPUT_GEOID_TRACT_FIELD_NAME: str}, # EJSCREEN writes the word "None" for NA data. na_values=["None"], low_memory=False, ) # rename ID to Tract ID - self.df.rename( + self.output_df = self.df.rename( columns={ - "ID": self.GEOID_TRACT_FIELD_NAME, + self.INPUT_GEOID_TRACT_FIELD_NAME: self.GEOID_TRACT_FIELD_NAME, "ACSTOTPOP": field_names.TOTAL_POP_FIELD, "CANCER": field_names.AIR_TOXICS_CANCER_RISK_FIELD, "RESP": field_names.RESPIRATORY_HAZARD_FIELD, @@ -80,13 +84,4 @@ class EJSCREENETL(ExtractTransformLoad): "PRE1960PCT": field_names.LEAD_PAINT_FIELD, "UST": field_names.UST_FIELD, # added for 2021 update }, - inplace=True, - ) - - def load(self) -> None: - logger.info("Saving EJScreen CSV") - # write nationwide csv - self.CSV_PATH.mkdir(parents=True, exist_ok=True) - self.df[self.COLUMNS_TO_KEEP].to_csv( - self.CSV_PATH / "usa.csv", index=False ) diff --git a/data/data-pipeline/data_pipeline/etl/sources/fsf_flood_risk/etl.py b/data/data-pipeline/data_pipeline/etl/sources/fsf_flood_risk/etl.py index 4937a2b2..fc440e21 100644 --- a/data/data-pipeline/data_pipeline/etl/sources/fsf_flood_risk/etl.py +++ b/data/data-pipeline/data_pipeline/etl/sources/fsf_flood_risk/etl.py @@ -14,8 +14,11 @@ class FloodRiskETL(ExtractTransformLoad): """ETL class for the First Street Foundation flood risk dataset""" NAME = "fsf_flood_risk" + # These data were emailed to the J40 team while first street got + # their official data sharing channels setup. SOURCE_URL = settings.AWS_JUSTICE40_DATASOURCES_URL + "/fsf_flood.zip" GEO_LEVEL = ValidGeoLevel.CENSUS_TRACT + LOAD_YAML_CONFIG: bool = True # Output score variables (values set on datasets.yml) for linting purposes COUNT_PROPERTIES: str diff --git a/data/data-pipeline/data_pipeline/etl/sources/fsf_wildfire_risk/etl.py b/data/data-pipeline/data_pipeline/etl/sources/fsf_wildfire_risk/etl.py index b623206c..5989d7e5 100644 --- a/data/data-pipeline/data_pipeline/etl/sources/fsf_wildfire_risk/etl.py +++ b/data/data-pipeline/data_pipeline/etl/sources/fsf_wildfire_risk/etl.py @@ -14,9 +14,12 @@ class WildfireRiskETL(ExtractTransformLoad): """ETL class for the First Street Foundation wildfire risk dataset""" NAME = "fsf_wildfire_risk" + # These data were emailed to the J40 team while first street got + # their official data sharing channels setup. SOURCE_URL = settings.AWS_JUSTICE40_DATASOURCES_URL + "/fsf_fire.zip" GEO_LEVEL = ValidGeoLevel.CENSUS_TRACT PUERTO_RICO_EXPECTED_IN_DATA = False + LOAD_YAML_CONFIG: bool = True ALASKA_AND_HAWAII_EXPECTED_IN_DATA = False # Output score variables (values set on datasets.yml) for linting purposes diff --git a/data/data-pipeline/data_pipeline/etl/sources/geocorr/etl.py b/data/data-pipeline/data_pipeline/etl/sources/geocorr/etl.py index ed088cae..c8e30d10 100644 --- a/data/data-pipeline/data_pipeline/etl/sources/geocorr/etl.py +++ b/data/data-pipeline/data_pipeline/etl/sources/geocorr/etl.py @@ -1,7 +1,7 @@ import pandas as pd from data_pipeline.config import settings -from data_pipeline.etl.base import ExtractTransformLoad +from data_pipeline.etl.base import ExtractTransformLoad, ValidGeoLevel from data_pipeline.utils import ( get_module_logger, unzip_file_from_url, @@ -11,6 +11,10 @@ logger = get_module_logger(__name__) class GeoCorrETL(ExtractTransformLoad): + NAME = "geocorr" + GEO_LEVEL: ValidGeoLevel = ValidGeoLevel.CENSUS_TRACT + PUERTO_RICO_EXPECTED_IN_DATA = False + def __init__(self): self.OUTPUT_PATH = self.DATA_PATH / "dataset" / "geocorr" @@ -24,6 +28,10 @@ class GeoCorrETL(ExtractTransformLoad): self.GEOCORR_PLACES_URL = "https://justice40-data.s3.amazonaws.com/data-sources/geocorr_urban_rural.csv.zip" self.GEOCORR_GEOID_FIELD_NAME = "GEOID10_TRACT" self.URBAN_HEURISTIC_FIELD_NAME = "Urban Heuristic Flag" + self.COLUMNS_TO_KEEP = [ + self.GEOID_TRACT_FIELD_NAME, + self.URBAN_HEURISTIC_FIELD_NAME, + ] self.df: pd.DataFrame @@ -35,13 +43,11 @@ class GeoCorrETL(ExtractTransformLoad): file_url=settings.AWS_JUSTICE40_DATASOURCES_URL + "/geocorr_urban_rural.csv.zip", download_path=self.get_tmp_path(), - unzipped_file_path=self.get_tmp_path() / "geocorr", + unzipped_file_path=self.get_tmp_path(), ) self.df = pd.read_csv( - filepath_or_buffer=self.get_tmp_path() - / "geocorr" - / "geocorr_urban_rural.csv", + filepath_or_buffer=self.get_tmp_path() / "geocorr_urban_rural.csv", dtype={ self.GEOCORR_GEOID_FIELD_NAME: "string", }, @@ -50,22 +56,10 @@ class GeoCorrETL(ExtractTransformLoad): def transform(self) -> None: logger.info("Starting GeoCorr Urban Rural Map transform") + # Put in logic from Jupyter Notebook transform when we switch in the hyperlink to Geocorr - self.df.rename( + self.output_df = self.df.rename( columns={ "urban_heuristic_flag": self.URBAN_HEURISTIC_FIELD_NAME, }, - inplace=True, ) - - pass - - # Put in logic from Jupyter Notebook transform when we switch in the hyperlink to Geocorr - - def load(self) -> None: - logger.info("Saving GeoCorr Urban Rural Map Data") - - # mkdir census - self.OUTPUT_PATH.mkdir(parents=True, exist_ok=True) - - self.df.to_csv(path_or_buf=self.OUTPUT_PATH / "usa.csv", index=False) diff --git a/data/data-pipeline/data_pipeline/etl/sources/historic_redlining/etl.py b/data/data-pipeline/data_pipeline/etl/sources/historic_redlining/etl.py index 1099bf83..39467fcd 100644 --- a/data/data-pipeline/data_pipeline/etl/sources/historic_redlining/etl.py +++ b/data/data-pipeline/data_pipeline/etl/sources/historic_redlining/etl.py @@ -1,6 +1,6 @@ import pandas as pd -from data_pipeline.etl.base import ExtractTransformLoad +from data_pipeline.etl.base import ExtractTransformLoad, ValidGeoLevel from data_pipeline.utils import get_module_logger from data_pipeline.config import settings @@ -8,11 +8,28 @@ logger = get_module_logger(__name__) class HistoricRedliningETL(ExtractTransformLoad): + NAME = "historic_redlining" + GEO_LEVEL: ValidGeoLevel = ValidGeoLevel.CENSUS_TRACT + EXPECTED_MISSING_STATES = [ + "10", + "11", + "16", + "23", + "30", + "32", + "35", + "38", + "46", + "50", + "56", + ] + PUERTO_RICO_EXPECTED_IN_DATA = False + ALASKA_AND_HAWAII_EXPECTED_IN_DATA: bool = False + SOURCE_URL = settings.AWS_JUSTICE40_DATASOURCES_URL + "/HRS_2010.zip" + def __init__(self): self.CSV_PATH = self.DATA_PATH / "dataset" / "historic_redlining" - self.HISTORIC_REDLINING_URL = ( - settings.AWS_JUSTICE40_DATASOURCES_URL + "/HRS_2010.zip" - ) + self.HISTORIC_REDLINING_FILE_PATH = ( self.get_tmp_path() / "HRS_2010.xlsx" ) @@ -25,13 +42,6 @@ class HistoricRedliningETL(ExtractTransformLoad): ] self.df: pd.DataFrame - def extract(self) -> None: - logger.info("Downloading Historic Redlining Data") - super().extract( - self.HISTORIC_REDLINING_URL, - self.get_tmp_path(), - ) - def transform(self) -> None: logger.info("Transforming Historic Redlining Data") # this is obviously temporary @@ -57,16 +67,4 @@ class HistoricRedliningETL(ExtractTransformLoad): f"{self.REDLINING_SCALAR} meets or exceeds {round(threshold, 2)}" ) - self.df = historic_redlining_data - - def load(self) -> None: - logger.info("Saving Historic Redlining CSV") - # write selected states csv - self.CSV_PATH.mkdir(parents=True, exist_ok=True) - self.df[self.COLUMNS_TO_KEEP].to_csv( - self.CSV_PATH / "usa.csv", index=False - ) - - def validate(self) -> None: - logger.info("Validating Historic Redlining Data") - pass + self.output_df = historic_redlining_data diff --git a/data/data-pipeline/data_pipeline/etl/sources/hud_housing/etl.py b/data/data-pipeline/data_pipeline/etl/sources/hud_housing/etl.py index 6a6a70bb..85406ffc 100644 --- a/data/data-pipeline/data_pipeline/etl/sources/hud_housing/etl.py +++ b/data/data-pipeline/data_pipeline/etl/sources/hud_housing/etl.py @@ -1,16 +1,18 @@ import pandas as pd -from data_pipeline.etl.base import ExtractTransformLoad +from data_pipeline.etl.base import ExtractTransformLoad, ValidGeoLevel from data_pipeline.utils import get_module_logger logger = get_module_logger(__name__) class HudHousingETL(ExtractTransformLoad): + NAME = "hud_housing" + GEO_LEVEL: ValidGeoLevel = ValidGeoLevel.CENSUS_TRACT + def __init__(self): - self.OUTPUT_PATH = self.DATA_PATH / "dataset" / "hud_housing" self.GEOID_TRACT_FIELD_NAME = "GEOID10_TRACT" self.HOUSING_FTP_URL = "https://www.huduser.gov/portal/datasets/cp/2014thru2018-140-csv.zip" - self.HOUSING_ZIP_FILE_DIR = self.get_tmp_path() / "hud_housing" + self.HOUSING_ZIP_FILE_DIR = self.get_tmp_path() # We measure households earning less than 80% of HUD Area Median Family Income by county # and paying greater than 30% of their income to housing costs. @@ -22,6 +24,14 @@ class HudHousingETL(ExtractTransformLoad): self.NO_KITCHEN_OR_INDOOR_PLUMBING_FIELD_NAME = ( "Share of homes with no kitchen or indoor plumbing (percent)" ) + self.COLUMNS_TO_KEEP = [ + self.GEOID_TRACT_FIELD_NAME, + self.HOUSING_BURDEN_NUMERATOR_FIELD_NAME, + self.HOUSING_BURDEN_DENOMINATOR_FIELD_NAME, + self.HOUSING_BURDEN_FIELD_NAME, + self.NO_KITCHEN_OR_INDOOR_PLUMBING_FIELD_NAME, + "DENOM INCL NOT COMPUTED", + ] # Note: some variable definitions. # HUD-adjusted median family income (HAMFI). @@ -234,19 +244,4 @@ class HudHousingETL(ExtractTransformLoad): float ) - def load(self) -> None: - logger.info("Saving HUD Housing Data") - - self.OUTPUT_PATH.mkdir(parents=True, exist_ok=True) - - # Drop unnecessary fields - self.df[ - [ - self.GEOID_TRACT_FIELD_NAME, - self.HOUSING_BURDEN_NUMERATOR_FIELD_NAME, - self.HOUSING_BURDEN_DENOMINATOR_FIELD_NAME, - self.HOUSING_BURDEN_FIELD_NAME, - self.NO_KITCHEN_OR_INDOOR_PLUMBING_FIELD_NAME, - "DENOM INCL NOT COMPUTED", - ] - ].to_csv(path_or_buf=self.OUTPUT_PATH / "usa.csv", index=False) + self.output_df = self.df diff --git a/data/data-pipeline/data_pipeline/etl/sources/national_risk_index/etl.py b/data/data-pipeline/data_pipeline/etl/sources/national_risk_index/etl.py index 57681974..db59973f 100644 --- a/data/data-pipeline/data_pipeline/etl/sources/national_risk_index/etl.py +++ b/data/data-pipeline/data_pipeline/etl/sources/national_risk_index/etl.py @@ -18,6 +18,7 @@ class NationalRiskIndexETL(ExtractTransformLoad): SOURCE_URL = "https://hazards.fema.gov/nri/Content/StaticDocuments/DataDownload//NRI_Table_CensusTracts/NRI_Table_CensusTracts.zip" GEO_LEVEL = ValidGeoLevel.CENSUS_TRACT PUERTO_RICO_EXPECTED_IN_DATA = False + LOAD_YAML_CONFIG: bool = True # Output score variables (values set on datasets.yml) for linting purposes RISK_INDEX_EXPECTED_ANNUAL_LOSS_SCORE_FIELD_NAME: str diff --git a/data/data-pipeline/data_pipeline/etl/sources/nlcd_nature_deprived/etl.py b/data/data-pipeline/data_pipeline/etl/sources/nlcd_nature_deprived/etl.py index 27849a82..ce2dd66d 100644 --- a/data/data-pipeline/data_pipeline/etl/sources/nlcd_nature_deprived/etl.py +++ b/data/data-pipeline/data_pipeline/etl/sources/nlcd_nature_deprived/etl.py @@ -20,6 +20,7 @@ class NatureDeprivedETL(ExtractTransformLoad): ) GEO_LEVEL = ValidGeoLevel.CENSUS_TRACT PUERTO_RICO_EXPECTED_IN_DATA = False + LOAD_YAML_CONFIG: bool = True ALASKA_AND_HAWAII_EXPECTED_IN_DATA = False # Output score variables (values set on datasets.yml) for linting purposes @@ -65,7 +66,7 @@ class NatureDeprivedETL(ExtractTransformLoad): df_ncld[self.TRACT_ACRES_FIELD_NAME] >= self.TRACT_ACRES_LOWER_BOUND ) df_ncld[self.TRACT_PERCENT_NON_NATURAL_FIELD_NAME] = ( - 1 - df_ncld[self.PERCENT_NATURAL_FIELD_NAME] + 100 - df_ncld[self.PERCENT_NATURAL_FIELD_NAME] ) # Assign the final df to the class' output_df for the load method with rename diff --git a/data/data-pipeline/data_pipeline/etl/sources/persistent_poverty/etl.py b/data/data-pipeline/data_pipeline/etl/sources/persistent_poverty/etl.py index 44d35b4e..c35ae9f2 100644 --- a/data/data-pipeline/data_pipeline/etl/sources/persistent_poverty/etl.py +++ b/data/data-pipeline/data_pipeline/etl/sources/persistent_poverty/etl.py @@ -2,7 +2,7 @@ import functools import pandas as pd from data_pipeline.config import settings -from data_pipeline.etl.base import ExtractTransformLoad +from data_pipeline.etl.base import ExtractTransformLoad, ValidGeoLevel from data_pipeline.utils import ( get_module_logger, unzip_file_from_url, @@ -19,6 +19,10 @@ class PersistentPovertyETL(ExtractTransformLoad): Codebook: `https://s4.ad.brown.edu/Projects/Diversity/Researcher/LTBDDload/Dfiles/codebooks.pdf`. """ + NAME = "persistent_poverty" + GEO_LEVEL: ValidGeoLevel = ValidGeoLevel.CENSUS_TRACT + PUERTO_RICO_EXPECTED_IN_DATA = False + def __init__(self): self.OUTPUT_PATH = self.DATA_PATH / "dataset" / "persistent_poverty" @@ -75,7 +79,7 @@ class PersistentPovertyETL(ExtractTransformLoad): def extract(self) -> None: logger.info("Starting to download 86MB persistent poverty file.") - unzipped_file_path = self.get_tmp_path() / "persistent_poverty" + unzipped_file_path = self.get_tmp_path() unzip_file_from_url( file_url=settings.AWS_JUSTICE40_DATASOURCES_URL @@ -155,14 +159,4 @@ class PersistentPovertyETL(ExtractTransformLoad): ) ) - self.df = transformed_df - - def load(self) -> None: - logger.info("Saving persistent poverty data.") - - # mkdir census - self.OUTPUT_PATH.mkdir(parents=True, exist_ok=True) - - self.df[self.COLUMNS_TO_KEEP].to_csv( - path_or_buf=self.OUTPUT_PATH / "usa.csv", index=False - ) + self.output_df = transformed_df diff --git a/data/data-pipeline/data_pipeline/etl/sources/us_army_fuds/etl.py b/data/data-pipeline/data_pipeline/etl/sources/us_army_fuds/etl.py index 945f1039..30f2ca39 100644 --- a/data/data-pipeline/data_pipeline/etl/sources/us_army_fuds/etl.py +++ b/data/data-pipeline/data_pipeline/etl/sources/us_army_fuds/etl.py @@ -19,6 +19,7 @@ class USArmyFUDS(ExtractTransformLoad): INELIGIBLE_FUDS_COUNT_FIELD_NAME: str ELIGIBLE_FUDS_BINARY_FIELD_NAME: str GEO_LEVEL: ValidGeoLevel = ValidGeoLevel.CENSUS_TRACT + LOAD_YAML_CONFIG: bool = True ISLAND_AREAS_EXPECTED_IN_DATA = True diff --git a/data/data-pipeline/data_pipeline/tests/sources/cdc_life_expectancy/__init__.py b/data/data-pipeline/data_pipeline/tests/sources/cdc_life_expectancy/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/data/data-pipeline/data_pipeline/tests/sources/cdc_life_expectancy/data/ME_A.CSV b/data/data-pipeline/data_pipeline/tests/sources/cdc_life_expectancy/data/ME_A.CSV new file mode 100644 index 00000000..6c316fa3 --- /dev/null +++ b/data/data-pipeline/data_pipeline/tests/sources/cdc_life_expectancy/data/ME_A.CSV @@ -0,0 +1,2 @@ +"Tract ID","STATE2KX","CNTY2KX","TRACT2KX","e(0)","se(e(0))","Abridged life table flag" +23001010100,23,001,010100,72.3,2.2928,3 diff --git a/data/data-pipeline/data_pipeline/tests/sources/cdc_life_expectancy/data/US_A.CSV b/data/data-pipeline/data_pipeline/tests/sources/cdc_life_expectancy/data/US_A.CSV new file mode 100644 index 00000000..f021be4f --- /dev/null +++ b/data/data-pipeline/data_pipeline/tests/sources/cdc_life_expectancy/data/US_A.CSV @@ -0,0 +1,16 @@ +Tract ID,STATE2KX,CNTY2KX,TRACT2KX,e(0),se(e(0)),Abridged life table flag +15001021010,15,001,021010,77.4,1.6548,2 +15001021101,15,001,021101,82.5,3.9086,3 +15001021402,15,001,021402,80.4,1.093,2 +15001021800,15,001,021800,79.5,1.132,2 +15003010201,15,003,010201,79.4,1.5261,3 +15007040603,15,007,040603,86.3,2.2285,3 +15007040604,15,007,040604,84.9,2.1995,3 +15007040700,15,007,040700,80.4,0.7571,2 +15009030100,15,009,030100,77.2,1.8736,3 +15009030402,15,009,030402,83.5,1.8267,3 +15009030800,15,009,030800,82.2,1.6251,3 +06027000800,06,007,040500,99.1,3.1415,3 +06069000802,06,001,020100,99.1,3.1415,3 +06061021322,06,007,040300,99.1,3.1415,3 +15009030201,15,009,030201,99.1,3.1415,3 diff --git a/data/data-pipeline/data_pipeline/tests/sources/cdc_life_expectancy/data/WI_A.CSV b/data/data-pipeline/data_pipeline/tests/sources/cdc_life_expectancy/data/WI_A.CSV new file mode 100644 index 00000000..b166804e --- /dev/null +++ b/data/data-pipeline/data_pipeline/tests/sources/cdc_life_expectancy/data/WI_A.CSV @@ -0,0 +1,2 @@ +"Tract ID","STATE2KX","CNTY2KX","TRACT2KX","e(0)","se(e(0))","Abridged life table flag" +55001950201,55,001,950201,74.5,2.5471,3 diff --git a/data/data-pipeline/data_pipeline/tests/sources/cdc_life_expectancy/data/extract.csv b/data/data-pipeline/data_pipeline/tests/sources/cdc_life_expectancy/data/extract.csv new file mode 100644 index 00000000..17e0a827 --- /dev/null +++ b/data/data-pipeline/data_pipeline/tests/sources/cdc_life_expectancy/data/extract.csv @@ -0,0 +1,16 @@ +Tract ID,STATE2KX,CNTY2KX,TRACT2KX,e(0),se(e(0)),Abridged life table flag +15001021010,15,1,21010,77.4000000000,1.6548000000,2 +15001021101,15,1,21101,82.5000000000,3.9086000000,3 +15001021402,15,1,21402,80.4000000000,1.0930000000,2 +15001021800,15,1,21800,79.5000000000,1.1320000000,2 +15003010201,15,3,10201,79.4000000000,1.5261000000,3 +15007040603,15,7,40603,86.3000000000,2.2285000000,3 +15007040604,15,7,40604,84.9000000000,2.1995000000,3 +15007040700,15,7,40700,80.4000000000,0.7571000000,2 +15009030100,15,9,30100,77.2000000000,1.8736000000,3 +15009030402,15,9,30402,83.5000000000,1.8267000000,3 +15009030800,15,9,30800,82.2000000000,1.6251000000,3 +6027000800,6,7,40500,99.1000000000,3.1415000000,3 +6069000802,6,1,20100,99.1000000000,3.1415000000,3 +6061021322,6,7,40300,99.1000000000,3.1415000000,3 +15009030201,15,9,30201,99.1000000000,3.1415000000,3 diff --git a/data/data-pipeline/data_pipeline/tests/sources/cdc_life_expectancy/data/output.csv b/data/data-pipeline/data_pipeline/tests/sources/cdc_life_expectancy/data/output.csv new file mode 100644 index 00000000..ca890942 --- /dev/null +++ b/data/data-pipeline/data_pipeline/tests/sources/cdc_life_expectancy/data/output.csv @@ -0,0 +1,18 @@ +GEOID10_TRACT,Life expectancy (years) +15001021010,77.4000000000 +15001021101,82.5000000000 +15001021402,80.4000000000 +15001021800,79.5000000000 +15003010201,79.4000000000 +15007040603,86.3000000000 +15007040604,84.9000000000 +15007040700,80.4000000000 +15009030100,77.2000000000 +15009030402,83.5000000000 +15009030800,82.2000000000 +06027000800,99.1000000000 +06069000802,99.1000000000 +06061021322,99.1000000000 +15009030201,99.1000000000 +23001010100,72.3000000000 +55001950201,74.5000000000 diff --git a/data/data-pipeline/data_pipeline/tests/sources/cdc_life_expectancy/data/transform.csv b/data/data-pipeline/data_pipeline/tests/sources/cdc_life_expectancy/data/transform.csv new file mode 100644 index 00000000..6cff770b --- /dev/null +++ b/data/data-pipeline/data_pipeline/tests/sources/cdc_life_expectancy/data/transform.csv @@ -0,0 +1,18 @@ +GEOID10_TRACT,STATE2KX,CNTY2KX,TRACT2KX,Life expectancy (years),se(e(0)),Abridged life table flag +15001021010,15,1,21010,77.4000000000,1.6548000000,2 +15001021101,15,1,21101,82.5000000000,3.9086000000,3 +15001021402,15,1,21402,80.4000000000,1.0930000000,2 +15001021800,15,1,21800,79.5000000000,1.1320000000,2 +15003010201,15,3,10201,79.4000000000,1.5261000000,3 +15007040603,15,7,40603,86.3000000000,2.2285000000,3 +15007040604,15,7,40604,84.9000000000,2.1995000000,3 +15007040700,15,7,40700,80.4000000000,0.7571000000,2 +15009030100,15,9,30100,77.2000000000,1.8736000000,3 +15009030402,15,9,30402,83.5000000000,1.8267000000,3 +15009030800,15,9,30800,82.2000000000,1.6251000000,3 +06027000800,06,7,40500,99.1000000000,3.1415000000,3 +06069000802,06,1,20100,99.1000000000,3.1415000000,3 +06061021322,06,7,40300,99.1000000000,3.1415000000,3 +15009030201,15,9,30201,99.1000000000,3.1415000000,3 +23001010100,23,1,10100,72.3000000000,2.2928000000,3 +55001950201,55,1,950201,74.5000000000,2.5471000000,3 diff --git a/data/data-pipeline/data_pipeline/tests/sources/cdc_life_expectancy/test_etl.py b/data/data-pipeline/data_pipeline/tests/sources/cdc_life_expectancy/test_etl.py new file mode 100644 index 00000000..bf8413aa --- /dev/null +++ b/data/data-pipeline/data_pipeline/tests/sources/cdc_life_expectancy/test_etl.py @@ -0,0 +1,112 @@ +# pylint: disable=protected-access +import pathlib +from unittest import mock +import requests +from data_pipeline.etl.base import ExtractTransformLoad +from data_pipeline.etl.sources.cdc_life_expectancy.etl import CDCLifeExpectancy +from data_pipeline.tests.sources.example.test_etl import TestETL +from data_pipeline.utils import get_module_logger + +logger = get_module_logger(__name__) + + +class TestCDCLifeExpectency(TestETL): + """Tests the CDC Life Expectancy ETL. + + This uses pytest-snapshot. + To update individual snapshots: $ poetry run pytest + data_pipeline/tests/sources/cdc_life_expectancy/test_etl.py::TestClassNameETL:: + --snapshot-update + """ + + _ETL_CLASS = CDCLifeExpectancy + + _SAMPLE_DATA_PATH = pathlib.Path(__file__).parents[0] / "data" + _SAMPLE_DATA_FILE_NAME = "US_A.CSV" + _SAMPLE_DATA_ZIP_FILE_NAME = None + _EXTRACT_TMP_FOLDER_NAME = "CDCLifeExpectanc" + _EXTRACT_CSV_FILE_NAME = "extract.csv" + _FIXTURES_SHARED_TRACT_IDS = TestETL._FIXTURES_SHARED_TRACT_IDS + [ + "55001950201", # WI + "23001010100", # ME + ] + + def setup_method(self, _method, filename=__file__): + """Invoke `setup_method` from Parent, but using the current file name. + + This code can be copied identically between all child classes. + """ + super().setup_method(_method=_method, filename=filename) + + def _setup_etl_instance_and_run_extract( + self, mock_etl, mock_paths + ) -> ExtractTransformLoad: + """Method to setup an ETL instance with proper upstream mocks to run extract. + This must be re-implemented in every child class. + + This method can be used by multiple tests that need to run the same fixtures + that need these same mocks. + + In order to re-implement this method, usually it will involve a + decent amount of work to monkeypatch `requests` or another method that's + used to retrieve data in order to force that method to retrieve the fixture + data. A basic version of that patching is included here for classes that can use it. + """ + + with mock.patch( + "data_pipeline.utils.requests" + ) as requests_mock, mock.patch( + "data_pipeline.etl.score.etl_utils.get_state_fips_codes" + ) as mock_get_state_fips_codes: + tmp_path = mock_paths[1] + + def fake_get(url, *args, **kwargs): + file_path = url.split("/")[-1] + with open( + self._DATA_DIRECTORY_FOR_TEST / file_path, + "rb", + ) as file: + file_contents = file.read() + + response_mock = requests.Response() + response_mock.status_code = 200 + # pylint: disable=protected-access + # Return text fixture: + response_mock._content = file_contents + return response_mock + + requests_mock.get = fake_get + mock_get_state_fips_codes.return_value = [ + x[0:2] for x in self._FIXTURES_SHARED_TRACT_IDS + ] + # Instantiate the ETL class. + etl = self._get_instance_of_etl_class() + + # Monkey-patch the temporary directory to the one used in the test + etl.TMP_PATH = tmp_path + + # Run the extract method. + etl.extract() + return etl + + def test_init(self, mock_etl, mock_paths): + etl = self._ETL_CLASS() + data_path, _ = mock_paths + assert etl.DATA_PATH == data_path + assert etl.COLUMNS_TO_KEEP == [ + "GEOID10_TRACT", + "Life expectancy (years)", + ] + assert etl.INPUT_GEOID_TRACT_FIELD_NAME == "Tract ID" + assert etl.LIFE_EXPECTANCY_FIELD_NAME == "Life expectancy (years)" + + def test_get_output_file_path(self, mock_etl, mock_paths): + """Tests the right file name is returned.""" + etl = self._ETL_CLASS() + data_path, tmp_path = mock_paths + + output_file_path = etl._get_output_file_path() + expected_output_file_path = ( + data_path / "dataset" / "cdc_life_expectancy" / "usa.csv" + ) + assert output_file_path == expected_output_file_path diff --git a/data/data-pipeline/data_pipeline/tests/sources/cdc_places/__init__.py b/data/data-pipeline/data_pipeline/tests/sources/cdc_places/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/data/data-pipeline/data_pipeline/tests/sources/cdc_places/data/census_tract.csv b/data/data-pipeline/data_pipeline/tests/sources/cdc_places/data/census_tract.csv new file mode 100644 index 00000000..3530d645 --- /dev/null +++ b/data/data-pipeline/data_pipeline/tests/sources/cdc_places/data/census_tract.csv @@ -0,0 +1,451 @@ +Year,StateAbbr,StateDesc,CountyName,CountyFIPS,LocationName,DataSource,Category,Measure,Data_Value_Unit,Data_Value_Type,Data_Value,Data_Value_Footnote_Symbol,Data_Value_Footnote,Low_Confidence_Limit,High_Confidence_Limit,TotalPopulation,Geolocation,LocationID,CategoryID,MeasureId,DataValueTypeID,Short_Question_Text +2019,CA,California,Inyo,06027,06027000800,BRFSS,Health Outcomes,High blood pressure among adults aged >=18 years,%,Crude prevalence,36.1,,,35.2,36.8,3378,POINT (-117.1176757 36.25159703),06027000800,HLTHOUT,BPHIGH,CrdPrv,High Blood Pressure +2019,CA,California,Inyo,06027,06027000800,BRFSS,Health Outcomes,Diagnosed diabetes among adults aged >=18 years,%,Crude prevalence,13.1,,,12.6,13.6,3378,POINT (-117.1176757 36.25159703),06027000800,HLTHOUT,DIABETES,CrdPrv,Diabetes +2019,CA,California,Inyo,06027,06027000800,BRFSS,Health Status,Mental health not good for >=14 days among adults aged >=18 years,%,Crude prevalence,15.1,,,14.4,15.8,3378,POINT (-117.1176757 36.25159703),06027000800,HLTHSTAT,MHLTH,CrdPrv,Mental Health +2018,CA,California,Inyo,06027,06027000800,BRFSS,Prevention,Cervical cancer screening among adult women aged 21-65 years,%,Crude prevalence,82.0,,,80.5,83.4,3378,POINT (-117.1176757 36.25159703),06027000800,PREVENT,CERVICAL,CrdPrv,Cervical Cancer Screening +2019,CA,California,Inyo,06027,06027000800,BRFSS,Prevention,Current lack of health insurance among adults aged 18-64 years,%,Crude prevalence,14.2,,,12.8,15.6,3378,POINT (-117.1176757 36.25159703),06027000800,PREVENT,ACCESS2,CrdPrv,Health Insurance +2018,CA,California,Inyo,06027,06027000800,BRFSS,Health Risk Behaviors,Sleeping less than 7 hours among adults aged >=18 years,%,Crude prevalence,33.1,,,32.1,34.1,3378,POINT (-117.1176757 36.25159703),06027000800,RISKBEH,SLEEP,CrdPrv,Sleep <7 hours +2019,CA,California,Inyo,06027,06027000800,BRFSS,Prevention,Cholesterol screening among adults aged >=18 years,%,Crude prevalence,86.4,,,86.1,86.7,3378,POINT (-117.1176757 36.25159703),06027000800,PREVENT,CHOLSCREEN,CrdPrv,Cholesterol Screening +2019,CA,California,Inyo,06027,06027000800,BRFSS,Health Risk Behaviors,Current smoking among adults aged >=18 years,%,Crude prevalence,17.2,,,15.8,18.5,3378,POINT (-117.1176757 36.25159703),06027000800,RISKBEH,CSMOKING,CrdPrv,Current Smoking +2019,CA,California,Inyo,06027,06027000800,BRFSS,Health Outcomes,Coronary heart disease among adults aged >=18 years,%,Crude prevalence,8.6,,,8.1,9.1,3378,POINT (-117.1176757 36.25159703),06027000800,HLTHOUT,CHD,CrdPrv,Coronary Heart Disease +2019,CA,California,Inyo,06027,06027000800,BRFSS,Health Outcomes,Obesity among adults aged >=18 years,%,Crude prevalence,30.2,,,29.4,31.0,3378,POINT (-117.1176757 36.25159703),06027000800,HLTHOUT,OBESITY,CrdPrv,Obesity +2019,CA,California,Inyo,06027,06027000800,BRFSS,Health Status,Fair or poor self-rated health status among adults aged >=18 years,%,Crude prevalence,24.2,,,22.4,25.9,3378,POINT (-117.1176757 36.25159703),06027000800,HLTHSTAT,GHLTH,CrdPrv,General Health +2019,CA,California,Inyo,06027,06027000800,BRFSS,Prevention,Taking medicine for high blood pressure control among adults aged >=18 years with high blood pressure,%,Crude prevalence,74.3,,,73.6,74.9,3378,POINT (-117.1176757 36.25159703),06027000800,PREVENT,BPMED,CrdPrv,Taking BP Medication +2019,CA,California,Inyo,06027,06027000800,BRFSS,Health Outcomes,Chronic kidney disease among adults aged >=18 years,%,Crude prevalence,3.9,,,3.8,4.1,3378,POINT (-117.1176757 36.25159703),06027000800,HLTHOUT,KIDNEY,CrdPrv,Chronic Kidney Disease +2019,CA,California,Inyo,06027,06027000800,BRFSS,Health Status,Physical health not good for >=14 days among adults aged >=18 years,%,Crude prevalence,16.8,,,15.8,17.7,3378,POINT (-117.1176757 36.25159703),06027000800,HLTHSTAT,PHLTH,CrdPrv,Physical Health +2018,CA,California,Inyo,06027,06027000800,BRFSS,Prevention,"Older adult women aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening, and Mammogram past 2 years",%,Crude prevalence,25.6,,,22.4,29.2,3378,POINT (-117.1176757 36.25159703),06027000800,PREVENT,COREW,CrdPrv,Core preventive services for older women +2018,CA,California,Inyo,06027,06027000800,BRFSS,Prevention,"Older adult men aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening",%,Crude prevalence,26.5,,,24.0,29.1,3378,POINT (-117.1176757 36.25159703),06027000800,PREVENT,COREM,CrdPrv,Core preventive services for older men +2019,CA,California,Inyo,06027,06027000800,BRFSS,Prevention,Visits to doctor for routine checkup within the past year among adults aged >=18 years,%,Crude prevalence,72.5,,,71.9,73.0,3378,POINT (-117.1176757 36.25159703),06027000800,PREVENT,CHECKUP,CrdPrv,Annual Checkup +2019,CA,California,Inyo,06027,06027000800,BRFSS,Health Risk Behaviors,No leisure-time physical activity among adults aged >=18 years,%,Crude prevalence,26.9,,,25.3,28.4,3378,POINT (-117.1176757 36.25159703),06027000800,RISKBEH,LPA,CrdPrv,Physical Inactivity +2019,CA,California,Inyo,06027,06027000800,BRFSS,Health Outcomes,High cholesterol among adults aged >=18 years who have been screened in the past 5 years,%,Crude prevalence,35.8,,,35.2,36.5,3378,POINT (-117.1176757 36.25159703),06027000800,HLTHOUT,HIGHCHOL,CrdPrv,High Cholesterol +2019,CA,California,Inyo,06027,06027000800,BRFSS,Health Outcomes,Cancer (excluding skin cancer) among adults aged >=18 years,%,Crude prevalence,8.3,,,8.0,8.5,3378,POINT (-117.1176757 36.25159703),06027000800,HLTHOUT,CANCER,CrdPrv,Cancer (except skin) +2019,CA,California,Inyo,06027,06027000800,BRFSS,Health Outcomes,Depression among adults aged >=18 years,%,Crude prevalence,19.5,,,18.9,20.2,3378,POINT (-117.1176757 36.25159703),06027000800,HLTHOUT,DEPRESSION,CrdPrv,Depression +2019,CA,California,Inyo,06027,06027000800,BRFSS,Health Outcomes,Chronic obstructive pulmonary disease among adults aged >=18 years,%,Crude prevalence,9.2,,,8.4,10.0,3378,POINT (-117.1176757 36.25159703),06027000800,HLTHOUT,COPD,CrdPrv,COPD +2019,CA,California,Inyo,06027,06027000800,BRFSS,Health Risk Behaviors,Binge drinking among adults aged >=18 years,%,Crude prevalence,16.2,,,15.8,16.6,3378,POINT (-117.1176757 36.25159703),06027000800,RISKBEH,BINGE,CrdPrv,Binge Drinking +2018,CA,California,Inyo,06027,06027000800,BRFSS,Health Outcomes,All teeth lost among adults aged >=65 years,%,Crude prevalence,17.7,,,14.2,21.6,3378,POINT (-117.1176757 36.25159703),06027000800,HLTHOUT,TEETHLOST,CrdPrv,All Teeth Lost +2019,CA,California,Inyo,06027,06027000800,BRFSS,Health Outcomes,Current asthma among adults aged >=18 years,%,Crude prevalence,10.1,,,9.7,10.4,3378,POINT (-117.1176757 36.25159703),06027000800,HLTHOUT,CASTHMA,CrdPrv,Current Asthma +2018,CA,California,Inyo,06027,06027000800,BRFSS,Prevention,Mammography use among women aged 50-74 years,%,Crude prevalence,74.9,,,72.7,77.0,3378,POINT (-117.1176757 36.25159703),06027000800,PREVENT,MAMMOUSE,CrdPrv,Mammography +2018,CA,California,Inyo,06027,06027000800,BRFSS,Prevention,Visits to dentist or dental clinic among adults aged >=18 years,%,Crude prevalence,58.1,,,55.6,59.9,3378,POINT (-117.1176757 36.25159703),06027000800,PREVENT,DENTAL,CrdPrv,Dental Visit +2018,CA,California,Inyo,06027,06027000800,BRFSS,Prevention,"Fecal occult blood test, sigmoidoscopy, or colonoscopy among adults aged 50-75 years",%,Crude prevalence,61.5,,,59.2,63.6,3378,POINT (-117.1176757 36.25159703),06027000800,PREVENT,COLON_SCREEN,CrdPrv,Colorectal Cancer Screening +2019,CA,California,Inyo,06027,06027000800,BRFSS,Health Outcomes,Arthritis among adults aged >=18 years,%,Crude prevalence,28.5,,,27.8,29.2,3378,POINT (-117.1176757 36.25159703),06027000800,HLTHOUT,ARTHRITIS,CrdPrv,Arthritis +2019,CA,California,Inyo,06027,06027000800,BRFSS,Health Outcomes,Stroke among adults aged >=18 years,%,Crude prevalence,4.6,,,4.3,4.9,3378,POINT (-117.1176757 36.25159703),06027000800,HLTHOUT,STROKE,CrdPrv,Stroke +2019,CA,California,Placer,06061,06061021322,BRFSS,Health Outcomes,Obesity among adults aged >=18 years,%,Crude prevalence,23.0,,,22.2,24.0,8762,POINT (-121.4057179 38.84598382),06061021322,HLTHOUT,OBESITY,CrdPrv,Obesity +2019,CA,California,Placer,06061,06061021322,BRFSS,Health Outcomes,High blood pressure among adults aged >=18 years,%,Crude prevalence,23.2,,,22.5,23.9,8762,POINT (-121.4057179 38.84598382),06061021322,HLTHOUT,BPHIGH,CrdPrv,High Blood Pressure +2019,CA,California,Placer,06061,06061021322,BRFSS,Health Outcomes,Coronary heart disease among adults aged >=18 years,%,Crude prevalence,3.9,,,3.7,4.2,8762,POINT (-121.4057179 38.84598382),06061021322,HLTHOUT,CHD,CrdPrv,Coronary Heart Disease +2019,CA,California,Placer,06061,06061021322,BRFSS,Health Risk Behaviors,No leisure-time physical activity among adults aged >=18 years,%,Crude prevalence,16.6,,,15.2,18.1,8762,POINT (-121.4057179 38.84598382),06061021322,RISKBEH,LPA,CrdPrv,Physical Inactivity +2019,CA,California,Placer,06061,06061021322,BRFSS,Health Outcomes,Depression among adults aged >=18 years,%,Crude prevalence,17.2,,,16.4,18.0,8762,POINT (-121.4057179 38.84598382),06061021322,HLTHOUT,DEPRESSION,CrdPrv,Depression +2019,CA,California,Placer,06061,06061021322,BRFSS,Health Risk Behaviors,Binge drinking among adults aged >=18 years,%,Crude prevalence,18.9,,,18.3,19.5,8762,POINT (-121.4057179 38.84598382),06061021322,RISKBEH,BINGE,CrdPrv,Binge Drinking +2018,CA,California,Placer,06061,06061021322,BRFSS,Prevention,"Older adult men aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening",%,Crude prevalence,35.7,,,31.8,39.8,8762,POINT (-121.4057179 38.84598382),06061021322,PREVENT,COREM,CrdPrv,Core preventive services for older men +2018,CA,California,Placer,06061,06061021322,BRFSS,Health Risk Behaviors,Sleeping less than 7 hours among adults aged >=18 years,%,Crude prevalence,31.9,,,30.6,33.2,8762,POINT (-121.4057179 38.84598382),06061021322,RISKBEH,SLEEP,CrdPrv,Sleep <7 hours +2019,CA,California,Placer,06061,06061021322,BRFSS,Health Outcomes,Chronic kidney disease among adults aged >=18 years,%,Crude prevalence,2.2,,,2.1,2.3,8762,POINT (-121.4057179 38.84598382),06061021322,HLTHOUT,KIDNEY,CrdPrv,Chronic Kidney Disease +2019,CA,California,Placer,06061,06061021322,BRFSS,Health Outcomes,Current asthma among adults aged >=18 years,%,Crude prevalence,8.6,,,8.2,8.9,8762,POINT (-121.4057179 38.84598382),06061021322,HLTHOUT,CASTHMA,CrdPrv,Current Asthma +2019,CA,California,Placer,06061,06061021322,BRFSS,Health Status,Mental health not good for >=14 days among adults aged >=18 years,%,Crude prevalence,12.0,,,11.1,13.0,8762,POINT (-121.4057179 38.84598382),06061021322,HLTHSTAT,MHLTH,CrdPrv,Mental Health +2018,CA,California,Placer,06061,06061021322,BRFSS,Prevention,Cervical cancer screening among adult women aged 21-65 years,%,Crude prevalence,83.4,,,81.6,85.2,8762,POINT (-121.4057179 38.84598382),06061021322,PREVENT,CERVICAL,CrdPrv,Cervical Cancer Screening +2019,CA,California,Placer,06061,06061021322,BRFSS,Health Outcomes,Diagnosed diabetes among adults aged >=18 years,%,Crude prevalence,6.9,,,6.6,7.3,8762,POINT (-121.4057179 38.84598382),06061021322,HLTHOUT,DIABETES,CrdPrv,Diabetes +2018,CA,California,Placer,06061,06061021322,BRFSS,Prevention,"Older adult women aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening, and Mammogram past 2 years",%,Crude prevalence,34.4,,,30.3,38.5,8762,POINT (-121.4057179 38.84598382),06061021322,PREVENT,COREW,CrdPrv,Core preventive services for older women +2019,CA,California,Placer,06061,06061021322,BRFSS,Health Outcomes,High cholesterol among adults aged >=18 years who have been screened in the past 5 years,%,Crude prevalence,28.8,,,28.2,29.4,8762,POINT (-121.4057179 38.84598382),06061021322,HLTHOUT,HIGHCHOL,CrdPrv,High Cholesterol +2019,CA,California,Placer,06061,06061021322,BRFSS,Prevention,Cholesterol screening among adults aged >=18 years,%,Crude prevalence,86.2,,,85.7,86.8,8762,POINT (-121.4057179 38.84598382),06061021322,PREVENT,CHOLSCREEN,CrdPrv,Cholesterol Screening +2018,CA,California,Placer,06061,06061021322,BRFSS,Prevention,Mammography use among women aged 50-74 years,%,Crude prevalence,78.2,,,75.6,80.3,8762,POINT (-121.4057179 38.84598382),06061021322,PREVENT,MAMMOUSE,CrdPrv,Mammography +2019,CA,California,Placer,06061,06061021322,BRFSS,Health Outcomes,Cancer (excluding skin cancer) among adults aged >=18 years,%,Crude prevalence,5.9,,,5.7,6.1,8762,POINT (-121.4057179 38.84598382),06061021322,HLTHOUT,CANCER,CrdPrv,Cancer (except skin) +2019,CA,California,Placer,06061,06061021322,BRFSS,Health Outcomes,Stroke among adults aged >=18 years,%,Crude prevalence,2.2,,,2.0,2.4,8762,POINT (-121.4057179 38.84598382),06061021322,HLTHOUT,STROKE,CrdPrv,Stroke +2018,CA,California,Placer,06061,06061021322,BRFSS,Prevention,"Fecal occult blood test, sigmoidoscopy, or colonoscopy among adults aged 50-75 years",%,Crude prevalence,72.1,,,69.9,74.0,8762,POINT (-121.4057179 38.84598382),06061021322,PREVENT,COLON_SCREEN,CrdPrv,Colorectal Cancer Screening +2019,CA,California,Placer,06061,06061021322,BRFSS,Prevention,Current lack of health insurance among adults aged 18-64 years,%,Crude prevalence,9.0,,,7.8,10.6,8762,POINT (-121.4057179 38.84598382),06061021322,PREVENT,ACCESS2,CrdPrv,Health Insurance +2019,CA,California,Placer,06061,06061021322,BRFSS,Health Risk Behaviors,Current smoking among adults aged >=18 years,%,Crude prevalence,11.4,,,9.8,13.2,8762,POINT (-121.4057179 38.84598382),06061021322,RISKBEH,CSMOKING,CrdPrv,Current Smoking +2019,CA,California,Placer,06061,06061021322,BRFSS,Prevention,Taking medicine for high blood pressure control among adults aged >=18 years with high blood pressure,%,Crude prevalence,67.4,,,66.4,68.4,8762,POINT (-121.4057179 38.84598382),06061021322,PREVENT,BPMED,CrdPrv,Taking BP Medication +2019,CA,California,Placer,06061,06061021322,BRFSS,Health Status,Physical health not good for >=14 days among adults aged >=18 years,%,Crude prevalence,9.4,,,8.7,10.1,8762,POINT (-121.4057179 38.84598382),06061021322,HLTHSTAT,PHLTH,CrdPrv,Physical Health +2019,CA,California,Placer,06061,06061021322,BRFSS,Prevention,Visits to doctor for routine checkup within the past year among adults aged >=18 years,%,Crude prevalence,70.2,,,69.4,70.9,8762,POINT (-121.4057179 38.84598382),06061021322,PREVENT,CHECKUP,CrdPrv,Annual Checkup +2018,CA,California,Placer,06061,06061021322,BRFSS,Health Outcomes,All teeth lost among adults aged >=65 years,%,Crude prevalence,7.6,,,5.6,10.0,8762,POINT (-121.4057179 38.84598382),06061021322,HLTHOUT,TEETHLOST,CrdPrv,All Teeth Lost +2019,CA,California,Placer,06061,06061021322,BRFSS,Health Status,Fair or poor self-rated health status among adults aged >=18 years,%,Crude prevalence,12.3,,,11.0,13.6,8762,POINT (-121.4057179 38.84598382),06061021322,HLTHSTAT,GHLTH,CrdPrv,General Health +2019,CA,California,Placer,06061,06061021322,BRFSS,Health Outcomes,Chronic obstructive pulmonary disease among adults aged >=18 years,%,Crude prevalence,4.2,,,3.8,4.8,8762,POINT (-121.4057179 38.84598382),06061021322,HLTHOUT,COPD,CrdPrv,COPD +2019,CA,California,Placer,06061,06061021322,BRFSS,Health Outcomes,Arthritis among adults aged >=18 years,%,Crude prevalence,18.7,,,18.0,19.4,8762,POINT (-121.4057179 38.84598382),06061021322,HLTHOUT,ARTHRITIS,CrdPrv,Arthritis +2018,CA,California,Placer,06061,06061021322,BRFSS,Prevention,Visits to dentist or dental clinic among adults aged >=18 years,%,Crude prevalence,71.8,,,69.5,74.1,8762,POINT (-121.4057179 38.84598382),06061021322,PREVENT,DENTAL,CrdPrv,Dental Visit +2019,CA,California,San Benito,06069,06069000802,BRFSS,Health Outcomes,Chronic obstructive pulmonary disease among adults aged >=18 years,%,Crude prevalence,6.5,,,5.9,7.0,2534,POINT (-121.0070559 36.54987144),06069000802,HLTHOUT,COPD,CrdPrv,COPD +2018,CA,California,San Benito,06069,06069000802,BRFSS,Health Outcomes,All teeth lost among adults aged >=65 years,%,Crude prevalence,12.3,,,9.6,15.5,2534,POINT (-121.0070559 36.54987144),06069000802,HLTHOUT,TEETHLOST,CrdPrv,All Teeth Lost +2018,CA,California,San Benito,06069,06069000802,BRFSS,Prevention,Mammography use among women aged 50-74 years,%,Crude prevalence,77.6,,,75.4,79.6,2534,POINT (-121.0070559 36.54987144),06069000802,PREVENT,MAMMOUSE,CrdPrv,Mammography +2019,CA,California,San Benito,06069,06069000802,BRFSS,Health Outcomes,Current asthma among adults aged >=18 years,%,Crude prevalence,9.3,,,9.0,9.6,2534,POINT (-121.0070559 36.54987144),06069000802,HLTHOUT,CASTHMA,CrdPrv,Current Asthma +2019,CA,California,San Benito,06069,06069000802,BRFSS,Health Outcomes,Arthritis among adults aged >=18 years,%,Crude prevalence,23.6,,,23.0,24.3,2534,POINT (-121.0070559 36.54987144),06069000802,HLTHOUT,ARTHRITIS,CrdPrv,Arthritis +2018,CA,California,San Benito,06069,06069000802,BRFSS,Prevention,Visits to dentist or dental clinic among adults aged >=18 years,%,Crude prevalence,64.8,,,62.9,67.0,2534,POINT (-121.0070559 36.54987144),06069000802,PREVENT,DENTAL,CrdPrv,Dental Visit +2019,CA,California,San Benito,06069,06069000802,BRFSS,Health Outcomes,Chronic kidney disease among adults aged >=18 years,%,Crude prevalence,2.9,,,2.8,3.0,2534,POINT (-121.0070559 36.54987144),06069000802,HLTHOUT,KIDNEY,CrdPrv,Chronic Kidney Disease +2018,CA,California,San Benito,06069,06069000802,BRFSS,Prevention,Cervical cancer screening among adult women aged 21-65 years,%,Crude prevalence,84.7,,,83.5,86.0,2534,POINT (-121.0070559 36.54987144),06069000802,PREVENT,CERVICAL,CrdPrv,Cervical Cancer Screening +2019,CA,California,San Benito,06069,06069000802,BRFSS,Health Risk Behaviors,Binge drinking among adults aged >=18 years,%,Crude prevalence,18.7,,,18.3,19.1,2534,POINT (-121.0070559 36.54987144),06069000802,RISKBEH,BINGE,CrdPrv,Binge Drinking +2019,CA,California,San Benito,06069,06069000802,BRFSS,Health Status,Mental health not good for >=14 days among adults aged >=18 years,%,Crude prevalence,13.5,,,12.9,14.2,2534,POINT (-121.0070559 36.54987144),06069000802,HLTHSTAT,MHLTH,CrdPrv,Mental Health +2018,CA,California,San Benito,06069,06069000802,BRFSS,Prevention,"Older adult men aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening",%,Crude prevalence,30.3,,,26.8,34.1,2534,POINT (-121.0070559 36.54987144),06069000802,PREVENT,COREM,CrdPrv,Core preventive services for older men +2019,CA,California,San Benito,06069,06069000802,BRFSS,Health Status,Physical health not good for >=14 days among adults aged >=18 years,%,Crude prevalence,13.4,,,12.7,14.2,2534,POINT (-121.0070559 36.54987144),06069000802,HLTHSTAT,PHLTH,CrdPrv,Physical Health +2019,CA,California,San Benito,06069,06069000802,BRFSS,Health Outcomes,Depression among adults aged >=18 years,%,Crude prevalence,19.2,,,18.6,19.9,2534,POINT (-121.0070559 36.54987144),06069000802,HLTHOUT,DEPRESSION,CrdPrv,Depression +2019,CA,California,San Benito,06069,06069000802,BRFSS,Health Outcomes,Cancer (excluding skin cancer) among adults aged >=18 years,%,Crude prevalence,7.0,,,6.8,7.2,2534,POINT (-121.0070559 36.54987144),06069000802,HLTHOUT,CANCER,CrdPrv,Cancer (except skin) +2019,CA,California,San Benito,06069,06069000802,BRFSS,Health Outcomes,High cholesterol among adults aged >=18 years who have been screened in the past 5 years,%,Crude prevalence,32.7,,,32.1,33.3,2534,POINT (-121.0070559 36.54987144),06069000802,HLTHOUT,HIGHCHOL,CrdPrv,High Cholesterol +2019,CA,California,San Benito,06069,06069000802,BRFSS,Prevention,Cholesterol screening among adults aged >=18 years,%,Crude prevalence,87.1,,,86.9,87.2,2534,POINT (-121.0070559 36.54987144),06069000802,PREVENT,CHOLSCREEN,CrdPrv,Cholesterol Screening +2018,CA,California,San Benito,06069,06069000802,BRFSS,Health Risk Behaviors,Sleeping less than 7 hours among adults aged >=18 years,%,Crude prevalence,31.9,,,30.7,32.8,2534,POINT (-121.0070559 36.54987144),06069000802,RISKBEH,SLEEP,CrdPrv,Sleep <7 hours +2019,CA,California,San Benito,06069,06069000802,BRFSS,Health Status,Fair or poor self-rated health status among adults aged >=18 years,%,Crude prevalence,18.9,,,17.5,20.4,2534,POINT (-121.0070559 36.54987144),06069000802,HLTHSTAT,GHLTH,CrdPrv,General Health +2019,CA,California,San Benito,06069,06069000802,BRFSS,Prevention,Taking medicine for high blood pressure control among adults aged >=18 years with high blood pressure,%,Crude prevalence,70.6,,,69.8,71.2,2534,POINT (-121.0070559 36.54987144),06069000802,PREVENT,BPMED,CrdPrv,Taking BP Medication +2018,CA,California,San Benito,06069,06069000802,BRFSS,Prevention,"Older adult women aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening, and Mammogram past 2 years",%,Crude prevalence,29.3,,,26.0,32.8,2534,POINT (-121.0070559 36.54987144),06069000802,PREVENT,COREW,CrdPrv,Core preventive services for older women +2019,CA,California,San Benito,06069,06069000802,BRFSS,Prevention,Visits to doctor for routine checkup within the past year among adults aged >=18 years,%,Crude prevalence,71.1,,,70.5,71.7,2534,POINT (-121.0070559 36.54987144),06069000802,PREVENT,CHECKUP,CrdPrv,Annual Checkup +2019,CA,California,San Benito,06069,06069000802,BRFSS,Health Outcomes,Stroke among adults aged >=18 years,%,Crude prevalence,3.2,,,3.0,3.4,2534,POINT (-121.0070559 36.54987144),06069000802,HLTHOUT,STROKE,CrdPrv,Stroke +2018,CA,California,San Benito,06069,06069000802,BRFSS,Prevention,"Fecal occult blood test, sigmoidoscopy, or colonoscopy among adults aged 50-75 years",%,Crude prevalence,65.7,,,63.5,67.7,2534,POINT (-121.0070559 36.54987144),06069000802,PREVENT,COLON_SCREEN,CrdPrv,Colorectal Cancer Screening +2019,CA,California,San Benito,06069,06069000802,BRFSS,Health Risk Behaviors,No leisure-time physical activity among adults aged >=18 years,%,Crude prevalence,22.7,,,21.4,24.1,2534,POINT (-121.0070559 36.54987144),06069000802,RISKBEH,LPA,CrdPrv,Physical Inactivity +2019,CA,California,San Benito,06069,06069000802,BRFSS,Prevention,Current lack of health insurance among adults aged 18-64 years,%,Crude prevalence,13.2,,,11.9,14.5,2534,POINT (-121.0070559 36.54987144),06069000802,PREVENT,ACCESS2,CrdPrv,Health Insurance +2019,CA,California,San Benito,06069,06069000802,BRFSS,Health Outcomes,Diagnosed diabetes among adults aged >=18 years,%,Crude prevalence,10.2,,,9.8,10.6,2534,POINT (-121.0070559 36.54987144),06069000802,HLTHOUT,DIABETES,CrdPrv,Diabetes +2019,CA,California,San Benito,06069,06069000802,BRFSS,Health Outcomes,High blood pressure among adults aged >=18 years,%,Crude prevalence,30.2,,,29.5,30.9,2534,POINT (-121.0070559 36.54987144),06069000802,HLTHOUT,BPHIGH,CrdPrv,High Blood Pressure +2019,CA,California,San Benito,06069,06069000802,BRFSS,Health Outcomes,Coronary heart disease among adults aged >=18 years,%,Crude prevalence,5.8,,,5.5,6.1,2534,POINT (-121.0070559 36.54987144),06069000802,HLTHOUT,CHD,CrdPrv,Coronary Heart Disease +2019,CA,California,San Benito,06069,06069000802,BRFSS,Health Outcomes,Obesity among adults aged >=18 years,%,Crude prevalence,29.2,,,28.4,30.0,2534,POINT (-121.0070559 36.54987144),06069000802,HLTHOUT,OBESITY,CrdPrv,Obesity +2019,CA,California,San Benito,06069,06069000802,BRFSS,Health Risk Behaviors,Current smoking among adults aged >=18 years,%,Crude prevalence,14.5,,,13.3,15.7,2534,POINT (-121.0070559 36.54987144),06069000802,RISKBEH,CSMOKING,CrdPrv,Current Smoking +2019,HI,Hawaii,Hawaii,15001,15001021800,BRFSS,Prevention,Taking medicine for high blood pressure control among adults aged >=18 years with high blood pressure,%,Crude prevalence,73.5,,,73.1,73.8,6322,POINT (-155.8112721 20.16059783),15001021800,PREVENT,BPMED,CrdPrv,Taking BP Medication +2019,HI,Hawaii,Hawaii,15001,15001021010,BRFSS,Health Outcomes,Obesity among adults aged >=18 years,%,Crude prevalence,29.4,,,29.0,29.8,7884,POINT (-155.1037996 19.49754656),15001021010,HLTHOUT,OBESITY,CrdPrv,Obesity +2018,HI,Hawaii,Hawaii,15001,15001021101,BRFSS,Prevention,"Older adult women aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening, and Mammogram past 2 years",%,Crude prevalence,23.4,,,20.4,26.5,3531,POINT (-154.8953489 19.44949565),15001021101,PREVENT,COREW,CrdPrv,Core preventive services for older women +2019,HI,Hawaii,Hawaii,15001,15001021010,BRFSS,Health Risk Behaviors,Binge drinking among adults aged >=18 years,%,Crude prevalence,20.7,,,20.5,20.9,7884,POINT (-155.1037996 19.49754656),15001021010,RISKBEH,BINGE,CrdPrv,Binge Drinking +2019,HI,Hawaii,Hawaii,15001,15001021402,BRFSS,Health Status,Physical health not good for >=14 days among adults aged >=18 years,%,Crude prevalence,12.4,,,11.9,12.9,4025,POINT (-155.906965 19.51804981),15001021402,HLTHSTAT,PHLTH,CrdPrv,Physical Health +2019,HI,Hawaii,Hawaii,15001,15001021800,BRFSS,Health Outcomes,Obesity among adults aged >=18 years,%,Crude prevalence,24.3,,,24.0,24.7,6322,POINT (-155.8112721 20.16059783),15001021800,HLTHOUT,OBESITY,CrdPrv,Obesity +2018,HI,Hawaii,Hawaii,15001,15001021402,BRFSS,Prevention,"Fecal occult blood test, sigmoidoscopy, or colonoscopy among adults aged 50-75 years",%,Crude prevalence,64.9,,,63.7,66.2,4025,POINT (-155.906965 19.51804981),15001021402,PREVENT,COLON_SCREEN,CrdPrv,Colorectal Cancer Screening +2019,HI,Hawaii,Hawaii,15001,15001021402,BRFSS,Health Outcomes,Depression among adults aged >=18 years,%,Crude prevalence,15.9,,,15.6,16.3,4025,POINT (-155.906965 19.51804981),15001021402,HLTHOUT,DEPRESSION,CrdPrv,Depression +2019,HI,Hawaii,Hawaii,15001,15001021800,BRFSS,Prevention,Current lack of health insurance among adults aged 18-64 years,%,Crude prevalence,10.8,,,10.3,11.3,6322,POINT (-155.8112721 20.16059783),15001021800,PREVENT,ACCESS2,CrdPrv,Health Insurance +2019,HI,Hawaii,Hawaii,15001,15001021402,BRFSS,Health Risk Behaviors,No leisure-time physical activity among adults aged >=18 years,%,Crude prevalence,24.9,,,23.8,25.9,4025,POINT (-155.906965 19.51804981),15001021402,RISKBEH,LPA,CrdPrv,Physical Inactivity +2019,HI,Hawaii,Hawaii,15001,15001021800,BRFSS,Health Outcomes,Chronic kidney disease among adults aged >=18 years,%,Crude prevalence,3.0,,,3.0,3.1,6322,POINT (-155.8112721 20.16059783),15001021800,HLTHOUT,KIDNEY,CrdPrv,Chronic Kidney Disease +2019,HI,Hawaii,Hawaii,15001,15001021101,BRFSS,Health Status,Physical health not good for >=14 days among adults aged >=18 years,%,Crude prevalence,16.0,,,14.9,17.2,3531,POINT (-154.8953489 19.44949565),15001021101,HLTHSTAT,PHLTH,CrdPrv,Physical Health +2019,HI,Hawaii,Hawaii,15001,15001021010,BRFSS,Health Outcomes,Depression among adults aged >=18 years,%,Crude prevalence,21.1,,,20.8,21.4,7884,POINT (-155.1037996 19.49754656),15001021010,HLTHOUT,DEPRESSION,CrdPrv,Depression +2019,HI,Hawaii,Hawaii,15001,15001021010,BRFSS,Health Outcomes,Current asthma among adults aged >=18 years,%,Crude prevalence,11.3,,,11.1,11.5,7884,POINT (-155.1037996 19.49754656),15001021010,HLTHOUT,CASTHMA,CrdPrv,Current Asthma +2018,HI,Hawaii,Hawaii,15001,15001021402,BRFSS,Prevention,Cervical cancer screening among adult women aged 21-65 years,%,Crude prevalence,82.3,,,81.3,83.2,4025,POINT (-155.906965 19.51804981),15001021402,PREVENT,CERVICAL,CrdPrv,Cervical Cancer Screening +2019,HI,Hawaii,Hawaii,15001,15001021402,BRFSS,Health Outcomes,Chronic kidney disease among adults aged >=18 years,%,Crude prevalence,2.9,,,2.8,3.0,4025,POINT (-155.906965 19.51804981),15001021402,HLTHOUT,KIDNEY,CrdPrv,Chronic Kidney Disease +2019,HI,Hawaii,Hawaii,15001,15001021101,BRFSS,Prevention,Visits to doctor for routine checkup within the past year among adults aged >=18 years,%,Crude prevalence,74.5,,,73.9,75.1,3531,POINT (-154.8953489 19.44949565),15001021101,PREVENT,CHECKUP,CrdPrv,Annual Checkup +2019,HI,Hawaii,Hawaii,15001,15001021800,BRFSS,Health Outcomes,Chronic obstructive pulmonary disease among adults aged >=18 years,%,Crude prevalence,5.8,,,5.5,6.0,6322,POINT (-155.8112721 20.16059783),15001021800,HLTHOUT,COPD,CrdPrv,COPD +2019,HI,Hawaii,Hawaii,15001,15001021101,BRFSS,Health Status,Mental health not good for >=14 days among adults aged >=18 years,%,Crude prevalence,15.6,,,14.7,16.5,3531,POINT (-154.8953489 19.44949565),15001021101,HLTHSTAT,MHLTH,CrdPrv,Mental Health +2019,HI,Hawaii,Hawaii,15001,15001021402,BRFSS,Health Risk Behaviors,Binge drinking among adults aged >=18 years,%,Crude prevalence,18.2,,,18.0,18.4,4025,POINT (-155.906965 19.51804981),15001021402,RISKBEH,BINGE,CrdPrv,Binge Drinking +2018,HI,Hawaii,Hawaii,15001,15001021101,BRFSS,Prevention,"Older adult men aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening",%,Crude prevalence,20.3,,,17.4,23.4,3531,POINT (-154.8953489 19.44949565),15001021101,PREVENT,COREM,CrdPrv,Core preventive services for older men +2019,HI,Hawaii,Hawaii,15001,15001021101,BRFSS,Health Outcomes,Diagnosed diabetes among adults aged >=18 years,%,Crude prevalence,11.5,,,10.9,12.2,3531,POINT (-154.8953489 19.44949565),15001021101,HLTHOUT,DIABETES,CrdPrv,Diabetes +2019,HI,Hawaii,Hawaii,15001,15001021010,BRFSS,Health Outcomes,High cholesterol among adults aged >=18 years who have been screened in the past 5 years,%,Crude prevalence,28.8,,,28.6,29.1,7884,POINT (-155.1037996 19.49754656),15001021010,HLTHOUT,HIGHCHOL,CrdPrv,High Cholesterol +2018,HI,Hawaii,Hawaii,15001,15001021800,BRFSS,Health Outcomes,All teeth lost among adults aged >=65 years,%,Crude prevalence,10.1,,,9.1,11.3,6322,POINT (-155.8112721 20.16059783),15001021800,HLTHOUT,TEETHLOST,CrdPrv,All Teeth Lost +2018,HI,Hawaii,Hawaii,15001,15001021010,BRFSS,Prevention,"Older adult women aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening, and Mammogram past 2 years",%,Crude prevalence,22.6,,,21.1,24.3,7884,POINT (-155.1037996 19.49754656),15001021010,PREVENT,COREW,CrdPrv,Core preventive services for older women +2019,HI,Hawaii,Hawaii,15001,15001021800,BRFSS,Health Status,Mental health not good for >=14 days among adults aged >=18 years,%,Crude prevalence,14.0,,,13.6,14.3,6322,POINT (-155.8112721 20.16059783),15001021800,HLTHSTAT,MHLTH,CrdPrv,Mental Health +2019,HI,Hawaii,Hawaii,15001,15001021010,BRFSS,Prevention,Visits to doctor for routine checkup within the past year among adults aged >=18 years,%,Crude prevalence,72.6,,,72.3,72.9,7884,POINT (-155.1037996 19.49754656),15001021010,PREVENT,CHECKUP,CrdPrv,Annual Checkup +2019,HI,Hawaii,Hawaii,15001,15001021101,BRFSS,Health Outcomes,High blood pressure among adults aged >=18 years,%,Crude prevalence,32.6,,,31.6,33.6,3531,POINT (-154.8953489 19.44949565),15001021101,HLTHOUT,BPHIGH,CrdPrv,High Blood Pressure +2018,HI,Hawaii,Hawaii,15001,15001021800,BRFSS,Prevention,Cervical cancer screening among adult women aged 21-65 years,%,Crude prevalence,83.2,,,82.6,83.9,6322,POINT (-155.8112721 20.16059783),15001021800,PREVENT,CERVICAL,CrdPrv,Cervical Cancer Screening +2018,HI,Hawaii,Hawaii,15001,15001021402,BRFSS,Health Outcomes,All teeth lost among adults aged >=65 years,%,Crude prevalence,8.5,,,7.2,10.1,4025,POINT (-155.906965 19.51804981),15001021402,HLTHOUT,TEETHLOST,CrdPrv,All Teeth Lost +2019,HI,Hawaii,Hawaii,15001,15001021101,BRFSS,Health Outcomes,Depression among adults aged >=18 years,%,Crude prevalence,19.8,,,19.1,20.6,3531,POINT (-154.8953489 19.44949565),15001021101,HLTHOUT,DEPRESSION,CrdPrv,Depression +2019,HI,Hawaii,Hawaii,15001,15001021010,BRFSS,Health Status,Mental health not good for >=14 days among adults aged >=18 years,%,Crude prevalence,17.8,,,17.4,18.3,7884,POINT (-155.1037996 19.49754656),15001021010,HLTHSTAT,MHLTH,CrdPrv,Mental Health +2019,HI,Hawaii,Hawaii,15001,15001021800,BRFSS,Health Outcomes,High cholesterol among adults aged >=18 years who have been screened in the past 5 years,%,Crude prevalence,29.5,,,29.2,29.8,6322,POINT (-155.8112721 20.16059783),15001021800,HLTHOUT,HIGHCHOL,CrdPrv,High Cholesterol +2019,HI,Hawaii,Hawaii,15001,15001021402,BRFSS,Prevention,Cholesterol screening among adults aged >=18 years,%,Crude prevalence,83.8,,,83.7,83.9,4025,POINT (-155.906965 19.51804981),15001021402,PREVENT,CHOLSCREEN,CrdPrv,Cholesterol Screening +2018,HI,Hawaii,Hawaii,15001,15001021010,BRFSS,Prevention,Visits to dentist or dental clinic among adults aged >=18 years,%,Crude prevalence,61.9,,,60.9,63.0,7884,POINT (-155.1037996 19.49754656),15001021010,PREVENT,DENTAL,CrdPrv,Dental Visit +2019,HI,Hawaii,Hawaii,15001,15001021010,BRFSS,Health Outcomes,Cancer (excluding skin cancer) among adults aged >=18 years,%,Crude prevalence,5.9,,,5.9,6.1,7884,POINT (-155.1037996 19.49754656),15001021010,HLTHOUT,CANCER,CrdPrv,Cancer (except skin) +2018,HI,Hawaii,Hawaii,15001,15001021010,BRFSS,Health Outcomes,All teeth lost among adults aged >=65 years,%,Crude prevalence,13.3,,,11.7,15.1,7884,POINT (-155.1037996 19.49754656),15001021010,HLTHOUT,TEETHLOST,CrdPrv,All Teeth Lost +2019,HI,Hawaii,Hawaii,15001,15001021800,BRFSS,Health Status,Physical health not good for >=14 days among adults aged >=18 years,%,Crude prevalence,13.2,,,12.9,13.6,6322,POINT (-155.8112721 20.16059783),15001021800,HLTHSTAT,PHLTH,CrdPrv,Physical Health +2019,HI,Hawaii,Hawaii,15001,15001021101,BRFSS,Prevention,Cholesterol screening among adults aged >=18 years,%,Crude prevalence,82.5,,,82.4,82.6,3531,POINT (-154.8953489 19.44949565),15001021101,PREVENT,CHOLSCREEN,CrdPrv,Cholesterol Screening +2019,HI,Hawaii,Hawaii,15001,15001021010,BRFSS,Health Outcomes,Arthritis among adults aged >=18 years,%,Crude prevalence,23.8,,,23.4,24.2,7884,POINT (-155.1037996 19.49754656),15001021010,HLTHOUT,ARTHRITIS,CrdPrv,Arthritis +2019,HI,Hawaii,Hawaii,15001,15001021402,BRFSS,Health Outcomes,Current asthma among adults aged >=18 years,%,Crude prevalence,9.2,,,9.0,9.4,4025,POINT (-155.906965 19.51804981),15001021402,HLTHOUT,CASTHMA,CrdPrv,Current Asthma +2019,HI,Hawaii,Hawaii,15001,15001021402,BRFSS,Health Outcomes,Diagnosed diabetes among adults aged >=18 years,%,Crude prevalence,11.2,,,10.8,11.5,4025,POINT (-155.906965 19.51804981),15001021402,HLTHOUT,DIABETES,CrdPrv,Diabetes +2019,HI,Hawaii,Hawaii,15001,15001021101,BRFSS,Health Outcomes,Current asthma among adults aged >=18 years,%,Crude prevalence,10.4,,,10.0,10.7,3531,POINT (-154.8953489 19.44949565),15001021101,HLTHOUT,CASTHMA,CrdPrv,Current Asthma +2018,HI,Hawaii,Hawaii,15001,15001021800,BRFSS,Prevention,"Older adult men aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening",%,Crude prevalence,20.5,,,19.1,22.0,6322,POINT (-155.8112721 20.16059783),15001021800,PREVENT,COREM,CrdPrv,Core preventive services for older men +2019,HI,Hawaii,Hawaii,15001,15001021010,BRFSS,Prevention,Cholesterol screening among adults aged >=18 years,%,Crude prevalence,79.8,,,79.7,79.9,7884,POINT (-155.1037996 19.49754656),15001021010,PREVENT,CHOLSCREEN,CrdPrv,Cholesterol Screening +2018,HI,Hawaii,Hawaii,15001,15001021101,BRFSS,Health Risk Behaviors,Sleeping less than 7 hours among adults aged >=18 years,%,Crude prevalence,39.4,,,38.2,41.2,3531,POINT (-154.8953489 19.44949565),15001021101,RISKBEH,SLEEP,CrdPrv,Sleep <7 hours +2019,HI,Hawaii,Hawaii,15001,15001021402,BRFSS,Health Outcomes,High blood pressure among adults aged >=18 years,%,Crude prevalence,29.6,,,29.1,30.2,4025,POINT (-155.906965 19.51804981),15001021402,HLTHOUT,BPHIGH,CrdPrv,High Blood Pressure +2019,HI,Hawaii,Hawaii,15001,15001021800,BRFSS,Health Outcomes,High blood pressure among adults aged >=18 years,%,Crude prevalence,29.8,,,29.4,30.2,6322,POINT (-155.8112721 20.16059783),15001021800,HLTHOUT,BPHIGH,CrdPrv,High Blood Pressure +2019,HI,Hawaii,Hawaii,15001,15001021402,BRFSS,Health Outcomes,Obesity among adults aged >=18 years,%,Crude prevalence,21.7,,,21.3,22.1,4025,POINT (-155.906965 19.51804981),15001021402,HLTHOUT,OBESITY,CrdPrv,Obesity +2019,HI,Hawaii,Hawaii,15001,15001021800,BRFSS,Health Outcomes,Coronary heart disease among adults aged >=18 years,%,Crude prevalence,6.4,,,6.2,6.6,6322,POINT (-155.8112721 20.16059783),15001021800,HLTHOUT,CHD,CrdPrv,Coronary Heart Disease +2019,HI,Hawaii,Hawaii,15001,15001021010,BRFSS,Health Outcomes,Chronic kidney disease among adults aged >=18 years,%,Crude prevalence,3.2,,,3.1,3.3,7884,POINT (-155.1037996 19.49754656),15001021010,HLTHOUT,KIDNEY,CrdPrv,Chronic Kidney Disease +2019,HI,Hawaii,Hawaii,15001,15001021010,BRFSS,Health Risk Behaviors,Current smoking among adults aged >=18 years,%,Crude prevalence,22.6,,,21.7,23.6,7884,POINT (-155.1037996 19.49754656),15001021010,RISKBEH,CSMOKING,CrdPrv,Current Smoking +2019,HI,Hawaii,Hawaii,15001,15001021800,BRFSS,Health Outcomes,Diagnosed diabetes among adults aged >=18 years,%,Crude prevalence,10.7,,,10.5,11.0,6322,POINT (-155.8112721 20.16059783),15001021800,HLTHOUT,DIABETES,CrdPrv,Diabetes +2019,HI,Hawaii,Hawaii,15001,15001021101,BRFSS,Health Risk Behaviors,Binge drinking among adults aged >=18 years,%,Crude prevalence,19.1,,,18.7,19.5,3531,POINT (-154.8953489 19.44949565),15001021101,RISKBEH,BINGE,CrdPrv,Binge Drinking +2019,HI,Hawaii,Hawaii,15001,15001021402,BRFSS,Prevention,Current lack of health insurance among adults aged 18-64 years,%,Crude prevalence,11.4,,,10.6,12.1,4025,POINT (-155.906965 19.51804981),15001021402,PREVENT,ACCESS2,CrdPrv,Health Insurance +2018,HI,Hawaii,Hawaii,15001,15001021800,BRFSS,Prevention,Mammography use among women aged 50-74 years,%,Crude prevalence,76.2,,,75.3,77.1,6322,POINT (-155.8112721 20.16059783),15001021800,PREVENT,MAMMOUSE,CrdPrv,Mammography +2019,HI,Hawaii,Hawaii,15001,15001021010,BRFSS,Prevention,Taking medicine for high blood pressure control among adults aged >=18 years with high blood pressure,%,Crude prevalence,70.6,,,70.1,71.0,7884,POINT (-155.1037996 19.49754656),15001021010,PREVENT,BPMED,CrdPrv,Taking BP Medication +2019,HI,Hawaii,Hawaii,15001,15001021101,BRFSS,Health Outcomes,Arthritis among adults aged >=18 years,%,Crude prevalence,25.7,,,24.8,26.6,3531,POINT (-154.8953489 19.44949565),15001021101,HLTHOUT,ARTHRITIS,CrdPrv,Arthritis +2018,HI,Hawaii,Hawaii,15001,15001021402,BRFSS,Prevention,"Older adult men aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening",%,Crude prevalence,21.4,,,19.4,23.6,4025,POINT (-155.906965 19.51804981),15001021402,PREVENT,COREM,CrdPrv,Core preventive services for older men +2019,HI,Hawaii,Hawaii,15001,15001021800,BRFSS,Health Outcomes,Current asthma among adults aged >=18 years,%,Crude prevalence,9.8,,,9.6,9.9,6322,POINT (-155.8112721 20.16059783),15001021800,HLTHOUT,CASTHMA,CrdPrv,Current Asthma +2019,HI,Hawaii,Hawaii,15001,15001021010,BRFSS,Prevention,Current lack of health insurance among adults aged 18-64 years,%,Crude prevalence,12.5,,,11.9,13.1,7884,POINT (-155.1037996 19.49754656),15001021010,PREVENT,ACCESS2,CrdPrv,Health Insurance +2019,HI,Hawaii,Hawaii,15001,15001021402,BRFSS,Health Outcomes,Stroke among adults aged >=18 years,%,Crude prevalence,3.6,,,3.4,3.7,4025,POINT (-155.906965 19.51804981),15001021402,HLTHOUT,STROKE,CrdPrv,Stroke +2018,HI,Hawaii,Hawaii,15001,15001021101,BRFSS,Prevention,Visits to dentist or dental clinic among adults aged >=18 years,%,Crude prevalence,65.5,,,63.5,68.0,3531,POINT (-154.8953489 19.44949565),15001021101,PREVENT,DENTAL,CrdPrv,Dental Visit +2018,HI,Hawaii,Hawaii,15001,15001021800,BRFSS,Health Risk Behaviors,Sleeping less than 7 hours among adults aged >=18 years,%,Crude prevalence,40.2,,,39.7,40.7,6322,POINT (-155.8112721 20.16059783),15001021800,RISKBEH,SLEEP,CrdPrv,Sleep <7 hours +2019,HI,Hawaii,Hawaii,15001,15001021101,BRFSS,Health Outcomes,Chronic kidney disease among adults aged >=18 years,%,Crude prevalence,3.5,,,3.3,3.6,3531,POINT (-154.8953489 19.44949565),15001021101,HLTHOUT,KIDNEY,CrdPrv,Chronic Kidney Disease +2019,HI,Hawaii,Hawaii,15001,15001021010,BRFSS,Health Outcomes,Stroke among adults aged >=18 years,%,Crude prevalence,4.0,,,3.9,4.2,7884,POINT (-155.1037996 19.49754656),15001021010,HLTHOUT,STROKE,CrdPrv,Stroke +2018,HI,Hawaii,Hawaii,15001,15001021101,BRFSS,Prevention,"Fecal occult blood test, sigmoidoscopy, or colonoscopy among adults aged 50-75 years",%,Crude prevalence,63.8,,,61.3,66.1,3531,POINT (-154.8953489 19.44949565),15001021101,PREVENT,COLON_SCREEN,CrdPrv,Colorectal Cancer Screening +2018,HI,Hawaii,Hawaii,15001,15001021010,BRFSS,Health Risk Behaviors,Sleeping less than 7 hours among adults aged >=18 years,%,Crude prevalence,42.6,,,41.8,43.1,7884,POINT (-155.1037996 19.49754656),15001021010,RISKBEH,SLEEP,CrdPrv,Sleep <7 hours +2019,HI,Hawaii,Hawaii,15001,15001021800,BRFSS,Prevention,Cholesterol screening among adults aged >=18 years,%,Crude prevalence,83.1,,,83.0,83.3,6322,POINT (-155.8112721 20.16059783),15001021800,PREVENT,CHOLSCREEN,CrdPrv,Cholesterol Screening +2019,HI,Hawaii,Hawaii,15001,15001021010,BRFSS,Health Risk Behaviors,No leisure-time physical activity among adults aged >=18 years,%,Crude prevalence,27.3,,,26.5,28.1,7884,POINT (-155.1037996 19.49754656),15001021010,RISKBEH,LPA,CrdPrv,Physical Inactivity +2019,HI,Hawaii,Hawaii,15001,15001021010,BRFSS,Health Outcomes,Chronic obstructive pulmonary disease among adults aged >=18 years,%,Crude prevalence,7.4,,,7.1,7.8,7884,POINT (-155.1037996 19.49754656),15001021010,HLTHOUT,COPD,CrdPrv,COPD +2019,HI,Hawaii,Hawaii,15001,15001021101,BRFSS,Health Risk Behaviors,No leisure-time physical activity among adults aged >=18 years,%,Crude prevalence,25.8,,,24.0,27.7,3531,POINT (-154.8953489 19.44949565),15001021101,RISKBEH,LPA,CrdPrv,Physical Inactivity +2018,HI,Hawaii,Hawaii,15001,15001021010,BRFSS,Prevention,"Fecal occult blood test, sigmoidoscopy, or colonoscopy among adults aged 50-75 years",%,Crude prevalence,60.7,,,59.6,61.7,7884,POINT (-155.1037996 19.49754656),15001021010,PREVENT,COLON_SCREEN,CrdPrv,Colorectal Cancer Screening +2019,HI,Hawaii,Hawaii,15001,15001021800,BRFSS,Health Outcomes,Depression among adults aged >=18 years,%,Crude prevalence,17.7,,,17.4,17.9,6322,POINT (-155.8112721 20.16059783),15001021800,HLTHOUT,DEPRESSION,CrdPrv,Depression +2019,HI,Hawaii,Hawaii,15001,15001021402,BRFSS,Health Status,Fair or poor self-rated health status among adults aged >=18 years,%,Crude prevalence,18.0,,,17.1,19.0,4025,POINT (-155.906965 19.51804981),15001021402,HLTHSTAT,GHLTH,CrdPrv,General Health +2018,HI,Hawaii,Hawaii,15001,15001021800,BRFSS,Prevention,"Older adult women aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening, and Mammogram past 2 years",%,Crude prevalence,24.1,,,22.7,25.6,6322,POINT (-155.8112721 20.16059783),15001021800,PREVENT,COREW,CrdPrv,Core preventive services for older women +2018,HI,Hawaii,Hawaii,15001,15001021101,BRFSS,Prevention,Cervical cancer screening among adult women aged 21-65 years,%,Crude prevalence,83.4,,,82.0,84.8,3531,POINT (-154.8953489 19.44949565),15001021101,PREVENT,CERVICAL,CrdPrv,Cervical Cancer Screening +2019,HI,Hawaii,Hawaii,15001,15001021402,BRFSS,Prevention,Taking medicine for high blood pressure control among adults aged >=18 years with high blood pressure,%,Crude prevalence,74.4,,,73.9,74.9,4025,POINT (-155.906965 19.51804981),15001021402,PREVENT,BPMED,CrdPrv,Taking BP Medication +2019,HI,Hawaii,Hawaii,15001,15001021101,BRFSS,Health Outcomes,Stroke among adults aged >=18 years,%,Crude prevalence,4.2,,,3.9,4.5,3531,POINT (-154.8953489 19.44949565),15001021101,HLTHOUT,STROKE,CrdPrv,Stroke +2018,HI,Hawaii,Hawaii,15001,15001021402,BRFSS,Prevention,"Older adult women aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening, and Mammogram past 2 years",%,Crude prevalence,24.8,,,22.8,26.9,4025,POINT (-155.906965 19.51804981),15001021402,PREVENT,COREW,CrdPrv,Core preventive services for older women +2019,HI,Hawaii,Hawaii,15001,15001021402,BRFSS,Prevention,Visits to doctor for routine checkup within the past year among adults aged >=18 years,%,Crude prevalence,75.9,,,75.6,76.3,4025,POINT (-155.906965 19.51804981),15001021402,PREVENT,CHECKUP,CrdPrv,Annual Checkup +2019,HI,Hawaii,Hawaii,15001,15001021800,BRFSS,Health Status,Fair or poor self-rated health status among adults aged >=18 years,%,Crude prevalence,18.3,,,17.6,19.0,6322,POINT (-155.8112721 20.16059783),15001021800,HLTHSTAT,GHLTH,CrdPrv,General Health +2019,HI,Hawaii,Hawaii,15001,15001021800,BRFSS,Prevention,Visits to doctor for routine checkup within the past year among adults aged >=18 years,%,Crude prevalence,75.4,,,75.2,75.7,6322,POINT (-155.8112721 20.16059783),15001021800,PREVENT,CHECKUP,CrdPrv,Annual Checkup +2019,HI,Hawaii,Hawaii,15001,15001021800,BRFSS,Health Risk Behaviors,Binge drinking among adults aged >=18 years,%,Crude prevalence,19.2,,,19.0,19.4,6322,POINT (-155.8112721 20.16059783),15001021800,RISKBEH,BINGE,CrdPrv,Binge Drinking +2019,HI,Hawaii,Hawaii,15001,15001021402,BRFSS,Health Status,Mental health not good for >=14 days among adults aged >=18 years,%,Crude prevalence,13.2,,,12.7,13.6,4025,POINT (-155.906965 19.51804981),15001021402,HLTHSTAT,MHLTH,CrdPrv,Mental Health +2019,HI,Hawaii,Hawaii,15001,15001021402,BRFSS,Health Outcomes,Coronary heart disease among adults aged >=18 years,%,Crude prevalence,6.0,,,5.8,6.3,4025,POINT (-155.906965 19.51804981),15001021402,HLTHOUT,CHD,CrdPrv,Coronary Heart Disease +2019,HI,Hawaii,Hawaii,15001,15001021800,BRFSS,Health Risk Behaviors,No leisure-time physical activity among adults aged >=18 years,%,Crude prevalence,24.0,,,23.3,24.8,6322,POINT (-155.8112721 20.16059783),15001021800,RISKBEH,LPA,CrdPrv,Physical Inactivity +2019,HI,Hawaii,Hawaii,15001,15001021010,BRFSS,Health Outcomes,Diagnosed diabetes among adults aged >=18 years,%,Crude prevalence,11.2,,,10.9,11.4,7884,POINT (-155.1037996 19.49754656),15001021010,HLTHOUT,DIABETES,CrdPrv,Diabetes +2019,HI,Hawaii,Hawaii,15001,15001021010,BRFSS,Health Outcomes,High blood pressure among adults aged >=18 years,%,Crude prevalence,30.6,,,30.2,31.0,7884,POINT (-155.1037996 19.49754656),15001021010,HLTHOUT,BPHIGH,CrdPrv,High Blood Pressure +2019,HI,Hawaii,Hawaii,15001,15001021800,BRFSS,Health Outcomes,Cancer (excluding skin cancer) among adults aged >=18 years,%,Crude prevalence,6.7,,,6.6,6.8,6322,POINT (-155.8112721 20.16059783),15001021800,HLTHOUT,CANCER,CrdPrv,Cancer (except skin) +2019,HI,Hawaii,Hawaii,15001,15001021101,BRFSS,Health Risk Behaviors,Current smoking among adults aged >=18 years,%,Crude prevalence,19.3,,,17.2,21.4,3531,POINT (-154.8953489 19.44949565),15001021101,RISKBEH,CSMOKING,CrdPrv,Current Smoking +2018,HI,Hawaii,Hawaii,15001,15001021402,BRFSS,Health Risk Behaviors,Sleeping less than 7 hours among adults aged >=18 years,%,Crude prevalence,40.2,,,39.3,41.0,4025,POINT (-155.906965 19.51804981),15001021402,RISKBEH,SLEEP,CrdPrv,Sleep <7 hours +2019,HI,Hawaii,Hawaii,15001,15001021101,BRFSS,Prevention,Current lack of health insurance among adults aged 18-64 years,%,Crude prevalence,10.2,,,9.1,11.4,3531,POINT (-154.8953489 19.44949565),15001021101,PREVENT,ACCESS2,CrdPrv,Health Insurance +2019,HI,Hawaii,Hawaii,15001,15001021010,BRFSS,Health Status,Physical health not good for >=14 days among adults aged >=18 years,%,Crude prevalence,16.7,,,16.2,17.2,7884,POINT (-155.1037996 19.49754656),15001021010,HLTHSTAT,PHLTH,CrdPrv,Physical Health +2018,HI,Hawaii,Hawaii,15001,15001021800,BRFSS,Prevention,"Fecal occult blood test, sigmoidoscopy, or colonoscopy among adults aged 50-75 years",%,Crude prevalence,66.0,,,65.0,67.0,6322,POINT (-155.8112721 20.16059783),15001021800,PREVENT,COLON_SCREEN,CrdPrv,Colorectal Cancer Screening +2019,HI,Hawaii,Hawaii,15001,15001021402,BRFSS,Health Outcomes,Chronic obstructive pulmonary disease among adults aged >=18 years,%,Crude prevalence,5.2,,,4.9,5.5,4025,POINT (-155.906965 19.51804981),15001021402,HLTHOUT,COPD,CrdPrv,COPD +2018,HI,Hawaii,Hawaii,15001,15001021800,BRFSS,Prevention,Visits to dentist or dental clinic among adults aged >=18 years,%,Crude prevalence,67.4,,,66.4,68.4,6322,POINT (-155.8112721 20.16059783),15001021800,PREVENT,DENTAL,CrdPrv,Dental Visit +2019,HI,Hawaii,Hawaii,15001,15001021101,BRFSS,Health Outcomes,Cancer (excluding skin cancer) among adults aged >=18 years,%,Crude prevalence,6.8,,,6.6,7.0,3531,POINT (-154.8953489 19.44949565),15001021101,HLTHOUT,CANCER,CrdPrv,Cancer (except skin) +2019,HI,Hawaii,Hawaii,15001,15001021402,BRFSS,Health Outcomes,Arthritis among adults aged >=18 years,%,Crude prevalence,21.1,,,20.6,21.5,4025,POINT (-155.906965 19.51804981),15001021402,HLTHOUT,ARTHRITIS,CrdPrv,Arthritis +2018,HI,Hawaii,Hawaii,15001,15001021010,BRFSS,Prevention,Cervical cancer screening among adult women aged 21-65 years,%,Crude prevalence,83.0,,,82.5,83.7,7884,POINT (-155.1037996 19.49754656),15001021010,PREVENT,CERVICAL,CrdPrv,Cervical Cancer Screening +2019,HI,Hawaii,Hawaii,15001,15001021800,BRFSS,Health Outcomes,Arthritis among adults aged >=18 years,%,Crude prevalence,22.6,,,22.2,22.9,6322,POINT (-155.8112721 20.16059783),15001021800,HLTHOUT,ARTHRITIS,CrdPrv,Arthritis +2018,HI,Hawaii,Hawaii,15001,15001021402,BRFSS,Prevention,Mammography use among women aged 50-74 years,%,Crude prevalence,75.9,,,74.6,77.2,4025,POINT (-155.906965 19.51804981),15001021402,PREVENT,MAMMOUSE,CrdPrv,Mammography +2018,HI,Hawaii,Hawaii,15001,15001021402,BRFSS,Prevention,Visits to dentist or dental clinic among adults aged >=18 years,%,Crude prevalence,68.3,,,67.0,69.7,4025,POINT (-155.906965 19.51804981),15001021402,PREVENT,DENTAL,CrdPrv,Dental Visit +2019,HI,Hawaii,Hawaii,15001,15001021101,BRFSS,Health Outcomes,High cholesterol among adults aged >=18 years who have been screened in the past 5 years,%,Crude prevalence,30.8,,,30.2,31.4,3531,POINT (-154.8953489 19.44949565),15001021101,HLTHOUT,HIGHCHOL,CrdPrv,High Cholesterol +2019,HI,Hawaii,Hawaii,15001,15001021101,BRFSS,Prevention,Taking medicine for high blood pressure control among adults aged >=18 years with high blood pressure,%,Crude prevalence,74.6,,,73.7,75.4,3531,POINT (-154.8953489 19.44949565),15001021101,PREVENT,BPMED,CrdPrv,Taking BP Medication +2019,HI,Hawaii,Hawaii,15001,15001021402,BRFSS,Health Outcomes,Cancer (excluding skin cancer) among adults aged >=18 years,%,Crude prevalence,6.3,,,6.2,6.4,4025,POINT (-155.906965 19.51804981),15001021402,HLTHOUT,CANCER,CrdPrv,Cancer (except skin) +2019,HI,Hawaii,Hawaii,15001,15001021101,BRFSS,Health Outcomes,Obesity among adults aged >=18 years,%,Crude prevalence,27.9,,,27.0,28.8,3531,POINT (-154.8953489 19.44949565),15001021101,HLTHOUT,OBESITY,CrdPrv,Obesity +2019,HI,Hawaii,Hawaii,15001,15001021402,BRFSS,Health Outcomes,High cholesterol among adults aged >=18 years who have been screened in the past 5 years,%,Crude prevalence,29.8,,,29.4,30.2,4025,POINT (-155.906965 19.51804981),15001021402,HLTHOUT,HIGHCHOL,CrdPrv,High Cholesterol +2018,HI,Hawaii,Hawaii,15001,15001021010,BRFSS,Prevention,Mammography use among women aged 50-74 years,%,Crude prevalence,76.2,,,75.1,77.3,7884,POINT (-155.1037996 19.49754656),15001021010,PREVENT,MAMMOUSE,CrdPrv,Mammography +2019,HI,Hawaii,Hawaii,15001,15001021800,BRFSS,Health Outcomes,Stroke among adults aged >=18 years,%,Crude prevalence,3.7,,,3.6,3.8,6322,POINT (-155.8112721 20.16059783),15001021800,HLTHOUT,STROKE,CrdPrv,Stroke +2019,HI,Hawaii,Hawaii,15001,15001021101,BRFSS,Health Outcomes,Coronary heart disease among adults aged >=18 years,%,Crude prevalence,7.7,,,7.2,8.3,3531,POINT (-154.8953489 19.44949565),15001021101,HLTHOUT,CHD,CrdPrv,Coronary Heart Disease +2018,HI,Hawaii,Hawaii,15001,15001021010,BRFSS,Prevention,"Older adult men aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening",%,Crude prevalence,18.5,,,17.0,20.1,7884,POINT (-155.1037996 19.49754656),15001021010,PREVENT,COREM,CrdPrv,Core preventive services for older men +2019,HI,Hawaii,Hawaii,15001,15001021800,BRFSS,Health Risk Behaviors,Current smoking among adults aged >=18 years,%,Crude prevalence,16.8,,,16.1,17.6,6322,POINT (-155.8112721 20.16059783),15001021800,RISKBEH,CSMOKING,CrdPrv,Current Smoking +2019,HI,Hawaii,Hawaii,15001,15001021010,BRFSS,Health Outcomes,Coronary heart disease among adults aged >=18 years,%,Crude prevalence,6.9,,,6.7,7.1,7884,POINT (-155.1037996 19.49754656),15001021010,HLTHOUT,CHD,CrdPrv,Coronary Heart Disease +2018,HI,Hawaii,Hawaii,15001,15001021101,BRFSS,Health Outcomes,All teeth lost among adults aged >=65 years,%,Crude prevalence,11.9,,,8.9,15.0,3531,POINT (-154.8953489 19.44949565),15001021101,HLTHOUT,TEETHLOST,CrdPrv,All Teeth Lost +2018,HI,Hawaii,Hawaii,15001,15001021101,BRFSS,Prevention,Mammography use among women aged 50-74 years,%,Crude prevalence,77.6,,,75.5,79.5,3531,POINT (-154.8953489 19.44949565),15001021101,PREVENT,MAMMOUSE,CrdPrv,Mammography +2019,HI,Hawaii,Hawaii,15001,15001021101,BRFSS,Health Status,Fair or poor self-rated health status among adults aged >=18 years,%,Crude prevalence,21.0,,,19.1,23.0,3531,POINT (-154.8953489 19.44949565),15001021101,HLTHSTAT,GHLTH,CrdPrv,General Health +2019,HI,Hawaii,Hawaii,15001,15001021402,BRFSS,Health Risk Behaviors,Current smoking among adults aged >=18 years,%,Crude prevalence,16.1,,,15.1,17.0,4025,POINT (-155.906965 19.51804981),15001021402,RISKBEH,CSMOKING,CrdPrv,Current Smoking +2019,HI,Hawaii,Hawaii,15001,15001021010,BRFSS,Health Status,Fair or poor self-rated health status among adults aged >=18 years,%,Crude prevalence,22.9,,,22.1,23.7,7884,POINT (-155.1037996 19.49754656),15001021010,HLTHSTAT,GHLTH,CrdPrv,General Health +2019,HI,Hawaii,Hawaii,15001,15001021101,BRFSS,Health Outcomes,Chronic obstructive pulmonary disease among adults aged >=18 years,%,Crude prevalence,7.3,,,6.5,8.2,3531,POINT (-154.8953489 19.44949565),15001021101,HLTHOUT,COPD,CrdPrv,COPD +2019,HI,Hawaii,Maui,15009,15009030201,BRFSS,Health Status,Fair or poor self-rated health status among adults aged >=18 years,%,Crude prevalence,15.7,,,14.4,17.0,2453,POINT (-156.2504199 20.86252093),15009030201,HLTHSTAT,GHLTH,CrdPrv,General Health +2019,HI,Hawaii,Maui,15009,15009030800,BRFSS,Health Outcomes,Stroke among adults aged >=18 years,%,Crude prevalence,2.8,,,2.7,2.9,6907,POINT (-156.5426669 20.90996813),15009030800,HLTHOUT,STROKE,CrdPrv,Stroke +2019,HI,Hawaii,Kauai,15007,15007040604,BRFSS,Health Outcomes,Cancer (excluding skin cancer) among adults aged >=18 years,%,Crude prevalence,6.7,,,6.6,6.9,3139,POINT (-159.4840794 21.90956079),15007040604,HLTHOUT,CANCER,CrdPrv,Cancer (except skin) +2018,HI,Hawaii,Honolulu,15003,15003010201,BRFSS,Prevention,"Older adult women aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening, and Mammogram past 2 years",%,Crude prevalence,27.0,,,25.1,28.9,5882,POINT (-157.8941068 21.55452063),15003010201,PREVENT,COREW,CrdPrv,Core preventive services for older women +2019,HI,Hawaii,Kauai,15007,15007040700,BRFSS,Health Outcomes,Diagnosed diabetes among adults aged >=18 years,%,Crude prevalence,10.7,,,10.3,11.1,8403,POINT (-159.5219447 21.94546074),15007040700,HLTHOUT,DIABETES,CrdPrv,Diabetes +2019,HI,Hawaii,Maui,15009,15009030100,BRFSS,Health Outcomes,Depression among adults aged >=18 years,%,Crude prevalence,17.2,,,16.7,17.6,2291,POINT (-156.1446943 20.72704536),15009030100,HLTHOUT,DEPRESSION,CrdPrv,Depression +2019,HI,Hawaii,Maui,15009,15009030800,BRFSS,Health Outcomes,Current asthma among adults aged >=18 years,%,Crude prevalence,9.1,,,8.9,9.3,6907,POINT (-156.5426669 20.90996813),15009030800,HLTHOUT,CASTHMA,CrdPrv,Current Asthma +2019,HI,Hawaii,Maui,15009,15009030402,BRFSS,Health Outcomes,Arthritis among adults aged >=18 years,%,Crude prevalence,20.4,,,19.8,21.0,8652,POINT (-156.3303372 20.82505697),15009030402,HLTHOUT,ARTHRITIS,CrdPrv,Arthritis +2019,HI,Hawaii,Honolulu,15003,15003010201,BRFSS,Health Outcomes,Arthritis among adults aged >=18 years,%,Crude prevalence,22.7,,,22.3,23.1,5882,POINT (-157.8941068 21.55452063),15003010201,HLTHOUT,ARTHRITIS,CrdPrv,Arthritis +2018,HI,Hawaii,Maui,15009,15009030800,BRFSS,Prevention,"Fecal occult blood test, sigmoidoscopy, or colonoscopy among adults aged 50-75 years",%,Crude prevalence,61.8,,,60.6,63.0,6907,POINT (-156.5426669 20.90996813),15009030800,PREVENT,COLON_SCREEN,CrdPrv,Colorectal Cancer Screening +2019,HI,Hawaii,Kauai,15007,15007040604,BRFSS,Prevention,Current lack of health insurance among adults aged 18-64 years,%,Crude prevalence,11.7,,,11.0,12.4,3139,POINT (-159.4840794 21.90956079),15007040604,PREVENT,ACCESS2,CrdPrv,Health Insurance +2019,HI,Hawaii,Honolulu,15003,15003010201,BRFSS,Health Outcomes,Stroke among adults aged >=18 years,%,Crude prevalence,3.5,,,3.3,3.6,5882,POINT (-157.8941068 21.55452063),15003010201,HLTHOUT,STROKE,CrdPrv,Stroke +2018,HI,Hawaii,Kauai,15007,15007040700,BRFSS,Prevention,Cervical cancer screening among adult women aged 21-65 years,%,Crude prevalence,84.8,,,83.9,85.7,8403,POINT (-159.5219447 21.94546074),15007040700,PREVENT,CERVICAL,CrdPrv,Cervical Cancer Screening +2019,HI,Hawaii,Maui,15009,15009030100,BRFSS,Health Risk Behaviors,Binge drinking among adults aged >=18 years,%,Crude prevalence,23.0,,,22.7,23.4,2291,POINT (-156.1446943 20.72704536),15009030100,RISKBEH,BINGE,CrdPrv,Binge Drinking +2019,HI,Hawaii,Kauai,15007,15007040603,BRFSS,Health Outcomes,Obesity among adults aged >=18 years,%,Crude prevalence,24.3,,,23.7,25.0,2544,POINT (-159.4384998 21.90703588),15007040603,HLTHOUT,OBESITY,CrdPrv,Obesity +2018,HI,Hawaii,Kauai,15007,15007040604,BRFSS,Prevention,Visits to dentist or dental clinic among adults aged >=18 years,%,Crude prevalence,65.5,,,64.3,66.8,3139,POINT (-159.4840794 21.90956079),15007040604,PREVENT,DENTAL,CrdPrv,Dental Visit +2018,HI,Hawaii,Maui,15009,15009030402,BRFSS,Health Risk Behaviors,Sleeping less than 7 hours among adults aged >=18 years,%,Crude prevalence,38.7,,,38.0,39.4,8652,POINT (-156.3303372 20.82505697),15009030402,RISKBEH,SLEEP,CrdPrv,Sleep <7 hours +2019,HI,Hawaii,Kauai,15007,15007040700,BRFSS,Health Outcomes,Arthritis among adults aged >=18 years,%,Crude prevalence,22.6,,,22.0,23.1,8403,POINT (-159.5219447 21.94546074),15007040700,HLTHOUT,ARTHRITIS,CrdPrv,Arthritis +2019,HI,Hawaii,Maui,15009,15009030402,BRFSS,Health Risk Behaviors,No leisure-time physical activity among adults aged >=18 years,%,Crude prevalence,22.7,,,21.4,24.2,8652,POINT (-156.3303372 20.82505697),15009030402,RISKBEH,LPA,CrdPrv,Physical Inactivity +2019,HI,Hawaii,Kauai,15007,15007040604,BRFSS,Health Outcomes,High cholesterol among adults aged >=18 years who have been screened in the past 5 years,%,Crude prevalence,29.3,,,28.9,29.7,3139,POINT (-159.4840794 21.90956079),15007040604,HLTHOUT,HIGHCHOL,CrdPrv,High Cholesterol +2019,HI,Hawaii,Maui,15009,15009030201,BRFSS,Health Outcomes,Obesity among adults aged >=18 years,%,Crude prevalence,27.5,,,26.6,28.4,2453,POINT (-156.2504199 20.86252093),15009030201,HLTHOUT,OBESITY,CrdPrv,Obesity +2019,HI,Hawaii,Maui,15009,15009030201,BRFSS,Prevention,Visits to doctor for routine checkup within the past year among adults aged >=18 years,%,Crude prevalence,73.0,,,72.4,73.6,2453,POINT (-156.2504199 20.86252093),15009030201,PREVENT,CHECKUP,CrdPrv,Annual Checkup +2019,HI,Hawaii,Maui,15009,15009030100,BRFSS,Health Status,Mental health not good for >=14 days among adults aged >=18 years,%,Crude prevalence,14.1,,,13.6,14.7,2291,POINT (-156.1446943 20.72704536),15009030100,HLTHSTAT,MHLTH,CrdPrv,Mental Health +2019,HI,Hawaii,Kauai,15007,15007040700,BRFSS,Health Risk Behaviors,No leisure-time physical activity among adults aged >=18 years,%,Crude prevalence,24.3,,,23.2,25.5,8403,POINT (-159.5219447 21.94546074),15007040700,RISKBEH,LPA,CrdPrv,Physical Inactivity +2018,HI,Hawaii,Maui,15009,15009030402,BRFSS,Prevention,Visits to dentist or dental clinic among adults aged >=18 years,%,Crude prevalence,71.5,,,69.7,73.4,8652,POINT (-156.3303372 20.82505697),15009030402,PREVENT,DENTAL,CrdPrv,Dental Visit +2019,HI,Hawaii,Kauai,15007,15007040603,BRFSS,Health Risk Behaviors,Current smoking among adults aged >=18 years,%,Crude prevalence,14.9,,,13.7,16.2,2544,POINT (-159.4384998 21.90703588),15007040603,RISKBEH,CSMOKING,CrdPrv,Current Smoking +2019,HI,Hawaii,Kauai,15007,15007040700,BRFSS,Health Outcomes,Cancer (excluding skin cancer) among adults aged >=18 years,%,Crude prevalence,6.3,,,6.1,6.4,8403,POINT (-159.5219447 21.94546074),15007040700,HLTHOUT,CANCER,CrdPrv,Cancer (except skin) +2019,HI,Hawaii,Maui,15009,15009030201,BRFSS,Prevention,Taking medicine for high blood pressure control among adults aged >=18 years with high blood pressure,%,Crude prevalence,69.2,,,68.1,70.1,2453,POINT (-156.2504199 20.86252093),15009030201,PREVENT,BPMED,CrdPrv,Taking BP Medication +2018,HI,Hawaii,Maui,15009,15009030100,BRFSS,Health Outcomes,All teeth lost among adults aged >=65 years,%,Crude prevalence,11.2,,,9.4,13.1,2291,POINT (-156.1446943 20.72704536),15009030100,HLTHOUT,TEETHLOST,CrdPrv,All Teeth Lost +2019,HI,Hawaii,Kauai,15007,15007040603,BRFSS,Prevention,Cholesterol screening among adults aged >=18 years,%,Crude prevalence,83.6,,,83.3,83.8,2544,POINT (-159.4384998 21.90703588),15007040603,PREVENT,CHOLSCREEN,CrdPrv,Cholesterol Screening +2018,HI,Hawaii,Maui,15009,15009030800,BRFSS,Prevention,Visits to dentist or dental clinic among adults aged >=18 years,%,Crude prevalence,70.2,,,69.0,71.4,6907,POINT (-156.5426669 20.90996813),15009030800,PREVENT,DENTAL,CrdPrv,Dental Visit +2019,HI,Hawaii,Maui,15009,15009030800,BRFSS,Health Outcomes,Arthritis among adults aged >=18 years,%,Crude prevalence,18.6,,,18.2,18.9,6907,POINT (-156.5426669 20.90996813),15009030800,HLTHOUT,ARTHRITIS,CrdPrv,Arthritis +2018,HI,Hawaii,Maui,15009,15009030100,BRFSS,Prevention,Cervical cancer screening among adult women aged 21-65 years,%,Crude prevalence,85.6,,,84.8,86.6,2291,POINT (-156.1446943 20.72704536),15009030100,PREVENT,CERVICAL,CrdPrv,Cervical Cancer Screening +2019,HI,Hawaii,Maui,15009,15009030100,BRFSS,Health Outcomes,Chronic obstructive pulmonary disease among adults aged >=18 years,%,Crude prevalence,6.1,,,5.7,6.6,2291,POINT (-156.1446943 20.72704536),15009030100,HLTHOUT,COPD,CrdPrv,COPD +2019,HI,Hawaii,Kauai,15007,15007040604,BRFSS,Health Outcomes,High blood pressure among adults aged >=18 years,%,Crude prevalence,31.0,,,30.5,31.5,3139,POINT (-159.4840794 21.90956079),15007040604,HLTHOUT,BPHIGH,CrdPrv,High Blood Pressure +2019,HI,Hawaii,Maui,15009,15009030402,BRFSS,Health Status,Physical health not good for >=14 days among adults aged >=18 years,%,Crude prevalence,11.0,,,10.4,11.6,8652,POINT (-156.3303372 20.82505697),15009030402,HLTHSTAT,PHLTH,CrdPrv,Physical Health +2019,HI,Hawaii,Kauai,15007,15007040604,BRFSS,Health Status,Mental health not good for >=14 days among adults aged >=18 years,%,Crude prevalence,13.9,,,13.4,14.3,3139,POINT (-159.4840794 21.90956079),15007040604,HLTHSTAT,MHLTH,CrdPrv,Mental Health +2018,HI,Hawaii,Maui,15009,15009030201,BRFSS,Prevention,"Older adult women aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening, and Mammogram past 2 years",%,Crude prevalence,25.3,,,21.7,29.2,2453,POINT (-156.2504199 20.86252093),15009030201,PREVENT,COREW,CrdPrv,Core preventive services for older women +2018,HI,Hawaii,Honolulu,15003,15003010201,BRFSS,Prevention,Mammography use among women aged 50-74 years,%,Crude prevalence,82.1,,,81.0,83.1,5882,POINT (-157.8941068 21.55452063),15003010201,PREVENT,MAMMOUSE,CrdPrv,Mammography +2019,HI,Hawaii,Kauai,15007,15007040700,BRFSS,Health Status,Fair or poor self-rated health status among adults aged >=18 years,%,Crude prevalence,15.7,,,14.8,16.7,8403,POINT (-159.5219447 21.94546074),15007040700,HLTHSTAT,GHLTH,CrdPrv,General Health +2019,HI,Hawaii,Kauai,15007,15007040700,BRFSS,Health Status,Physical health not good for >=14 days among adults aged >=18 years,%,Crude prevalence,10.8,,,10.3,11.3,8403,POINT (-159.5219447 21.94546074),15007040700,HLTHSTAT,PHLTH,CrdPrv,Physical Health +2019,HI,Hawaii,Kauai,15007,15007040603,BRFSS,Health Outcomes,High blood pressure among adults aged >=18 years,%,Crude prevalence,31.1,,,30.3,31.8,2544,POINT (-159.4384998 21.90703588),15007040603,HLTHOUT,BPHIGH,CrdPrv,High Blood Pressure +2019,HI,Hawaii,Honolulu,15003,15003010201,BRFSS,Health Outcomes,Current asthma among adults aged >=18 years,%,Crude prevalence,12.2,,,12.0,12.5,5882,POINT (-157.8941068 21.55452063),15003010201,HLTHOUT,CASTHMA,CrdPrv,Current Asthma +2019,HI,Hawaii,Kauai,15007,15007040603,BRFSS,Health Risk Behaviors,No leisure-time physical activity among adults aged >=18 years,%,Crude prevalence,27.0,,,25.3,28.5,2544,POINT (-159.4384998 21.90703588),15007040603,RISKBEH,LPA,CrdPrv,Physical Inactivity +2019,HI,Hawaii,Maui,15009,15009030100,BRFSS,Health Outcomes,Obesity among adults aged >=18 years,%,Crude prevalence,29.2,,,28.6,29.8,2291,POINT (-156.1446943 20.72704536),15009030100,HLTHOUT,OBESITY,CrdPrv,Obesity +2018,HI,Hawaii,Maui,15009,15009030402,BRFSS,Prevention,Cervical cancer screening among adult women aged 21-65 years,%,Crude prevalence,85.0,,,83.8,86.2,8652,POINT (-156.3303372 20.82505697),15009030402,PREVENT,CERVICAL,CrdPrv,Cervical Cancer Screening +2019,HI,Hawaii,Maui,15009,15009030402,BRFSS,Health Outcomes,Chronic obstructive pulmonary disease among adults aged >=18 years,%,Crude prevalence,4.6,,,4.3,5.0,8652,POINT (-156.3303372 20.82505697),15009030402,HLTHOUT,COPD,CrdPrv,COPD +2019,HI,Hawaii,Kauai,15007,15007040604,BRFSS,Prevention,Visits to doctor for routine checkup within the past year among adults aged >=18 years,%,Crude prevalence,77.8,,,77.5,78.1,3139,POINT (-159.4840794 21.90956079),15007040604,PREVENT,CHECKUP,CrdPrv,Annual Checkup +2018,HI,Hawaii,Kauai,15007,15007040700,BRFSS,Health Outcomes,All teeth lost among adults aged >=65 years,%,Crude prevalence,6.2,,,5.1,7.5,8403,POINT (-159.5219447 21.94546074),15007040700,HLTHOUT,TEETHLOST,CrdPrv,All Teeth Lost +2019,HI,Hawaii,Maui,15009,15009030201,BRFSS,Health Outcomes,Diagnosed diabetes among adults aged >=18 years,%,Crude prevalence,8.9,,,8.5,9.4,2453,POINT (-156.2504199 20.86252093),15009030201,HLTHOUT,DIABETES,CrdPrv,Diabetes +2018,HI,Hawaii,Kauai,15007,15007040700,BRFSS,Prevention,"Older adult women aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening, and Mammogram past 2 years",%,Crude prevalence,25.6,,,23.3,27.9,8403,POINT (-159.5219447 21.94546074),15007040700,PREVENT,COREW,CrdPrv,Core preventive services for older women +2019,HI,Hawaii,Maui,15009,15009030800,BRFSS,Health Status,Mental health not good for >=14 days among adults aged >=18 years,%,Crude prevalence,12.9,,,12.5,13.4,6907,POINT (-156.5426669 20.90996813),15009030800,HLTHSTAT,MHLTH,CrdPrv,Mental Health +2018,HI,Hawaii,Maui,15009,15009030100,BRFSS,Prevention,Visits to dentist or dental clinic among adults aged >=18 years,%,Crude prevalence,67.9,,,66.6,69.3,2291,POINT (-156.1446943 20.72704536),15009030100,PREVENT,DENTAL,CrdPrv,Dental Visit +2019,HI,Hawaii,Kauai,15007,15007040604,BRFSS,Health Outcomes,Chronic obstructive pulmonary disease among adults aged >=18 years,%,Crude prevalence,5.8,,,5.5,6.2,3139,POINT (-159.4840794 21.90956079),15007040604,HLTHOUT,COPD,CrdPrv,COPD +2019,HI,Hawaii,Honolulu,15003,15003010201,BRFSS,Health Status,Fair or poor self-rated health status among adults aged >=18 years,%,Crude prevalence,20.0,,,19.0,21.0,5882,POINT (-157.8941068 21.55452063),15003010201,HLTHSTAT,GHLTH,CrdPrv,General Health +2018,HI,Hawaii,Kauai,15007,15007040603,BRFSS,Prevention,Cervical cancer screening among adult women aged 21-65 years,%,Crude prevalence,82.4,,,80.9,84.0,2544,POINT (-159.4384998 21.90703588),15007040603,PREVENT,CERVICAL,CrdPrv,Cervical Cancer Screening +2018,HI,Hawaii,Maui,15009,15009030800,BRFSS,Prevention,Cervical cancer screening among adult women aged 21-65 years,%,Crude prevalence,84.2,,,83.4,85.1,6907,POINT (-156.5426669 20.90996813),15009030800,PREVENT,CERVICAL,CrdPrv,Cervical Cancer Screening +2019,HI,Hawaii,Kauai,15007,15007040603,BRFSS,Health Outcomes,Arthritis among adults aged >=18 years,%,Crude prevalence,25.3,,,24.6,26.0,2544,POINT (-159.4384998 21.90703588),15007040603,HLTHOUT,ARTHRITIS,CrdPrv,Arthritis +2018,HI,Hawaii,Kauai,15007,15007040700,BRFSS,Prevention,"Fecal occult blood test, sigmoidoscopy, or colonoscopy among adults aged 50-75 years",%,Crude prevalence,69.0,,,67.5,70.4,8403,POINT (-159.5219447 21.94546074),15007040700,PREVENT,COLON_SCREEN,CrdPrv,Colorectal Cancer Screening +2019,HI,Hawaii,Maui,15009,15009030100,BRFSS,Health Outcomes,Current asthma among adults aged >=18 years,%,Crude prevalence,10.2,,,10.0,10.5,2291,POINT (-156.1446943 20.72704536),15009030100,HLTHOUT,CASTHMA,CrdPrv,Current Asthma +2019,HI,Hawaii,Kauai,15007,15007040603,BRFSS,Health Outcomes,Diagnosed diabetes among adults aged >=18 years,%,Crude prevalence,12.2,,,11.5,12.8,2544,POINT (-159.4384998 21.90703588),15007040603,HLTHOUT,DIABETES,CrdPrv,Diabetes +2019,HI,Hawaii,Honolulu,15003,15003010201,BRFSS,Health Risk Behaviors,Binge drinking among adults aged >=18 years,%,Crude prevalence,21.3,,,21.0,21.6,5882,POINT (-157.8941068 21.55452063),15003010201,RISKBEH,BINGE,CrdPrv,Binge Drinking +2018,HI,Hawaii,Kauai,15007,15007040604,BRFSS,Health Risk Behaviors,Sleeping less than 7 hours among adults aged >=18 years,%,Crude prevalence,37.5,,,36.9,38.1,3139,POINT (-159.4840794 21.90956079),15007040604,RISKBEH,SLEEP,CrdPrv,Sleep <7 hours +2018,HI,Hawaii,Honolulu,15003,15003010201,BRFSS,Health Outcomes,All teeth lost among adults aged >=65 years,%,Crude prevalence,10.1,,,8.7,11.6,5882,POINT (-157.8941068 21.55452063),15003010201,HLTHOUT,TEETHLOST,CrdPrv,All Teeth Lost +2018,HI,Hawaii,Kauai,15007,15007040603,BRFSS,Health Outcomes,All teeth lost among adults aged >=65 years,%,Crude prevalence,8.1,,,6.2,10.2,2544,POINT (-159.4384998 21.90703588),15007040603,HLTHOUT,TEETHLOST,CrdPrv,All Teeth Lost +2019,HI,Hawaii,Maui,15009,15009030201,BRFSS,Health Outcomes,Cancer (excluding skin cancer) among adults aged >=18 years,%,Crude prevalence,6.5,,,6.3,6.8,2453,POINT (-156.2504199 20.86252093),15009030201,HLTHOUT,CANCER,CrdPrv,Cancer (except skin) +2019,HI,Hawaii,Kauai,15007,15007040700,BRFSS,Health Risk Behaviors,Binge drinking among adults aged >=18 years,%,Crude prevalence,19.2,,,18.9,19.6,8403,POINT (-159.5219447 21.94546074),15007040700,RISKBEH,BINGE,CrdPrv,Binge Drinking +2018,HI,Hawaii,Maui,15009,15009030800,BRFSS,Health Risk Behaviors,Sleeping less than 7 hours among adults aged >=18 years,%,Crude prevalence,40.9,,,40.1,41.6,6907,POINT (-156.5426669 20.90996813),15009030800,RISKBEH,SLEEP,CrdPrv,Sleep <7 hours +2019,HI,Hawaii,Maui,15009,15009030402,BRFSS,Health Outcomes,Depression among adults aged >=18 years,%,Crude prevalence,15.5,,,15.0,16.0,8652,POINT (-156.3303372 20.82505697),15009030402,HLTHOUT,DEPRESSION,CrdPrv,Depression +2019,HI,Hawaii,Honolulu,15003,15003010201,BRFSS,Health Risk Behaviors,Current smoking among adults aged >=18 years,%,Crude prevalence,17.5,,,16.4,18.7,5882,POINT (-157.8941068 21.55452063),15003010201,RISKBEH,CSMOKING,CrdPrv,Current Smoking +2019,HI,Hawaii,Maui,15009,15009030800,BRFSS,Health Outcomes,Depression among adults aged >=18 years,%,Crude prevalence,15.2,,,14.9,15.6,6907,POINT (-156.5426669 20.90996813),15009030800,HLTHOUT,DEPRESSION,CrdPrv,Depression +2019,HI,Hawaii,Maui,15009,15009030402,BRFSS,Health Risk Behaviors,Binge drinking among adults aged >=18 years,%,Crude prevalence,21.0,,,20.6,21.4,8652,POINT (-156.3303372 20.82505697),15009030402,RISKBEH,BINGE,CrdPrv,Binge Drinking +2019,HI,Hawaii,Maui,15009,15009030100,BRFSS,Health Outcomes,High cholesterol among adults aged >=18 years who have been screened in the past 5 years,%,Crude prevalence,27.0,,,26.6,27.4,2291,POINT (-156.1446943 20.72704536),15009030100,HLTHOUT,HIGHCHOL,CrdPrv,High Cholesterol +2018,HI,Hawaii,Kauai,15007,15007040603,BRFSS,Prevention,"Fecal occult blood test, sigmoidoscopy, or colonoscopy among adults aged 50-75 years",%,Crude prevalence,67.1,,,65.3,68.9,2544,POINT (-159.4384998 21.90703588),15007040603,PREVENT,COLON_SCREEN,CrdPrv,Colorectal Cancer Screening +2019,HI,Hawaii,Maui,15009,15009030201,BRFSS,Health Outcomes,High cholesterol among adults aged >=18 years who have been screened in the past 5 years,%,Crude prevalence,26.4,,,25.8,26.9,2453,POINT (-156.2504199 20.86252093),15009030201,HLTHOUT,HIGHCHOL,CrdPrv,High Cholesterol +2019,HI,Hawaii,Maui,15009,15009030100,BRFSS,Health Outcomes,High blood pressure among adults aged >=18 years,%,Crude prevalence,28.1,,,27.6,28.7,2291,POINT (-156.1446943 20.72704536),15009030100,HLTHOUT,BPHIGH,CrdPrv,High Blood Pressure +2019,HI,Hawaii,Honolulu,15003,15003010201,BRFSS,Health Outcomes,Cancer (excluding skin cancer) among adults aged >=18 years,%,Crude prevalence,6.4,,,6.3,6.5,5882,POINT (-157.8941068 21.55452063),15003010201,HLTHOUT,CANCER,CrdPrv,Cancer (except skin) +2019,HI,Hawaii,Kauai,15007,15007040604,BRFSS,Health Outcomes,Depression among adults aged >=18 years,%,Crude prevalence,15.9,,,15.6,16.2,3139,POINT (-159.4840794 21.90956079),15007040604,HLTHOUT,DEPRESSION,CrdPrv,Depression +2019,HI,Hawaii,Honolulu,15003,15003010201,BRFSS,Prevention,Cholesterol screening among adults aged >=18 years,%,Crude prevalence,82.9,,,82.7,83.0,5882,POINT (-157.8941068 21.55452063),15003010201,PREVENT,CHOLSCREEN,CrdPrv,Cholesterol Screening +2019,HI,Hawaii,Kauai,15007,15007040700,BRFSS,Health Outcomes,Chronic kidney disease among adults aged >=18 years,%,Crude prevalence,2.7,,,2.6,2.8,8403,POINT (-159.5219447 21.94546074),15007040700,HLTHOUT,KIDNEY,CrdPrv,Chronic Kidney Disease +2019,HI,Hawaii,Maui,15009,15009030800,BRFSS,Health Risk Behaviors,Binge drinking among adults aged >=18 years,%,Crude prevalence,21.8,,,21.5,22.1,6907,POINT (-156.5426669 20.90996813),15009030800,RISKBEH,BINGE,CrdPrv,Binge Drinking +2019,HI,Hawaii,Maui,15009,15009030100,BRFSS,Health Outcomes,Coronary heart disease among adults aged >=18 years,%,Crude prevalence,6.0,,,5.8,6.3,2291,POINT (-156.1446943 20.72704536),15009030100,HLTHOUT,CHD,CrdPrv,Coronary Heart Disease +2019,HI,Hawaii,Maui,15009,15009030402,BRFSS,Health Outcomes,Chronic kidney disease among adults aged >=18 years,%,Crude prevalence,2.5,,,2.5,2.6,8652,POINT (-156.3303372 20.82505697),15009030402,HLTHOUT,KIDNEY,CrdPrv,Chronic Kidney Disease +2019,HI,Hawaii,Maui,15009,15009030201,BRFSS,Prevention,Cholesterol screening among adults aged >=18 years,%,Crude prevalence,82.9,,,82.9,83.0,2453,POINT (-156.2504199 20.86252093),15009030201,PREVENT,CHOLSCREEN,CrdPrv,Cholesterol Screening +2019,HI,Hawaii,Kauai,15007,15007040604,BRFSS,Health Outcomes,Arthritis among adults aged >=18 years,%,Crude prevalence,25.5,,,24.9,25.9,3139,POINT (-159.4840794 21.90956079),15007040604,HLTHOUT,ARTHRITIS,CrdPrv,Arthritis +2019,HI,Hawaii,Maui,15009,15009030100,BRFSS,Health Outcomes,Diagnosed diabetes among adults aged >=18 years,%,Crude prevalence,10.7,,,10.4,11.1,2291,POINT (-156.1446943 20.72704536),15009030100,HLTHOUT,DIABETES,CrdPrv,Diabetes +2018,HI,Hawaii,Kauai,15007,15007040603,BRFSS,Prevention,Visits to dentist or dental clinic among adults aged >=18 years,%,Crude prevalence,68.0,,,66.2,69.9,2544,POINT (-159.4384998 21.90703588),15007040603,PREVENT,DENTAL,CrdPrv,Dental Visit +2019,HI,Hawaii,Kauai,15007,15007040603,BRFSS,Health Status,Physical health not good for >=14 days among adults aged >=18 years,%,Crude prevalence,12.3,,,11.6,13.0,2544,POINT (-159.4384998 21.90703588),15007040603,HLTHSTAT,PHLTH,CrdPrv,Physical Health +2019,HI,Hawaii,Maui,15009,15009030402,BRFSS,Health Outcomes,Current asthma among adults aged >=18 years,%,Crude prevalence,8.9,,,8.7,9.2,8652,POINT (-156.3303372 20.82505697),15009030402,HLTHOUT,CASTHMA,CrdPrv,Current Asthma +2019,HI,Hawaii,Maui,15009,15009030402,BRFSS,Health Outcomes,Stroke among adults aged >=18 years,%,Crude prevalence,3.0,,,2.8,3.2,8652,POINT (-156.3303372 20.82505697),15009030402,HLTHOUT,STROKE,CrdPrv,Stroke +2019,HI,Hawaii,Kauai,15007,15007040604,BRFSS,Health Status,Fair or poor self-rated health status among adults aged >=18 years,%,Crude prevalence,19.5,,,18.6,20.5,3139,POINT (-159.4840794 21.90956079),15007040604,HLTHSTAT,GHLTH,CrdPrv,General Health +2019,HI,Hawaii,Maui,15009,15009030201,BRFSS,Health Status,Physical health not good for >=14 days among adults aged >=18 years,%,Crude prevalence,12.4,,,11.6,13.1,2453,POINT (-156.2504199 20.86252093),15009030201,HLTHSTAT,PHLTH,CrdPrv,Physical Health +2019,HI,Hawaii,Kauai,15007,15007040603,BRFSS,Health Outcomes,Chronic kidney disease among adults aged >=18 years,%,Crude prevalence,3.1,,,3.0,3.2,2544,POINT (-159.4384998 21.90703588),15007040603,HLTHOUT,KIDNEY,CrdPrv,Chronic Kidney Disease +2019,HI,Hawaii,Honolulu,15003,15003010201,BRFSS,Health Risk Behaviors,No leisure-time physical activity among adults aged >=18 years,%,Crude prevalence,24.8,,,23.8,25.9,5882,POINT (-157.8941068 21.55452063),15003010201,RISKBEH,LPA,CrdPrv,Physical Inactivity +2018,HI,Hawaii,Maui,15009,15009030201,BRFSS,Prevention,"Older adult men aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening",%,Crude prevalence,26.1,,,22.4,30.0,2453,POINT (-156.2504199 20.86252093),15009030201,PREVENT,COREM,CrdPrv,Core preventive services for older men +2019,HI,Hawaii,Kauai,15007,15007040700,BRFSS,Health Outcomes,Obesity among adults aged >=18 years,%,Crude prevalence,23.1,,,22.6,23.6,8403,POINT (-159.5219447 21.94546074),15007040700,HLTHOUT,OBESITY,CrdPrv,Obesity +2019,HI,Hawaii,Kauai,15007,15007040604,BRFSS,Health Outcomes,Diagnosed diabetes among adults aged >=18 years,%,Crude prevalence,12.3,,,12.0,12.7,3139,POINT (-159.4840794 21.90956079),15007040604,HLTHOUT,DIABETES,CrdPrv,Diabetes +2018,HI,Hawaii,Maui,15009,15009030800,BRFSS,Prevention,"Older adult women aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening, and Mammogram past 2 years",%,Crude prevalence,24.0,,,22.0,25.9,6907,POINT (-156.5426669 20.90996813),15009030800,PREVENT,COREW,CrdPrv,Core preventive services for older women +2019,HI,Hawaii,Kauai,15007,15007040604,BRFSS,Health Outcomes,Coronary heart disease among adults aged >=18 years,%,Crude prevalence,6.4,,,6.1,6.7,3139,POINT (-159.4840794 21.90956079),15007040604,HLTHOUT,CHD,CrdPrv,Coronary Heart Disease +2019,HI,Hawaii,Maui,15009,15009030402,BRFSS,Health Outcomes,Obesity among adults aged >=18 years,%,Crude prevalence,23.7,,,23.0,24.3,8652,POINT (-156.3303372 20.82505697),15009030402,HLTHOUT,OBESITY,CrdPrv,Obesity +2019,HI,Hawaii,Kauai,15007,15007040700,BRFSS,Prevention,Cholesterol screening among adults aged >=18 years,%,Crude prevalence,84.2,,,84.1,84.4,8403,POINT (-159.5219447 21.94546074),15007040700,PREVENT,CHOLSCREEN,CrdPrv,Cholesterol Screening +2019,HI,Hawaii,Maui,15009,15009030800,BRFSS,Health Status,Fair or poor self-rated health status among adults aged >=18 years,%,Crude prevalence,15.1,,,14.3,15.8,6907,POINT (-156.5426669 20.90996813),15009030800,HLTHSTAT,GHLTH,CrdPrv,General Health +2019,HI,Hawaii,Maui,15009,15009030100,BRFSS,Health Outcomes,Stroke among adults aged >=18 years,%,Crude prevalence,3.7,,,3.5,3.9,2291,POINT (-156.1446943 20.72704536),15009030100,HLTHOUT,STROKE,CrdPrv,Stroke +2019,HI,Hawaii,Honolulu,15003,15003010201,BRFSS,Prevention,Visits to doctor for routine checkup within the past year among adults aged >=18 years,%,Crude prevalence,79.1,,,78.8,79.4,5882,POINT (-157.8941068 21.55452063),15003010201,PREVENT,CHECKUP,CrdPrv,Annual Checkup +2018,HI,Hawaii,Kauai,15007,15007040604,BRFSS,Prevention,"Older adult men aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening",%,Crude prevalence,25.0,,,22.9,27.3,3139,POINT (-159.4840794 21.90956079),15007040604,PREVENT,COREM,CrdPrv,Core preventive services for older men +2019,HI,Hawaii,Maui,15009,15009030402,BRFSS,Prevention,Cholesterol screening among adults aged >=18 years,%,Crude prevalence,82.9,,,82.5,83.3,8652,POINT (-156.3303372 20.82505697),15009030402,PREVENT,CHOLSCREEN,CrdPrv,Cholesterol Screening +2019,HI,Hawaii,Kauai,15007,15007040700,BRFSS,Health Risk Behaviors,Current smoking among adults aged >=18 years,%,Crude prevalence,13.6,,,12.6,14.6,8403,POINT (-159.5219447 21.94546074),15007040700,RISKBEH,CSMOKING,CrdPrv,Current Smoking +2019,HI,Hawaii,Kauai,15007,15007040700,BRFSS,Health Outcomes,High cholesterol among adults aged >=18 years who have been screened in the past 5 years,%,Crude prevalence,28.3,,,27.9,28.7,8403,POINT (-159.5219447 21.94546074),15007040700,HLTHOUT,HIGHCHOL,CrdPrv,High Cholesterol +2019,HI,Hawaii,Kauai,15007,15007040603,BRFSS,Health Outcomes,Current asthma among adults aged >=18 years,%,Crude prevalence,9.0,,,8.7,9.2,2544,POINT (-159.4384998 21.90703588),15007040603,HLTHOUT,CASTHMA,CrdPrv,Current Asthma +2019,HI,Hawaii,Maui,15009,15009030201,BRFSS,Health Risk Behaviors,No leisure-time physical activity among adults aged >=18 years,%,Crude prevalence,22.1,,,20.7,23.7,2453,POINT (-156.2504199 20.86252093),15009030201,RISKBEH,LPA,CrdPrv,Physical Inactivity +2018,HI,Hawaii,Kauai,15007,15007040604,BRFSS,Prevention,Mammography use among women aged 50-74 years,%,Crude prevalence,77.5,,,76.1,78.9,3139,POINT (-159.4840794 21.90956079),15007040604,PREVENT,MAMMOUSE,CrdPrv,Mammography +2019,HI,Hawaii,Kauai,15007,15007040603,BRFSS,Health Outcomes,Depression among adults aged >=18 years,%,Crude prevalence,14.7,,,14.2,15.2,2544,POINT (-159.4384998 21.90703588),15007040603,HLTHOUT,DEPRESSION,CrdPrv,Depression +2018,HI,Hawaii,Maui,15009,15009030100,BRFSS,Prevention,"Fecal occult blood test, sigmoidoscopy, or colonoscopy among adults aged 50-75 years",%,Crude prevalence,63.2,,,61.5,64.8,2291,POINT (-156.1446943 20.72704536),15009030100,PREVENT,COLON_SCREEN,CrdPrv,Colorectal Cancer Screening +2019,HI,Hawaii,Kauai,15007,15007040604,BRFSS,Health Outcomes,Stroke among adults aged >=18 years,%,Crude prevalence,3.8,,,3.7,4.0,3139,POINT (-159.4840794 21.90956079),15007040604,HLTHOUT,STROKE,CrdPrv,Stroke +2019,HI,Hawaii,Honolulu,15003,15003010201,BRFSS,Health Outcomes,Diagnosed diabetes among adults aged >=18 years,%,Crude prevalence,10.2,,,10.0,10.5,5882,POINT (-157.8941068 21.55452063),15003010201,HLTHOUT,DIABETES,CrdPrv,Diabetes +2018,HI,Hawaii,Maui,15009,15009030402,BRFSS,Prevention,"Older adult men aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening",%,Crude prevalence,25.4,,,22.7,28.5,8652,POINT (-156.3303372 20.82505697),15009030402,PREVENT,COREM,CrdPrv,Core preventive services for older men +2019,HI,Hawaii,Maui,15009,15009030800,BRFSS,Prevention,Cholesterol screening among adults aged >=18 years,%,Crude prevalence,82.0,,,81.7,82.4,6907,POINT (-156.5426669 20.90996813),15009030800,PREVENT,CHOLSCREEN,CrdPrv,Cholesterol Screening +2019,HI,Hawaii,Maui,15009,15009030201,BRFSS,Health Outcomes,Depression among adults aged >=18 years,%,Crude prevalence,18.5,,,17.9,19.2,2453,POINT (-156.2504199 20.86252093),15009030201,HLTHOUT,DEPRESSION,CrdPrv,Depression +2018,HI,Hawaii,Maui,15009,15009030800,BRFSS,Prevention,Mammography use among women aged 50-74 years,%,Crude prevalence,79.6,,,78.6,80.7,6907,POINT (-156.5426669 20.90996813),15009030800,PREVENT,MAMMOUSE,CrdPrv,Mammography +2019,HI,Hawaii,Maui,15009,15009030100,BRFSS,Prevention,Current lack of health insurance among adults aged 18-64 years,%,Crude prevalence,11.1,,,10.3,11.9,2291,POINT (-156.1446943 20.72704536),15009030100,PREVENT,ACCESS2,CrdPrv,Health Insurance +2019,HI,Hawaii,Maui,15009,15009030201,BRFSS,Health Risk Behaviors,Binge drinking among adults aged >=18 years,%,Crude prevalence,23.5,,,23.0,24.0,2453,POINT (-156.2504199 20.86252093),15009030201,RISKBEH,BINGE,CrdPrv,Binge Drinking +2019,HI,Hawaii,Maui,15009,15009030100,BRFSS,Health Risk Behaviors,Current smoking among adults aged >=18 years,%,Crude prevalence,18.4,,,17.3,19.6,2291,POINT (-156.1446943 20.72704536),15009030100,RISKBEH,CSMOKING,CrdPrv,Current Smoking +2019,HI,Hawaii,Maui,15009,15009030402,BRFSS,Health Outcomes,Diagnosed diabetes among adults aged >=18 years,%,Crude prevalence,9.6,,,9.2,10.1,8652,POINT (-156.3303372 20.82505697),15009030402,HLTHOUT,DIABETES,CrdPrv,Diabetes +2018,HI,Hawaii,Kauai,15007,15007040603,BRFSS,Prevention,"Older adult men aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening",%,Crude prevalence,26.3,,,23.2,29.7,2544,POINT (-159.4384998 21.90703588),15007040603,PREVENT,COREM,CrdPrv,Core preventive services for older men +2019,HI,Hawaii,Maui,15009,15009030100,BRFSS,Prevention,Taking medicine for high blood pressure control among adults aged >=18 years with high blood pressure,%,Crude prevalence,70.6,,,70.0,71.4,2291,POINT (-156.1446943 20.72704536),15009030100,PREVENT,BPMED,CrdPrv,Taking BP Medication +2019,HI,Hawaii,Maui,15009,15009030201,BRFSS,Health Outcomes,Current asthma among adults aged >=18 years,%,Crude prevalence,9.7,,,9.4,10.0,2453,POINT (-156.2504199 20.86252093),15009030201,HLTHOUT,CASTHMA,CrdPrv,Current Asthma +2019,HI,Hawaii,Kauai,15007,15007040603,BRFSS,Health Outcomes,Chronic obstructive pulmonary disease among adults aged >=18 years,%,Crude prevalence,5.6,,,5.1,6.1,2544,POINT (-159.4384998 21.90703588),15007040603,HLTHOUT,COPD,CrdPrv,COPD +2018,HI,Hawaii,Kauai,15007,15007040603,BRFSS,Health Risk Behaviors,Sleeping less than 7 hours among adults aged >=18 years,%,Crude prevalence,36.8,,,35.8,37.7,2544,POINT (-159.4384998 21.90703588),15007040603,RISKBEH,SLEEP,CrdPrv,Sleep <7 hours +2018,HI,Hawaii,Maui,15009,15009030402,BRFSS,Health Outcomes,All teeth lost among adults aged >=65 years,%,Crude prevalence,7.9,,,6.2,9.9,8652,POINT (-156.3303372 20.82505697),15009030402,HLTHOUT,TEETHLOST,CrdPrv,All Teeth Lost +2019,HI,Hawaii,Kauai,15007,15007040604,BRFSS,Health Risk Behaviors,No leisure-time physical activity among adults aged >=18 years,%,Crude prevalence,27.7,,,26.6,28.7,3139,POINT (-159.4840794 21.90956079),15007040604,RISKBEH,LPA,CrdPrv,Physical Inactivity +2019,HI,Hawaii,Maui,15009,15009030402,BRFSS,Health Outcomes,High cholesterol among adults aged >=18 years who have been screened in the past 5 years,%,Crude prevalence,26.2,,,25.7,26.6,8652,POINT (-156.3303372 20.82505697),15009030402,HLTHOUT,HIGHCHOL,CrdPrv,High Cholesterol +2018,HI,Hawaii,Maui,15009,15009030201,BRFSS,Prevention,Mammography use among women aged 50-74 years,%,Crude prevalence,80.7,,,78.6,82.6,2453,POINT (-156.2504199 20.86252093),15009030201,PREVENT,MAMMOUSE,CrdPrv,Mammography +2019,HI,Hawaii,Maui,15009,15009030100,BRFSS,Health Status,Fair or poor self-rated health status among adults aged >=18 years,%,Crude prevalence,18.1,,,17.1,19.1,2291,POINT (-156.1446943 20.72704536),15009030100,HLTHSTAT,GHLTH,CrdPrv,General Health +2019,HI,Hawaii,Kauai,15007,15007040700,BRFSS,Health Outcomes,Stroke among adults aged >=18 years,%,Crude prevalence,3.1,,,2.9,3.2,8403,POINT (-159.5219447 21.94546074),15007040700,HLTHOUT,STROKE,CrdPrv,Stroke +2019,HI,Hawaii,Maui,15009,15009030100,BRFSS,Health Risk Behaviors,No leisure-time physical activity among adults aged >=18 years,%,Crude prevalence,23.8,,,22.7,24.9,2291,POINT (-156.1446943 20.72704536),15009030100,RISKBEH,LPA,CrdPrv,Physical Inactivity +2018,HI,Hawaii,Maui,15009,15009030201,BRFSS,Health Outcomes,All teeth lost among adults aged >=65 years,%,Crude prevalence,8.2,,,6.4,10.4,2453,POINT (-156.2504199 20.86252093),15009030201,HLTHOUT,TEETHLOST,CrdPrv,All Teeth Lost +2018,HI,Hawaii,Maui,15009,15009030800,BRFSS,Prevention,"Older adult men aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening",%,Crude prevalence,24.8,,,22.9,27.0,6907,POINT (-156.5426669 20.90996813),15009030800,PREVENT,COREM,CrdPrv,Core preventive services for older men +2019,HI,Hawaii,Honolulu,15003,15003010201,BRFSS,Health Status,Physical health not good for >=14 days among adults aged >=18 years,%,Crude prevalence,13.0,,,12.5,13.5,5882,POINT (-157.8941068 21.55452063),15003010201,HLTHSTAT,PHLTH,CrdPrv,Physical Health +2019,HI,Hawaii,Kauai,15007,15007040604,BRFSS,Health Risk Behaviors,Binge drinking among adults aged >=18 years,%,Crude prevalence,18.5,,,18.3,18.8,3139,POINT (-159.4840794 21.90956079),15007040604,RISKBEH,BINGE,CrdPrv,Binge Drinking +2019,HI,Hawaii,Maui,15009,15009030800,BRFSS,Health Outcomes,Diagnosed diabetes among adults aged >=18 years,%,Crude prevalence,9.3,,,9.0,9.5,6907,POINT (-156.5426669 20.90996813),15009030800,HLTHOUT,DIABETES,CrdPrv,Diabetes +2019,HI,Hawaii,Maui,15009,15009030402,BRFSS,Health Outcomes,Cancer (excluding skin cancer) among adults aged >=18 years,%,Crude prevalence,6.1,,,6.0,6.3,8652,POINT (-156.3303372 20.82505697),15009030402,HLTHOUT,CANCER,CrdPrv,Cancer (except skin) +2018,HI,Hawaii,Honolulu,15003,15003010201,BRFSS,Prevention,"Fecal occult blood test, sigmoidoscopy, or colonoscopy among adults aged 50-75 years",%,Crude prevalence,69.2,,,68.0,70.5,5882,POINT (-157.8941068 21.55452063),15003010201,PREVENT,COLON_SCREEN,CrdPrv,Colorectal Cancer Screening +2019,HI,Hawaii,Kauai,15007,15007040603,BRFSS,Prevention,Current lack of health insurance among adults aged 18-64 years,%,Crude prevalence,11.4,,,10.4,12.6,2544,POINT (-159.4384998 21.90703588),15007040603,PREVENT,ACCESS2,CrdPrv,Health Insurance +2019,HI,Hawaii,Kauai,15007,15007040700,BRFSS,Health Outcomes,High blood pressure among adults aged >=18 years,%,Crude prevalence,27.9,,,27.3,28.5,8403,POINT (-159.5219447 21.94546074),15007040700,HLTHOUT,BPHIGH,CrdPrv,High Blood Pressure +2019,HI,Hawaii,Maui,15009,15009030201,BRFSS,Health Outcomes,Chronic kidney disease among adults aged >=18 years,%,Crude prevalence,2.6,,,2.5,2.7,2453,POINT (-156.2504199 20.86252093),15009030201,HLTHOUT,KIDNEY,CrdPrv,Chronic Kidney Disease +2018,HI,Hawaii,Honolulu,15003,15003010201,BRFSS,Prevention,Visits to dentist or dental clinic among adults aged >=18 years,%,Crude prevalence,72.1,,,70.8,73.3,5882,POINT (-157.8941068 21.55452063),15003010201,PREVENT,DENTAL,CrdPrv,Dental Visit +2018,HI,Hawaii,Maui,15009,15009030100,BRFSS,Prevention,"Older adult women aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening, and Mammogram past 2 years",%,Crude prevalence,23.0,,,21.0,25.0,2291,POINT (-156.1446943 20.72704536),15009030100,PREVENT,COREW,CrdPrv,Core preventive services for older women +2019,HI,Hawaii,Honolulu,15003,15003010201,BRFSS,Prevention,Taking medicine for high blood pressure control among adults aged >=18 years with high blood pressure,%,Crude prevalence,69.9,,,69.4,70.4,5882,POINT (-157.8941068 21.55452063),15003010201,PREVENT,BPMED,CrdPrv,Taking BP Medication +2019,HI,Hawaii,Kauai,15007,15007040700,BRFSS,Health Status,Mental health not good for >=14 days among adults aged >=18 years,%,Crude prevalence,12.0,,,11.5,12.5,8403,POINT (-159.5219447 21.94546074),15007040700,HLTHSTAT,MHLTH,CrdPrv,Mental Health +2019,HI,Hawaii,Kauai,15007,15007040603,BRFSS,Health Outcomes,Cancer (excluding skin cancer) among adults aged >=18 years,%,Crude prevalence,7.0,,,6.8,7.2,2544,POINT (-159.4384998 21.90703588),15007040603,HLTHOUT,CANCER,CrdPrv,Cancer (except skin) +2019,HI,Hawaii,Honolulu,15003,15003010201,BRFSS,Health Outcomes,Chronic kidney disease among adults aged >=18 years,%,Crude prevalence,2.9,,,2.8,2.9,5882,POINT (-157.8941068 21.55452063),15003010201,HLTHOUT,KIDNEY,CrdPrv,Chronic Kidney Disease +2019,HI,Hawaii,Maui,15009,15009030800,BRFSS,Prevention,Visits to doctor for routine checkup within the past year among adults aged >=18 years,%,Crude prevalence,73.2,,,72.9,73.6,6907,POINT (-156.5426669 20.90996813),15009030800,PREVENT,CHECKUP,CrdPrv,Annual Checkup +2019,HI,Hawaii,Kauai,15007,15007040603,BRFSS,Health Outcomes,High cholesterol among adults aged >=18 years who have been screened in the past 5 years,%,Crude prevalence,30.0,,,29.4,30.6,2544,POINT (-159.4384998 21.90703588),15007040603,HLTHOUT,HIGHCHOL,CrdPrv,High Cholesterol +2019,HI,Hawaii,Kauai,15007,15007040700,BRFSS,Prevention,Visits to doctor for routine checkup within the past year among adults aged >=18 years,%,Crude prevalence,77.8,,,77.5,78.2,8403,POINT (-159.5219447 21.94546074),15007040700,PREVENT,CHECKUP,CrdPrv,Annual Checkup +2019,HI,Hawaii,Maui,15009,15009030800,BRFSS,Health Outcomes,High cholesterol among adults aged >=18 years who have been screened in the past 5 years,%,Crude prevalence,24.5,,,24.2,24.8,6907,POINT (-156.5426669 20.90996813),15009030800,HLTHOUT,HIGHCHOL,CrdPrv,High Cholesterol +2018,HI,Hawaii,Maui,15009,15009030100,BRFSS,Health Risk Behaviors,Sleeping less than 7 hours among adults aged >=18 years,%,Crude prevalence,40.4,,,39.6,41.5,2291,POINT (-156.1446943 20.72704536),15009030100,RISKBEH,SLEEP,CrdPrv,Sleep <7 hours +2019,HI,Hawaii,Maui,15009,15009030402,BRFSS,Prevention,Visits to doctor for routine checkup within the past year among adults aged >=18 years,%,Crude prevalence,74.1,,,73.6,74.6,8652,POINT (-156.3303372 20.82505697),15009030402,PREVENT,CHECKUP,CrdPrv,Annual Checkup +2018,HI,Hawaii,Kauai,15007,15007040604,BRFSS,Health Outcomes,All teeth lost among adults aged >=65 years,%,Crude prevalence,9.6,,,7.8,11.5,3139,POINT (-159.4840794 21.90956079),15007040604,HLTHOUT,TEETHLOST,CrdPrv,All Teeth Lost +2019,HI,Hawaii,Maui,15009,15009030800,BRFSS,Health Outcomes,High blood pressure among adults aged >=18 years,%,Crude prevalence,23.8,,,23.3,24.1,6907,POINT (-156.5426669 20.90996813),15009030800,HLTHOUT,BPHIGH,CrdPrv,High Blood Pressure +2018,HI,Hawaii,Kauai,15007,15007040604,BRFSS,Prevention,Cervical cancer screening among adult women aged 21-65 years,%,Crude prevalence,82.1,,,81.2,82.9,3139,POINT (-159.4840794 21.90956079),15007040604,PREVENT,CERVICAL,CrdPrv,Cervical Cancer Screening +2019,HI,Hawaii,Maui,15009,15009030100,BRFSS,Prevention,Cholesterol screening among adults aged >=18 years,%,Crude prevalence,82.6,,,82.3,83.0,2291,POINT (-156.1446943 20.72704536),15009030100,PREVENT,CHOLSCREEN,CrdPrv,Cholesterol Screening +2018,HI,Hawaii,Maui,15009,15009030201,BRFSS,Prevention,Visits to dentist or dental clinic among adults aged >=18 years,%,Crude prevalence,73.2,,,71.2,74.9,2453,POINT (-156.2504199 20.86252093),15009030201,PREVENT,DENTAL,CrdPrv,Dental Visit +2018,HI,Hawaii,Kauai,15007,15007040604,BRFSS,Prevention,"Older adult women aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening, and Mammogram past 2 years",%,Crude prevalence,21.3,,,19.5,23.3,3139,POINT (-159.4840794 21.90956079),15007040604,PREVENT,COREW,CrdPrv,Core preventive services for older women +2019,HI,Hawaii,Honolulu,15003,15003010201,BRFSS,Health Outcomes,Obesity among adults aged >=18 years,%,Crude prevalence,33.4,,,32.9,34.0,5882,POINT (-157.8941068 21.55452063),15003010201,HLTHOUT,OBESITY,CrdPrv,Obesity +2019,HI,Hawaii,Maui,15009,15009030402,BRFSS,Prevention,Taking medicine for high blood pressure control among adults aged >=18 years with high blood pressure,%,Crude prevalence,70.7,,,69.9,71.4,8652,POINT (-156.3303372 20.82505697),15009030402,PREVENT,BPMED,CrdPrv,Taking BP Medication +2019,HI,Hawaii,Kauai,15007,15007040603,BRFSS,Health Status,Mental health not good for >=14 days among adults aged >=18 years,%,Crude prevalence,12.6,,,12.0,13.1,2544,POINT (-159.4384998 21.90703588),15007040603,HLTHSTAT,MHLTH,CrdPrv,Mental Health +2019,HI,Hawaii,Honolulu,15003,15003010201,BRFSS,Health Outcomes,Coronary heart disease among adults aged >=18 years,%,Crude prevalence,5.5,,,5.3,5.7,5882,POINT (-157.8941068 21.55452063),15003010201,HLTHOUT,CHD,CrdPrv,Coronary Heart Disease +2019,HI,Hawaii,Maui,15009,15009030201,BRFSS,Health Outcomes,Arthritis among adults aged >=18 years,%,Crude prevalence,23.0,,,22.2,23.7,2453,POINT (-156.2504199 20.86252093),15009030201,HLTHOUT,ARTHRITIS,CrdPrv,Arthritis +2018,HI,Hawaii,Maui,15009,15009030402,BRFSS,Prevention,"Older adult women aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening, and Mammogram past 2 years",%,Crude prevalence,24.7,,,22.1,27.4,8652,POINT (-156.3303372 20.82505697),15009030402,PREVENT,COREW,CrdPrv,Core preventive services for older women +2019,HI,Hawaii,Maui,15009,15009030800,BRFSS,Health Status,Physical health not good for >=14 days among adults aged >=18 years,%,Crude prevalence,10.7,,,10.3,11.0,6907,POINT (-156.5426669 20.90996813),15009030800,HLTHSTAT,PHLTH,CrdPrv,Physical Health +2019,HI,Hawaii,Maui,15009,15009030402,BRFSS,Health Status,Mental health not good for >=14 days among adults aged >=18 years,%,Crude prevalence,12.5,,,11.8,13.2,8652,POINT (-156.3303372 20.82505697),15009030402,HLTHSTAT,MHLTH,CrdPrv,Mental Health +2019,HI,Hawaii,Kauai,15007,15007040604,BRFSS,Health Status,Physical health not good for >=14 days among adults aged >=18 years,%,Crude prevalence,13.2,,,12.7,13.8,3139,POINT (-159.4840794 21.90956079),15007040604,HLTHSTAT,PHLTH,CrdPrv,Physical Health +2018,HI,Hawaii,Kauai,15007,15007040700,BRFSS,Prevention,Visits to dentist or dental clinic among adults aged >=18 years,%,Crude prevalence,72.2,,,70.8,73.5,8403,POINT (-159.5219447 21.94546074),15007040700,PREVENT,DENTAL,CrdPrv,Dental Visit +2019,HI,Hawaii,Maui,15009,15009030100,BRFSS,Health Outcomes,Chronic kidney disease among adults aged >=18 years,%,Crude prevalence,2.9,,,2.8,3.0,2291,POINT (-156.1446943 20.72704536),15009030100,HLTHOUT,KIDNEY,CrdPrv,Chronic Kidney Disease +2019,HI,Hawaii,Maui,15009,15009030402,BRFSS,Health Status,Fair or poor self-rated health status among adults aged >=18 years,%,Crude prevalence,15.2,,,14.1,16.4,8652,POINT (-156.3303372 20.82505697),15009030402,HLTHSTAT,GHLTH,CrdPrv,General Health +2019,HI,Hawaii,Maui,15009,15009030201,BRFSS,Health Outcomes,Chronic obstructive pulmonary disease among adults aged >=18 years,%,Crude prevalence,5.5,,,5.0,6.1,2453,POINT (-156.2504199 20.86252093),15009030201,HLTHOUT,COPD,CrdPrv,COPD +2019,HI,Hawaii,Kauai,15007,15007040603,BRFSS,Health Outcomes,Stroke among adults aged >=18 years,%,Crude prevalence,3.7,,,3.4,3.9,2544,POINT (-159.4384998 21.90703588),15007040603,HLTHOUT,STROKE,CrdPrv,Stroke +2019,HI,Hawaii,Maui,15009,15009030402,BRFSS,Prevention,Current lack of health insurance among adults aged 18-64 years,%,Crude prevalence,10.3,,,9.3,11.3,8652,POINT (-156.3303372 20.82505697),15009030402,PREVENT,ACCESS2,CrdPrv,Health Insurance +2019,HI,Hawaii,Kauai,15007,15007040604,BRFSS,Health Outcomes,Chronic kidney disease among adults aged >=18 years,%,Crude prevalence,3.3,,,3.2,3.4,3139,POINT (-159.4840794 21.90956079),15007040604,HLTHOUT,KIDNEY,CrdPrv,Chronic Kidney Disease +2019,HI,Hawaii,Maui,15009,15009030201,BRFSS,Health Status,Mental health not good for >=14 days among adults aged >=18 years,%,Crude prevalence,13.9,,,13.2,14.6,2453,POINT (-156.2504199 20.86252093),15009030201,HLTHSTAT,MHLTH,CrdPrv,Mental Health +2019,HI,Hawaii,Maui,15009,15009030100,BRFSS,Health Outcomes,Arthritis among adults aged >=18 years,%,Crude prevalence,23.2,,,22.7,23.8,2291,POINT (-156.1446943 20.72704536),15009030100,HLTHOUT,ARTHRITIS,CrdPrv,Arthritis +2019,HI,Hawaii,Kauai,15007,15007040604,BRFSS,Health Risk Behaviors,Current smoking among adults aged >=18 years,%,Crude prevalence,15.6,,,14.8,16.5,3139,POINT (-159.4840794 21.90956079),15007040604,RISKBEH,CSMOKING,CrdPrv,Current Smoking +2019,HI,Hawaii,Maui,15009,15009030201,BRFSS,Health Outcomes,High blood pressure among adults aged >=18 years,%,Crude prevalence,25.6,,,24.8,26.4,2453,POINT (-156.2504199 20.86252093),15009030201,HLTHOUT,BPHIGH,CrdPrv,High Blood Pressure +2019,HI,Hawaii,Maui,15009,15009030800,BRFSS,Health Risk Behaviors,No leisure-time physical activity among adults aged >=18 years,%,Crude prevalence,22.6,,,21.7,23.6,6907,POINT (-156.5426669 20.90996813),15009030800,RISKBEH,LPA,CrdPrv,Physical Inactivity +2018,HI,Hawaii,Kauai,15007,15007040700,BRFSS,Prevention,Mammography use among women aged 50-74 years,%,Crude prevalence,77.6,,,76.1,79.2,8403,POINT (-159.5219447 21.94546074),15007040700,PREVENT,MAMMOUSE,CrdPrv,Mammography +2019,HI,Hawaii,Honolulu,15003,15003010201,BRFSS,Health Outcomes,Chronic obstructive pulmonary disease among adults aged >=18 years,%,Crude prevalence,5.9,,,5.6,6.2,5882,POINT (-157.8941068 21.55452063),15003010201,HLTHOUT,COPD,CrdPrv,COPD +2019,HI,Hawaii,Honolulu,15003,15003010201,BRFSS,Prevention,Current lack of health insurance among adults aged 18-64 years,%,Crude prevalence,10.2,,,9.5,11.0,5882,POINT (-157.8941068 21.55452063),15003010201,PREVENT,ACCESS2,CrdPrv,Health Insurance +2019,HI,Hawaii,Maui,15009,15009030800,BRFSS,Health Outcomes,Chronic obstructive pulmonary disease among adults aged >=18 years,%,Crude prevalence,4.3,,,4.1,4.6,6907,POINT (-156.5426669 20.90996813),15009030800,HLTHOUT,COPD,CrdPrv,COPD +2019,HI,Hawaii,Maui,15009,15009030201,BRFSS,Health Outcomes,Coronary heart disease among adults aged >=18 years,%,Crude prevalence,5.0,,,4.7,5.4,2453,POINT (-156.2504199 20.86252093),15009030201,HLTHOUT,CHD,CrdPrv,Coronary Heart Disease +2019,HI,Hawaii,Kauai,15007,15007040604,BRFSS,Health Outcomes,Current asthma among adults aged >=18 years,%,Crude prevalence,9.6,,,9.4,9.8,3139,POINT (-159.4840794 21.90956079),15007040604,HLTHOUT,CASTHMA,CrdPrv,Current Asthma +2019,HI,Hawaii,Maui,15009,15009030402,BRFSS,Health Risk Behaviors,Current smoking among adults aged >=18 years,%,Crude prevalence,15.3,,,14.0,16.7,8652,POINT (-156.3303372 20.82505697),15009030402,RISKBEH,CSMOKING,CrdPrv,Current Smoking +2019,HI,Hawaii,Kauai,15007,15007040700,BRFSS,Health Outcomes,Chronic obstructive pulmonary disease among adults aged >=18 years,%,Crude prevalence,4.5,,,4.2,4.8,8403,POINT (-159.5219447 21.94546074),15007040700,HLTHOUT,COPD,CrdPrv,COPD +2019,HI,Hawaii,Honolulu,15003,15003010201,BRFSS,Health Outcomes,Depression among adults aged >=18 years,%,Crude prevalence,18.5,,,18.0,18.9,5882,POINT (-157.8941068 21.55452063),15003010201,HLTHOUT,DEPRESSION,CrdPrv,Depression +2018,HI,Hawaii,Kauai,15007,15007040603,BRFSS,Prevention,"Older adult women aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening, and Mammogram past 2 years",%,Crude prevalence,24.1,,,21.1,27.2,2544,POINT (-159.4384998 21.90703588),15007040603,PREVENT,COREW,CrdPrv,Core preventive services for older women +2019,HI,Hawaii,Kauai,15007,15007040700,BRFSS,Health Outcomes,Depression among adults aged >=18 years,%,Crude prevalence,14.5,,,14.2,14.9,8403,POINT (-159.5219447 21.94546074),15007040700,HLTHOUT,DEPRESSION,CrdPrv,Depression +2019,HI,Hawaii,Maui,15009,15009030800,BRFSS,Health Outcomes,Cancer (excluding skin cancer) among adults aged >=18 years,%,Crude prevalence,5.4,,,5.3,5.5,6907,POINT (-156.5426669 20.90996813),15009030800,HLTHOUT,CANCER,CrdPrv,Cancer (except skin) +2019,HI,Hawaii,Maui,15009,15009030201,BRFSS,Prevention,Current lack of health insurance among adults aged 18-64 years,%,Crude prevalence,9.3,,,8.3,10.2,2453,POINT (-156.2504199 20.86252093),15009030201,PREVENT,ACCESS2,CrdPrv,Health Insurance +2019,HI,Hawaii,Kauai,15007,15007040603,BRFSS,Prevention,Visits to doctor for routine checkup within the past year among adults aged >=18 years,%,Crude prevalence,78.5,,,78.0,78.9,2544,POINT (-159.4384998 21.90703588),15007040603,PREVENT,CHECKUP,CrdPrv,Annual Checkup +2019,HI,Hawaii,Maui,15009,15009030201,BRFSS,Health Outcomes,Stroke among adults aged >=18 years,%,Crude prevalence,3.1,,,2.9,3.3,2453,POINT (-156.2504199 20.86252093),15009030201,HLTHOUT,STROKE,CrdPrv,Stroke +2018,HI,Hawaii,Maui,15009,15009030100,BRFSS,Prevention,Mammography use among women aged 50-74 years,%,Crude prevalence,78.5,,,77.1,79.9,2291,POINT (-156.1446943 20.72704536),15009030100,PREVENT,MAMMOUSE,CrdPrv,Mammography +2018,HI,Hawaii,Maui,15009,15009030100,BRFSS,Prevention,"Older adult men aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening",%,Crude prevalence,22.7,,,20.4,25.1,2291,POINT (-156.1446943 20.72704536),15009030100,PREVENT,COREM,CrdPrv,Core preventive services for older men +2018,HI,Hawaii,Honolulu,15003,15003010201,BRFSS,Health Risk Behaviors,Sleeping less than 7 hours among adults aged >=18 years,%,Crude prevalence,48.2,,,47.6,48.9,5882,POINT (-157.8941068 21.55452063),15003010201,RISKBEH,SLEEP,CrdPrv,Sleep <7 hours +2019,HI,Hawaii,Maui,15009,15009030800,BRFSS,Health Risk Behaviors,Current smoking among adults aged >=18 years,%,Crude prevalence,16.1,,,15.1,17.0,6907,POINT (-156.5426669 20.90996813),15009030800,RISKBEH,CSMOKING,CrdPrv,Current Smoking +2018,HI,Hawaii,Maui,15009,15009030402,BRFSS,Prevention,"Fecal occult blood test, sigmoidoscopy, or colonoscopy among adults aged 50-75 years",%,Crude prevalence,63.8,,,61.5,65.9,8652,POINT (-156.3303372 20.82505697),15009030402,PREVENT,COLON_SCREEN,CrdPrv,Colorectal Cancer Screening +2019,HI,Hawaii,Kauai,15007,15007040700,BRFSS,Prevention,Taking medicine for high blood pressure control among adults aged >=18 years with high blood pressure,%,Crude prevalence,74.8,,,74.3,75.4,8403,POINT (-159.5219447 21.94546074),15007040700,PREVENT,BPMED,CrdPrv,Taking BP Medication +2019,HI,Hawaii,Maui,15009,15009030800,BRFSS,Prevention,Current lack of health insurance among adults aged 18-64 years,%,Crude prevalence,10.6,,,10.0,11.3,6907,POINT (-156.5426669 20.90996813),15009030800,PREVENT,ACCESS2,CrdPrv,Health Insurance +2019,HI,Hawaii,Maui,15009,15009030201,BRFSS,Health Risk Behaviors,Current smoking among adults aged >=18 years,%,Crude prevalence,17.5,,,15.9,19.2,2453,POINT (-156.2504199 20.86252093),15009030201,RISKBEH,CSMOKING,CrdPrv,Current Smoking +2018,HI,Hawaii,Maui,15009,15009030800,BRFSS,Health Outcomes,All teeth lost among adults aged >=65 years,%,Crude prevalence,8.1,,,7.1,9.3,6907,POINT (-156.5426669 20.90996813),15009030800,HLTHOUT,TEETHLOST,CrdPrv,All Teeth Lost +2019,HI,Hawaii,Kauai,15007,15007040700,BRFSS,Health Outcomes,Coronary heart disease among adults aged >=18 years,%,Crude prevalence,5.0,,,4.8,5.3,8403,POINT (-159.5219447 21.94546074),15007040700,HLTHOUT,CHD,CrdPrv,Coronary Heart Disease +2019,HI,Hawaii,Kauai,15007,15007040700,BRFSS,Health Outcomes,Current asthma among adults aged >=18 years,%,Crude prevalence,8.7,,,8.5,8.9,8403,POINT (-159.5219447 21.94546074),15007040700,HLTHOUT,CASTHMA,CrdPrv,Current Asthma +2019,HI,Hawaii,Maui,15009,15009030100,BRFSS,Health Status,Physical health not good for >=14 days among adults aged >=18 years,%,Crude prevalence,13.0,,,12.4,13.5,2291,POINT (-156.1446943 20.72704536),15009030100,HLTHSTAT,PHLTH,CrdPrv,Physical Health +2019,HI,Hawaii,Maui,15009,15009030402,BRFSS,Health Outcomes,Coronary heart disease among adults aged >=18 years,%,Crude prevalence,4.8,,,4.5,5.1,8652,POINT (-156.3303372 20.82505697),15009030402,HLTHOUT,CHD,CrdPrv,Coronary Heart Disease +2019,HI,Hawaii,Kauai,15007,15007040603,BRFSS,Health Status,Fair or poor self-rated health status among adults aged >=18 years,%,Crude prevalence,18.2,,,16.8,19.5,2544,POINT (-159.4384998 21.90703588),15007040603,HLTHSTAT,GHLTH,CrdPrv,General Health +2019,HI,Hawaii,Maui,15009,15009030100,BRFSS,Prevention,Visits to doctor for routine checkup within the past year among adults aged >=18 years,%,Crude prevalence,74.2,,,73.8,74.6,2291,POINT (-156.1446943 20.72704536),15009030100,PREVENT,CHECKUP,CrdPrv,Annual Checkup +2019,HI,Hawaii,Kauai,15007,15007040603,BRFSS,Prevention,Taking medicine for high blood pressure control among adults aged >=18 years with high blood pressure,%,Crude prevalence,77.1,,,76.5,77.7,2544,POINT (-159.4384998 21.90703588),15007040603,PREVENT,BPMED,CrdPrv,Taking BP Medication +2018,HI,Hawaii,Maui,15009,15009030201,BRFSS,Health Risk Behaviors,Sleeping less than 7 hours among adults aged >=18 years,%,Crude prevalence,37.8,,,37.1,38.6,2453,POINT (-156.2504199 20.86252093),15009030201,RISKBEH,SLEEP,CrdPrv,Sleep <7 hours +2019,HI,Hawaii,Kauai,15007,15007040700,BRFSS,Prevention,Current lack of health insurance among adults aged 18-64 years,%,Crude prevalence,10.2,,,9.5,11.1,8403,POINT (-159.5219447 21.94546074),15007040700,PREVENT,ACCESS2,CrdPrv,Health Insurance +2019,HI,Hawaii,Honolulu,15003,15003010201,BRFSS,Health Outcomes,High cholesterol among adults aged >=18 years who have been screened in the past 5 years,%,Crude prevalence,29.8,,,29.4,30.1,5882,POINT (-157.8941068 21.55452063),15003010201,HLTHOUT,HIGHCHOL,CrdPrv,High Cholesterol +2018,HI,Hawaii,Maui,15009,15009030201,BRFSS,Prevention,"Fecal occult blood test, sigmoidoscopy, or colonoscopy among adults aged 50-75 years",%,Crude prevalence,65.4,,,63.1,67.3,2453,POINT (-156.2504199 20.86252093),15009030201,PREVENT,COLON_SCREEN,CrdPrv,Colorectal Cancer Screening +2019,HI,Hawaii,Maui,15009,15009030800,BRFSS,Prevention,Taking medicine for high blood pressure control among adults aged >=18 years with high blood pressure,%,Crude prevalence,67.7,,,67.1,68.2,6907,POINT (-156.5426669 20.90996813),15009030800,PREVENT,BPMED,CrdPrv,Taking BP Medication +2019,HI,Hawaii,Kauai,15007,15007040604,BRFSS,Prevention,Taking medicine for high blood pressure control among adults aged >=18 years with high blood pressure,%,Crude prevalence,76.2,,,75.8,76.7,3139,POINT (-159.4840794 21.90956079),15007040604,PREVENT,BPMED,CrdPrv,Taking BP Medication +2018,HI,Hawaii,Maui,15009,15009030402,BRFSS,Prevention,Mammography use among women aged 50-74 years,%,Crude prevalence,79.6,,,77.8,81.3,8652,POINT (-156.3303372 20.82505697),15009030402,PREVENT,MAMMOUSE,CrdPrv,Mammography +2019,HI,Hawaii,Maui,15009,15009030800,BRFSS,Health Outcomes,Obesity among adults aged >=18 years,%,Crude prevalence,24.3,,,23.8,24.7,6907,POINT (-156.5426669 20.90996813),15009030800,HLTHOUT,OBESITY,CrdPrv,Obesity +2018,HI,Hawaii,Honolulu,15003,15003010201,BRFSS,Prevention,Cervical cancer screening among adult women aged 21-65 years,%,Crude prevalence,87.3,,,86.7,88.1,5882,POINT (-157.8941068 21.55452063),15003010201,PREVENT,CERVICAL,CrdPrv,Cervical Cancer Screening +2019,HI,Hawaii,Kauai,15007,15007040603,BRFSS,Health Outcomes,Coronary heart disease among adults aged >=18 years,%,Crude prevalence,6.1,,,5.8,6.5,2544,POINT (-159.4384998 21.90703588),15007040603,HLTHOUT,CHD,CrdPrv,Coronary Heart Disease +2019,HI,Hawaii,Kauai,15007,15007040603,BRFSS,Health Risk Behaviors,Binge drinking among adults aged >=18 years,%,Crude prevalence,17.9,,,17.5,18.2,2544,POINT (-159.4384998 21.90703588),15007040603,RISKBEH,BINGE,CrdPrv,Binge Drinking +2019,HI,Hawaii,Kauai,15007,15007040604,BRFSS,Health Outcomes,Obesity among adults aged >=18 years,%,Crude prevalence,26.0,,,25.6,26.5,3139,POINT (-159.4840794 21.90956079),15007040604,HLTHOUT,OBESITY,CrdPrv,Obesity +2018,HI,Hawaii,Kauai,15007,15007040603,BRFSS,Prevention,Mammography use among women aged 50-74 years,%,Crude prevalence,77.1,,,75.2,79.0,2544,POINT (-159.4384998 21.90703588),15007040603,PREVENT,MAMMOUSE,CrdPrv,Mammography +2019,HI,Hawaii,Maui,15009,15009030100,BRFSS,Health Outcomes,Cancer (excluding skin cancer) among adults aged >=18 years,%,Crude prevalence,6.9,,,6.8,7.1,2291,POINT (-156.1446943 20.72704536),15009030100,HLTHOUT,CANCER,CrdPrv,Cancer (except skin) +2019,HI,Hawaii,Honolulu,15003,15003010201,BRFSS,Health Outcomes,High blood pressure among adults aged >=18 years,%,Crude prevalence,31.2,,,30.8,31.8,5882,POINT (-157.8941068 21.55452063),15003010201,HLTHOUT,BPHIGH,CrdPrv,High Blood Pressure +2019,HI,Hawaii,Honolulu,15003,15003010201,BRFSS,Health Status,Mental health not good for >=14 days among adults aged >=18 years,%,Crude prevalence,15.1,,,14.5,15.7,5882,POINT (-157.8941068 21.55452063),15003010201,HLTHSTAT,MHLTH,CrdPrv,Mental Health +2018,HI,Hawaii,Maui,15009,15009030201,BRFSS,Prevention,Cervical cancer screening among adult women aged 21-65 years,%,Crude prevalence,87.7,,,86.6,88.8,2453,POINT (-156.2504199 20.86252093),15009030201,PREVENT,CERVICAL,CrdPrv,Cervical Cancer Screening +2019,HI,Hawaii,Maui,15009,15009030800,BRFSS,Health Outcomes,Coronary heart disease among adults aged >=18 years,%,Crude prevalence,4.3,,,4.2,4.4,6907,POINT (-156.5426669 20.90996813),15009030800,HLTHOUT,CHD,CrdPrv,Coronary Heart Disease +2018,HI,Hawaii,Kauai,15007,15007040700,BRFSS,Health Risk Behaviors,Sleeping less than 7 hours among adults aged >=18 years,%,Crude prevalence,36.4,,,35.6,36.9,8403,POINT (-159.5219447 21.94546074),15007040700,RISKBEH,SLEEP,CrdPrv,Sleep <7 hours +2019,HI,Hawaii,Kauai,15007,15007040604,BRFSS,Prevention,Cholesterol screening among adults aged >=18 years,%,Crude prevalence,82.8,,,82.7,82.9,3139,POINT (-159.4840794 21.90956079),15007040604,PREVENT,CHOLSCREEN,CrdPrv,Cholesterol Screening +2019,HI,Hawaii,Maui,15009,15009030402,BRFSS,Health Outcomes,High blood pressure among adults aged >=18 years,%,Crude prevalence,25.2,,,24.6,25.9,8652,POINT (-156.3303372 20.82505697),15009030402,HLTHOUT,BPHIGH,CrdPrv,High Blood Pressure +2018,HI,Hawaii,Kauai,15007,15007040700,BRFSS,Prevention,"Older adult men aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening",%,Crude prevalence,27.8,,,25.4,30.3,8403,POINT (-159.5219447 21.94546074),15007040700,PREVENT,COREM,CrdPrv,Core preventive services for older men +2019,HI,Hawaii,Maui,15009,15009030800,BRFSS,Health Outcomes,Chronic kidney disease among adults aged >=18 years,%,Crude prevalence,2.3,,,2.3,2.4,6907,POINT (-156.5426669 20.90996813),15009030800,HLTHOUT,KIDNEY,CrdPrv,Chronic Kidney Disease +2018,HI,Hawaii,Honolulu,15003,15003010201,BRFSS,Prevention,"Older adult men aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening",%,Crude prevalence,25.0,,,23.0,27.0,5882,POINT (-157.8941068 21.55452063),15003010201,PREVENT,COREM,CrdPrv,Core preventive services for older men +2018,HI,Hawaii,Kauai,15007,15007040604,BRFSS,Prevention,"Fecal occult blood test, sigmoidoscopy, or colonoscopy among adults aged 50-75 years",%,Crude prevalence,64.2,,,62.8,65.7,3139,POINT (-159.4840794 21.90956079),15007040604,PREVENT,COLON_SCREEN,CrdPrv,Colorectal Cancer Screening diff --git a/data/data-pipeline/data_pipeline/tests/sources/cdc_places/data/extract.csv b/data/data-pipeline/data_pipeline/tests/sources/cdc_places/data/extract.csv new file mode 100644 index 00000000..0dda6ca5 --- /dev/null +++ b/data/data-pipeline/data_pipeline/tests/sources/cdc_places/data/extract.csv @@ -0,0 +1,451 @@ +Year,StateAbbr,StateDesc,CountyName,CountyFIPS,LocationName,DataSource,Category,Measure,Data_Value_Unit,Data_Value_Type,Data_Value,Data_Value_Footnote_Symbol,Data_Value_Footnote,Low_Confidence_Limit,High_Confidence_Limit,TotalPopulation,Geolocation,LocationID,CategoryID,MeasureId,DataValueTypeID,Short_Question_Text +2019,CA,California,Inyo,6027,6027000800,BRFSS,Health Outcomes,High blood pressure among adults aged >=18 years,%,Crude prevalence,36.1000000000,,,35.2000000000,36.8000000000,3378,POINT (-117.1176757 36.25159703),6027000800,HLTHOUT,BPHIGH,CrdPrv,High Blood Pressure +2019,CA,California,Inyo,6027,6027000800,BRFSS,Health Outcomes,Diagnosed diabetes among adults aged >=18 years,%,Crude prevalence,13.1000000000,,,12.6000000000,13.6000000000,3378,POINT (-117.1176757 36.25159703),6027000800,HLTHOUT,DIABETES,CrdPrv,Diabetes +2019,CA,California,Inyo,6027,6027000800,BRFSS,Health Status,Mental health not good for >=14 days among adults aged >=18 years,%,Crude prevalence,15.1000000000,,,14.4000000000,15.8000000000,3378,POINT (-117.1176757 36.25159703),6027000800,HLTHSTAT,MHLTH,CrdPrv,Mental Health +2018,CA,California,Inyo,6027,6027000800,BRFSS,Prevention,Cervical cancer screening among adult women aged 21-65 years,%,Crude prevalence,82.0000000000,,,80.5000000000,83.4000000000,3378,POINT (-117.1176757 36.25159703),6027000800,PREVENT,CERVICAL,CrdPrv,Cervical Cancer Screening +2019,CA,California,Inyo,6027,6027000800,BRFSS,Prevention,Current lack of health insurance among adults aged 18-64 years,%,Crude prevalence,14.2000000000,,,12.8000000000,15.6000000000,3378,POINT (-117.1176757 36.25159703),6027000800,PREVENT,ACCESS2,CrdPrv,Health Insurance +2018,CA,California,Inyo,6027,6027000800,BRFSS,Health Risk Behaviors,Sleeping less than 7 hours among adults aged >=18 years,%,Crude prevalence,33.1000000000,,,32.1000000000,34.1000000000,3378,POINT (-117.1176757 36.25159703),6027000800,RISKBEH,SLEEP,CrdPrv,Sleep <7 hours +2019,CA,California,Inyo,6027,6027000800,BRFSS,Prevention,Cholesterol screening among adults aged >=18 years,%,Crude prevalence,86.4000000000,,,86.1000000000,86.7000000000,3378,POINT (-117.1176757 36.25159703),6027000800,PREVENT,CHOLSCREEN,CrdPrv,Cholesterol Screening +2019,CA,California,Inyo,6027,6027000800,BRFSS,Health Risk Behaviors,Current smoking among adults aged >=18 years,%,Crude prevalence,17.2000000000,,,15.8000000000,18.5000000000,3378,POINT (-117.1176757 36.25159703),6027000800,RISKBEH,CSMOKING,CrdPrv,Current Smoking +2019,CA,California,Inyo,6027,6027000800,BRFSS,Health Outcomes,Coronary heart disease among adults aged >=18 years,%,Crude prevalence,8.6000000000,,,8.1000000000,9.1000000000,3378,POINT (-117.1176757 36.25159703),6027000800,HLTHOUT,CHD,CrdPrv,Coronary Heart Disease +2019,CA,California,Inyo,6027,6027000800,BRFSS,Health Outcomes,Obesity among adults aged >=18 years,%,Crude prevalence,30.2000000000,,,29.4000000000,31.0000000000,3378,POINT (-117.1176757 36.25159703),6027000800,HLTHOUT,OBESITY,CrdPrv,Obesity +2019,CA,California,Inyo,6027,6027000800,BRFSS,Health Status,Fair or poor self-rated health status among adults aged >=18 years,%,Crude prevalence,24.2000000000,,,22.4000000000,25.9000000000,3378,POINT (-117.1176757 36.25159703),6027000800,HLTHSTAT,GHLTH,CrdPrv,General Health +2019,CA,California,Inyo,6027,6027000800,BRFSS,Prevention,Taking medicine for high blood pressure control among adults aged >=18 years with high blood pressure,%,Crude prevalence,74.3000000000,,,73.6000000000,74.9000000000,3378,POINT (-117.1176757 36.25159703),6027000800,PREVENT,BPMED,CrdPrv,Taking BP Medication +2019,CA,California,Inyo,6027,6027000800,BRFSS,Health Outcomes,Chronic kidney disease among adults aged >=18 years,%,Crude prevalence,3.9000000000,,,3.8000000000,4.1000000000,3378,POINT (-117.1176757 36.25159703),6027000800,HLTHOUT,KIDNEY,CrdPrv,Chronic Kidney Disease +2019,CA,California,Inyo,6027,6027000800,BRFSS,Health Status,Physical health not good for >=14 days among adults aged >=18 years,%,Crude prevalence,16.8000000000,,,15.8000000000,17.7000000000,3378,POINT (-117.1176757 36.25159703),6027000800,HLTHSTAT,PHLTH,CrdPrv,Physical Health +2018,CA,California,Inyo,6027,6027000800,BRFSS,Prevention,"Older adult women aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening, and Mammogram past 2 years",%,Crude prevalence,25.6000000000,,,22.4000000000,29.2000000000,3378,POINT (-117.1176757 36.25159703),6027000800,PREVENT,COREW,CrdPrv,Core preventive services for older women +2018,CA,California,Inyo,6027,6027000800,BRFSS,Prevention,"Older adult men aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening",%,Crude prevalence,26.5000000000,,,24.0000000000,29.1000000000,3378,POINT (-117.1176757 36.25159703),6027000800,PREVENT,COREM,CrdPrv,Core preventive services for older men +2019,CA,California,Inyo,6027,6027000800,BRFSS,Prevention,Visits to doctor for routine checkup within the past year among adults aged >=18 years,%,Crude prevalence,72.5000000000,,,71.9000000000,73.0000000000,3378,POINT (-117.1176757 36.25159703),6027000800,PREVENT,CHECKUP,CrdPrv,Annual Checkup +2019,CA,California,Inyo,6027,6027000800,BRFSS,Health Risk Behaviors,No leisure-time physical activity among adults aged >=18 years,%,Crude prevalence,26.9000000000,,,25.3000000000,28.4000000000,3378,POINT (-117.1176757 36.25159703),6027000800,RISKBEH,LPA,CrdPrv,Physical Inactivity +2019,CA,California,Inyo,6027,6027000800,BRFSS,Health Outcomes,High cholesterol among adults aged >=18 years who have been screened in the past 5 years,%,Crude prevalence,35.8000000000,,,35.2000000000,36.5000000000,3378,POINT (-117.1176757 36.25159703),6027000800,HLTHOUT,HIGHCHOL,CrdPrv,High Cholesterol +2019,CA,California,Inyo,6027,6027000800,BRFSS,Health Outcomes,Cancer (excluding skin cancer) among adults aged >=18 years,%,Crude prevalence,8.3000000000,,,8.0000000000,8.5000000000,3378,POINT (-117.1176757 36.25159703),6027000800,HLTHOUT,CANCER,CrdPrv,Cancer (except skin) +2019,CA,California,Inyo,6027,6027000800,BRFSS,Health Outcomes,Depression among adults aged >=18 years,%,Crude prevalence,19.5000000000,,,18.9000000000,20.2000000000,3378,POINT (-117.1176757 36.25159703),6027000800,HLTHOUT,DEPRESSION,CrdPrv,Depression +2019,CA,California,Inyo,6027,6027000800,BRFSS,Health Outcomes,Chronic obstructive pulmonary disease among adults aged >=18 years,%,Crude prevalence,9.2000000000,,,8.4000000000,10.0000000000,3378,POINT (-117.1176757 36.25159703),6027000800,HLTHOUT,COPD,CrdPrv,COPD +2019,CA,California,Inyo,6027,6027000800,BRFSS,Health Risk Behaviors,Binge drinking among adults aged >=18 years,%,Crude prevalence,16.2000000000,,,15.8000000000,16.6000000000,3378,POINT (-117.1176757 36.25159703),6027000800,RISKBEH,BINGE,CrdPrv,Binge Drinking +2018,CA,California,Inyo,6027,6027000800,BRFSS,Health Outcomes,All teeth lost among adults aged >=65 years,%,Crude prevalence,17.7000000000,,,14.2000000000,21.6000000000,3378,POINT (-117.1176757 36.25159703),6027000800,HLTHOUT,TEETHLOST,CrdPrv,All Teeth Lost +2019,CA,California,Inyo,6027,6027000800,BRFSS,Health Outcomes,Current asthma among adults aged >=18 years,%,Crude prevalence,10.1000000000,,,9.7000000000,10.4000000000,3378,POINT (-117.1176757 36.25159703),6027000800,HLTHOUT,CASTHMA,CrdPrv,Current Asthma +2018,CA,California,Inyo,6027,6027000800,BRFSS,Prevention,Mammography use among women aged 50-74 years,%,Crude prevalence,74.9000000000,,,72.7000000000,77.0000000000,3378,POINT (-117.1176757 36.25159703),6027000800,PREVENT,MAMMOUSE,CrdPrv,Mammography +2018,CA,California,Inyo,6027,6027000800,BRFSS,Prevention,Visits to dentist or dental clinic among adults aged >=18 years,%,Crude prevalence,58.1000000000,,,55.6000000000,59.9000000000,3378,POINT (-117.1176757 36.25159703),6027000800,PREVENT,DENTAL,CrdPrv,Dental Visit +2018,CA,California,Inyo,6027,6027000800,BRFSS,Prevention,"Fecal occult blood test, sigmoidoscopy, or colonoscopy among adults aged 50-75 years",%,Crude prevalence,61.5000000000,,,59.2000000000,63.6000000000,3378,POINT (-117.1176757 36.25159703),6027000800,PREVENT,COLON_SCREEN,CrdPrv,Colorectal Cancer Screening +2019,CA,California,Inyo,6027,6027000800,BRFSS,Health Outcomes,Arthritis among adults aged >=18 years,%,Crude prevalence,28.5000000000,,,27.8000000000,29.2000000000,3378,POINT (-117.1176757 36.25159703),6027000800,HLTHOUT,ARTHRITIS,CrdPrv,Arthritis +2019,CA,California,Inyo,6027,6027000800,BRFSS,Health Outcomes,Stroke among adults aged >=18 years,%,Crude prevalence,4.6000000000,,,4.3000000000,4.9000000000,3378,POINT (-117.1176757 36.25159703),6027000800,HLTHOUT,STROKE,CrdPrv,Stroke +2019,CA,California,Placer,6061,6061021322,BRFSS,Health Outcomes,Obesity among adults aged >=18 years,%,Crude prevalence,23.0000000000,,,22.2000000000,24.0000000000,8762,POINT (-121.4057179 38.84598382),6061021322,HLTHOUT,OBESITY,CrdPrv,Obesity +2019,CA,California,Placer,6061,6061021322,BRFSS,Health Outcomes,High blood pressure among adults aged >=18 years,%,Crude prevalence,23.2000000000,,,22.5000000000,23.9000000000,8762,POINT (-121.4057179 38.84598382),6061021322,HLTHOUT,BPHIGH,CrdPrv,High Blood Pressure +2019,CA,California,Placer,6061,6061021322,BRFSS,Health Outcomes,Coronary heart disease among adults aged >=18 years,%,Crude prevalence,3.9000000000,,,3.7000000000,4.2000000000,8762,POINT (-121.4057179 38.84598382),6061021322,HLTHOUT,CHD,CrdPrv,Coronary Heart Disease +2019,CA,California,Placer,6061,6061021322,BRFSS,Health Risk Behaviors,No leisure-time physical activity among adults aged >=18 years,%,Crude prevalence,16.6000000000,,,15.2000000000,18.1000000000,8762,POINT (-121.4057179 38.84598382),6061021322,RISKBEH,LPA,CrdPrv,Physical Inactivity +2019,CA,California,Placer,6061,6061021322,BRFSS,Health Outcomes,Depression among adults aged >=18 years,%,Crude prevalence,17.2000000000,,,16.4000000000,18.0000000000,8762,POINT (-121.4057179 38.84598382),6061021322,HLTHOUT,DEPRESSION,CrdPrv,Depression +2019,CA,California,Placer,6061,6061021322,BRFSS,Health Risk Behaviors,Binge drinking among adults aged >=18 years,%,Crude prevalence,18.9000000000,,,18.3000000000,19.5000000000,8762,POINT (-121.4057179 38.84598382),6061021322,RISKBEH,BINGE,CrdPrv,Binge Drinking +2018,CA,California,Placer,6061,6061021322,BRFSS,Prevention,"Older adult men aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening",%,Crude prevalence,35.7000000000,,,31.8000000000,39.8000000000,8762,POINT (-121.4057179 38.84598382),6061021322,PREVENT,COREM,CrdPrv,Core preventive services for older men +2018,CA,California,Placer,6061,6061021322,BRFSS,Health Risk Behaviors,Sleeping less than 7 hours among adults aged >=18 years,%,Crude prevalence,31.9000000000,,,30.6000000000,33.2000000000,8762,POINT (-121.4057179 38.84598382),6061021322,RISKBEH,SLEEP,CrdPrv,Sleep <7 hours +2019,CA,California,Placer,6061,6061021322,BRFSS,Health Outcomes,Chronic kidney disease among adults aged >=18 years,%,Crude prevalence,2.2000000000,,,2.1000000000,2.3000000000,8762,POINT (-121.4057179 38.84598382),6061021322,HLTHOUT,KIDNEY,CrdPrv,Chronic Kidney Disease +2019,CA,California,Placer,6061,6061021322,BRFSS,Health Outcomes,Current asthma among adults aged >=18 years,%,Crude prevalence,8.6000000000,,,8.2000000000,8.9000000000,8762,POINT (-121.4057179 38.84598382),6061021322,HLTHOUT,CASTHMA,CrdPrv,Current Asthma +2019,CA,California,Placer,6061,6061021322,BRFSS,Health Status,Mental health not good for >=14 days among adults aged >=18 years,%,Crude prevalence,12.0000000000,,,11.1000000000,13.0000000000,8762,POINT (-121.4057179 38.84598382),6061021322,HLTHSTAT,MHLTH,CrdPrv,Mental Health +2018,CA,California,Placer,6061,6061021322,BRFSS,Prevention,Cervical cancer screening among adult women aged 21-65 years,%,Crude prevalence,83.4000000000,,,81.6000000000,85.2000000000,8762,POINT (-121.4057179 38.84598382),6061021322,PREVENT,CERVICAL,CrdPrv,Cervical Cancer Screening +2019,CA,California,Placer,6061,6061021322,BRFSS,Health Outcomes,Diagnosed diabetes among adults aged >=18 years,%,Crude prevalence,6.9000000000,,,6.6000000000,7.3000000000,8762,POINT (-121.4057179 38.84598382),6061021322,HLTHOUT,DIABETES,CrdPrv,Diabetes +2018,CA,California,Placer,6061,6061021322,BRFSS,Prevention,"Older adult women aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening, and Mammogram past 2 years",%,Crude prevalence,34.4000000000,,,30.3000000000,38.5000000000,8762,POINT (-121.4057179 38.84598382),6061021322,PREVENT,COREW,CrdPrv,Core preventive services for older women +2019,CA,California,Placer,6061,6061021322,BRFSS,Health Outcomes,High cholesterol among adults aged >=18 years who have been screened in the past 5 years,%,Crude prevalence,28.8000000000,,,28.2000000000,29.4000000000,8762,POINT (-121.4057179 38.84598382),6061021322,HLTHOUT,HIGHCHOL,CrdPrv,High Cholesterol +2019,CA,California,Placer,6061,6061021322,BRFSS,Prevention,Cholesterol screening among adults aged >=18 years,%,Crude prevalence,86.2000000000,,,85.7000000000,86.8000000000,8762,POINT (-121.4057179 38.84598382),6061021322,PREVENT,CHOLSCREEN,CrdPrv,Cholesterol Screening +2018,CA,California,Placer,6061,6061021322,BRFSS,Prevention,Mammography use among women aged 50-74 years,%,Crude prevalence,78.2000000000,,,75.6000000000,80.3000000000,8762,POINT (-121.4057179 38.84598382),6061021322,PREVENT,MAMMOUSE,CrdPrv,Mammography +2019,CA,California,Placer,6061,6061021322,BRFSS,Health Outcomes,Cancer (excluding skin cancer) among adults aged >=18 years,%,Crude prevalence,5.9000000000,,,5.7000000000,6.1000000000,8762,POINT (-121.4057179 38.84598382),6061021322,HLTHOUT,CANCER,CrdPrv,Cancer (except skin) +2019,CA,California,Placer,6061,6061021322,BRFSS,Health Outcomes,Stroke among adults aged >=18 years,%,Crude prevalence,2.2000000000,,,2.0000000000,2.4000000000,8762,POINT (-121.4057179 38.84598382),6061021322,HLTHOUT,STROKE,CrdPrv,Stroke +2018,CA,California,Placer,6061,6061021322,BRFSS,Prevention,"Fecal occult blood test, sigmoidoscopy, or colonoscopy among adults aged 50-75 years",%,Crude prevalence,72.1000000000,,,69.9000000000,74.0000000000,8762,POINT (-121.4057179 38.84598382),6061021322,PREVENT,COLON_SCREEN,CrdPrv,Colorectal Cancer Screening +2019,CA,California,Placer,6061,6061021322,BRFSS,Prevention,Current lack of health insurance among adults aged 18-64 years,%,Crude prevalence,9.0000000000,,,7.8000000000,10.6000000000,8762,POINT (-121.4057179 38.84598382),6061021322,PREVENT,ACCESS2,CrdPrv,Health Insurance +2019,CA,California,Placer,6061,6061021322,BRFSS,Health Risk Behaviors,Current smoking among adults aged >=18 years,%,Crude prevalence,11.4000000000,,,9.8000000000,13.2000000000,8762,POINT (-121.4057179 38.84598382),6061021322,RISKBEH,CSMOKING,CrdPrv,Current Smoking +2019,CA,California,Placer,6061,6061021322,BRFSS,Prevention,Taking medicine for high blood pressure control among adults aged >=18 years with high blood pressure,%,Crude prevalence,67.4000000000,,,66.4000000000,68.4000000000,8762,POINT (-121.4057179 38.84598382),6061021322,PREVENT,BPMED,CrdPrv,Taking BP Medication +2019,CA,California,Placer,6061,6061021322,BRFSS,Health Status,Physical health not good for >=14 days among adults aged >=18 years,%,Crude prevalence,9.4000000000,,,8.7000000000,10.1000000000,8762,POINT (-121.4057179 38.84598382),6061021322,HLTHSTAT,PHLTH,CrdPrv,Physical Health +2019,CA,California,Placer,6061,6061021322,BRFSS,Prevention,Visits to doctor for routine checkup within the past year among adults aged >=18 years,%,Crude prevalence,70.2000000000,,,69.4000000000,70.9000000000,8762,POINT (-121.4057179 38.84598382),6061021322,PREVENT,CHECKUP,CrdPrv,Annual Checkup +2018,CA,California,Placer,6061,6061021322,BRFSS,Health Outcomes,All teeth lost among adults aged >=65 years,%,Crude prevalence,7.6000000000,,,5.6000000000,10.0000000000,8762,POINT (-121.4057179 38.84598382),6061021322,HLTHOUT,TEETHLOST,CrdPrv,All Teeth Lost +2019,CA,California,Placer,6061,6061021322,BRFSS,Health Status,Fair or poor self-rated health status among adults aged >=18 years,%,Crude prevalence,12.3000000000,,,11.0000000000,13.6000000000,8762,POINT (-121.4057179 38.84598382),6061021322,HLTHSTAT,GHLTH,CrdPrv,General Health +2019,CA,California,Placer,6061,6061021322,BRFSS,Health Outcomes,Chronic obstructive pulmonary disease among adults aged >=18 years,%,Crude prevalence,4.2000000000,,,3.8000000000,4.8000000000,8762,POINT (-121.4057179 38.84598382),6061021322,HLTHOUT,COPD,CrdPrv,COPD +2019,CA,California,Placer,6061,6061021322,BRFSS,Health Outcomes,Arthritis among adults aged >=18 years,%,Crude prevalence,18.7000000000,,,18.0000000000,19.4000000000,8762,POINT (-121.4057179 38.84598382),6061021322,HLTHOUT,ARTHRITIS,CrdPrv,Arthritis +2018,CA,California,Placer,6061,6061021322,BRFSS,Prevention,Visits to dentist or dental clinic among adults aged >=18 years,%,Crude prevalence,71.8000000000,,,69.5000000000,74.1000000000,8762,POINT (-121.4057179 38.84598382),6061021322,PREVENT,DENTAL,CrdPrv,Dental Visit +2019,CA,California,San Benito,6069,6069000802,BRFSS,Health Outcomes,Chronic obstructive pulmonary disease among adults aged >=18 years,%,Crude prevalence,6.5000000000,,,5.9000000000,7.0000000000,2534,POINT (-121.0070559 36.54987144),6069000802,HLTHOUT,COPD,CrdPrv,COPD +2018,CA,California,San Benito,6069,6069000802,BRFSS,Health Outcomes,All teeth lost among adults aged >=65 years,%,Crude prevalence,12.3000000000,,,9.6000000000,15.5000000000,2534,POINT (-121.0070559 36.54987144),6069000802,HLTHOUT,TEETHLOST,CrdPrv,All Teeth Lost +2018,CA,California,San Benito,6069,6069000802,BRFSS,Prevention,Mammography use among women aged 50-74 years,%,Crude prevalence,77.6000000000,,,75.4000000000,79.6000000000,2534,POINT (-121.0070559 36.54987144),6069000802,PREVENT,MAMMOUSE,CrdPrv,Mammography +2019,CA,California,San Benito,6069,6069000802,BRFSS,Health Outcomes,Current asthma among adults aged >=18 years,%,Crude prevalence,9.3000000000,,,9.0000000000,9.6000000000,2534,POINT (-121.0070559 36.54987144),6069000802,HLTHOUT,CASTHMA,CrdPrv,Current Asthma +2019,CA,California,San Benito,6069,6069000802,BRFSS,Health Outcomes,Arthritis among adults aged >=18 years,%,Crude prevalence,23.6000000000,,,23.0000000000,24.3000000000,2534,POINT (-121.0070559 36.54987144),6069000802,HLTHOUT,ARTHRITIS,CrdPrv,Arthritis +2018,CA,California,San Benito,6069,6069000802,BRFSS,Prevention,Visits to dentist or dental clinic among adults aged >=18 years,%,Crude prevalence,64.8000000000,,,62.9000000000,67.0000000000,2534,POINT (-121.0070559 36.54987144),6069000802,PREVENT,DENTAL,CrdPrv,Dental Visit +2019,CA,California,San Benito,6069,6069000802,BRFSS,Health Outcomes,Chronic kidney disease among adults aged >=18 years,%,Crude prevalence,2.9000000000,,,2.8000000000,3.0000000000,2534,POINT (-121.0070559 36.54987144),6069000802,HLTHOUT,KIDNEY,CrdPrv,Chronic Kidney Disease +2018,CA,California,San Benito,6069,6069000802,BRFSS,Prevention,Cervical cancer screening among adult women aged 21-65 years,%,Crude prevalence,84.7000000000,,,83.5000000000,86.0000000000,2534,POINT (-121.0070559 36.54987144),6069000802,PREVENT,CERVICAL,CrdPrv,Cervical Cancer Screening +2019,CA,California,San Benito,6069,6069000802,BRFSS,Health Risk Behaviors,Binge drinking among adults aged >=18 years,%,Crude prevalence,18.7000000000,,,18.3000000000,19.1000000000,2534,POINT (-121.0070559 36.54987144),6069000802,RISKBEH,BINGE,CrdPrv,Binge Drinking +2019,CA,California,San Benito,6069,6069000802,BRFSS,Health Status,Mental health not good for >=14 days among adults aged >=18 years,%,Crude prevalence,13.5000000000,,,12.9000000000,14.2000000000,2534,POINT (-121.0070559 36.54987144),6069000802,HLTHSTAT,MHLTH,CrdPrv,Mental Health +2018,CA,California,San Benito,6069,6069000802,BRFSS,Prevention,"Older adult men aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening",%,Crude prevalence,30.3000000000,,,26.8000000000,34.1000000000,2534,POINT (-121.0070559 36.54987144),6069000802,PREVENT,COREM,CrdPrv,Core preventive services for older men +2019,CA,California,San Benito,6069,6069000802,BRFSS,Health Status,Physical health not good for >=14 days among adults aged >=18 years,%,Crude prevalence,13.4000000000,,,12.7000000000,14.2000000000,2534,POINT (-121.0070559 36.54987144),6069000802,HLTHSTAT,PHLTH,CrdPrv,Physical Health +2019,CA,California,San Benito,6069,6069000802,BRFSS,Health Outcomes,Depression among adults aged >=18 years,%,Crude prevalence,19.2000000000,,,18.6000000000,19.9000000000,2534,POINT (-121.0070559 36.54987144),6069000802,HLTHOUT,DEPRESSION,CrdPrv,Depression +2019,CA,California,San Benito,6069,6069000802,BRFSS,Health Outcomes,Cancer (excluding skin cancer) among adults aged >=18 years,%,Crude prevalence,7.0000000000,,,6.8000000000,7.2000000000,2534,POINT (-121.0070559 36.54987144),6069000802,HLTHOUT,CANCER,CrdPrv,Cancer (except skin) +2019,CA,California,San Benito,6069,6069000802,BRFSS,Health Outcomes,High cholesterol among adults aged >=18 years who have been screened in the past 5 years,%,Crude prevalence,32.7000000000,,,32.1000000000,33.3000000000,2534,POINT (-121.0070559 36.54987144),6069000802,HLTHOUT,HIGHCHOL,CrdPrv,High Cholesterol +2019,CA,California,San Benito,6069,6069000802,BRFSS,Prevention,Cholesterol screening among adults aged >=18 years,%,Crude prevalence,87.1000000000,,,86.9000000000,87.2000000000,2534,POINT (-121.0070559 36.54987144),6069000802,PREVENT,CHOLSCREEN,CrdPrv,Cholesterol Screening +2018,CA,California,San Benito,6069,6069000802,BRFSS,Health Risk Behaviors,Sleeping less than 7 hours among adults aged >=18 years,%,Crude prevalence,31.9000000000,,,30.7000000000,32.8000000000,2534,POINT (-121.0070559 36.54987144),6069000802,RISKBEH,SLEEP,CrdPrv,Sleep <7 hours +2019,CA,California,San Benito,6069,6069000802,BRFSS,Health Status,Fair or poor self-rated health status among adults aged >=18 years,%,Crude prevalence,18.9000000000,,,17.5000000000,20.4000000000,2534,POINT (-121.0070559 36.54987144),6069000802,HLTHSTAT,GHLTH,CrdPrv,General Health +2019,CA,California,San Benito,6069,6069000802,BRFSS,Prevention,Taking medicine for high blood pressure control among adults aged >=18 years with high blood pressure,%,Crude prevalence,70.6000000000,,,69.8000000000,71.2000000000,2534,POINT (-121.0070559 36.54987144),6069000802,PREVENT,BPMED,CrdPrv,Taking BP Medication +2018,CA,California,San Benito,6069,6069000802,BRFSS,Prevention,"Older adult women aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening, and Mammogram past 2 years",%,Crude prevalence,29.3000000000,,,26.0000000000,32.8000000000,2534,POINT (-121.0070559 36.54987144),6069000802,PREVENT,COREW,CrdPrv,Core preventive services for older women +2019,CA,California,San Benito,6069,6069000802,BRFSS,Prevention,Visits to doctor for routine checkup within the past year among adults aged >=18 years,%,Crude prevalence,71.1000000000,,,70.5000000000,71.7000000000,2534,POINT (-121.0070559 36.54987144),6069000802,PREVENT,CHECKUP,CrdPrv,Annual Checkup +2019,CA,California,San Benito,6069,6069000802,BRFSS,Health Outcomes,Stroke among adults aged >=18 years,%,Crude prevalence,3.2000000000,,,3.0000000000,3.4000000000,2534,POINT (-121.0070559 36.54987144),6069000802,HLTHOUT,STROKE,CrdPrv,Stroke +2018,CA,California,San Benito,6069,6069000802,BRFSS,Prevention,"Fecal occult blood test, sigmoidoscopy, or colonoscopy among adults aged 50-75 years",%,Crude prevalence,65.7000000000,,,63.5000000000,67.7000000000,2534,POINT (-121.0070559 36.54987144),6069000802,PREVENT,COLON_SCREEN,CrdPrv,Colorectal Cancer Screening +2019,CA,California,San Benito,6069,6069000802,BRFSS,Health Risk Behaviors,No leisure-time physical activity among adults aged >=18 years,%,Crude prevalence,22.7000000000,,,21.4000000000,24.1000000000,2534,POINT (-121.0070559 36.54987144),6069000802,RISKBEH,LPA,CrdPrv,Physical Inactivity +2019,CA,California,San Benito,6069,6069000802,BRFSS,Prevention,Current lack of health insurance among adults aged 18-64 years,%,Crude prevalence,13.2000000000,,,11.9000000000,14.5000000000,2534,POINT (-121.0070559 36.54987144),6069000802,PREVENT,ACCESS2,CrdPrv,Health Insurance +2019,CA,California,San Benito,6069,6069000802,BRFSS,Health Outcomes,Diagnosed diabetes among adults aged >=18 years,%,Crude prevalence,10.2000000000,,,9.8000000000,10.6000000000,2534,POINT (-121.0070559 36.54987144),6069000802,HLTHOUT,DIABETES,CrdPrv,Diabetes +2019,CA,California,San Benito,6069,6069000802,BRFSS,Health Outcomes,High blood pressure among adults aged >=18 years,%,Crude prevalence,30.2000000000,,,29.5000000000,30.9000000000,2534,POINT (-121.0070559 36.54987144),6069000802,HLTHOUT,BPHIGH,CrdPrv,High Blood Pressure +2019,CA,California,San Benito,6069,6069000802,BRFSS,Health Outcomes,Coronary heart disease among adults aged >=18 years,%,Crude prevalence,5.8000000000,,,5.5000000000,6.1000000000,2534,POINT (-121.0070559 36.54987144),6069000802,HLTHOUT,CHD,CrdPrv,Coronary Heart Disease +2019,CA,California,San Benito,6069,6069000802,BRFSS,Health Outcomes,Obesity among adults aged >=18 years,%,Crude prevalence,29.2000000000,,,28.4000000000,30.0000000000,2534,POINT (-121.0070559 36.54987144),6069000802,HLTHOUT,OBESITY,CrdPrv,Obesity +2019,CA,California,San Benito,6069,6069000802,BRFSS,Health Risk Behaviors,Current smoking among adults aged >=18 years,%,Crude prevalence,14.5000000000,,,13.3000000000,15.7000000000,2534,POINT (-121.0070559 36.54987144),6069000802,RISKBEH,CSMOKING,CrdPrv,Current Smoking +2019,HI,Hawaii,Hawaii,15001,15001021800,BRFSS,Prevention,Taking medicine for high blood pressure control among adults aged >=18 years with high blood pressure,%,Crude prevalence,73.5000000000,,,73.1000000000,73.8000000000,6322,POINT (-155.8112721 20.16059783),15001021800,PREVENT,BPMED,CrdPrv,Taking BP Medication +2019,HI,Hawaii,Hawaii,15001,15001021010,BRFSS,Health Outcomes,Obesity among adults aged >=18 years,%,Crude prevalence,29.4000000000,,,29.0000000000,29.8000000000,7884,POINT (-155.1037996 19.49754656),15001021010,HLTHOUT,OBESITY,CrdPrv,Obesity +2018,HI,Hawaii,Hawaii,15001,15001021101,BRFSS,Prevention,"Older adult women aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening, and Mammogram past 2 years",%,Crude prevalence,23.4000000000,,,20.4000000000,26.5000000000,3531,POINT (-154.8953489 19.44949565),15001021101,PREVENT,COREW,CrdPrv,Core preventive services for older women +2019,HI,Hawaii,Hawaii,15001,15001021010,BRFSS,Health Risk Behaviors,Binge drinking among adults aged >=18 years,%,Crude prevalence,20.7000000000,,,20.5000000000,20.9000000000,7884,POINT (-155.1037996 19.49754656),15001021010,RISKBEH,BINGE,CrdPrv,Binge Drinking +2019,HI,Hawaii,Hawaii,15001,15001021402,BRFSS,Health Status,Physical health not good for >=14 days among adults aged >=18 years,%,Crude prevalence,12.4000000000,,,11.9000000000,12.9000000000,4025,POINT (-155.906965 19.51804981),15001021402,HLTHSTAT,PHLTH,CrdPrv,Physical Health +2019,HI,Hawaii,Hawaii,15001,15001021800,BRFSS,Health Outcomes,Obesity among adults aged >=18 years,%,Crude prevalence,24.3000000000,,,24.0000000000,24.7000000000,6322,POINT (-155.8112721 20.16059783),15001021800,HLTHOUT,OBESITY,CrdPrv,Obesity +2018,HI,Hawaii,Hawaii,15001,15001021402,BRFSS,Prevention,"Fecal occult blood test, sigmoidoscopy, or colonoscopy among adults aged 50-75 years",%,Crude prevalence,64.9000000000,,,63.7000000000,66.2000000000,4025,POINT (-155.906965 19.51804981),15001021402,PREVENT,COLON_SCREEN,CrdPrv,Colorectal Cancer Screening +2019,HI,Hawaii,Hawaii,15001,15001021402,BRFSS,Health Outcomes,Depression among adults aged >=18 years,%,Crude prevalence,15.9000000000,,,15.6000000000,16.3000000000,4025,POINT (-155.906965 19.51804981),15001021402,HLTHOUT,DEPRESSION,CrdPrv,Depression +2019,HI,Hawaii,Hawaii,15001,15001021800,BRFSS,Prevention,Current lack of health insurance among adults aged 18-64 years,%,Crude prevalence,10.8000000000,,,10.3000000000,11.3000000000,6322,POINT (-155.8112721 20.16059783),15001021800,PREVENT,ACCESS2,CrdPrv,Health Insurance +2019,HI,Hawaii,Hawaii,15001,15001021402,BRFSS,Health Risk Behaviors,No leisure-time physical activity among adults aged >=18 years,%,Crude prevalence,24.9000000000,,,23.8000000000,25.9000000000,4025,POINT (-155.906965 19.51804981),15001021402,RISKBEH,LPA,CrdPrv,Physical Inactivity +2019,HI,Hawaii,Hawaii,15001,15001021800,BRFSS,Health Outcomes,Chronic kidney disease among adults aged >=18 years,%,Crude prevalence,3.0000000000,,,3.0000000000,3.1000000000,6322,POINT (-155.8112721 20.16059783),15001021800,HLTHOUT,KIDNEY,CrdPrv,Chronic Kidney Disease +2019,HI,Hawaii,Hawaii,15001,15001021101,BRFSS,Health Status,Physical health not good for >=14 days among adults aged >=18 years,%,Crude prevalence,16.0000000000,,,14.9000000000,17.2000000000,3531,POINT (-154.8953489 19.44949565),15001021101,HLTHSTAT,PHLTH,CrdPrv,Physical Health +2019,HI,Hawaii,Hawaii,15001,15001021010,BRFSS,Health Outcomes,Depression among adults aged >=18 years,%,Crude prevalence,21.1000000000,,,20.8000000000,21.4000000000,7884,POINT (-155.1037996 19.49754656),15001021010,HLTHOUT,DEPRESSION,CrdPrv,Depression +2019,HI,Hawaii,Hawaii,15001,15001021010,BRFSS,Health Outcomes,Current asthma among adults aged >=18 years,%,Crude prevalence,11.3000000000,,,11.1000000000,11.5000000000,7884,POINT (-155.1037996 19.49754656),15001021010,HLTHOUT,CASTHMA,CrdPrv,Current Asthma +2018,HI,Hawaii,Hawaii,15001,15001021402,BRFSS,Prevention,Cervical cancer screening among adult women aged 21-65 years,%,Crude prevalence,82.3000000000,,,81.3000000000,83.2000000000,4025,POINT (-155.906965 19.51804981),15001021402,PREVENT,CERVICAL,CrdPrv,Cervical Cancer Screening +2019,HI,Hawaii,Hawaii,15001,15001021402,BRFSS,Health Outcomes,Chronic kidney disease among adults aged >=18 years,%,Crude prevalence,2.9000000000,,,2.8000000000,3.0000000000,4025,POINT (-155.906965 19.51804981),15001021402,HLTHOUT,KIDNEY,CrdPrv,Chronic Kidney Disease +2019,HI,Hawaii,Hawaii,15001,15001021101,BRFSS,Prevention,Visits to doctor for routine checkup within the past year among adults aged >=18 years,%,Crude prevalence,74.5000000000,,,73.9000000000,75.1000000000,3531,POINT (-154.8953489 19.44949565),15001021101,PREVENT,CHECKUP,CrdPrv,Annual Checkup +2019,HI,Hawaii,Hawaii,15001,15001021800,BRFSS,Health Outcomes,Chronic obstructive pulmonary disease among adults aged >=18 years,%,Crude prevalence,5.8000000000,,,5.5000000000,6.0000000000,6322,POINT (-155.8112721 20.16059783),15001021800,HLTHOUT,COPD,CrdPrv,COPD +2019,HI,Hawaii,Hawaii,15001,15001021101,BRFSS,Health Status,Mental health not good for >=14 days among adults aged >=18 years,%,Crude prevalence,15.6000000000,,,14.7000000000,16.5000000000,3531,POINT (-154.8953489 19.44949565),15001021101,HLTHSTAT,MHLTH,CrdPrv,Mental Health +2019,HI,Hawaii,Hawaii,15001,15001021402,BRFSS,Health Risk Behaviors,Binge drinking among adults aged >=18 years,%,Crude prevalence,18.2000000000,,,18.0000000000,18.4000000000,4025,POINT (-155.906965 19.51804981),15001021402,RISKBEH,BINGE,CrdPrv,Binge Drinking +2018,HI,Hawaii,Hawaii,15001,15001021101,BRFSS,Prevention,"Older adult men aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening",%,Crude prevalence,20.3000000000,,,17.4000000000,23.4000000000,3531,POINT (-154.8953489 19.44949565),15001021101,PREVENT,COREM,CrdPrv,Core preventive services for older men +2019,HI,Hawaii,Hawaii,15001,15001021101,BRFSS,Health Outcomes,Diagnosed diabetes among adults aged >=18 years,%,Crude prevalence,11.5000000000,,,10.9000000000,12.2000000000,3531,POINT (-154.8953489 19.44949565),15001021101,HLTHOUT,DIABETES,CrdPrv,Diabetes +2019,HI,Hawaii,Hawaii,15001,15001021010,BRFSS,Health Outcomes,High cholesterol among adults aged >=18 years who have been screened in the past 5 years,%,Crude prevalence,28.8000000000,,,28.6000000000,29.1000000000,7884,POINT (-155.1037996 19.49754656),15001021010,HLTHOUT,HIGHCHOL,CrdPrv,High Cholesterol +2018,HI,Hawaii,Hawaii,15001,15001021800,BRFSS,Health Outcomes,All teeth lost among adults aged >=65 years,%,Crude prevalence,10.1000000000,,,9.1000000000,11.3000000000,6322,POINT (-155.8112721 20.16059783),15001021800,HLTHOUT,TEETHLOST,CrdPrv,All Teeth Lost +2018,HI,Hawaii,Hawaii,15001,15001021010,BRFSS,Prevention,"Older adult women aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening, and Mammogram past 2 years",%,Crude prevalence,22.6000000000,,,21.1000000000,24.3000000000,7884,POINT (-155.1037996 19.49754656),15001021010,PREVENT,COREW,CrdPrv,Core preventive services for older women +2019,HI,Hawaii,Hawaii,15001,15001021800,BRFSS,Health Status,Mental health not good for >=14 days among adults aged >=18 years,%,Crude prevalence,14.0000000000,,,13.6000000000,14.3000000000,6322,POINT (-155.8112721 20.16059783),15001021800,HLTHSTAT,MHLTH,CrdPrv,Mental Health +2019,HI,Hawaii,Hawaii,15001,15001021010,BRFSS,Prevention,Visits to doctor for routine checkup within the past year among adults aged >=18 years,%,Crude prevalence,72.6000000000,,,72.3000000000,72.9000000000,7884,POINT (-155.1037996 19.49754656),15001021010,PREVENT,CHECKUP,CrdPrv,Annual Checkup +2019,HI,Hawaii,Hawaii,15001,15001021101,BRFSS,Health Outcomes,High blood pressure among adults aged >=18 years,%,Crude prevalence,32.6000000000,,,31.6000000000,33.6000000000,3531,POINT (-154.8953489 19.44949565),15001021101,HLTHOUT,BPHIGH,CrdPrv,High Blood Pressure +2018,HI,Hawaii,Hawaii,15001,15001021800,BRFSS,Prevention,Cervical cancer screening among adult women aged 21-65 years,%,Crude prevalence,83.2000000000,,,82.6000000000,83.9000000000,6322,POINT (-155.8112721 20.16059783),15001021800,PREVENT,CERVICAL,CrdPrv,Cervical Cancer Screening +2018,HI,Hawaii,Hawaii,15001,15001021402,BRFSS,Health Outcomes,All teeth lost among adults aged >=65 years,%,Crude prevalence,8.5000000000,,,7.2000000000,10.1000000000,4025,POINT (-155.906965 19.51804981),15001021402,HLTHOUT,TEETHLOST,CrdPrv,All Teeth Lost +2019,HI,Hawaii,Hawaii,15001,15001021101,BRFSS,Health Outcomes,Depression among adults aged >=18 years,%,Crude prevalence,19.8000000000,,,19.1000000000,20.6000000000,3531,POINT (-154.8953489 19.44949565),15001021101,HLTHOUT,DEPRESSION,CrdPrv,Depression +2019,HI,Hawaii,Hawaii,15001,15001021010,BRFSS,Health Status,Mental health not good for >=14 days among adults aged >=18 years,%,Crude prevalence,17.8000000000,,,17.4000000000,18.3000000000,7884,POINT (-155.1037996 19.49754656),15001021010,HLTHSTAT,MHLTH,CrdPrv,Mental Health +2019,HI,Hawaii,Hawaii,15001,15001021800,BRFSS,Health Outcomes,High cholesterol among adults aged >=18 years who have been screened in the past 5 years,%,Crude prevalence,29.5000000000,,,29.2000000000,29.8000000000,6322,POINT (-155.8112721 20.16059783),15001021800,HLTHOUT,HIGHCHOL,CrdPrv,High Cholesterol +2019,HI,Hawaii,Hawaii,15001,15001021402,BRFSS,Prevention,Cholesterol screening among adults aged >=18 years,%,Crude prevalence,83.8000000000,,,83.7000000000,83.9000000000,4025,POINT (-155.906965 19.51804981),15001021402,PREVENT,CHOLSCREEN,CrdPrv,Cholesterol Screening +2018,HI,Hawaii,Hawaii,15001,15001021010,BRFSS,Prevention,Visits to dentist or dental clinic among adults aged >=18 years,%,Crude prevalence,61.9000000000,,,60.9000000000,63.0000000000,7884,POINT (-155.1037996 19.49754656),15001021010,PREVENT,DENTAL,CrdPrv,Dental Visit +2019,HI,Hawaii,Hawaii,15001,15001021010,BRFSS,Health Outcomes,Cancer (excluding skin cancer) among adults aged >=18 years,%,Crude prevalence,5.9000000000,,,5.9000000000,6.1000000000,7884,POINT (-155.1037996 19.49754656),15001021010,HLTHOUT,CANCER,CrdPrv,Cancer (except skin) +2018,HI,Hawaii,Hawaii,15001,15001021010,BRFSS,Health Outcomes,All teeth lost among adults aged >=65 years,%,Crude prevalence,13.3000000000,,,11.7000000000,15.1000000000,7884,POINT (-155.1037996 19.49754656),15001021010,HLTHOUT,TEETHLOST,CrdPrv,All Teeth Lost +2019,HI,Hawaii,Hawaii,15001,15001021800,BRFSS,Health Status,Physical health not good for >=14 days among adults aged >=18 years,%,Crude prevalence,13.2000000000,,,12.9000000000,13.6000000000,6322,POINT (-155.8112721 20.16059783),15001021800,HLTHSTAT,PHLTH,CrdPrv,Physical Health +2019,HI,Hawaii,Hawaii,15001,15001021101,BRFSS,Prevention,Cholesterol screening among adults aged >=18 years,%,Crude prevalence,82.5000000000,,,82.4000000000,82.6000000000,3531,POINT (-154.8953489 19.44949565),15001021101,PREVENT,CHOLSCREEN,CrdPrv,Cholesterol Screening +2019,HI,Hawaii,Hawaii,15001,15001021010,BRFSS,Health Outcomes,Arthritis among adults aged >=18 years,%,Crude prevalence,23.8000000000,,,23.4000000000,24.2000000000,7884,POINT (-155.1037996 19.49754656),15001021010,HLTHOUT,ARTHRITIS,CrdPrv,Arthritis +2019,HI,Hawaii,Hawaii,15001,15001021402,BRFSS,Health Outcomes,Current asthma among adults aged >=18 years,%,Crude prevalence,9.2000000000,,,9.0000000000,9.4000000000,4025,POINT (-155.906965 19.51804981),15001021402,HLTHOUT,CASTHMA,CrdPrv,Current Asthma +2019,HI,Hawaii,Hawaii,15001,15001021402,BRFSS,Health Outcomes,Diagnosed diabetes among adults aged >=18 years,%,Crude prevalence,11.2000000000,,,10.8000000000,11.5000000000,4025,POINT (-155.906965 19.51804981),15001021402,HLTHOUT,DIABETES,CrdPrv,Diabetes +2019,HI,Hawaii,Hawaii,15001,15001021101,BRFSS,Health Outcomes,Current asthma among adults aged >=18 years,%,Crude prevalence,10.4000000000,,,10.0000000000,10.7000000000,3531,POINT (-154.8953489 19.44949565),15001021101,HLTHOUT,CASTHMA,CrdPrv,Current Asthma +2018,HI,Hawaii,Hawaii,15001,15001021800,BRFSS,Prevention,"Older adult men aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening",%,Crude prevalence,20.5000000000,,,19.1000000000,22.0000000000,6322,POINT (-155.8112721 20.16059783),15001021800,PREVENT,COREM,CrdPrv,Core preventive services for older men +2019,HI,Hawaii,Hawaii,15001,15001021010,BRFSS,Prevention,Cholesterol screening among adults aged >=18 years,%,Crude prevalence,79.8000000000,,,79.7000000000,79.9000000000,7884,POINT (-155.1037996 19.49754656),15001021010,PREVENT,CHOLSCREEN,CrdPrv,Cholesterol Screening +2018,HI,Hawaii,Hawaii,15001,15001021101,BRFSS,Health Risk Behaviors,Sleeping less than 7 hours among adults aged >=18 years,%,Crude prevalence,39.4000000000,,,38.2000000000,41.2000000000,3531,POINT (-154.8953489 19.44949565),15001021101,RISKBEH,SLEEP,CrdPrv,Sleep <7 hours +2019,HI,Hawaii,Hawaii,15001,15001021402,BRFSS,Health Outcomes,High blood pressure among adults aged >=18 years,%,Crude prevalence,29.6000000000,,,29.1000000000,30.2000000000,4025,POINT (-155.906965 19.51804981),15001021402,HLTHOUT,BPHIGH,CrdPrv,High Blood Pressure +2019,HI,Hawaii,Hawaii,15001,15001021800,BRFSS,Health Outcomes,High blood pressure among adults aged >=18 years,%,Crude prevalence,29.8000000000,,,29.4000000000,30.2000000000,6322,POINT (-155.8112721 20.16059783),15001021800,HLTHOUT,BPHIGH,CrdPrv,High Blood Pressure +2019,HI,Hawaii,Hawaii,15001,15001021402,BRFSS,Health Outcomes,Obesity among adults aged >=18 years,%,Crude prevalence,21.7000000000,,,21.3000000000,22.1000000000,4025,POINT (-155.906965 19.51804981),15001021402,HLTHOUT,OBESITY,CrdPrv,Obesity +2019,HI,Hawaii,Hawaii,15001,15001021800,BRFSS,Health Outcomes,Coronary heart disease among adults aged >=18 years,%,Crude prevalence,6.4000000000,,,6.2000000000,6.6000000000,6322,POINT (-155.8112721 20.16059783),15001021800,HLTHOUT,CHD,CrdPrv,Coronary Heart Disease +2019,HI,Hawaii,Hawaii,15001,15001021010,BRFSS,Health Outcomes,Chronic kidney disease among adults aged >=18 years,%,Crude prevalence,3.2000000000,,,3.1000000000,3.3000000000,7884,POINT (-155.1037996 19.49754656),15001021010,HLTHOUT,KIDNEY,CrdPrv,Chronic Kidney Disease +2019,HI,Hawaii,Hawaii,15001,15001021010,BRFSS,Health Risk Behaviors,Current smoking among adults aged >=18 years,%,Crude prevalence,22.6000000000,,,21.7000000000,23.6000000000,7884,POINT (-155.1037996 19.49754656),15001021010,RISKBEH,CSMOKING,CrdPrv,Current Smoking +2019,HI,Hawaii,Hawaii,15001,15001021800,BRFSS,Health Outcomes,Diagnosed diabetes among adults aged >=18 years,%,Crude prevalence,10.7000000000,,,10.5000000000,11.0000000000,6322,POINT (-155.8112721 20.16059783),15001021800,HLTHOUT,DIABETES,CrdPrv,Diabetes +2019,HI,Hawaii,Hawaii,15001,15001021101,BRFSS,Health Risk Behaviors,Binge drinking among adults aged >=18 years,%,Crude prevalence,19.1000000000,,,18.7000000000,19.5000000000,3531,POINT (-154.8953489 19.44949565),15001021101,RISKBEH,BINGE,CrdPrv,Binge Drinking +2019,HI,Hawaii,Hawaii,15001,15001021402,BRFSS,Prevention,Current lack of health insurance among adults aged 18-64 years,%,Crude prevalence,11.4000000000,,,10.6000000000,12.1000000000,4025,POINT (-155.906965 19.51804981),15001021402,PREVENT,ACCESS2,CrdPrv,Health Insurance +2018,HI,Hawaii,Hawaii,15001,15001021800,BRFSS,Prevention,Mammography use among women aged 50-74 years,%,Crude prevalence,76.2000000000,,,75.3000000000,77.1000000000,6322,POINT (-155.8112721 20.16059783),15001021800,PREVENT,MAMMOUSE,CrdPrv,Mammography +2019,HI,Hawaii,Hawaii,15001,15001021010,BRFSS,Prevention,Taking medicine for high blood pressure control among adults aged >=18 years with high blood pressure,%,Crude prevalence,70.6000000000,,,70.1000000000,71.0000000000,7884,POINT (-155.1037996 19.49754656),15001021010,PREVENT,BPMED,CrdPrv,Taking BP Medication +2019,HI,Hawaii,Hawaii,15001,15001021101,BRFSS,Health Outcomes,Arthritis among adults aged >=18 years,%,Crude prevalence,25.7000000000,,,24.8000000000,26.6000000000,3531,POINT (-154.8953489 19.44949565),15001021101,HLTHOUT,ARTHRITIS,CrdPrv,Arthritis +2018,HI,Hawaii,Hawaii,15001,15001021402,BRFSS,Prevention,"Older adult men aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening",%,Crude prevalence,21.4000000000,,,19.4000000000,23.6000000000,4025,POINT (-155.906965 19.51804981),15001021402,PREVENT,COREM,CrdPrv,Core preventive services for older men +2019,HI,Hawaii,Hawaii,15001,15001021800,BRFSS,Health Outcomes,Current asthma among adults aged >=18 years,%,Crude prevalence,9.8000000000,,,9.6000000000,9.9000000000,6322,POINT (-155.8112721 20.16059783),15001021800,HLTHOUT,CASTHMA,CrdPrv,Current Asthma +2019,HI,Hawaii,Hawaii,15001,15001021010,BRFSS,Prevention,Current lack of health insurance among adults aged 18-64 years,%,Crude prevalence,12.5000000000,,,11.9000000000,13.1000000000,7884,POINT (-155.1037996 19.49754656),15001021010,PREVENT,ACCESS2,CrdPrv,Health Insurance +2019,HI,Hawaii,Hawaii,15001,15001021402,BRFSS,Health Outcomes,Stroke among adults aged >=18 years,%,Crude prevalence,3.6000000000,,,3.4000000000,3.7000000000,4025,POINT (-155.906965 19.51804981),15001021402,HLTHOUT,STROKE,CrdPrv,Stroke +2018,HI,Hawaii,Hawaii,15001,15001021101,BRFSS,Prevention,Visits to dentist or dental clinic among adults aged >=18 years,%,Crude prevalence,65.5000000000,,,63.5000000000,68.0000000000,3531,POINT (-154.8953489 19.44949565),15001021101,PREVENT,DENTAL,CrdPrv,Dental Visit +2018,HI,Hawaii,Hawaii,15001,15001021800,BRFSS,Health Risk Behaviors,Sleeping less than 7 hours among adults aged >=18 years,%,Crude prevalence,40.2000000000,,,39.7000000000,40.7000000000,6322,POINT (-155.8112721 20.16059783),15001021800,RISKBEH,SLEEP,CrdPrv,Sleep <7 hours +2019,HI,Hawaii,Hawaii,15001,15001021101,BRFSS,Health Outcomes,Chronic kidney disease among adults aged >=18 years,%,Crude prevalence,3.5000000000,,,3.3000000000,3.6000000000,3531,POINT (-154.8953489 19.44949565),15001021101,HLTHOUT,KIDNEY,CrdPrv,Chronic Kidney Disease +2019,HI,Hawaii,Hawaii,15001,15001021010,BRFSS,Health Outcomes,Stroke among adults aged >=18 years,%,Crude prevalence,4.0000000000,,,3.9000000000,4.2000000000,7884,POINT (-155.1037996 19.49754656),15001021010,HLTHOUT,STROKE,CrdPrv,Stroke +2018,HI,Hawaii,Hawaii,15001,15001021101,BRFSS,Prevention,"Fecal occult blood test, sigmoidoscopy, or colonoscopy among adults aged 50-75 years",%,Crude prevalence,63.8000000000,,,61.3000000000,66.1000000000,3531,POINT (-154.8953489 19.44949565),15001021101,PREVENT,COLON_SCREEN,CrdPrv,Colorectal Cancer Screening +2018,HI,Hawaii,Hawaii,15001,15001021010,BRFSS,Health Risk Behaviors,Sleeping less than 7 hours among adults aged >=18 years,%,Crude prevalence,42.6000000000,,,41.8000000000,43.1000000000,7884,POINT (-155.1037996 19.49754656),15001021010,RISKBEH,SLEEP,CrdPrv,Sleep <7 hours +2019,HI,Hawaii,Hawaii,15001,15001021800,BRFSS,Prevention,Cholesterol screening among adults aged >=18 years,%,Crude prevalence,83.1000000000,,,83.0000000000,83.3000000000,6322,POINT (-155.8112721 20.16059783),15001021800,PREVENT,CHOLSCREEN,CrdPrv,Cholesterol Screening +2019,HI,Hawaii,Hawaii,15001,15001021010,BRFSS,Health Risk Behaviors,No leisure-time physical activity among adults aged >=18 years,%,Crude prevalence,27.3000000000,,,26.5000000000,28.1000000000,7884,POINT (-155.1037996 19.49754656),15001021010,RISKBEH,LPA,CrdPrv,Physical Inactivity +2019,HI,Hawaii,Hawaii,15001,15001021010,BRFSS,Health Outcomes,Chronic obstructive pulmonary disease among adults aged >=18 years,%,Crude prevalence,7.4000000000,,,7.1000000000,7.8000000000,7884,POINT (-155.1037996 19.49754656),15001021010,HLTHOUT,COPD,CrdPrv,COPD +2019,HI,Hawaii,Hawaii,15001,15001021101,BRFSS,Health Risk Behaviors,No leisure-time physical activity among adults aged >=18 years,%,Crude prevalence,25.8000000000,,,24.0000000000,27.7000000000,3531,POINT (-154.8953489 19.44949565),15001021101,RISKBEH,LPA,CrdPrv,Physical Inactivity +2018,HI,Hawaii,Hawaii,15001,15001021010,BRFSS,Prevention,"Fecal occult blood test, sigmoidoscopy, or colonoscopy among adults aged 50-75 years",%,Crude prevalence,60.7000000000,,,59.6000000000,61.7000000000,7884,POINT (-155.1037996 19.49754656),15001021010,PREVENT,COLON_SCREEN,CrdPrv,Colorectal Cancer Screening +2019,HI,Hawaii,Hawaii,15001,15001021800,BRFSS,Health Outcomes,Depression among adults aged >=18 years,%,Crude prevalence,17.7000000000,,,17.4000000000,17.9000000000,6322,POINT (-155.8112721 20.16059783),15001021800,HLTHOUT,DEPRESSION,CrdPrv,Depression +2019,HI,Hawaii,Hawaii,15001,15001021402,BRFSS,Health Status,Fair or poor self-rated health status among adults aged >=18 years,%,Crude prevalence,18.0000000000,,,17.1000000000,19.0000000000,4025,POINT (-155.906965 19.51804981),15001021402,HLTHSTAT,GHLTH,CrdPrv,General Health +2018,HI,Hawaii,Hawaii,15001,15001021800,BRFSS,Prevention,"Older adult women aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening, and Mammogram past 2 years",%,Crude prevalence,24.1000000000,,,22.7000000000,25.6000000000,6322,POINT (-155.8112721 20.16059783),15001021800,PREVENT,COREW,CrdPrv,Core preventive services for older women +2018,HI,Hawaii,Hawaii,15001,15001021101,BRFSS,Prevention,Cervical cancer screening among adult women aged 21-65 years,%,Crude prevalence,83.4000000000,,,82.0000000000,84.8000000000,3531,POINT (-154.8953489 19.44949565),15001021101,PREVENT,CERVICAL,CrdPrv,Cervical Cancer Screening +2019,HI,Hawaii,Hawaii,15001,15001021402,BRFSS,Prevention,Taking medicine for high blood pressure control among adults aged >=18 years with high blood pressure,%,Crude prevalence,74.4000000000,,,73.9000000000,74.9000000000,4025,POINT (-155.906965 19.51804981),15001021402,PREVENT,BPMED,CrdPrv,Taking BP Medication +2019,HI,Hawaii,Hawaii,15001,15001021101,BRFSS,Health Outcomes,Stroke among adults aged >=18 years,%,Crude prevalence,4.2000000000,,,3.9000000000,4.5000000000,3531,POINT (-154.8953489 19.44949565),15001021101,HLTHOUT,STROKE,CrdPrv,Stroke +2018,HI,Hawaii,Hawaii,15001,15001021402,BRFSS,Prevention,"Older adult women aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening, and Mammogram past 2 years",%,Crude prevalence,24.8000000000,,,22.8000000000,26.9000000000,4025,POINT (-155.906965 19.51804981),15001021402,PREVENT,COREW,CrdPrv,Core preventive services for older women +2019,HI,Hawaii,Hawaii,15001,15001021402,BRFSS,Prevention,Visits to doctor for routine checkup within the past year among adults aged >=18 years,%,Crude prevalence,75.9000000000,,,75.6000000000,76.3000000000,4025,POINT (-155.906965 19.51804981),15001021402,PREVENT,CHECKUP,CrdPrv,Annual Checkup +2019,HI,Hawaii,Hawaii,15001,15001021800,BRFSS,Health Status,Fair or poor self-rated health status among adults aged >=18 years,%,Crude prevalence,18.3000000000,,,17.6000000000,19.0000000000,6322,POINT (-155.8112721 20.16059783),15001021800,HLTHSTAT,GHLTH,CrdPrv,General Health +2019,HI,Hawaii,Hawaii,15001,15001021800,BRFSS,Prevention,Visits to doctor for routine checkup within the past year among adults aged >=18 years,%,Crude prevalence,75.4000000000,,,75.2000000000,75.7000000000,6322,POINT (-155.8112721 20.16059783),15001021800,PREVENT,CHECKUP,CrdPrv,Annual Checkup +2019,HI,Hawaii,Hawaii,15001,15001021800,BRFSS,Health Risk Behaviors,Binge drinking among adults aged >=18 years,%,Crude prevalence,19.2000000000,,,19.0000000000,19.4000000000,6322,POINT (-155.8112721 20.16059783),15001021800,RISKBEH,BINGE,CrdPrv,Binge Drinking +2019,HI,Hawaii,Hawaii,15001,15001021402,BRFSS,Health Status,Mental health not good for >=14 days among adults aged >=18 years,%,Crude prevalence,13.2000000000,,,12.7000000000,13.6000000000,4025,POINT (-155.906965 19.51804981),15001021402,HLTHSTAT,MHLTH,CrdPrv,Mental Health +2019,HI,Hawaii,Hawaii,15001,15001021402,BRFSS,Health Outcomes,Coronary heart disease among adults aged >=18 years,%,Crude prevalence,6.0000000000,,,5.8000000000,6.3000000000,4025,POINT (-155.906965 19.51804981),15001021402,HLTHOUT,CHD,CrdPrv,Coronary Heart Disease +2019,HI,Hawaii,Hawaii,15001,15001021800,BRFSS,Health Risk Behaviors,No leisure-time physical activity among adults aged >=18 years,%,Crude prevalence,24.0000000000,,,23.3000000000,24.8000000000,6322,POINT (-155.8112721 20.16059783),15001021800,RISKBEH,LPA,CrdPrv,Physical Inactivity +2019,HI,Hawaii,Hawaii,15001,15001021010,BRFSS,Health Outcomes,Diagnosed diabetes among adults aged >=18 years,%,Crude prevalence,11.2000000000,,,10.9000000000,11.4000000000,7884,POINT (-155.1037996 19.49754656),15001021010,HLTHOUT,DIABETES,CrdPrv,Diabetes +2019,HI,Hawaii,Hawaii,15001,15001021010,BRFSS,Health Outcomes,High blood pressure among adults aged >=18 years,%,Crude prevalence,30.6000000000,,,30.2000000000,31.0000000000,7884,POINT (-155.1037996 19.49754656),15001021010,HLTHOUT,BPHIGH,CrdPrv,High Blood Pressure +2019,HI,Hawaii,Hawaii,15001,15001021800,BRFSS,Health Outcomes,Cancer (excluding skin cancer) among adults aged >=18 years,%,Crude prevalence,6.7000000000,,,6.6000000000,6.8000000000,6322,POINT (-155.8112721 20.16059783),15001021800,HLTHOUT,CANCER,CrdPrv,Cancer (except skin) +2019,HI,Hawaii,Hawaii,15001,15001021101,BRFSS,Health Risk Behaviors,Current smoking among adults aged >=18 years,%,Crude prevalence,19.3000000000,,,17.2000000000,21.4000000000,3531,POINT (-154.8953489 19.44949565),15001021101,RISKBEH,CSMOKING,CrdPrv,Current Smoking +2018,HI,Hawaii,Hawaii,15001,15001021402,BRFSS,Health Risk Behaviors,Sleeping less than 7 hours among adults aged >=18 years,%,Crude prevalence,40.2000000000,,,39.3000000000,41.0000000000,4025,POINT (-155.906965 19.51804981),15001021402,RISKBEH,SLEEP,CrdPrv,Sleep <7 hours +2019,HI,Hawaii,Hawaii,15001,15001021101,BRFSS,Prevention,Current lack of health insurance among adults aged 18-64 years,%,Crude prevalence,10.2000000000,,,9.1000000000,11.4000000000,3531,POINT (-154.8953489 19.44949565),15001021101,PREVENT,ACCESS2,CrdPrv,Health Insurance +2019,HI,Hawaii,Hawaii,15001,15001021010,BRFSS,Health Status,Physical health not good for >=14 days among adults aged >=18 years,%,Crude prevalence,16.7000000000,,,16.2000000000,17.2000000000,7884,POINT (-155.1037996 19.49754656),15001021010,HLTHSTAT,PHLTH,CrdPrv,Physical Health +2018,HI,Hawaii,Hawaii,15001,15001021800,BRFSS,Prevention,"Fecal occult blood test, sigmoidoscopy, or colonoscopy among adults aged 50-75 years",%,Crude prevalence,66.0000000000,,,65.0000000000,67.0000000000,6322,POINT (-155.8112721 20.16059783),15001021800,PREVENT,COLON_SCREEN,CrdPrv,Colorectal Cancer Screening +2019,HI,Hawaii,Hawaii,15001,15001021402,BRFSS,Health Outcomes,Chronic obstructive pulmonary disease among adults aged >=18 years,%,Crude prevalence,5.2000000000,,,4.9000000000,5.5000000000,4025,POINT (-155.906965 19.51804981),15001021402,HLTHOUT,COPD,CrdPrv,COPD +2018,HI,Hawaii,Hawaii,15001,15001021800,BRFSS,Prevention,Visits to dentist or dental clinic among adults aged >=18 years,%,Crude prevalence,67.4000000000,,,66.4000000000,68.4000000000,6322,POINT (-155.8112721 20.16059783),15001021800,PREVENT,DENTAL,CrdPrv,Dental Visit +2019,HI,Hawaii,Hawaii,15001,15001021101,BRFSS,Health Outcomes,Cancer (excluding skin cancer) among adults aged >=18 years,%,Crude prevalence,6.8000000000,,,6.6000000000,7.0000000000,3531,POINT (-154.8953489 19.44949565),15001021101,HLTHOUT,CANCER,CrdPrv,Cancer (except skin) +2019,HI,Hawaii,Hawaii,15001,15001021402,BRFSS,Health Outcomes,Arthritis among adults aged >=18 years,%,Crude prevalence,21.1000000000,,,20.6000000000,21.5000000000,4025,POINT (-155.906965 19.51804981),15001021402,HLTHOUT,ARTHRITIS,CrdPrv,Arthritis +2018,HI,Hawaii,Hawaii,15001,15001021010,BRFSS,Prevention,Cervical cancer screening among adult women aged 21-65 years,%,Crude prevalence,83.0000000000,,,82.5000000000,83.7000000000,7884,POINT (-155.1037996 19.49754656),15001021010,PREVENT,CERVICAL,CrdPrv,Cervical Cancer Screening +2019,HI,Hawaii,Hawaii,15001,15001021800,BRFSS,Health Outcomes,Arthritis among adults aged >=18 years,%,Crude prevalence,22.6000000000,,,22.2000000000,22.9000000000,6322,POINT (-155.8112721 20.16059783),15001021800,HLTHOUT,ARTHRITIS,CrdPrv,Arthritis +2018,HI,Hawaii,Hawaii,15001,15001021402,BRFSS,Prevention,Mammography use among women aged 50-74 years,%,Crude prevalence,75.9000000000,,,74.6000000000,77.2000000000,4025,POINT (-155.906965 19.51804981),15001021402,PREVENT,MAMMOUSE,CrdPrv,Mammography +2018,HI,Hawaii,Hawaii,15001,15001021402,BRFSS,Prevention,Visits to dentist or dental clinic among adults aged >=18 years,%,Crude prevalence,68.3000000000,,,67.0000000000,69.7000000000,4025,POINT (-155.906965 19.51804981),15001021402,PREVENT,DENTAL,CrdPrv,Dental Visit +2019,HI,Hawaii,Hawaii,15001,15001021101,BRFSS,Health Outcomes,High cholesterol among adults aged >=18 years who have been screened in the past 5 years,%,Crude prevalence,30.8000000000,,,30.2000000000,31.4000000000,3531,POINT (-154.8953489 19.44949565),15001021101,HLTHOUT,HIGHCHOL,CrdPrv,High Cholesterol +2019,HI,Hawaii,Hawaii,15001,15001021101,BRFSS,Prevention,Taking medicine for high blood pressure control among adults aged >=18 years with high blood pressure,%,Crude prevalence,74.6000000000,,,73.7000000000,75.4000000000,3531,POINT (-154.8953489 19.44949565),15001021101,PREVENT,BPMED,CrdPrv,Taking BP Medication +2019,HI,Hawaii,Hawaii,15001,15001021402,BRFSS,Health Outcomes,Cancer (excluding skin cancer) among adults aged >=18 years,%,Crude prevalence,6.3000000000,,,6.2000000000,6.4000000000,4025,POINT (-155.906965 19.51804981),15001021402,HLTHOUT,CANCER,CrdPrv,Cancer (except skin) +2019,HI,Hawaii,Hawaii,15001,15001021101,BRFSS,Health Outcomes,Obesity among adults aged >=18 years,%,Crude prevalence,27.9000000000,,,27.0000000000,28.8000000000,3531,POINT (-154.8953489 19.44949565),15001021101,HLTHOUT,OBESITY,CrdPrv,Obesity +2019,HI,Hawaii,Hawaii,15001,15001021402,BRFSS,Health Outcomes,High cholesterol among adults aged >=18 years who have been screened in the past 5 years,%,Crude prevalence,29.8000000000,,,29.4000000000,30.2000000000,4025,POINT (-155.906965 19.51804981),15001021402,HLTHOUT,HIGHCHOL,CrdPrv,High Cholesterol +2018,HI,Hawaii,Hawaii,15001,15001021010,BRFSS,Prevention,Mammography use among women aged 50-74 years,%,Crude prevalence,76.2000000000,,,75.1000000000,77.3000000000,7884,POINT (-155.1037996 19.49754656),15001021010,PREVENT,MAMMOUSE,CrdPrv,Mammography +2019,HI,Hawaii,Hawaii,15001,15001021800,BRFSS,Health Outcomes,Stroke among adults aged >=18 years,%,Crude prevalence,3.7000000000,,,3.6000000000,3.8000000000,6322,POINT (-155.8112721 20.16059783),15001021800,HLTHOUT,STROKE,CrdPrv,Stroke +2019,HI,Hawaii,Hawaii,15001,15001021101,BRFSS,Health Outcomes,Coronary heart disease among adults aged >=18 years,%,Crude prevalence,7.7000000000,,,7.2000000000,8.3000000000,3531,POINT (-154.8953489 19.44949565),15001021101,HLTHOUT,CHD,CrdPrv,Coronary Heart Disease +2018,HI,Hawaii,Hawaii,15001,15001021010,BRFSS,Prevention,"Older adult men aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening",%,Crude prevalence,18.5000000000,,,17.0000000000,20.1000000000,7884,POINT (-155.1037996 19.49754656),15001021010,PREVENT,COREM,CrdPrv,Core preventive services for older men +2019,HI,Hawaii,Hawaii,15001,15001021800,BRFSS,Health Risk Behaviors,Current smoking among adults aged >=18 years,%,Crude prevalence,16.8000000000,,,16.1000000000,17.6000000000,6322,POINT (-155.8112721 20.16059783),15001021800,RISKBEH,CSMOKING,CrdPrv,Current Smoking +2019,HI,Hawaii,Hawaii,15001,15001021010,BRFSS,Health Outcomes,Coronary heart disease among adults aged >=18 years,%,Crude prevalence,6.9000000000,,,6.7000000000,7.1000000000,7884,POINT (-155.1037996 19.49754656),15001021010,HLTHOUT,CHD,CrdPrv,Coronary Heart Disease +2018,HI,Hawaii,Hawaii,15001,15001021101,BRFSS,Health Outcomes,All teeth lost among adults aged >=65 years,%,Crude prevalence,11.9000000000,,,8.9000000000,15.0000000000,3531,POINT (-154.8953489 19.44949565),15001021101,HLTHOUT,TEETHLOST,CrdPrv,All Teeth Lost +2018,HI,Hawaii,Hawaii,15001,15001021101,BRFSS,Prevention,Mammography use among women aged 50-74 years,%,Crude prevalence,77.6000000000,,,75.5000000000,79.5000000000,3531,POINT (-154.8953489 19.44949565),15001021101,PREVENT,MAMMOUSE,CrdPrv,Mammography +2019,HI,Hawaii,Hawaii,15001,15001021101,BRFSS,Health Status,Fair or poor self-rated health status among adults aged >=18 years,%,Crude prevalence,21.0000000000,,,19.1000000000,23.0000000000,3531,POINT (-154.8953489 19.44949565),15001021101,HLTHSTAT,GHLTH,CrdPrv,General Health +2019,HI,Hawaii,Hawaii,15001,15001021402,BRFSS,Health Risk Behaviors,Current smoking among adults aged >=18 years,%,Crude prevalence,16.1000000000,,,15.1000000000,17.0000000000,4025,POINT (-155.906965 19.51804981),15001021402,RISKBEH,CSMOKING,CrdPrv,Current Smoking +2019,HI,Hawaii,Hawaii,15001,15001021010,BRFSS,Health Status,Fair or poor self-rated health status among adults aged >=18 years,%,Crude prevalence,22.9000000000,,,22.1000000000,23.7000000000,7884,POINT (-155.1037996 19.49754656),15001021010,HLTHSTAT,GHLTH,CrdPrv,General Health +2019,HI,Hawaii,Hawaii,15001,15001021101,BRFSS,Health Outcomes,Chronic obstructive pulmonary disease among adults aged >=18 years,%,Crude prevalence,7.3000000000,,,6.5000000000,8.2000000000,3531,POINT (-154.8953489 19.44949565),15001021101,HLTHOUT,COPD,CrdPrv,COPD +2019,HI,Hawaii,Maui,15009,15009030201,BRFSS,Health Status,Fair or poor self-rated health status among adults aged >=18 years,%,Crude prevalence,15.7000000000,,,14.4000000000,17.0000000000,2453,POINT (-156.2504199 20.86252093),15009030201,HLTHSTAT,GHLTH,CrdPrv,General Health +2019,HI,Hawaii,Maui,15009,15009030800,BRFSS,Health Outcomes,Stroke among adults aged >=18 years,%,Crude prevalence,2.8000000000,,,2.7000000000,2.9000000000,6907,POINT (-156.5426669 20.90996813),15009030800,HLTHOUT,STROKE,CrdPrv,Stroke +2019,HI,Hawaii,Kauai,15007,15007040604,BRFSS,Health Outcomes,Cancer (excluding skin cancer) among adults aged >=18 years,%,Crude prevalence,6.7000000000,,,6.6000000000,6.9000000000,3139,POINT (-159.4840794 21.90956079),15007040604,HLTHOUT,CANCER,CrdPrv,Cancer (except skin) +2018,HI,Hawaii,Honolulu,15003,15003010201,BRFSS,Prevention,"Older adult women aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening, and Mammogram past 2 years",%,Crude prevalence,27.0000000000,,,25.1000000000,28.9000000000,5882,POINT (-157.8941068 21.55452063),15003010201,PREVENT,COREW,CrdPrv,Core preventive services for older women +2019,HI,Hawaii,Kauai,15007,15007040700,BRFSS,Health Outcomes,Diagnosed diabetes among adults aged >=18 years,%,Crude prevalence,10.7000000000,,,10.3000000000,11.1000000000,8403,POINT (-159.5219447 21.94546074),15007040700,HLTHOUT,DIABETES,CrdPrv,Diabetes +2019,HI,Hawaii,Maui,15009,15009030100,BRFSS,Health Outcomes,Depression among adults aged >=18 years,%,Crude prevalence,17.2000000000,,,16.7000000000,17.6000000000,2291,POINT (-156.1446943 20.72704536),15009030100,HLTHOUT,DEPRESSION,CrdPrv,Depression +2019,HI,Hawaii,Maui,15009,15009030800,BRFSS,Health Outcomes,Current asthma among adults aged >=18 years,%,Crude prevalence,9.1000000000,,,8.9000000000,9.3000000000,6907,POINT (-156.5426669 20.90996813),15009030800,HLTHOUT,CASTHMA,CrdPrv,Current Asthma +2019,HI,Hawaii,Maui,15009,15009030402,BRFSS,Health Outcomes,Arthritis among adults aged >=18 years,%,Crude prevalence,20.4000000000,,,19.8000000000,21.0000000000,8652,POINT (-156.3303372 20.82505697),15009030402,HLTHOUT,ARTHRITIS,CrdPrv,Arthritis +2019,HI,Hawaii,Honolulu,15003,15003010201,BRFSS,Health Outcomes,Arthritis among adults aged >=18 years,%,Crude prevalence,22.7000000000,,,22.3000000000,23.1000000000,5882,POINT (-157.8941068 21.55452063),15003010201,HLTHOUT,ARTHRITIS,CrdPrv,Arthritis +2018,HI,Hawaii,Maui,15009,15009030800,BRFSS,Prevention,"Fecal occult blood test, sigmoidoscopy, or colonoscopy among adults aged 50-75 years",%,Crude prevalence,61.8000000000,,,60.6000000000,63.0000000000,6907,POINT (-156.5426669 20.90996813),15009030800,PREVENT,COLON_SCREEN,CrdPrv,Colorectal Cancer Screening +2019,HI,Hawaii,Kauai,15007,15007040604,BRFSS,Prevention,Current lack of health insurance among adults aged 18-64 years,%,Crude prevalence,11.7000000000,,,11.0000000000,12.4000000000,3139,POINT (-159.4840794 21.90956079),15007040604,PREVENT,ACCESS2,CrdPrv,Health Insurance +2019,HI,Hawaii,Honolulu,15003,15003010201,BRFSS,Health Outcomes,Stroke among adults aged >=18 years,%,Crude prevalence,3.5000000000,,,3.3000000000,3.6000000000,5882,POINT (-157.8941068 21.55452063),15003010201,HLTHOUT,STROKE,CrdPrv,Stroke +2018,HI,Hawaii,Kauai,15007,15007040700,BRFSS,Prevention,Cervical cancer screening among adult women aged 21-65 years,%,Crude prevalence,84.8000000000,,,83.9000000000,85.7000000000,8403,POINT (-159.5219447 21.94546074),15007040700,PREVENT,CERVICAL,CrdPrv,Cervical Cancer Screening +2019,HI,Hawaii,Maui,15009,15009030100,BRFSS,Health Risk Behaviors,Binge drinking among adults aged >=18 years,%,Crude prevalence,23.0000000000,,,22.7000000000,23.4000000000,2291,POINT (-156.1446943 20.72704536),15009030100,RISKBEH,BINGE,CrdPrv,Binge Drinking +2019,HI,Hawaii,Kauai,15007,15007040603,BRFSS,Health Outcomes,Obesity among adults aged >=18 years,%,Crude prevalence,24.3000000000,,,23.7000000000,25.0000000000,2544,POINT (-159.4384998 21.90703588),15007040603,HLTHOUT,OBESITY,CrdPrv,Obesity +2018,HI,Hawaii,Kauai,15007,15007040604,BRFSS,Prevention,Visits to dentist or dental clinic among adults aged >=18 years,%,Crude prevalence,65.5000000000,,,64.3000000000,66.8000000000,3139,POINT (-159.4840794 21.90956079),15007040604,PREVENT,DENTAL,CrdPrv,Dental Visit +2018,HI,Hawaii,Maui,15009,15009030402,BRFSS,Health Risk Behaviors,Sleeping less than 7 hours among adults aged >=18 years,%,Crude prevalence,38.7000000000,,,38.0000000000,39.4000000000,8652,POINT (-156.3303372 20.82505697),15009030402,RISKBEH,SLEEP,CrdPrv,Sleep <7 hours +2019,HI,Hawaii,Kauai,15007,15007040700,BRFSS,Health Outcomes,Arthritis among adults aged >=18 years,%,Crude prevalence,22.6000000000,,,22.0000000000,23.1000000000,8403,POINT (-159.5219447 21.94546074),15007040700,HLTHOUT,ARTHRITIS,CrdPrv,Arthritis +2019,HI,Hawaii,Maui,15009,15009030402,BRFSS,Health Risk Behaviors,No leisure-time physical activity among adults aged >=18 years,%,Crude prevalence,22.7000000000,,,21.4000000000,24.2000000000,8652,POINT (-156.3303372 20.82505697),15009030402,RISKBEH,LPA,CrdPrv,Physical Inactivity +2019,HI,Hawaii,Kauai,15007,15007040604,BRFSS,Health Outcomes,High cholesterol among adults aged >=18 years who have been screened in the past 5 years,%,Crude prevalence,29.3000000000,,,28.9000000000,29.7000000000,3139,POINT (-159.4840794 21.90956079),15007040604,HLTHOUT,HIGHCHOL,CrdPrv,High Cholesterol +2019,HI,Hawaii,Maui,15009,15009030201,BRFSS,Health Outcomes,Obesity among adults aged >=18 years,%,Crude prevalence,27.5000000000,,,26.6000000000,28.4000000000,2453,POINT (-156.2504199 20.86252093),15009030201,HLTHOUT,OBESITY,CrdPrv,Obesity +2019,HI,Hawaii,Maui,15009,15009030201,BRFSS,Prevention,Visits to doctor for routine checkup within the past year among adults aged >=18 years,%,Crude prevalence,73.0000000000,,,72.4000000000,73.6000000000,2453,POINT (-156.2504199 20.86252093),15009030201,PREVENT,CHECKUP,CrdPrv,Annual Checkup +2019,HI,Hawaii,Maui,15009,15009030100,BRFSS,Health Status,Mental health not good for >=14 days among adults aged >=18 years,%,Crude prevalence,14.1000000000,,,13.6000000000,14.7000000000,2291,POINT (-156.1446943 20.72704536),15009030100,HLTHSTAT,MHLTH,CrdPrv,Mental Health +2019,HI,Hawaii,Kauai,15007,15007040700,BRFSS,Health Risk Behaviors,No leisure-time physical activity among adults aged >=18 years,%,Crude prevalence,24.3000000000,,,23.2000000000,25.5000000000,8403,POINT (-159.5219447 21.94546074),15007040700,RISKBEH,LPA,CrdPrv,Physical Inactivity +2018,HI,Hawaii,Maui,15009,15009030402,BRFSS,Prevention,Visits to dentist or dental clinic among adults aged >=18 years,%,Crude prevalence,71.5000000000,,,69.7000000000,73.4000000000,8652,POINT (-156.3303372 20.82505697),15009030402,PREVENT,DENTAL,CrdPrv,Dental Visit +2019,HI,Hawaii,Kauai,15007,15007040603,BRFSS,Health Risk Behaviors,Current smoking among adults aged >=18 years,%,Crude prevalence,14.9000000000,,,13.7000000000,16.2000000000,2544,POINT (-159.4384998 21.90703588),15007040603,RISKBEH,CSMOKING,CrdPrv,Current Smoking +2019,HI,Hawaii,Kauai,15007,15007040700,BRFSS,Health Outcomes,Cancer (excluding skin cancer) among adults aged >=18 years,%,Crude prevalence,6.3000000000,,,6.1000000000,6.4000000000,8403,POINT (-159.5219447 21.94546074),15007040700,HLTHOUT,CANCER,CrdPrv,Cancer (except skin) +2019,HI,Hawaii,Maui,15009,15009030201,BRFSS,Prevention,Taking medicine for high blood pressure control among adults aged >=18 years with high blood pressure,%,Crude prevalence,69.2000000000,,,68.1000000000,70.1000000000,2453,POINT (-156.2504199 20.86252093),15009030201,PREVENT,BPMED,CrdPrv,Taking BP Medication +2018,HI,Hawaii,Maui,15009,15009030100,BRFSS,Health Outcomes,All teeth lost among adults aged >=65 years,%,Crude prevalence,11.2000000000,,,9.4000000000,13.1000000000,2291,POINT (-156.1446943 20.72704536),15009030100,HLTHOUT,TEETHLOST,CrdPrv,All Teeth Lost +2019,HI,Hawaii,Kauai,15007,15007040603,BRFSS,Prevention,Cholesterol screening among adults aged >=18 years,%,Crude prevalence,83.6000000000,,,83.3000000000,83.8000000000,2544,POINT (-159.4384998 21.90703588),15007040603,PREVENT,CHOLSCREEN,CrdPrv,Cholesterol Screening +2018,HI,Hawaii,Maui,15009,15009030800,BRFSS,Prevention,Visits to dentist or dental clinic among adults aged >=18 years,%,Crude prevalence,70.2000000000,,,69.0000000000,71.4000000000,6907,POINT (-156.5426669 20.90996813),15009030800,PREVENT,DENTAL,CrdPrv,Dental Visit +2019,HI,Hawaii,Maui,15009,15009030800,BRFSS,Health Outcomes,Arthritis among adults aged >=18 years,%,Crude prevalence,18.6000000000,,,18.2000000000,18.9000000000,6907,POINT (-156.5426669 20.90996813),15009030800,HLTHOUT,ARTHRITIS,CrdPrv,Arthritis +2018,HI,Hawaii,Maui,15009,15009030100,BRFSS,Prevention,Cervical cancer screening among adult women aged 21-65 years,%,Crude prevalence,85.6000000000,,,84.8000000000,86.6000000000,2291,POINT (-156.1446943 20.72704536),15009030100,PREVENT,CERVICAL,CrdPrv,Cervical Cancer Screening +2019,HI,Hawaii,Maui,15009,15009030100,BRFSS,Health Outcomes,Chronic obstructive pulmonary disease among adults aged >=18 years,%,Crude prevalence,6.1000000000,,,5.7000000000,6.6000000000,2291,POINT (-156.1446943 20.72704536),15009030100,HLTHOUT,COPD,CrdPrv,COPD +2019,HI,Hawaii,Kauai,15007,15007040604,BRFSS,Health Outcomes,High blood pressure among adults aged >=18 years,%,Crude prevalence,31.0000000000,,,30.5000000000,31.5000000000,3139,POINT (-159.4840794 21.90956079),15007040604,HLTHOUT,BPHIGH,CrdPrv,High Blood Pressure +2019,HI,Hawaii,Maui,15009,15009030402,BRFSS,Health Status,Physical health not good for >=14 days among adults aged >=18 years,%,Crude prevalence,11.0000000000,,,10.4000000000,11.6000000000,8652,POINT (-156.3303372 20.82505697),15009030402,HLTHSTAT,PHLTH,CrdPrv,Physical Health +2019,HI,Hawaii,Kauai,15007,15007040604,BRFSS,Health Status,Mental health not good for >=14 days among adults aged >=18 years,%,Crude prevalence,13.9000000000,,,13.4000000000,14.3000000000,3139,POINT (-159.4840794 21.90956079),15007040604,HLTHSTAT,MHLTH,CrdPrv,Mental Health +2018,HI,Hawaii,Maui,15009,15009030201,BRFSS,Prevention,"Older adult women aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening, and Mammogram past 2 years",%,Crude prevalence,25.3000000000,,,21.7000000000,29.2000000000,2453,POINT (-156.2504199 20.86252093),15009030201,PREVENT,COREW,CrdPrv,Core preventive services for older women +2018,HI,Hawaii,Honolulu,15003,15003010201,BRFSS,Prevention,Mammography use among women aged 50-74 years,%,Crude prevalence,82.1000000000,,,81.0000000000,83.1000000000,5882,POINT (-157.8941068 21.55452063),15003010201,PREVENT,MAMMOUSE,CrdPrv,Mammography +2019,HI,Hawaii,Kauai,15007,15007040700,BRFSS,Health Status,Fair or poor self-rated health status among adults aged >=18 years,%,Crude prevalence,15.7000000000,,,14.8000000000,16.7000000000,8403,POINT (-159.5219447 21.94546074),15007040700,HLTHSTAT,GHLTH,CrdPrv,General Health +2019,HI,Hawaii,Kauai,15007,15007040700,BRFSS,Health Status,Physical health not good for >=14 days among adults aged >=18 years,%,Crude prevalence,10.8000000000,,,10.3000000000,11.3000000000,8403,POINT (-159.5219447 21.94546074),15007040700,HLTHSTAT,PHLTH,CrdPrv,Physical Health +2019,HI,Hawaii,Kauai,15007,15007040603,BRFSS,Health Outcomes,High blood pressure among adults aged >=18 years,%,Crude prevalence,31.1000000000,,,30.3000000000,31.8000000000,2544,POINT (-159.4384998 21.90703588),15007040603,HLTHOUT,BPHIGH,CrdPrv,High Blood Pressure +2019,HI,Hawaii,Honolulu,15003,15003010201,BRFSS,Health Outcomes,Current asthma among adults aged >=18 years,%,Crude prevalence,12.2000000000,,,12.0000000000,12.5000000000,5882,POINT (-157.8941068 21.55452063),15003010201,HLTHOUT,CASTHMA,CrdPrv,Current Asthma +2019,HI,Hawaii,Kauai,15007,15007040603,BRFSS,Health Risk Behaviors,No leisure-time physical activity among adults aged >=18 years,%,Crude prevalence,27.0000000000,,,25.3000000000,28.5000000000,2544,POINT (-159.4384998 21.90703588),15007040603,RISKBEH,LPA,CrdPrv,Physical Inactivity +2019,HI,Hawaii,Maui,15009,15009030100,BRFSS,Health Outcomes,Obesity among adults aged >=18 years,%,Crude prevalence,29.2000000000,,,28.6000000000,29.8000000000,2291,POINT (-156.1446943 20.72704536),15009030100,HLTHOUT,OBESITY,CrdPrv,Obesity +2018,HI,Hawaii,Maui,15009,15009030402,BRFSS,Prevention,Cervical cancer screening among adult women aged 21-65 years,%,Crude prevalence,85.0000000000,,,83.8000000000,86.2000000000,8652,POINT (-156.3303372 20.82505697),15009030402,PREVENT,CERVICAL,CrdPrv,Cervical Cancer Screening +2019,HI,Hawaii,Maui,15009,15009030402,BRFSS,Health Outcomes,Chronic obstructive pulmonary disease among adults aged >=18 years,%,Crude prevalence,4.6000000000,,,4.3000000000,5.0000000000,8652,POINT (-156.3303372 20.82505697),15009030402,HLTHOUT,COPD,CrdPrv,COPD +2019,HI,Hawaii,Kauai,15007,15007040604,BRFSS,Prevention,Visits to doctor for routine checkup within the past year among adults aged >=18 years,%,Crude prevalence,77.8000000000,,,77.5000000000,78.1000000000,3139,POINT (-159.4840794 21.90956079),15007040604,PREVENT,CHECKUP,CrdPrv,Annual Checkup +2018,HI,Hawaii,Kauai,15007,15007040700,BRFSS,Health Outcomes,All teeth lost among adults aged >=65 years,%,Crude prevalence,6.2000000000,,,5.1000000000,7.5000000000,8403,POINT (-159.5219447 21.94546074),15007040700,HLTHOUT,TEETHLOST,CrdPrv,All Teeth Lost +2019,HI,Hawaii,Maui,15009,15009030201,BRFSS,Health Outcomes,Diagnosed diabetes among adults aged >=18 years,%,Crude prevalence,8.9000000000,,,8.5000000000,9.4000000000,2453,POINT (-156.2504199 20.86252093),15009030201,HLTHOUT,DIABETES,CrdPrv,Diabetes +2018,HI,Hawaii,Kauai,15007,15007040700,BRFSS,Prevention,"Older adult women aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening, and Mammogram past 2 years",%,Crude prevalence,25.6000000000,,,23.3000000000,27.9000000000,8403,POINT (-159.5219447 21.94546074),15007040700,PREVENT,COREW,CrdPrv,Core preventive services for older women +2019,HI,Hawaii,Maui,15009,15009030800,BRFSS,Health Status,Mental health not good for >=14 days among adults aged >=18 years,%,Crude prevalence,12.9000000000,,,12.5000000000,13.4000000000,6907,POINT (-156.5426669 20.90996813),15009030800,HLTHSTAT,MHLTH,CrdPrv,Mental Health +2018,HI,Hawaii,Maui,15009,15009030100,BRFSS,Prevention,Visits to dentist or dental clinic among adults aged >=18 years,%,Crude prevalence,67.9000000000,,,66.6000000000,69.3000000000,2291,POINT (-156.1446943 20.72704536),15009030100,PREVENT,DENTAL,CrdPrv,Dental Visit +2019,HI,Hawaii,Kauai,15007,15007040604,BRFSS,Health Outcomes,Chronic obstructive pulmonary disease among adults aged >=18 years,%,Crude prevalence,5.8000000000,,,5.5000000000,6.2000000000,3139,POINT (-159.4840794 21.90956079),15007040604,HLTHOUT,COPD,CrdPrv,COPD +2019,HI,Hawaii,Honolulu,15003,15003010201,BRFSS,Health Status,Fair or poor self-rated health status among adults aged >=18 years,%,Crude prevalence,20.0000000000,,,19.0000000000,21.0000000000,5882,POINT (-157.8941068 21.55452063),15003010201,HLTHSTAT,GHLTH,CrdPrv,General Health +2018,HI,Hawaii,Kauai,15007,15007040603,BRFSS,Prevention,Cervical cancer screening among adult women aged 21-65 years,%,Crude prevalence,82.4000000000,,,80.9000000000,84.0000000000,2544,POINT (-159.4384998 21.90703588),15007040603,PREVENT,CERVICAL,CrdPrv,Cervical Cancer Screening +2018,HI,Hawaii,Maui,15009,15009030800,BRFSS,Prevention,Cervical cancer screening among adult women aged 21-65 years,%,Crude prevalence,84.2000000000,,,83.4000000000,85.1000000000,6907,POINT (-156.5426669 20.90996813),15009030800,PREVENT,CERVICAL,CrdPrv,Cervical Cancer Screening +2019,HI,Hawaii,Kauai,15007,15007040603,BRFSS,Health Outcomes,Arthritis among adults aged >=18 years,%,Crude prevalence,25.3000000000,,,24.6000000000,26.0000000000,2544,POINT (-159.4384998 21.90703588),15007040603,HLTHOUT,ARTHRITIS,CrdPrv,Arthritis +2018,HI,Hawaii,Kauai,15007,15007040700,BRFSS,Prevention,"Fecal occult blood test, sigmoidoscopy, or colonoscopy among adults aged 50-75 years",%,Crude prevalence,69.0000000000,,,67.5000000000,70.4000000000,8403,POINT (-159.5219447 21.94546074),15007040700,PREVENT,COLON_SCREEN,CrdPrv,Colorectal Cancer Screening +2019,HI,Hawaii,Maui,15009,15009030100,BRFSS,Health Outcomes,Current asthma among adults aged >=18 years,%,Crude prevalence,10.2000000000,,,10.0000000000,10.5000000000,2291,POINT (-156.1446943 20.72704536),15009030100,HLTHOUT,CASTHMA,CrdPrv,Current Asthma +2019,HI,Hawaii,Kauai,15007,15007040603,BRFSS,Health Outcomes,Diagnosed diabetes among adults aged >=18 years,%,Crude prevalence,12.2000000000,,,11.5000000000,12.8000000000,2544,POINT (-159.4384998 21.90703588),15007040603,HLTHOUT,DIABETES,CrdPrv,Diabetes +2019,HI,Hawaii,Honolulu,15003,15003010201,BRFSS,Health Risk Behaviors,Binge drinking among adults aged >=18 years,%,Crude prevalence,21.3000000000,,,21.0000000000,21.6000000000,5882,POINT (-157.8941068 21.55452063),15003010201,RISKBEH,BINGE,CrdPrv,Binge Drinking +2018,HI,Hawaii,Kauai,15007,15007040604,BRFSS,Health Risk Behaviors,Sleeping less than 7 hours among adults aged >=18 years,%,Crude prevalence,37.5000000000,,,36.9000000000,38.1000000000,3139,POINT (-159.4840794 21.90956079),15007040604,RISKBEH,SLEEP,CrdPrv,Sleep <7 hours +2018,HI,Hawaii,Honolulu,15003,15003010201,BRFSS,Health Outcomes,All teeth lost among adults aged >=65 years,%,Crude prevalence,10.1000000000,,,8.7000000000,11.6000000000,5882,POINT (-157.8941068 21.55452063),15003010201,HLTHOUT,TEETHLOST,CrdPrv,All Teeth Lost +2018,HI,Hawaii,Kauai,15007,15007040603,BRFSS,Health Outcomes,All teeth lost among adults aged >=65 years,%,Crude prevalence,8.1000000000,,,6.2000000000,10.2000000000,2544,POINT (-159.4384998 21.90703588),15007040603,HLTHOUT,TEETHLOST,CrdPrv,All Teeth Lost +2019,HI,Hawaii,Maui,15009,15009030201,BRFSS,Health Outcomes,Cancer (excluding skin cancer) among adults aged >=18 years,%,Crude prevalence,6.5000000000,,,6.3000000000,6.8000000000,2453,POINT (-156.2504199 20.86252093),15009030201,HLTHOUT,CANCER,CrdPrv,Cancer (except skin) +2019,HI,Hawaii,Kauai,15007,15007040700,BRFSS,Health Risk Behaviors,Binge drinking among adults aged >=18 years,%,Crude prevalence,19.2000000000,,,18.9000000000,19.6000000000,8403,POINT (-159.5219447 21.94546074),15007040700,RISKBEH,BINGE,CrdPrv,Binge Drinking +2018,HI,Hawaii,Maui,15009,15009030800,BRFSS,Health Risk Behaviors,Sleeping less than 7 hours among adults aged >=18 years,%,Crude prevalence,40.9000000000,,,40.1000000000,41.6000000000,6907,POINT (-156.5426669 20.90996813),15009030800,RISKBEH,SLEEP,CrdPrv,Sleep <7 hours +2019,HI,Hawaii,Maui,15009,15009030402,BRFSS,Health Outcomes,Depression among adults aged >=18 years,%,Crude prevalence,15.5000000000,,,15.0000000000,16.0000000000,8652,POINT (-156.3303372 20.82505697),15009030402,HLTHOUT,DEPRESSION,CrdPrv,Depression +2019,HI,Hawaii,Honolulu,15003,15003010201,BRFSS,Health Risk Behaviors,Current smoking among adults aged >=18 years,%,Crude prevalence,17.5000000000,,,16.4000000000,18.7000000000,5882,POINT (-157.8941068 21.55452063),15003010201,RISKBEH,CSMOKING,CrdPrv,Current Smoking +2019,HI,Hawaii,Maui,15009,15009030800,BRFSS,Health Outcomes,Depression among adults aged >=18 years,%,Crude prevalence,15.2000000000,,,14.9000000000,15.6000000000,6907,POINT (-156.5426669 20.90996813),15009030800,HLTHOUT,DEPRESSION,CrdPrv,Depression +2019,HI,Hawaii,Maui,15009,15009030402,BRFSS,Health Risk Behaviors,Binge drinking among adults aged >=18 years,%,Crude prevalence,21.0000000000,,,20.6000000000,21.4000000000,8652,POINT (-156.3303372 20.82505697),15009030402,RISKBEH,BINGE,CrdPrv,Binge Drinking +2019,HI,Hawaii,Maui,15009,15009030100,BRFSS,Health Outcomes,High cholesterol among adults aged >=18 years who have been screened in the past 5 years,%,Crude prevalence,27.0000000000,,,26.6000000000,27.4000000000,2291,POINT (-156.1446943 20.72704536),15009030100,HLTHOUT,HIGHCHOL,CrdPrv,High Cholesterol +2018,HI,Hawaii,Kauai,15007,15007040603,BRFSS,Prevention,"Fecal occult blood test, sigmoidoscopy, or colonoscopy among adults aged 50-75 years",%,Crude prevalence,67.1000000000,,,65.3000000000,68.9000000000,2544,POINT (-159.4384998 21.90703588),15007040603,PREVENT,COLON_SCREEN,CrdPrv,Colorectal Cancer Screening +2019,HI,Hawaii,Maui,15009,15009030201,BRFSS,Health Outcomes,High cholesterol among adults aged >=18 years who have been screened in the past 5 years,%,Crude prevalence,26.4000000000,,,25.8000000000,26.9000000000,2453,POINT (-156.2504199 20.86252093),15009030201,HLTHOUT,HIGHCHOL,CrdPrv,High Cholesterol +2019,HI,Hawaii,Maui,15009,15009030100,BRFSS,Health Outcomes,High blood pressure among adults aged >=18 years,%,Crude prevalence,28.1000000000,,,27.6000000000,28.7000000000,2291,POINT (-156.1446943 20.72704536),15009030100,HLTHOUT,BPHIGH,CrdPrv,High Blood Pressure +2019,HI,Hawaii,Honolulu,15003,15003010201,BRFSS,Health Outcomes,Cancer (excluding skin cancer) among adults aged >=18 years,%,Crude prevalence,6.4000000000,,,6.3000000000,6.5000000000,5882,POINT (-157.8941068 21.55452063),15003010201,HLTHOUT,CANCER,CrdPrv,Cancer (except skin) +2019,HI,Hawaii,Kauai,15007,15007040604,BRFSS,Health Outcomes,Depression among adults aged >=18 years,%,Crude prevalence,15.9000000000,,,15.6000000000,16.2000000000,3139,POINT (-159.4840794 21.90956079),15007040604,HLTHOUT,DEPRESSION,CrdPrv,Depression +2019,HI,Hawaii,Honolulu,15003,15003010201,BRFSS,Prevention,Cholesterol screening among adults aged >=18 years,%,Crude prevalence,82.9000000000,,,82.7000000000,83.0000000000,5882,POINT (-157.8941068 21.55452063),15003010201,PREVENT,CHOLSCREEN,CrdPrv,Cholesterol Screening +2019,HI,Hawaii,Kauai,15007,15007040700,BRFSS,Health Outcomes,Chronic kidney disease among adults aged >=18 years,%,Crude prevalence,2.7000000000,,,2.6000000000,2.8000000000,8403,POINT (-159.5219447 21.94546074),15007040700,HLTHOUT,KIDNEY,CrdPrv,Chronic Kidney Disease +2019,HI,Hawaii,Maui,15009,15009030800,BRFSS,Health Risk Behaviors,Binge drinking among adults aged >=18 years,%,Crude prevalence,21.8000000000,,,21.5000000000,22.1000000000,6907,POINT (-156.5426669 20.90996813),15009030800,RISKBEH,BINGE,CrdPrv,Binge Drinking +2019,HI,Hawaii,Maui,15009,15009030100,BRFSS,Health Outcomes,Coronary heart disease among adults aged >=18 years,%,Crude prevalence,6.0000000000,,,5.8000000000,6.3000000000,2291,POINT (-156.1446943 20.72704536),15009030100,HLTHOUT,CHD,CrdPrv,Coronary Heart Disease +2019,HI,Hawaii,Maui,15009,15009030402,BRFSS,Health Outcomes,Chronic kidney disease among adults aged >=18 years,%,Crude prevalence,2.5000000000,,,2.5000000000,2.6000000000,8652,POINT (-156.3303372 20.82505697),15009030402,HLTHOUT,KIDNEY,CrdPrv,Chronic Kidney Disease +2019,HI,Hawaii,Maui,15009,15009030201,BRFSS,Prevention,Cholesterol screening among adults aged >=18 years,%,Crude prevalence,82.9000000000,,,82.9000000000,83.0000000000,2453,POINT (-156.2504199 20.86252093),15009030201,PREVENT,CHOLSCREEN,CrdPrv,Cholesterol Screening +2019,HI,Hawaii,Kauai,15007,15007040604,BRFSS,Health Outcomes,Arthritis among adults aged >=18 years,%,Crude prevalence,25.5000000000,,,24.9000000000,25.9000000000,3139,POINT (-159.4840794 21.90956079),15007040604,HLTHOUT,ARTHRITIS,CrdPrv,Arthritis +2019,HI,Hawaii,Maui,15009,15009030100,BRFSS,Health Outcomes,Diagnosed diabetes among adults aged >=18 years,%,Crude prevalence,10.7000000000,,,10.4000000000,11.1000000000,2291,POINT (-156.1446943 20.72704536),15009030100,HLTHOUT,DIABETES,CrdPrv,Diabetes +2018,HI,Hawaii,Kauai,15007,15007040603,BRFSS,Prevention,Visits to dentist or dental clinic among adults aged >=18 years,%,Crude prevalence,68.0000000000,,,66.2000000000,69.9000000000,2544,POINT (-159.4384998 21.90703588),15007040603,PREVENT,DENTAL,CrdPrv,Dental Visit +2019,HI,Hawaii,Kauai,15007,15007040603,BRFSS,Health Status,Physical health not good for >=14 days among adults aged >=18 years,%,Crude prevalence,12.3000000000,,,11.6000000000,13.0000000000,2544,POINT (-159.4384998 21.90703588),15007040603,HLTHSTAT,PHLTH,CrdPrv,Physical Health +2019,HI,Hawaii,Maui,15009,15009030402,BRFSS,Health Outcomes,Current asthma among adults aged >=18 years,%,Crude prevalence,8.9000000000,,,8.7000000000,9.2000000000,8652,POINT (-156.3303372 20.82505697),15009030402,HLTHOUT,CASTHMA,CrdPrv,Current Asthma +2019,HI,Hawaii,Maui,15009,15009030402,BRFSS,Health Outcomes,Stroke among adults aged >=18 years,%,Crude prevalence,3.0000000000,,,2.8000000000,3.2000000000,8652,POINT (-156.3303372 20.82505697),15009030402,HLTHOUT,STROKE,CrdPrv,Stroke +2019,HI,Hawaii,Kauai,15007,15007040604,BRFSS,Health Status,Fair or poor self-rated health status among adults aged >=18 years,%,Crude prevalence,19.5000000000,,,18.6000000000,20.5000000000,3139,POINT (-159.4840794 21.90956079),15007040604,HLTHSTAT,GHLTH,CrdPrv,General Health +2019,HI,Hawaii,Maui,15009,15009030201,BRFSS,Health Status,Physical health not good for >=14 days among adults aged >=18 years,%,Crude prevalence,12.4000000000,,,11.6000000000,13.1000000000,2453,POINT (-156.2504199 20.86252093),15009030201,HLTHSTAT,PHLTH,CrdPrv,Physical Health +2019,HI,Hawaii,Kauai,15007,15007040603,BRFSS,Health Outcomes,Chronic kidney disease among adults aged >=18 years,%,Crude prevalence,3.1000000000,,,3.0000000000,3.2000000000,2544,POINT (-159.4384998 21.90703588),15007040603,HLTHOUT,KIDNEY,CrdPrv,Chronic Kidney Disease +2019,HI,Hawaii,Honolulu,15003,15003010201,BRFSS,Health Risk Behaviors,No leisure-time physical activity among adults aged >=18 years,%,Crude prevalence,24.8000000000,,,23.8000000000,25.9000000000,5882,POINT (-157.8941068 21.55452063),15003010201,RISKBEH,LPA,CrdPrv,Physical Inactivity +2018,HI,Hawaii,Maui,15009,15009030201,BRFSS,Prevention,"Older adult men aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening",%,Crude prevalence,26.1000000000,,,22.4000000000,30.0000000000,2453,POINT (-156.2504199 20.86252093),15009030201,PREVENT,COREM,CrdPrv,Core preventive services for older men +2019,HI,Hawaii,Kauai,15007,15007040700,BRFSS,Health Outcomes,Obesity among adults aged >=18 years,%,Crude prevalence,23.1000000000,,,22.6000000000,23.6000000000,8403,POINT (-159.5219447 21.94546074),15007040700,HLTHOUT,OBESITY,CrdPrv,Obesity +2019,HI,Hawaii,Kauai,15007,15007040604,BRFSS,Health Outcomes,Diagnosed diabetes among adults aged >=18 years,%,Crude prevalence,12.3000000000,,,12.0000000000,12.7000000000,3139,POINT (-159.4840794 21.90956079),15007040604,HLTHOUT,DIABETES,CrdPrv,Diabetes +2018,HI,Hawaii,Maui,15009,15009030800,BRFSS,Prevention,"Older adult women aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening, and Mammogram past 2 years",%,Crude prevalence,24.0000000000,,,22.0000000000,25.9000000000,6907,POINT (-156.5426669 20.90996813),15009030800,PREVENT,COREW,CrdPrv,Core preventive services for older women +2019,HI,Hawaii,Kauai,15007,15007040604,BRFSS,Health Outcomes,Coronary heart disease among adults aged >=18 years,%,Crude prevalence,6.4000000000,,,6.1000000000,6.7000000000,3139,POINT (-159.4840794 21.90956079),15007040604,HLTHOUT,CHD,CrdPrv,Coronary Heart Disease +2019,HI,Hawaii,Maui,15009,15009030402,BRFSS,Health Outcomes,Obesity among adults aged >=18 years,%,Crude prevalence,23.7000000000,,,23.0000000000,24.3000000000,8652,POINT (-156.3303372 20.82505697),15009030402,HLTHOUT,OBESITY,CrdPrv,Obesity +2019,HI,Hawaii,Kauai,15007,15007040700,BRFSS,Prevention,Cholesterol screening among adults aged >=18 years,%,Crude prevalence,84.2000000000,,,84.1000000000,84.4000000000,8403,POINT (-159.5219447 21.94546074),15007040700,PREVENT,CHOLSCREEN,CrdPrv,Cholesterol Screening +2019,HI,Hawaii,Maui,15009,15009030800,BRFSS,Health Status,Fair or poor self-rated health status among adults aged >=18 years,%,Crude prevalence,15.1000000000,,,14.3000000000,15.8000000000,6907,POINT (-156.5426669 20.90996813),15009030800,HLTHSTAT,GHLTH,CrdPrv,General Health +2019,HI,Hawaii,Maui,15009,15009030100,BRFSS,Health Outcomes,Stroke among adults aged >=18 years,%,Crude prevalence,3.7000000000,,,3.5000000000,3.9000000000,2291,POINT (-156.1446943 20.72704536),15009030100,HLTHOUT,STROKE,CrdPrv,Stroke +2019,HI,Hawaii,Honolulu,15003,15003010201,BRFSS,Prevention,Visits to doctor for routine checkup within the past year among adults aged >=18 years,%,Crude prevalence,79.1000000000,,,78.8000000000,79.4000000000,5882,POINT (-157.8941068 21.55452063),15003010201,PREVENT,CHECKUP,CrdPrv,Annual Checkup +2018,HI,Hawaii,Kauai,15007,15007040604,BRFSS,Prevention,"Older adult men aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening",%,Crude prevalence,25.0000000000,,,22.9000000000,27.3000000000,3139,POINT (-159.4840794 21.90956079),15007040604,PREVENT,COREM,CrdPrv,Core preventive services for older men +2019,HI,Hawaii,Maui,15009,15009030402,BRFSS,Prevention,Cholesterol screening among adults aged >=18 years,%,Crude prevalence,82.9000000000,,,82.5000000000,83.3000000000,8652,POINT (-156.3303372 20.82505697),15009030402,PREVENT,CHOLSCREEN,CrdPrv,Cholesterol Screening +2019,HI,Hawaii,Kauai,15007,15007040700,BRFSS,Health Risk Behaviors,Current smoking among adults aged >=18 years,%,Crude prevalence,13.6000000000,,,12.6000000000,14.6000000000,8403,POINT (-159.5219447 21.94546074),15007040700,RISKBEH,CSMOKING,CrdPrv,Current Smoking +2019,HI,Hawaii,Kauai,15007,15007040700,BRFSS,Health Outcomes,High cholesterol among adults aged >=18 years who have been screened in the past 5 years,%,Crude prevalence,28.3000000000,,,27.9000000000,28.7000000000,8403,POINT (-159.5219447 21.94546074),15007040700,HLTHOUT,HIGHCHOL,CrdPrv,High Cholesterol +2019,HI,Hawaii,Kauai,15007,15007040603,BRFSS,Health Outcomes,Current asthma among adults aged >=18 years,%,Crude prevalence,9.0000000000,,,8.7000000000,9.2000000000,2544,POINT (-159.4384998 21.90703588),15007040603,HLTHOUT,CASTHMA,CrdPrv,Current Asthma +2019,HI,Hawaii,Maui,15009,15009030201,BRFSS,Health Risk Behaviors,No leisure-time physical activity among adults aged >=18 years,%,Crude prevalence,22.1000000000,,,20.7000000000,23.7000000000,2453,POINT (-156.2504199 20.86252093),15009030201,RISKBEH,LPA,CrdPrv,Physical Inactivity +2018,HI,Hawaii,Kauai,15007,15007040604,BRFSS,Prevention,Mammography use among women aged 50-74 years,%,Crude prevalence,77.5000000000,,,76.1000000000,78.9000000000,3139,POINT (-159.4840794 21.90956079),15007040604,PREVENT,MAMMOUSE,CrdPrv,Mammography +2019,HI,Hawaii,Kauai,15007,15007040603,BRFSS,Health Outcomes,Depression among adults aged >=18 years,%,Crude prevalence,14.7000000000,,,14.2000000000,15.2000000000,2544,POINT (-159.4384998 21.90703588),15007040603,HLTHOUT,DEPRESSION,CrdPrv,Depression +2018,HI,Hawaii,Maui,15009,15009030100,BRFSS,Prevention,"Fecal occult blood test, sigmoidoscopy, or colonoscopy among adults aged 50-75 years",%,Crude prevalence,63.2000000000,,,61.5000000000,64.8000000000,2291,POINT (-156.1446943 20.72704536),15009030100,PREVENT,COLON_SCREEN,CrdPrv,Colorectal Cancer Screening +2019,HI,Hawaii,Kauai,15007,15007040604,BRFSS,Health Outcomes,Stroke among adults aged >=18 years,%,Crude prevalence,3.8000000000,,,3.7000000000,4.0000000000,3139,POINT (-159.4840794 21.90956079),15007040604,HLTHOUT,STROKE,CrdPrv,Stroke +2019,HI,Hawaii,Honolulu,15003,15003010201,BRFSS,Health Outcomes,Diagnosed diabetes among adults aged >=18 years,%,Crude prevalence,10.2000000000,,,10.0000000000,10.5000000000,5882,POINT (-157.8941068 21.55452063),15003010201,HLTHOUT,DIABETES,CrdPrv,Diabetes +2018,HI,Hawaii,Maui,15009,15009030402,BRFSS,Prevention,"Older adult men aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening",%,Crude prevalence,25.4000000000,,,22.7000000000,28.5000000000,8652,POINT (-156.3303372 20.82505697),15009030402,PREVENT,COREM,CrdPrv,Core preventive services for older men +2019,HI,Hawaii,Maui,15009,15009030800,BRFSS,Prevention,Cholesterol screening among adults aged >=18 years,%,Crude prevalence,82.0000000000,,,81.7000000000,82.4000000000,6907,POINT (-156.5426669 20.90996813),15009030800,PREVENT,CHOLSCREEN,CrdPrv,Cholesterol Screening +2019,HI,Hawaii,Maui,15009,15009030201,BRFSS,Health Outcomes,Depression among adults aged >=18 years,%,Crude prevalence,18.5000000000,,,17.9000000000,19.2000000000,2453,POINT (-156.2504199 20.86252093),15009030201,HLTHOUT,DEPRESSION,CrdPrv,Depression +2018,HI,Hawaii,Maui,15009,15009030800,BRFSS,Prevention,Mammography use among women aged 50-74 years,%,Crude prevalence,79.6000000000,,,78.6000000000,80.7000000000,6907,POINT (-156.5426669 20.90996813),15009030800,PREVENT,MAMMOUSE,CrdPrv,Mammography +2019,HI,Hawaii,Maui,15009,15009030100,BRFSS,Prevention,Current lack of health insurance among adults aged 18-64 years,%,Crude prevalence,11.1000000000,,,10.3000000000,11.9000000000,2291,POINT (-156.1446943 20.72704536),15009030100,PREVENT,ACCESS2,CrdPrv,Health Insurance +2019,HI,Hawaii,Maui,15009,15009030201,BRFSS,Health Risk Behaviors,Binge drinking among adults aged >=18 years,%,Crude prevalence,23.5000000000,,,23.0000000000,24.0000000000,2453,POINT (-156.2504199 20.86252093),15009030201,RISKBEH,BINGE,CrdPrv,Binge Drinking +2019,HI,Hawaii,Maui,15009,15009030100,BRFSS,Health Risk Behaviors,Current smoking among adults aged >=18 years,%,Crude prevalence,18.4000000000,,,17.3000000000,19.6000000000,2291,POINT (-156.1446943 20.72704536),15009030100,RISKBEH,CSMOKING,CrdPrv,Current Smoking +2019,HI,Hawaii,Maui,15009,15009030402,BRFSS,Health Outcomes,Diagnosed diabetes among adults aged >=18 years,%,Crude prevalence,9.6000000000,,,9.2000000000,10.1000000000,8652,POINT (-156.3303372 20.82505697),15009030402,HLTHOUT,DIABETES,CrdPrv,Diabetes +2018,HI,Hawaii,Kauai,15007,15007040603,BRFSS,Prevention,"Older adult men aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening",%,Crude prevalence,26.3000000000,,,23.2000000000,29.7000000000,2544,POINT (-159.4384998 21.90703588),15007040603,PREVENT,COREM,CrdPrv,Core preventive services for older men +2019,HI,Hawaii,Maui,15009,15009030100,BRFSS,Prevention,Taking medicine for high blood pressure control among adults aged >=18 years with high blood pressure,%,Crude prevalence,70.6000000000,,,70.0000000000,71.4000000000,2291,POINT (-156.1446943 20.72704536),15009030100,PREVENT,BPMED,CrdPrv,Taking BP Medication +2019,HI,Hawaii,Maui,15009,15009030201,BRFSS,Health Outcomes,Current asthma among adults aged >=18 years,%,Crude prevalence,9.7000000000,,,9.4000000000,10.0000000000,2453,POINT (-156.2504199 20.86252093),15009030201,HLTHOUT,CASTHMA,CrdPrv,Current Asthma +2019,HI,Hawaii,Kauai,15007,15007040603,BRFSS,Health Outcomes,Chronic obstructive pulmonary disease among adults aged >=18 years,%,Crude prevalence,5.6000000000,,,5.1000000000,6.1000000000,2544,POINT (-159.4384998 21.90703588),15007040603,HLTHOUT,COPD,CrdPrv,COPD +2018,HI,Hawaii,Kauai,15007,15007040603,BRFSS,Health Risk Behaviors,Sleeping less than 7 hours among adults aged >=18 years,%,Crude prevalence,36.8000000000,,,35.8000000000,37.7000000000,2544,POINT (-159.4384998 21.90703588),15007040603,RISKBEH,SLEEP,CrdPrv,Sleep <7 hours +2018,HI,Hawaii,Maui,15009,15009030402,BRFSS,Health Outcomes,All teeth lost among adults aged >=65 years,%,Crude prevalence,7.9000000000,,,6.2000000000,9.9000000000,8652,POINT (-156.3303372 20.82505697),15009030402,HLTHOUT,TEETHLOST,CrdPrv,All Teeth Lost +2019,HI,Hawaii,Kauai,15007,15007040604,BRFSS,Health Risk Behaviors,No leisure-time physical activity among adults aged >=18 years,%,Crude prevalence,27.7000000000,,,26.6000000000,28.7000000000,3139,POINT (-159.4840794 21.90956079),15007040604,RISKBEH,LPA,CrdPrv,Physical Inactivity +2019,HI,Hawaii,Maui,15009,15009030402,BRFSS,Health Outcomes,High cholesterol among adults aged >=18 years who have been screened in the past 5 years,%,Crude prevalence,26.2000000000,,,25.7000000000,26.6000000000,8652,POINT (-156.3303372 20.82505697),15009030402,HLTHOUT,HIGHCHOL,CrdPrv,High Cholesterol +2018,HI,Hawaii,Maui,15009,15009030201,BRFSS,Prevention,Mammography use among women aged 50-74 years,%,Crude prevalence,80.7000000000,,,78.6000000000,82.6000000000,2453,POINT (-156.2504199 20.86252093),15009030201,PREVENT,MAMMOUSE,CrdPrv,Mammography +2019,HI,Hawaii,Maui,15009,15009030100,BRFSS,Health Status,Fair or poor self-rated health status among adults aged >=18 years,%,Crude prevalence,18.1000000000,,,17.1000000000,19.1000000000,2291,POINT (-156.1446943 20.72704536),15009030100,HLTHSTAT,GHLTH,CrdPrv,General Health +2019,HI,Hawaii,Kauai,15007,15007040700,BRFSS,Health Outcomes,Stroke among adults aged >=18 years,%,Crude prevalence,3.1000000000,,,2.9000000000,3.2000000000,8403,POINT (-159.5219447 21.94546074),15007040700,HLTHOUT,STROKE,CrdPrv,Stroke +2019,HI,Hawaii,Maui,15009,15009030100,BRFSS,Health Risk Behaviors,No leisure-time physical activity among adults aged >=18 years,%,Crude prevalence,23.8000000000,,,22.7000000000,24.9000000000,2291,POINT (-156.1446943 20.72704536),15009030100,RISKBEH,LPA,CrdPrv,Physical Inactivity +2018,HI,Hawaii,Maui,15009,15009030201,BRFSS,Health Outcomes,All teeth lost among adults aged >=65 years,%,Crude prevalence,8.2000000000,,,6.4000000000,10.4000000000,2453,POINT (-156.2504199 20.86252093),15009030201,HLTHOUT,TEETHLOST,CrdPrv,All Teeth Lost +2018,HI,Hawaii,Maui,15009,15009030800,BRFSS,Prevention,"Older adult men aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening",%,Crude prevalence,24.8000000000,,,22.9000000000,27.0000000000,6907,POINT (-156.5426669 20.90996813),15009030800,PREVENT,COREM,CrdPrv,Core preventive services for older men +2019,HI,Hawaii,Honolulu,15003,15003010201,BRFSS,Health Status,Physical health not good for >=14 days among adults aged >=18 years,%,Crude prevalence,13.0000000000,,,12.5000000000,13.5000000000,5882,POINT (-157.8941068 21.55452063),15003010201,HLTHSTAT,PHLTH,CrdPrv,Physical Health +2019,HI,Hawaii,Kauai,15007,15007040604,BRFSS,Health Risk Behaviors,Binge drinking among adults aged >=18 years,%,Crude prevalence,18.5000000000,,,18.3000000000,18.8000000000,3139,POINT (-159.4840794 21.90956079),15007040604,RISKBEH,BINGE,CrdPrv,Binge Drinking +2019,HI,Hawaii,Maui,15009,15009030800,BRFSS,Health Outcomes,Diagnosed diabetes among adults aged >=18 years,%,Crude prevalence,9.3000000000,,,9.0000000000,9.5000000000,6907,POINT (-156.5426669 20.90996813),15009030800,HLTHOUT,DIABETES,CrdPrv,Diabetes +2019,HI,Hawaii,Maui,15009,15009030402,BRFSS,Health Outcomes,Cancer (excluding skin cancer) among adults aged >=18 years,%,Crude prevalence,6.1000000000,,,6.0000000000,6.3000000000,8652,POINT (-156.3303372 20.82505697),15009030402,HLTHOUT,CANCER,CrdPrv,Cancer (except skin) +2018,HI,Hawaii,Honolulu,15003,15003010201,BRFSS,Prevention,"Fecal occult blood test, sigmoidoscopy, or colonoscopy among adults aged 50-75 years",%,Crude prevalence,69.2000000000,,,68.0000000000,70.5000000000,5882,POINT (-157.8941068 21.55452063),15003010201,PREVENT,COLON_SCREEN,CrdPrv,Colorectal Cancer Screening +2019,HI,Hawaii,Kauai,15007,15007040603,BRFSS,Prevention,Current lack of health insurance among adults aged 18-64 years,%,Crude prevalence,11.4000000000,,,10.4000000000,12.6000000000,2544,POINT (-159.4384998 21.90703588),15007040603,PREVENT,ACCESS2,CrdPrv,Health Insurance +2019,HI,Hawaii,Kauai,15007,15007040700,BRFSS,Health Outcomes,High blood pressure among adults aged >=18 years,%,Crude prevalence,27.9000000000,,,27.3000000000,28.5000000000,8403,POINT (-159.5219447 21.94546074),15007040700,HLTHOUT,BPHIGH,CrdPrv,High Blood Pressure +2019,HI,Hawaii,Maui,15009,15009030201,BRFSS,Health Outcomes,Chronic kidney disease among adults aged >=18 years,%,Crude prevalence,2.6000000000,,,2.5000000000,2.7000000000,2453,POINT (-156.2504199 20.86252093),15009030201,HLTHOUT,KIDNEY,CrdPrv,Chronic Kidney Disease +2018,HI,Hawaii,Honolulu,15003,15003010201,BRFSS,Prevention,Visits to dentist or dental clinic among adults aged >=18 years,%,Crude prevalence,72.1000000000,,,70.8000000000,73.3000000000,5882,POINT (-157.8941068 21.55452063),15003010201,PREVENT,DENTAL,CrdPrv,Dental Visit +2018,HI,Hawaii,Maui,15009,15009030100,BRFSS,Prevention,"Older adult women aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening, and Mammogram past 2 years",%,Crude prevalence,23.0000000000,,,21.0000000000,25.0000000000,2291,POINT (-156.1446943 20.72704536),15009030100,PREVENT,COREW,CrdPrv,Core preventive services for older women +2019,HI,Hawaii,Honolulu,15003,15003010201,BRFSS,Prevention,Taking medicine for high blood pressure control among adults aged >=18 years with high blood pressure,%,Crude prevalence,69.9000000000,,,69.4000000000,70.4000000000,5882,POINT (-157.8941068 21.55452063),15003010201,PREVENT,BPMED,CrdPrv,Taking BP Medication +2019,HI,Hawaii,Kauai,15007,15007040700,BRFSS,Health Status,Mental health not good for >=14 days among adults aged >=18 years,%,Crude prevalence,12.0000000000,,,11.5000000000,12.5000000000,8403,POINT (-159.5219447 21.94546074),15007040700,HLTHSTAT,MHLTH,CrdPrv,Mental Health +2019,HI,Hawaii,Kauai,15007,15007040603,BRFSS,Health Outcomes,Cancer (excluding skin cancer) among adults aged >=18 years,%,Crude prevalence,7.0000000000,,,6.8000000000,7.2000000000,2544,POINT (-159.4384998 21.90703588),15007040603,HLTHOUT,CANCER,CrdPrv,Cancer (except skin) +2019,HI,Hawaii,Honolulu,15003,15003010201,BRFSS,Health Outcomes,Chronic kidney disease among adults aged >=18 years,%,Crude prevalence,2.9000000000,,,2.8000000000,2.9000000000,5882,POINT (-157.8941068 21.55452063),15003010201,HLTHOUT,KIDNEY,CrdPrv,Chronic Kidney Disease +2019,HI,Hawaii,Maui,15009,15009030800,BRFSS,Prevention,Visits to doctor for routine checkup within the past year among adults aged >=18 years,%,Crude prevalence,73.2000000000,,,72.9000000000,73.6000000000,6907,POINT (-156.5426669 20.90996813),15009030800,PREVENT,CHECKUP,CrdPrv,Annual Checkup +2019,HI,Hawaii,Kauai,15007,15007040603,BRFSS,Health Outcomes,High cholesterol among adults aged >=18 years who have been screened in the past 5 years,%,Crude prevalence,30.0000000000,,,29.4000000000,30.6000000000,2544,POINT (-159.4384998 21.90703588),15007040603,HLTHOUT,HIGHCHOL,CrdPrv,High Cholesterol +2019,HI,Hawaii,Kauai,15007,15007040700,BRFSS,Prevention,Visits to doctor for routine checkup within the past year among adults aged >=18 years,%,Crude prevalence,77.8000000000,,,77.5000000000,78.2000000000,8403,POINT (-159.5219447 21.94546074),15007040700,PREVENT,CHECKUP,CrdPrv,Annual Checkup +2019,HI,Hawaii,Maui,15009,15009030800,BRFSS,Health Outcomes,High cholesterol among adults aged >=18 years who have been screened in the past 5 years,%,Crude prevalence,24.5000000000,,,24.2000000000,24.8000000000,6907,POINT (-156.5426669 20.90996813),15009030800,HLTHOUT,HIGHCHOL,CrdPrv,High Cholesterol +2018,HI,Hawaii,Maui,15009,15009030100,BRFSS,Health Risk Behaviors,Sleeping less than 7 hours among adults aged >=18 years,%,Crude prevalence,40.4000000000,,,39.6000000000,41.5000000000,2291,POINT (-156.1446943 20.72704536),15009030100,RISKBEH,SLEEP,CrdPrv,Sleep <7 hours +2019,HI,Hawaii,Maui,15009,15009030402,BRFSS,Prevention,Visits to doctor for routine checkup within the past year among adults aged >=18 years,%,Crude prevalence,74.1000000000,,,73.6000000000,74.6000000000,8652,POINT (-156.3303372 20.82505697),15009030402,PREVENT,CHECKUP,CrdPrv,Annual Checkup +2018,HI,Hawaii,Kauai,15007,15007040604,BRFSS,Health Outcomes,All teeth lost among adults aged >=65 years,%,Crude prevalence,9.6000000000,,,7.8000000000,11.5000000000,3139,POINT (-159.4840794 21.90956079),15007040604,HLTHOUT,TEETHLOST,CrdPrv,All Teeth Lost +2019,HI,Hawaii,Maui,15009,15009030800,BRFSS,Health Outcomes,High blood pressure among adults aged >=18 years,%,Crude prevalence,23.8000000000,,,23.3000000000,24.1000000000,6907,POINT (-156.5426669 20.90996813),15009030800,HLTHOUT,BPHIGH,CrdPrv,High Blood Pressure +2018,HI,Hawaii,Kauai,15007,15007040604,BRFSS,Prevention,Cervical cancer screening among adult women aged 21-65 years,%,Crude prevalence,82.1000000000,,,81.2000000000,82.9000000000,3139,POINT (-159.4840794 21.90956079),15007040604,PREVENT,CERVICAL,CrdPrv,Cervical Cancer Screening +2019,HI,Hawaii,Maui,15009,15009030100,BRFSS,Prevention,Cholesterol screening among adults aged >=18 years,%,Crude prevalence,82.6000000000,,,82.3000000000,83.0000000000,2291,POINT (-156.1446943 20.72704536),15009030100,PREVENT,CHOLSCREEN,CrdPrv,Cholesterol Screening +2018,HI,Hawaii,Maui,15009,15009030201,BRFSS,Prevention,Visits to dentist or dental clinic among adults aged >=18 years,%,Crude prevalence,73.2000000000,,,71.2000000000,74.9000000000,2453,POINT (-156.2504199 20.86252093),15009030201,PREVENT,DENTAL,CrdPrv,Dental Visit +2018,HI,Hawaii,Kauai,15007,15007040604,BRFSS,Prevention,"Older adult women aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening, and Mammogram past 2 years",%,Crude prevalence,21.3000000000,,,19.5000000000,23.3000000000,3139,POINT (-159.4840794 21.90956079),15007040604,PREVENT,COREW,CrdPrv,Core preventive services for older women +2019,HI,Hawaii,Honolulu,15003,15003010201,BRFSS,Health Outcomes,Obesity among adults aged >=18 years,%,Crude prevalence,33.4000000000,,,32.9000000000,34.0000000000,5882,POINT (-157.8941068 21.55452063),15003010201,HLTHOUT,OBESITY,CrdPrv,Obesity +2019,HI,Hawaii,Maui,15009,15009030402,BRFSS,Prevention,Taking medicine for high blood pressure control among adults aged >=18 years with high blood pressure,%,Crude prevalence,70.7000000000,,,69.9000000000,71.4000000000,8652,POINT (-156.3303372 20.82505697),15009030402,PREVENT,BPMED,CrdPrv,Taking BP Medication +2019,HI,Hawaii,Kauai,15007,15007040603,BRFSS,Health Status,Mental health not good for >=14 days among adults aged >=18 years,%,Crude prevalence,12.6000000000,,,12.0000000000,13.1000000000,2544,POINT (-159.4384998 21.90703588),15007040603,HLTHSTAT,MHLTH,CrdPrv,Mental Health +2019,HI,Hawaii,Honolulu,15003,15003010201,BRFSS,Health Outcomes,Coronary heart disease among adults aged >=18 years,%,Crude prevalence,5.5000000000,,,5.3000000000,5.7000000000,5882,POINT (-157.8941068 21.55452063),15003010201,HLTHOUT,CHD,CrdPrv,Coronary Heart Disease +2019,HI,Hawaii,Maui,15009,15009030201,BRFSS,Health Outcomes,Arthritis among adults aged >=18 years,%,Crude prevalence,23.0000000000,,,22.2000000000,23.7000000000,2453,POINT (-156.2504199 20.86252093),15009030201,HLTHOUT,ARTHRITIS,CrdPrv,Arthritis +2018,HI,Hawaii,Maui,15009,15009030402,BRFSS,Prevention,"Older adult women aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening, and Mammogram past 2 years",%,Crude prevalence,24.7000000000,,,22.1000000000,27.4000000000,8652,POINT (-156.3303372 20.82505697),15009030402,PREVENT,COREW,CrdPrv,Core preventive services for older women +2019,HI,Hawaii,Maui,15009,15009030800,BRFSS,Health Status,Physical health not good for >=14 days among adults aged >=18 years,%,Crude prevalence,10.7000000000,,,10.3000000000,11.0000000000,6907,POINT (-156.5426669 20.90996813),15009030800,HLTHSTAT,PHLTH,CrdPrv,Physical Health +2019,HI,Hawaii,Maui,15009,15009030402,BRFSS,Health Status,Mental health not good for >=14 days among adults aged >=18 years,%,Crude prevalence,12.5000000000,,,11.8000000000,13.2000000000,8652,POINT (-156.3303372 20.82505697),15009030402,HLTHSTAT,MHLTH,CrdPrv,Mental Health +2019,HI,Hawaii,Kauai,15007,15007040604,BRFSS,Health Status,Physical health not good for >=14 days among adults aged >=18 years,%,Crude prevalence,13.2000000000,,,12.7000000000,13.8000000000,3139,POINT (-159.4840794 21.90956079),15007040604,HLTHSTAT,PHLTH,CrdPrv,Physical Health +2018,HI,Hawaii,Kauai,15007,15007040700,BRFSS,Prevention,Visits to dentist or dental clinic among adults aged >=18 years,%,Crude prevalence,72.2000000000,,,70.8000000000,73.5000000000,8403,POINT (-159.5219447 21.94546074),15007040700,PREVENT,DENTAL,CrdPrv,Dental Visit +2019,HI,Hawaii,Maui,15009,15009030100,BRFSS,Health Outcomes,Chronic kidney disease among adults aged >=18 years,%,Crude prevalence,2.9000000000,,,2.8000000000,3.0000000000,2291,POINT (-156.1446943 20.72704536),15009030100,HLTHOUT,KIDNEY,CrdPrv,Chronic Kidney Disease +2019,HI,Hawaii,Maui,15009,15009030402,BRFSS,Health Status,Fair or poor self-rated health status among adults aged >=18 years,%,Crude prevalence,15.2000000000,,,14.1000000000,16.4000000000,8652,POINT (-156.3303372 20.82505697),15009030402,HLTHSTAT,GHLTH,CrdPrv,General Health +2019,HI,Hawaii,Maui,15009,15009030201,BRFSS,Health Outcomes,Chronic obstructive pulmonary disease among adults aged >=18 years,%,Crude prevalence,5.5000000000,,,5.0000000000,6.1000000000,2453,POINT (-156.2504199 20.86252093),15009030201,HLTHOUT,COPD,CrdPrv,COPD +2019,HI,Hawaii,Kauai,15007,15007040603,BRFSS,Health Outcomes,Stroke among adults aged >=18 years,%,Crude prevalence,3.7000000000,,,3.4000000000,3.9000000000,2544,POINT (-159.4384998 21.90703588),15007040603,HLTHOUT,STROKE,CrdPrv,Stroke +2019,HI,Hawaii,Maui,15009,15009030402,BRFSS,Prevention,Current lack of health insurance among adults aged 18-64 years,%,Crude prevalence,10.3000000000,,,9.3000000000,11.3000000000,8652,POINT (-156.3303372 20.82505697),15009030402,PREVENT,ACCESS2,CrdPrv,Health Insurance +2019,HI,Hawaii,Kauai,15007,15007040604,BRFSS,Health Outcomes,Chronic kidney disease among adults aged >=18 years,%,Crude prevalence,3.3000000000,,,3.2000000000,3.4000000000,3139,POINT (-159.4840794 21.90956079),15007040604,HLTHOUT,KIDNEY,CrdPrv,Chronic Kidney Disease +2019,HI,Hawaii,Maui,15009,15009030201,BRFSS,Health Status,Mental health not good for >=14 days among adults aged >=18 years,%,Crude prevalence,13.9000000000,,,13.2000000000,14.6000000000,2453,POINT (-156.2504199 20.86252093),15009030201,HLTHSTAT,MHLTH,CrdPrv,Mental Health +2019,HI,Hawaii,Maui,15009,15009030100,BRFSS,Health Outcomes,Arthritis among adults aged >=18 years,%,Crude prevalence,23.2000000000,,,22.7000000000,23.8000000000,2291,POINT (-156.1446943 20.72704536),15009030100,HLTHOUT,ARTHRITIS,CrdPrv,Arthritis +2019,HI,Hawaii,Kauai,15007,15007040604,BRFSS,Health Risk Behaviors,Current smoking among adults aged >=18 years,%,Crude prevalence,15.6000000000,,,14.8000000000,16.5000000000,3139,POINT (-159.4840794 21.90956079),15007040604,RISKBEH,CSMOKING,CrdPrv,Current Smoking +2019,HI,Hawaii,Maui,15009,15009030201,BRFSS,Health Outcomes,High blood pressure among adults aged >=18 years,%,Crude prevalence,25.6000000000,,,24.8000000000,26.4000000000,2453,POINT (-156.2504199 20.86252093),15009030201,HLTHOUT,BPHIGH,CrdPrv,High Blood Pressure +2019,HI,Hawaii,Maui,15009,15009030800,BRFSS,Health Risk Behaviors,No leisure-time physical activity among adults aged >=18 years,%,Crude prevalence,22.6000000000,,,21.7000000000,23.6000000000,6907,POINT (-156.5426669 20.90996813),15009030800,RISKBEH,LPA,CrdPrv,Physical Inactivity +2018,HI,Hawaii,Kauai,15007,15007040700,BRFSS,Prevention,Mammography use among women aged 50-74 years,%,Crude prevalence,77.6000000000,,,76.1000000000,79.2000000000,8403,POINT (-159.5219447 21.94546074),15007040700,PREVENT,MAMMOUSE,CrdPrv,Mammography +2019,HI,Hawaii,Honolulu,15003,15003010201,BRFSS,Health Outcomes,Chronic obstructive pulmonary disease among adults aged >=18 years,%,Crude prevalence,5.9000000000,,,5.6000000000,6.2000000000,5882,POINT (-157.8941068 21.55452063),15003010201,HLTHOUT,COPD,CrdPrv,COPD +2019,HI,Hawaii,Honolulu,15003,15003010201,BRFSS,Prevention,Current lack of health insurance among adults aged 18-64 years,%,Crude prevalence,10.2000000000,,,9.5000000000,11.0000000000,5882,POINT (-157.8941068 21.55452063),15003010201,PREVENT,ACCESS2,CrdPrv,Health Insurance +2019,HI,Hawaii,Maui,15009,15009030800,BRFSS,Health Outcomes,Chronic obstructive pulmonary disease among adults aged >=18 years,%,Crude prevalence,4.3000000000,,,4.1000000000,4.6000000000,6907,POINT (-156.5426669 20.90996813),15009030800,HLTHOUT,COPD,CrdPrv,COPD +2019,HI,Hawaii,Maui,15009,15009030201,BRFSS,Health Outcomes,Coronary heart disease among adults aged >=18 years,%,Crude prevalence,5.0000000000,,,4.7000000000,5.4000000000,2453,POINT (-156.2504199 20.86252093),15009030201,HLTHOUT,CHD,CrdPrv,Coronary Heart Disease +2019,HI,Hawaii,Kauai,15007,15007040604,BRFSS,Health Outcomes,Current asthma among adults aged >=18 years,%,Crude prevalence,9.6000000000,,,9.4000000000,9.8000000000,3139,POINT (-159.4840794 21.90956079),15007040604,HLTHOUT,CASTHMA,CrdPrv,Current Asthma +2019,HI,Hawaii,Maui,15009,15009030402,BRFSS,Health Risk Behaviors,Current smoking among adults aged >=18 years,%,Crude prevalence,15.3000000000,,,14.0000000000,16.7000000000,8652,POINT (-156.3303372 20.82505697),15009030402,RISKBEH,CSMOKING,CrdPrv,Current Smoking +2019,HI,Hawaii,Kauai,15007,15007040700,BRFSS,Health Outcomes,Chronic obstructive pulmonary disease among adults aged >=18 years,%,Crude prevalence,4.5000000000,,,4.2000000000,4.8000000000,8403,POINT (-159.5219447 21.94546074),15007040700,HLTHOUT,COPD,CrdPrv,COPD +2019,HI,Hawaii,Honolulu,15003,15003010201,BRFSS,Health Outcomes,Depression among adults aged >=18 years,%,Crude prevalence,18.5000000000,,,18.0000000000,18.9000000000,5882,POINT (-157.8941068 21.55452063),15003010201,HLTHOUT,DEPRESSION,CrdPrv,Depression +2018,HI,Hawaii,Kauai,15007,15007040603,BRFSS,Prevention,"Older adult women aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening, and Mammogram past 2 years",%,Crude prevalence,24.1000000000,,,21.1000000000,27.2000000000,2544,POINT (-159.4384998 21.90703588),15007040603,PREVENT,COREW,CrdPrv,Core preventive services for older women +2019,HI,Hawaii,Kauai,15007,15007040700,BRFSS,Health Outcomes,Depression among adults aged >=18 years,%,Crude prevalence,14.5000000000,,,14.2000000000,14.9000000000,8403,POINT (-159.5219447 21.94546074),15007040700,HLTHOUT,DEPRESSION,CrdPrv,Depression +2019,HI,Hawaii,Maui,15009,15009030800,BRFSS,Health Outcomes,Cancer (excluding skin cancer) among adults aged >=18 years,%,Crude prevalence,5.4000000000,,,5.3000000000,5.5000000000,6907,POINT (-156.5426669 20.90996813),15009030800,HLTHOUT,CANCER,CrdPrv,Cancer (except skin) +2019,HI,Hawaii,Maui,15009,15009030201,BRFSS,Prevention,Current lack of health insurance among adults aged 18-64 years,%,Crude prevalence,9.3000000000,,,8.3000000000,10.2000000000,2453,POINT (-156.2504199 20.86252093),15009030201,PREVENT,ACCESS2,CrdPrv,Health Insurance +2019,HI,Hawaii,Kauai,15007,15007040603,BRFSS,Prevention,Visits to doctor for routine checkup within the past year among adults aged >=18 years,%,Crude prevalence,78.5000000000,,,78.0000000000,78.9000000000,2544,POINT (-159.4384998 21.90703588),15007040603,PREVENT,CHECKUP,CrdPrv,Annual Checkup +2019,HI,Hawaii,Maui,15009,15009030201,BRFSS,Health Outcomes,Stroke among adults aged >=18 years,%,Crude prevalence,3.1000000000,,,2.9000000000,3.3000000000,2453,POINT (-156.2504199 20.86252093),15009030201,HLTHOUT,STROKE,CrdPrv,Stroke +2018,HI,Hawaii,Maui,15009,15009030100,BRFSS,Prevention,Mammography use among women aged 50-74 years,%,Crude prevalence,78.5000000000,,,77.1000000000,79.9000000000,2291,POINT (-156.1446943 20.72704536),15009030100,PREVENT,MAMMOUSE,CrdPrv,Mammography +2018,HI,Hawaii,Maui,15009,15009030100,BRFSS,Prevention,"Older adult men aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening",%,Crude prevalence,22.7000000000,,,20.4000000000,25.1000000000,2291,POINT (-156.1446943 20.72704536),15009030100,PREVENT,COREM,CrdPrv,Core preventive services for older men +2018,HI,Hawaii,Honolulu,15003,15003010201,BRFSS,Health Risk Behaviors,Sleeping less than 7 hours among adults aged >=18 years,%,Crude prevalence,48.2000000000,,,47.6000000000,48.9000000000,5882,POINT (-157.8941068 21.55452063),15003010201,RISKBEH,SLEEP,CrdPrv,Sleep <7 hours +2019,HI,Hawaii,Maui,15009,15009030800,BRFSS,Health Risk Behaviors,Current smoking among adults aged >=18 years,%,Crude prevalence,16.1000000000,,,15.1000000000,17.0000000000,6907,POINT (-156.5426669 20.90996813),15009030800,RISKBEH,CSMOKING,CrdPrv,Current Smoking +2018,HI,Hawaii,Maui,15009,15009030402,BRFSS,Prevention,"Fecal occult blood test, sigmoidoscopy, or colonoscopy among adults aged 50-75 years",%,Crude prevalence,63.8000000000,,,61.5000000000,65.9000000000,8652,POINT (-156.3303372 20.82505697),15009030402,PREVENT,COLON_SCREEN,CrdPrv,Colorectal Cancer Screening +2019,HI,Hawaii,Kauai,15007,15007040700,BRFSS,Prevention,Taking medicine for high blood pressure control among adults aged >=18 years with high blood pressure,%,Crude prevalence,74.8000000000,,,74.3000000000,75.4000000000,8403,POINT (-159.5219447 21.94546074),15007040700,PREVENT,BPMED,CrdPrv,Taking BP Medication +2019,HI,Hawaii,Maui,15009,15009030800,BRFSS,Prevention,Current lack of health insurance among adults aged 18-64 years,%,Crude prevalence,10.6000000000,,,10.0000000000,11.3000000000,6907,POINT (-156.5426669 20.90996813),15009030800,PREVENT,ACCESS2,CrdPrv,Health Insurance +2019,HI,Hawaii,Maui,15009,15009030201,BRFSS,Health Risk Behaviors,Current smoking among adults aged >=18 years,%,Crude prevalence,17.5000000000,,,15.9000000000,19.2000000000,2453,POINT (-156.2504199 20.86252093),15009030201,RISKBEH,CSMOKING,CrdPrv,Current Smoking +2018,HI,Hawaii,Maui,15009,15009030800,BRFSS,Health Outcomes,All teeth lost among adults aged >=65 years,%,Crude prevalence,8.1000000000,,,7.1000000000,9.3000000000,6907,POINT (-156.5426669 20.90996813),15009030800,HLTHOUT,TEETHLOST,CrdPrv,All Teeth Lost +2019,HI,Hawaii,Kauai,15007,15007040700,BRFSS,Health Outcomes,Coronary heart disease among adults aged >=18 years,%,Crude prevalence,5.0000000000,,,4.8000000000,5.3000000000,8403,POINT (-159.5219447 21.94546074),15007040700,HLTHOUT,CHD,CrdPrv,Coronary Heart Disease +2019,HI,Hawaii,Kauai,15007,15007040700,BRFSS,Health Outcomes,Current asthma among adults aged >=18 years,%,Crude prevalence,8.7000000000,,,8.5000000000,8.9000000000,8403,POINT (-159.5219447 21.94546074),15007040700,HLTHOUT,CASTHMA,CrdPrv,Current Asthma +2019,HI,Hawaii,Maui,15009,15009030100,BRFSS,Health Status,Physical health not good for >=14 days among adults aged >=18 years,%,Crude prevalence,13.0000000000,,,12.4000000000,13.5000000000,2291,POINT (-156.1446943 20.72704536),15009030100,HLTHSTAT,PHLTH,CrdPrv,Physical Health +2019,HI,Hawaii,Maui,15009,15009030402,BRFSS,Health Outcomes,Coronary heart disease among adults aged >=18 years,%,Crude prevalence,4.8000000000,,,4.5000000000,5.1000000000,8652,POINT (-156.3303372 20.82505697),15009030402,HLTHOUT,CHD,CrdPrv,Coronary Heart Disease +2019,HI,Hawaii,Kauai,15007,15007040603,BRFSS,Health Status,Fair or poor self-rated health status among adults aged >=18 years,%,Crude prevalence,18.2000000000,,,16.8000000000,19.5000000000,2544,POINT (-159.4384998 21.90703588),15007040603,HLTHSTAT,GHLTH,CrdPrv,General Health +2019,HI,Hawaii,Maui,15009,15009030100,BRFSS,Prevention,Visits to doctor for routine checkup within the past year among adults aged >=18 years,%,Crude prevalence,74.2000000000,,,73.8000000000,74.6000000000,2291,POINT (-156.1446943 20.72704536),15009030100,PREVENT,CHECKUP,CrdPrv,Annual Checkup +2019,HI,Hawaii,Kauai,15007,15007040603,BRFSS,Prevention,Taking medicine for high blood pressure control among adults aged >=18 years with high blood pressure,%,Crude prevalence,77.1000000000,,,76.5000000000,77.7000000000,2544,POINT (-159.4384998 21.90703588),15007040603,PREVENT,BPMED,CrdPrv,Taking BP Medication +2018,HI,Hawaii,Maui,15009,15009030201,BRFSS,Health Risk Behaviors,Sleeping less than 7 hours among adults aged >=18 years,%,Crude prevalence,37.8000000000,,,37.1000000000,38.6000000000,2453,POINT (-156.2504199 20.86252093),15009030201,RISKBEH,SLEEP,CrdPrv,Sleep <7 hours +2019,HI,Hawaii,Kauai,15007,15007040700,BRFSS,Prevention,Current lack of health insurance among adults aged 18-64 years,%,Crude prevalence,10.2000000000,,,9.5000000000,11.1000000000,8403,POINT (-159.5219447 21.94546074),15007040700,PREVENT,ACCESS2,CrdPrv,Health Insurance +2019,HI,Hawaii,Honolulu,15003,15003010201,BRFSS,Health Outcomes,High cholesterol among adults aged >=18 years who have been screened in the past 5 years,%,Crude prevalence,29.8000000000,,,29.4000000000,30.1000000000,5882,POINT (-157.8941068 21.55452063),15003010201,HLTHOUT,HIGHCHOL,CrdPrv,High Cholesterol +2018,HI,Hawaii,Maui,15009,15009030201,BRFSS,Prevention,"Fecal occult blood test, sigmoidoscopy, or colonoscopy among adults aged 50-75 years",%,Crude prevalence,65.4000000000,,,63.1000000000,67.3000000000,2453,POINT (-156.2504199 20.86252093),15009030201,PREVENT,COLON_SCREEN,CrdPrv,Colorectal Cancer Screening +2019,HI,Hawaii,Maui,15009,15009030800,BRFSS,Prevention,Taking medicine for high blood pressure control among adults aged >=18 years with high blood pressure,%,Crude prevalence,67.7000000000,,,67.1000000000,68.2000000000,6907,POINT (-156.5426669 20.90996813),15009030800,PREVENT,BPMED,CrdPrv,Taking BP Medication +2019,HI,Hawaii,Kauai,15007,15007040604,BRFSS,Prevention,Taking medicine for high blood pressure control among adults aged >=18 years with high blood pressure,%,Crude prevalence,76.2000000000,,,75.8000000000,76.7000000000,3139,POINT (-159.4840794 21.90956079),15007040604,PREVENT,BPMED,CrdPrv,Taking BP Medication +2018,HI,Hawaii,Maui,15009,15009030402,BRFSS,Prevention,Mammography use among women aged 50-74 years,%,Crude prevalence,79.6000000000,,,77.8000000000,81.3000000000,8652,POINT (-156.3303372 20.82505697),15009030402,PREVENT,MAMMOUSE,CrdPrv,Mammography +2019,HI,Hawaii,Maui,15009,15009030800,BRFSS,Health Outcomes,Obesity among adults aged >=18 years,%,Crude prevalence,24.3000000000,,,23.8000000000,24.7000000000,6907,POINT (-156.5426669 20.90996813),15009030800,HLTHOUT,OBESITY,CrdPrv,Obesity +2018,HI,Hawaii,Honolulu,15003,15003010201,BRFSS,Prevention,Cervical cancer screening among adult women aged 21-65 years,%,Crude prevalence,87.3000000000,,,86.7000000000,88.1000000000,5882,POINT (-157.8941068 21.55452063),15003010201,PREVENT,CERVICAL,CrdPrv,Cervical Cancer Screening +2019,HI,Hawaii,Kauai,15007,15007040603,BRFSS,Health Outcomes,Coronary heart disease among adults aged >=18 years,%,Crude prevalence,6.1000000000,,,5.8000000000,6.5000000000,2544,POINT (-159.4384998 21.90703588),15007040603,HLTHOUT,CHD,CrdPrv,Coronary Heart Disease +2019,HI,Hawaii,Kauai,15007,15007040603,BRFSS,Health Risk Behaviors,Binge drinking among adults aged >=18 years,%,Crude prevalence,17.9000000000,,,17.5000000000,18.2000000000,2544,POINT (-159.4384998 21.90703588),15007040603,RISKBEH,BINGE,CrdPrv,Binge Drinking +2019,HI,Hawaii,Kauai,15007,15007040604,BRFSS,Health Outcomes,Obesity among adults aged >=18 years,%,Crude prevalence,26.0000000000,,,25.6000000000,26.5000000000,3139,POINT (-159.4840794 21.90956079),15007040604,HLTHOUT,OBESITY,CrdPrv,Obesity +2018,HI,Hawaii,Kauai,15007,15007040603,BRFSS,Prevention,Mammography use among women aged 50-74 years,%,Crude prevalence,77.1000000000,,,75.2000000000,79.0000000000,2544,POINT (-159.4384998 21.90703588),15007040603,PREVENT,MAMMOUSE,CrdPrv,Mammography +2019,HI,Hawaii,Maui,15009,15009030100,BRFSS,Health Outcomes,Cancer (excluding skin cancer) among adults aged >=18 years,%,Crude prevalence,6.9000000000,,,6.8000000000,7.1000000000,2291,POINT (-156.1446943 20.72704536),15009030100,HLTHOUT,CANCER,CrdPrv,Cancer (except skin) +2019,HI,Hawaii,Honolulu,15003,15003010201,BRFSS,Health Outcomes,High blood pressure among adults aged >=18 years,%,Crude prevalence,31.2000000000,,,30.8000000000,31.8000000000,5882,POINT (-157.8941068 21.55452063),15003010201,HLTHOUT,BPHIGH,CrdPrv,High Blood Pressure +2019,HI,Hawaii,Honolulu,15003,15003010201,BRFSS,Health Status,Mental health not good for >=14 days among adults aged >=18 years,%,Crude prevalence,15.1000000000,,,14.5000000000,15.7000000000,5882,POINT (-157.8941068 21.55452063),15003010201,HLTHSTAT,MHLTH,CrdPrv,Mental Health +2018,HI,Hawaii,Maui,15009,15009030201,BRFSS,Prevention,Cervical cancer screening among adult women aged 21-65 years,%,Crude prevalence,87.7000000000,,,86.6000000000,88.8000000000,2453,POINT (-156.2504199 20.86252093),15009030201,PREVENT,CERVICAL,CrdPrv,Cervical Cancer Screening +2019,HI,Hawaii,Maui,15009,15009030800,BRFSS,Health Outcomes,Coronary heart disease among adults aged >=18 years,%,Crude prevalence,4.3000000000,,,4.2000000000,4.4000000000,6907,POINT (-156.5426669 20.90996813),15009030800,HLTHOUT,CHD,CrdPrv,Coronary Heart Disease +2018,HI,Hawaii,Kauai,15007,15007040700,BRFSS,Health Risk Behaviors,Sleeping less than 7 hours among adults aged >=18 years,%,Crude prevalence,36.4000000000,,,35.6000000000,36.9000000000,8403,POINT (-159.5219447 21.94546074),15007040700,RISKBEH,SLEEP,CrdPrv,Sleep <7 hours +2019,HI,Hawaii,Kauai,15007,15007040604,BRFSS,Prevention,Cholesterol screening among adults aged >=18 years,%,Crude prevalence,82.8000000000,,,82.7000000000,82.9000000000,3139,POINT (-159.4840794 21.90956079),15007040604,PREVENT,CHOLSCREEN,CrdPrv,Cholesterol Screening +2019,HI,Hawaii,Maui,15009,15009030402,BRFSS,Health Outcomes,High blood pressure among adults aged >=18 years,%,Crude prevalence,25.2000000000,,,24.6000000000,25.9000000000,8652,POINT (-156.3303372 20.82505697),15009030402,HLTHOUT,BPHIGH,CrdPrv,High Blood Pressure +2018,HI,Hawaii,Kauai,15007,15007040700,BRFSS,Prevention,"Older adult men aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening",%,Crude prevalence,27.8000000000,,,25.4000000000,30.3000000000,8403,POINT (-159.5219447 21.94546074),15007040700,PREVENT,COREM,CrdPrv,Core preventive services for older men +2019,HI,Hawaii,Maui,15009,15009030800,BRFSS,Health Outcomes,Chronic kidney disease among adults aged >=18 years,%,Crude prevalence,2.3000000000,,,2.3000000000,2.4000000000,6907,POINT (-156.5426669 20.90996813),15009030800,HLTHOUT,KIDNEY,CrdPrv,Chronic Kidney Disease +2018,HI,Hawaii,Honolulu,15003,15003010201,BRFSS,Prevention,"Older adult men aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening",%,Crude prevalence,25.0000000000,,,23.0000000000,27.0000000000,5882,POINT (-157.8941068 21.55452063),15003010201,PREVENT,COREM,CrdPrv,Core preventive services for older men +2018,HI,Hawaii,Kauai,15007,15007040604,BRFSS,Prevention,"Fecal occult blood test, sigmoidoscopy, or colonoscopy among adults aged 50-75 years",%,Crude prevalence,64.2000000000,,,62.8000000000,65.7000000000,3139,POINT (-159.4840794 21.90956079),15007040604,PREVENT,COLON_SCREEN,CrdPrv,Colorectal Cancer Screening diff --git a/data/data-pipeline/data_pipeline/tests/sources/cdc_places/data/output.csv b/data/data-pipeline/data_pipeline/tests/sources/cdc_places/data/output.csv new file mode 100644 index 00000000..f5bc346b --- /dev/null +++ b/data/data-pipeline/data_pipeline/tests/sources/cdc_places/data/output.csv @@ -0,0 +1,16 @@ +GEOID10_TRACT,Diagnosed diabetes among adults aged greater than or equal to 18 years,Current asthma among adults aged greater than or equal to 18 years,Coronary heart disease among adults aged greater than or equal to 18 years,Cancer (excluding skin cancer) among adults aged greater than or equal to 18 years,Current lack of health insurance among adults aged 18-64 years,Physical health not good for greater than or equal to 14 days among adults aged greater than or equal to 18 years +06027000800,13.1000000000,10.1000000000,8.6000000000,8.3000000000,14.2000000000,16.8000000000 +06061021322,6.9000000000,8.6000000000,3.9000000000,5.9000000000,9.0000000000,9.4000000000 +06069000802,10.2000000000,9.3000000000,5.8000000000,7.0000000000,13.2000000000,13.4000000000 +15001021010,11.2000000000,11.3000000000,6.9000000000,5.9000000000,12.5000000000,16.7000000000 +15001021101,11.5000000000,10.4000000000,7.7000000000,6.8000000000,10.2000000000,16.0000000000 +15001021402,11.2000000000,9.2000000000,6.0000000000,6.3000000000,11.4000000000,12.4000000000 +15001021800,10.7000000000,9.8000000000,6.4000000000,6.7000000000,10.8000000000,13.2000000000 +15003010201,10.2000000000,12.2000000000,5.5000000000,6.4000000000,10.2000000000,13.0000000000 +15007040603,12.2000000000,9.0000000000,6.1000000000,7.0000000000,11.4000000000,12.3000000000 +15007040604,12.3000000000,9.6000000000,6.4000000000,6.7000000000,11.7000000000,13.2000000000 +15007040700,10.7000000000,8.7000000000,5.0000000000,6.3000000000,10.2000000000,10.8000000000 +15009030100,10.7000000000,10.2000000000,6.0000000000,6.9000000000,11.1000000000,13.0000000000 +15009030201,8.9000000000,9.7000000000,5.0000000000,6.5000000000,9.3000000000,12.4000000000 +15009030402,9.6000000000,8.9000000000,4.8000000000,6.1000000000,10.3000000000,11.0000000000 +15009030800,9.3000000000,9.1000000000,4.3000000000,5.4000000000,10.6000000000,10.7000000000 diff --git a/data/data-pipeline/data_pipeline/tests/sources/cdc_places/data/transform.csv b/data/data-pipeline/data_pipeline/tests/sources/cdc_places/data/transform.csv new file mode 100644 index 00000000..947e039e --- /dev/null +++ b/data/data-pipeline/data_pipeline/tests/sources/cdc_places/data/transform.csv @@ -0,0 +1,16 @@ +GEOID10_TRACT,All teeth lost among adults aged >=65 years,Arthritis among adults aged >=18 years,Binge drinking among adults aged >=18 years,Cancer (excluding skin cancer) among adults aged greater than or equal to 18 years,Cervical cancer screening among adult women aged 21-65 years,Cholesterol screening among adults aged >=18 years,Chronic kidney disease among adults aged >=18 years,Chronic obstructive pulmonary disease among adults aged >=18 years,Coronary heart disease among adults aged greater than or equal to 18 years,Current asthma among adults aged greater than or equal to 18 years,Current lack of health insurance among adults aged 18-64 years,Current smoking among adults aged >=18 years,Depression among adults aged >=18 years,Diagnosed diabetes among adults aged greater than or equal to 18 years,Fair or poor self-rated health status among adults aged >=18 years,"Fecal occult blood test, sigmoidoscopy, or colonoscopy among adults aged 50-75 years",High blood pressure among adults aged >=18 years,High cholesterol among adults aged >=18 years who have been screened in the past 5 years,Mammography use among women aged 50-74 years,Mental health not good for >=14 days among adults aged >=18 years,No leisure-time physical activity among adults aged >=18 years,Obesity among adults aged >=18 years,"Older adult men aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening","Older adult women aged >=65 years who are up to date on a core set of clinical preventive services: Flu shot past year, PPV shot ever, Colorectal cancer screening, and Mammogram past 2 years",Physical health not good for greater than or equal to 14 days among adults aged greater than or equal to 18 years,Sleeping less than 7 hours among adults aged >=18 years,Stroke among adults aged >=18 years,Taking medicine for high blood pressure control among adults aged >=18 years with high blood pressure,Visits to dentist or dental clinic among adults aged >=18 years,Visits to doctor for routine checkup within the past year among adults aged >=18 years +06027000800,17.7000000000,28.5000000000,16.2000000000,8.3000000000,82.0000000000,86.4000000000,3.9000000000,9.2000000000,8.6000000000,10.1000000000,14.2000000000,17.2000000000,19.5000000000,13.1000000000,24.2000000000,61.5000000000,36.1000000000,35.8000000000,74.9000000000,15.1000000000,26.9000000000,30.2000000000,26.5000000000,25.6000000000,16.8000000000,33.1000000000,4.6000000000,74.3000000000,58.1000000000,72.5000000000 +06061021322,7.6000000000,18.7000000000,18.9000000000,5.9000000000,83.4000000000,86.2000000000,2.2000000000,4.2000000000,3.9000000000,8.6000000000,9.0000000000,11.4000000000,17.2000000000,6.9000000000,12.3000000000,72.1000000000,23.2000000000,28.8000000000,78.2000000000,12.0000000000,16.6000000000,23.0000000000,35.7000000000,34.4000000000,9.4000000000,31.9000000000,2.2000000000,67.4000000000,71.8000000000,70.2000000000 +06069000802,12.3000000000,23.6000000000,18.7000000000,7.0000000000,84.7000000000,87.1000000000,2.9000000000,6.5000000000,5.8000000000,9.3000000000,13.2000000000,14.5000000000,19.2000000000,10.2000000000,18.9000000000,65.7000000000,30.2000000000,32.7000000000,77.6000000000,13.5000000000,22.7000000000,29.2000000000,30.3000000000,29.3000000000,13.4000000000,31.9000000000,3.2000000000,70.6000000000,64.8000000000,71.1000000000 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b/data/data-pipeline/data_pipeline/tests/sources/cdc_places/test_etl.py new file mode 100644 index 00000000..dbffdfa5 --- /dev/null +++ b/data/data-pipeline/data_pipeline/tests/sources/cdc_places/test_etl.py @@ -0,0 +1,25 @@ +import pathlib +from data_pipeline.tests.sources.example.test_etl import TestETL +from data_pipeline.etl.sources.cdc_places.etl import CDCPlacesETL + + +class TestCDCPlacesETL(TestETL): + _ETL_CLASS = CDCPlacesETL + + _SAMPLE_DATA_PATH = pathlib.Path(__file__).parents[0] / "data" + _SAMPLE_DATA_FILE_NAME = "census_tract.csv" + _SAMPLE_DATA_ZIP_FILE_NAME = None + _EXTRACT_TMP_FOLDER_NAME = "cdc_places" + + def setup_method(self, _method, filename=__file__): + """Invoke `setup_method` from Parent, but using the current file name. + + This code can be copied identically between all child classes. + """ + super().setup_method(_method=_method, filename=filename) + + def test_sample_data_exists(self): + """This will test that the sample data exists where it's supposed to as it's supposed to + As per conversation with Jorge, here we can *just* test that the zip file exists. + """ + assert (self._SAMPLE_DATA_PATH / self._SAMPLE_DATA_FILE_NAME).exists() diff --git a/data/data-pipeline/data_pipeline/tests/sources/doe_energy_burden/test_etl.py b/data/data-pipeline/data_pipeline/tests/sources/doe_energy_burden/test_etl.py index bb24ba3e..70b8a285 100644 --- a/data/data-pipeline/data_pipeline/tests/sources/doe_energy_burden/test_etl.py +++ b/data/data-pipeline/data_pipeline/tests/sources/doe_energy_burden/test_etl.py @@ -59,3 +59,13 @@ class TestDOEEnergyBurdenETL(TestETL): data_path / "dataset" / "doe_energy_burden" / "usa.csv" ) assert output_file_path == expected_output_file_path + + def test_tract_id_lengths(self, mock_etl, mock_paths): + etl = self._setup_etl_instance_and_run_extract( + mock_etl=mock_etl, mock_paths=mock_paths + ) + etl.transform() + etl.validate() + etl.load() + df = etl.get_data_frame() + assert 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b/data/data-pipeline/data_pipeline/tests/sources/ejscreen/data/extract.csv @@ -0,0 +1,16 @@ 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+4529,6027000800,3054,3009,2337,1420,2067,1443,1218,0.3988212181,1210,0.4021269525,475,0.2032520325,134,0.0943661972,129,0.0422396857,747,0.2445972495,62,0.0429660430,763,0.3691340106,0.4004740853,0.2309005559,1223.0478564307,705.1702977293,135.9429095904,144.8520486255,0.0162608457,20.0000000000,0.2000000000,134.3731709435,0.0000000476,0.0088169702,0.0161739005,0.0231458734,59.8143830065,5.9332945205,0.0271801764,50.1811514356,2.2105466749,2718.8581918080,27.1885819181,18267.0798289539,0.0000064773,1.1986045786,2.1987270931,3.1465173743,8131.3412612630,806.5893205801,3.6949522625,California,CA,9,58.2565807824,70.8357682483,82.0300855712,83.4211514441,22.4791060804,91.4310072487,20.6342392033,44.8003303446,69.4492207493,64.4805710566,73.9747591523,41.2001973366,69.9936559849,0.4881982980,32.2031638835,14.4688811492,33.6358789383,2.7793036790,3.1380644255,0.3541522801,2.0598614138,97.6642425963,3.6388096802,6.3535808084,71.4956721564,59.1319320934,61.5316181718,60.9745786385,62.4689837463,62.0864910202,59.8317854029,59.0710337447,59.2599060994,64.9284478117,62.2619591744,60.9702180540,6,8,9,9,3,10,3,5,7,7,8,5,7,1,4,2,4,1,1,1,1,11,1,1,8,6,7,7,7,7,6,6,6,7,7,7,40% (58%ile),40% (70%ile),20% (82%ile),9% (83%ile),4% (22%ile),24% (91%ile),4% (44%ile),40% (64%ile),0.37 = fraction pre-1960 (69%ile),71%ile,0.0163 ug/m3 (0%ile),59%ile,20 lifetime risk per million (32%ile),61%ile,0.2 (14%ile),60%ile,130 daily vehicles/meters distance (33%ile),62%ile,0.000000048 toxicity-weighted concentration/meters distance (2%ile),62%ile,0.0088 sites/km distance (3%ile),59%ile,0.016 facilities/km distance (0%ile),59%ile,0.023 facilities/km distance (2%ile),59%ile,59.8 ppb (97%ile),64%ile,5.93 ug/m3 (3%ile),62%ile,0.027 facilities/sq km area (6%ile),60%ile,17743852489.0000000000,41257887.0000000000,0,1,969231.5231135677,27404749177.8422279358 +8028,6061021322,20899,20874,13290,6549,6904,9172,9199,0.4401646012,3881,0.1859250743,825,0.0620767494,225,0.0343563903,1429,0.0683764773,2939,0.1406287382,312,0.0340165722,231,0.0334588644,0.3130448377,0.1552546718,6542.3240634282,3244.6673856589,-896.9052371663,-589.6780917541,0.1849562857,30.0000000000,0.5000000000,12.5173455346,0.2667203153,0.0687928975,0.4515663958,0.2027045525,52.7832287582,12.1102756164,0.0258826940,-30.0094307337,-165.8882612555,-26907.1571149896,-448.4526185832,-11226.8727654026,-239.2228476257,-61.7007100657,-405.0122653138,-181.8067747336,-47341.5543077505,-10861.7696239112,-23.2143238368,California,CA,9,61.7694531724,28.3124099080,32.2625612545,63.3138029183,65.9392366308,44.1611446180,92.1063805127,31.2336817151,19.3531578232,52.0599864076,48.1147912182,98.1253263672,8.5598852754,35.4160437794,83.7767623034,95.2520218071,6.7786023570,88.6613290583,53.5138135020,56.0049245976,28.8270859466,89.7745222973,94.2035706464,6.2511191138,43.0185694890,24.7769097248,17.2770098374,9.5647689629,49.9350307593,5.0850465016,20.5837755437,15.4478896201,34.6338200533,14.8104044330,10.3206402564,53.0011626680,7,3,4,7,7,5,10,4,2,6,5,11,1,4,9,11,1,9,6,6,3,9,10,1,5,3,2,1,5,1,3,2,4,2,2,6,44% (61%ile),19% (28%ile),6% (32%ile),3% (63%ile),7% (65%ile),14% (44%ile),3% (31%ile),31% (52%ile),0.033 = fraction pre-1960 (19%ile),43%ile,0.185 ug/m3 (35%ile),24%ile,30 lifetime risk per million (83%ile),17%ile,0.5 (95%ile),9%ile,13 daily vehicles/meters distance (6%ile),49%ile,0.27 toxicity-weighted concentration/meters distance (88%ile),5%ile,0.069 sites/km distance (53%ile),20%ile,0.45 facilities/km distance (56%ile),15%ile,0.2 facilities/km distance (28%ile),34%ile,52.8 ppb (89%ile),14%ile,12.1 ug/m3 (94%ile),10%ile,0.026 facilities/sq km area (6%ile),53%ile,258653359.0000000000,119890.0000000000,0,0,124755.3452199987,427225089.6229769588 +8849,6069000802,3049,3045,2076,955,1119,1493,1247,0.4089865530,747,0.2453201970,307,0.1478805395,31,0.0324607330,240,0.0787143326,468,0.1534929485,93,0.0622906899,390,0.3485254692,0.3271533750,0.1778092173,997.4906403941,542.1403034316,-87.8345013597,-17.2605942492,0.0375346206,20.0000000000,0.2000000000,15.7944927934,,0.0396183204,0.0811927061,0.1674220356,47.0434058824,7.4113546849,0.0102735941,-30.6125607956,-3.2968346872,-1756.6900271942,-17.5669002719,-1387.3013987358,,-3.4798554127,-7.1315208575,-14.7054310128,-4132.0340979390,-650.9726431509,-0.9023760119,California,CA,9,59.1858457424,41.3904741949,69.9513617378,62.0187896062,79.0518001240,52.1216510370,37.3180569516,68.3483551403,67.5701406274,54.3994266601,57.9926859232,26.1831217492,58.7612911558,2.0014414700,32.2031638835,14.4688811492,8.1570460385,,34.5749415665,10.3739430074,25.1131375379,84.5333172848,19.2864164585,4.9410824602,42.8621394303,58.0471933934,56.5430390950,57.0023528116,55.7266348497,,54.6373148803,57.1359685902,54.8116596007,56.2167239668,56.9568759225,56.2801621878,6,5,7,7,8,6,4,7,7,6,6,3,6,1,4,2,1,0,4,2,3,9,2,1,5,6,6,6,6,0,6,6,6,6,6,6,41% (59%ile),25% (41%ile),15% (69%ile),3% (62%ile),8% (79%ile),15% (52%ile),6% (68%ile),33% (54%ile),0.35 = fraction pre-1960 (67%ile),42%ile,0.0375 ug/m3 (2%ile),58%ile,20 lifetime risk per million (32%ile),56%ile,0.2 (14%ile),57%ile,16 daily vehicles/meters distance (8%ile),55%ile,,,0.04 sites/km distance (34%ile),54%ile,0.081 facilities/km distance (10%ile),57%ile,0.17 facilities/km distance (25%ile),54%ile,47 ppb (84%ile),56%ile,7.41 ug/m3 (19%ile),56%ile,0.01 facilities/sq km area (4%ile),56%ile,2987635876.0000000000,3272257.0000000000,1,0,422237.6856758550,4643687820.1565904617 +20324,15001021010,8606,8586,6124,3300,4089,3602,5362,0.6230536835,4430,0.5159562078,425,0.0693990856,36,0.0109090909,315,0.0366023704,1715,0.1992795724,502,0.1393670183,46,0.0112496943,0.5695049456,0.2425333351,4901.1595620778,2087.2418818153,1837.7590471768,508.2966127298,0.0067389217,10.0000000000,0.1000000000,0.1074143214,,0.0027318608,0.0478749209,0.0931096253,,,0.0259838494,20.6742274811,12.3845143014,18377.5904717679,183.7759047177,197.4016409694,,5.0205019537,87.9825690670,171.1130563323,,,47.7520542990,Hawaii,HI,9,74.7108013633,85.0291087110,36.7675143964,39.4832933303,15.2054293702,77.9602931979,98.1974410889,95.8100562593,9.2273848439,81.1726508957,76.5777942789,91.7961653862,85.2496673015,0.0699884723,1.8303662611,1.0748659980,0.5930748980,,0.1022787768,3.9663081684,14.5954101870,,,6.2654376121,66.8695670869,60.7245800447,74.1372134844,71.3832220072,58.5855777989,,62.7945832024,66.7236133386,67.8259227785,,,62.6039374599,8,9,4,4,2,8,11,11,1,9,8,10,9,1,1,1,1,0,1,1,2,0,0,1,7,7,8,8,6,0,7,7,7,0,0,7,62% (74%ile),52% (85%ile),7% (36%ile),1% (39%ile),4% (15%ile),20% (77%ile),14% (95%ile),57% (81%ile),0.011 = fraction pre-1960 (9%ile),66%ile,0.00674 ug/m3 (0%ile),60%ile,10 lifetime risk per million (1%ile),74%ile,0.1 (1%ile),71%ile,0.11 daily vehicles/meters distance (0%ile),58%ile,,,0.0027 sites/km distance (0%ile),62%ile,0.048 facilities/km distance (3%ile),66%ile,0.093 facilities/km distance (14%ile),67%ile,,,,,0.026 facilities/sq km area (6%ile),62%ile,151184621.0000000000,0.0000000000,0,0,71817.3979516648,171030272.0024483502 +20327,15001021101,3054,3049,2569,1543,1958,1227,1086,0.3555992141,1450,0.4755657593,159,0.0618917867,30,0.0194426442,92,0.0301244270,909,0.2976424361,144,0.1173594132,33,0.0168539326,0.4155824867,0.2067110446,1269.1889143982,631.2955301209,182.0839675579,70.9772810171,0.0033713587,10.0000000000,0.1000000000,1.7167679255,,0.0025910486,0.2484740667,0.2746856427,,,0.0375389154,3.0688309139,0.6138703733,1820.8396755785,18.2083967558,312.5959152482,,0.4717884163,45.2431438983,50.0158516497,,,6.8352346500,Hawaii,HI,9,53.9485559986,80.7725127831,32.1386664749,50.6977998974,9.0084816593,96.6414513706,60.5643076662,92.9519087655,12.2637725970,66.3100091245,67.7573127614,34.5728724616,65.2982705045,0.0304238852,1.8303662611,1.0748659980,2.1438079687,,0.0856704529,41.7496844562,33.9762800526,,,7.1437776548,61.7192931562,58.8385531222,60.6418036749,60.2753222588,58.6611251941,,59.1844884925,63.7189045047,63.4569851575,,,61.1560978740,6,9,4,6,1,11,7,10,2,7,7,4,7,1,1,1,1,0,1,5,4,0,0,1,7,6,7,7,6,0,6,7,7,0,0,7,36% (53%ile),48% (80%ile),6% (32%ile),2% (50%ile),3% (9%ile),30% (96%ile),12% (92%ile),42% (66%ile),0.017 = fraction pre-1960 (12%ile),61%ile,0.00337 ug/m3 (0%ile),58%ile,10 lifetime risk per million (1%ile),60%ile,0.1 (1%ile),60%ile,1.7 daily vehicles/meters distance (2%ile),58%ile,,,0.0026 sites/km distance (0%ile),59%ile,0.25 facilities/km distance (41%ile),63%ile,0.27 facilities/km distance (33%ile),63%ile,,,,,0.038 facilities/sq km area (7%ile),61%ile,106332317.0000000000,11164968.0000000000,0,1,61396.6485753379,132838116.6897320002 +20331,15001021402,3778,3755,2731,1374,1583,1803,3034,0.8030704076,705,0.1877496671,214,0.0783595752,56,0.0407569141,284,0.0751720487,933,0.2469560614,23,0.0127565169,276,0.1743524953,0.4954100374,0.2386774457,1871.6591211718,901.7233898617,526.8383978048,208.5726547229,0.0131608945,10.0000000000,0.1000000000,635.9981128640,,0.0033357209,0.0225482603,0.6278707343,,,0.5088713177,91.8555892572,6.9336645713,5268.3839780481,52.6838397805,335068.2267881447,,1.7573858477,11.8792893273,330.7864116735,,,268.0929496982,Hawaii,HI,9,84.7051677398,28.7308989413,41.8348284107,67.0398022547,74.9080519616,91.8116448026,3.9836481634,4.5613171192,47.5766504879,74.5975822746,75.7504514060,55.7976616902,73.4870488517,0.2827998785,1.8303662611,1.0748659980,72.8760884284,,0.2741900811,0.7727230166,47.2090589343,,,27.1111550629,75.5651255220,59.8939075539,64.0364159199,62.9860278691,78.3054903833,,60.2827950818,60.4356420863,71.4747506314,,,66.1787674526,9,3,5,7,8,10,1,1,5,8,8,6,8,1,1,1,8,0,1,1,5,0,0,3,8,6,7,7,8,0,7,7,8,0,0,7,80% (84%ile),19% (28%ile),8% (41%ile),4% (67%ile),8% (74%ile),25% (91%ile),1% (4%ile),50% (74%ile),0.17 = fraction pre-1960 (47%ile),75%ile,0.0132 ug/m3 (0%ile),59%ile,10 lifetime risk per million (1%ile),64%ile,0.1 (1%ile),62%ile,640 daily vehicles/meters distance (72%ile),78%ile,,,0.0033 sites/km distance (0%ile),60%ile,0.023 facilities/km distance (0%ile),60%ile,0.63 facilities/km distance (47%ile),71%ile,,,,,0.51 facilities/sq km area (27%ile),66%ile,41940841.0000000000,6313950.0000000000,0,1,49320.0395726063,54601507.7207057551 +20340,15001021800,5998,5977,4357,2112,2631,3179,4020,0.6702234078,1613,0.2698678267,180,0.0413128299,76,0.0359848485,352,0.0586862287,1411,0.2352450817,241,0.0758100031,441,0.1676168757,0.4700456172,0.2185533706,2819.3336121800,1310.8831165809,684.2794304026,210.4283496771,0.0049503455,10.0000000000,0.1000000000,0.0743045071,,0.0038298946,0.0402733327,0.0410968274,,,0.1071290552,114.6967802385,3.3874195708,6842.7943040262,68.4279430403,50.8450457862,,2.6207181028,27.5582131707,28.1217136206,,,73.3062089025,Hawaii,HI,9,77.3411360526,46.5995830170,19.2008132329,64.3649112351,50.1501995746,89.5998226210,84.7007475319,78.6950985799,46.5822171086,72.0610072717,71.0200691830,75.5958544882,73.5619777633,0.0461211856,1.8303662611,1.0748659980,0.4752886632,,0.3337575583,2.8011103792,5.4042726335,,,11.7520153164,77.1764003076,59.3164619569,65.4523902797,64.1879137098,58.4409328484,,60.9620163260,62.2019174282,62.1288554973,,,63.2369902585,8,5,2,7,6,9,9,8,5,8,8,8,8,1,1,1,1,0,1,1,1,0,0,2,8,6,7,7,6,0,7,7,7,0,0,7,67% (77%ile),27% (46%ile),4% (19%ile),4% (64%ile),6% (50%ile),24% (89%ile),8% (78%ile),47% (72%ile),0.17 = fraction pre-1960 (46%ile),77%ile,0.00495 ug/m3 (0%ile),59%ile,10 lifetime risk per million (1%ile),65%ile,0.1 (1%ile),64%ile,0.074 daily vehicles/meters distance (0%ile),58%ile,,,0.0038 sites/km distance (0%ile),60%ile,0.04 facilities/km distance (2%ile),62%ile,0.041 facilities/km distance (5%ile),62%ile,,,,,0.11 facilities/sq km area (11%ile),63%ile,365110254.0000000000,37900489.0000000000,0,0,92961.9049100969,459707845.8010936975 +20560,15003010201,4936,4798,3182,1441,1938,2266,3695,0.7485818476,1439,0.2999166319,231,0.0725958517,49,0.0340041638,476,0.0964343598,651,0.1318881686,115,0.0507502207,413,0.2131062951,0.5242492398,0.2305701706,2587.6942476032,1138.0943619037,830.6706662005,232.4850372225,0.0171119880,10.0000000000,0.1000000000,1493.8870892160,,0.0694550700,0.0548137804,0.4080845621,,,0.0995447326,177.0211481635,14.2144264416,8306.7066620049,83.0670666200,1240928.1836273719,,57.6942892272,45.5321994534,338.9838750865,,,82.6888893711,Hawaii,HI,9,81.7629198374,52.7224313484,38.5909198362,63.0702607098,91.9817357703,38.9013315993,48.2935747432,55.4563410918,53.0116816593,77.2470730276,73.8991238733,68.7030659293,74.5973100320,0.5552831600,1.8303662611,1.0748659980,88.8972054263,,53.8623224639,5.1063675682,39.9537688137,,,11.2751492958,80.6790478991,60.9966060167,66.7493132622,65.1696166202,90.1940360178,,78.6444132849,63.7434121326,71.6211763075,,,63.4227438652,9,6,4,7,10,4,5,6,6,8,8,7,8,1,1,1,9,0,6,1,4,0,0,2,9,7,7,7,10,0,8,7,8,0,0,7,75% (81%ile),30% (52%ile),7% (38%ile),3% (63%ile),10% (91%ile),13% (38%ile),5% (55%ile),52% (77%ile),0.21 = fraction pre-1960 (53%ile),80%ile,0.0171 ug/m3 (0%ile),60%ile,10 lifetime risk per million (1%ile),66%ile,0.1 (1%ile),65%ile,1500 daily vehicles/meters distance (88%ile),90%ile,,,0.069 sites/km distance (53%ile),78%ile,0.055 facilities/km distance (5%ile),63%ile,0.41 facilities/km distance (39%ile),71%ile,,,,,0.1 facilities/sq km area (11%ile),63%ile,66256288.0000000000,7249455.0000000000,0,0,42997.4044651793,85395519.3857139796 +20614,15007040603,2984,2978,2104,1058,2064,1468,2011,0.6739276139,797,0.2676292814,138,0.0655893536,33,0.0311909263,168,0.0563002681,756,0.2533512064,64,0.0435967302,193,0.0935077519,0.4707784477,0.2246647750,1404.8028878442,670.3996884791,342.6152122150,122.9243592959,0.0225796264,10.0000000000,0.1000000000,255.5966484444,,0.0065810172,0.1042895043,0.5200441984,,,0.1610354485,32.0371782740,7.7361234992,3426.1521221502,34.2615212215,87571.2999482384,,2.2547565999,35.7311706322,178.1750534077,,,55.1731943631,Hawaii,HI,9,77.5456369017,46.0970850340,34.4418368771,61.1045870101,46.0008635792,92.7630589707,21.6211577108,45.6650954498,34.4080455626,72.1367523014,72.5017628853,38.1901457794,68.6478207991,0.9188048692,1.8303662611,1.0748659980,48.7907692784,,1.7474241413,15.4267424965,43.8004140051,,,14.6634418792,68.8698351102,60.0337698662,62.2468364816,61.5558275260,68.4712288534,,60.6727330898,62.8928664453,68.0134997705,,,62.8256530053,8,5,4,7,5,10,3,5,4,8,8,4,7,1,1,1,5,0,1,2,5,0,0,2,7,7,7,7,7,0,7,7,7,0,0,7,67% (77%ile),27% (46%ile),7% (34%ile),3% (61%ile),6% (46%ile),25% (92%ile),4% (45%ile),47% (72%ile),0.094 = fraction pre-1960 (34%ile),68%ile,0.0226 ug/m3 (0%ile),60%ile,10 lifetime risk per million (1%ile),62%ile,0.1 (1%ile),61%ile,260 daily vehicles/meters distance (48%ile),68%ile,,,0.0066 sites/km distance (1%ile),60%ile,0.1 facilities/km distance (15%ile),62%ile,0.52 facilities/km distance (43%ile),68%ile,,,,,0.16 facilities/sq km area (14%ile),62%ile,41255867.0000000000,7041518.0000000000,0,0,36855.9892981643,56378891.3118786365 +20615,15007040604,3529,3458,2370,1187,1625,1757,2203,0.6242561632,1275,0.3687102371,133,0.0561181435,42,0.0353833193,333,0.0943610088,632,0.1790875602,109,0.0620375640,322,0.1981538462,0.4964832002,0.2263194054,1752.0892134182,798.6811814815,495.9027833594,151.2145472028,0.0297040750,10.0000000000,0.1000000000,464.0468169721,,0.0064334940,0.1282189641,0.3810520320,,,0.2277699060,98.2650438411,14.7303334730,4959.0278335940,49.5902783359,230122.1081455294,,3.1903875793,63.5841411863,188.9647632974,,,112.9517303753,Hawaii,HI,9,74.7796778307,65.5138546463,28.4761545436,63.9820945208,90.9928060818,67.6211097498,45.4287763592,68.1087856009,51.0102487547,74.6953085329,72.8443904311,48.6363888652,70.3699602168,1.3729134893,1.8303662611,1.0748659980,64.8054389268,,1.7014251995,20.6071512154,38.9237463430,,,17.5743663328,76.0259182532,61.0699450431,63.7308514802,62.7533582560,75.0094643983,,61.3999056181,65.1045697304,68.3212418487,,,64.0124628341,8,7,3,7,10,7,5,7,6,8,8,5,8,1,1,1,7,0,1,3,4,0,0,2,8,7,7,7,8,0,7,7,7,0,0,7,62% (74%ile),37% (65%ile),6% (28%ile),4% (63%ile),9% (90%ile),18% (67%ile),6% (68%ile),50% (74%ile),0.2 = fraction pre-1960 (51%ile),76%ile,0.0297 ug/m3 (1%ile),61%ile,10 lifetime risk per million (1%ile),63%ile,0.1 (1%ile),62%ile,460 daily vehicles/meters distance (64%ile),75%ile,,,0.0064 sites/km distance (1%ile),61%ile,0.13 facilities/km distance (20%ile),65%ile,0.38 facilities/km distance (38%ile),68%ile,,,,,0.23 facilities/sq km area (17%ile),64%ile,21724894.0000000000,2371158.0000000000,0,1,27760.3117775823,28129042.7970332205 +20616,15007040700,9552,9523,6234,2895,3298,4974,7071,0.7402638191,1980,0.2079176730,309,0.0495668912,95,0.0328151986,772,0.0808207705,1834,0.1920016750,205,0.0412143144,346,0.1049120679,0.4740907460,0.2172310046,4528.5148062585,2074.9905558098,1128.3751689898,322.4823975128,0.0120486502,10.0000000000,0.1000000000,829.6297843840,,0.0062317499,0.2776903565,0.5315584393,,,0.8605507426,118.3801723682,13.5953976825,11283.7516898982,112.8375168990,936133.6481533048,,7.0317517976,313.3389029609,599.7973437979,,,971.0240895638,Hawaii,HI,9,81.2804870193,33.1925490446,24.3539720817,62.2442189921,81.2507708079,74.5290795692,78.3337372056,42.2880946853,36.4589583341,72.4690666385,70.6843205648,91.6638490724,78.6800785920,0.2182262554,1.8303662611,1.0748659980,78.8807149861,,1.6540510210,44.6852775053,44.1677595343,,,35.8025350464,77.4223719690,60.9007569981,69.0968995957,67.2008685709,87.5969776098,,64.0977043448,75.6532891441,75.2184280558,,,72.0462363568,9,4,3,7,9,8,8,5,4,8,8,10,8,1,1,1,8,0,1,5,5,0,0,4,8,7,7,7,9,0,7,8,8,0,0,8,74% (81%ile),21% (33%ile),5% (24%ile),3% (62%ile),8% (81%ile),19% (74%ile),4% (42%ile),47% (72%ile),0.1 = fraction pre-1960 (36%ile),77%ile,0.012 ug/m3 (0%ile),60%ile,10 lifetime risk per million (1%ile),69%ile,0.1 (1%ile),67%ile,830 daily vehicles/meters distance (78%ile),87%ile,,,0.0062 sites/km distance (1%ile),64%ile,0.28 facilities/km distance (44%ile),75%ile,0.53 facilities/km distance (44%ile),75%ile,,,,,0.86 facilities/sq km area (35%ile),72%ile,93005151.0000000000,5658877.0000000000,0,1,70950.4293149945,115233329.7073323578 +20624,15009030100,1405,1374,980,467,796,729,1060,0.7544483986,400,0.2911208151,44,0.0448979592,0,0.0000000000,124,0.0882562278,342,0.2434163701,41,0.0562414266,170,0.2135678392,0.5227846069,0.2370232951,734.5123726346,333.0177296537,234.3871433253,75.2419798205,0.0026846006,10.0000000000,0.1000000000,,,0.0046765532,0.0398066625,0.0329594792,,,0.0973247551,50.0575557353,0.6292358547,2343.8714332533,23.4387143325,,,1.0961239415,9.3301699117,7.7252781836,,,22.8116713113,Hawaii,HI,9,82.0728020535,51.0042892937,21.4097431797,22.2149776961,87.3264635475,91.2196103997,10.4895772204,61.9590696975,53.0589655984,77.1174117652,75.3619764353,7.5930221772,65.5506156308,0.0292580509,1.8303662611,1.0748659980,,,0.7827594098,2.7444494654,3.8365691660,,,11.1452377410,71.4770935032,58.8413451490,61.1806956436,60.6714580607,,,59.7579323526,60.0938330974,59.9756316335,,,61.8121208766,9,6,3,3,9,10,2,7,6,8,8,1,7,1,1,1,0,0,1,1,1,0,0,2,8,6,7,7,0,0,6,7,6,0,0,7,75% (82%ile),29% (51%ile),4% (21%ile),0% (22%ile),9% (87%ile),24% (91%ile),6% (61%ile),52% (77%ile),0.21 = fraction pre-1960 (53%ile),71%ile,0.00268 ug/m3 (0%ile),58%ile,10 lifetime risk per million (1%ile),61%ile,0.1 (1%ile),60%ile,,,,,0.0047 sites/km distance (0%ile),59%ile,0.04 facilities/km distance (2%ile),60%ile,0.033 facilities/km distance (3%ile),59%ile,,,,,0.097 facilities/sq km area (11%ile),61%ile,555262221.0000000000,25398369.0000000000,0,0,165450.9181509207,667169893.1947253942 +20625,15009030201,2340,2327,1879,842,1045,1395,992,0.4239316239,623,0.2677266867,62,0.0329962746,0,0.0000000000,150,0.0641025641,554,0.2367521368,133,0.0953405018,97,0.0928229665,0.3458291553,0.1709182144,809.2402234637,399.9486215874,-23.7085570230,-29.3718443271,0.0063521816,10.0000000000,0.1000000000,7.0868595222,,0.0053511202,0.1292001112,0.0908033666,,,0.0098923140,-2.2006985945,-0.1506010605,-237.0855702299,-2.3708557023,-168.0192130960,,-0.1268673373,-3.0631482031,-2.1528167944,,,-0.2345324900,Hawaii,HI,9,60.4734810662,46.1223337837,13.8610651481,22.2149776961,59.2951174656,89.9006101655,56.2295738392,87.6116027815,34.2663836750,57.2620275919,55.1869446757,12.9269845729,57.7176364334,0.0651806253,1.8303662611,1.0748659980,4.6368984700,,1.3707744996,20.8229399202,14.1722671160,,,4.9075810703,55.1341860078,58.6779670685,58.4263245996,58.4806520514,57.3658121936,,58.5702573228,58.1201119303,58.2216584263,,,56.4332033900,7,5,2,3,6,9,6,9,4,6,6,2,6,1,1,1,1,0,1,3,2,0,0,1,6,6,6,6,6,0,6,6,6,0,0,6,42% (60%ile),27% (46%ile),3% (13%ile),0% (22%ile),6% (59%ile),24% (89%ile),10% (87%ile),35% (57%ile),0.093 = fraction pre-1960 (34%ile),55%ile,0.00635 ug/m3 (0%ile),58%ile,10 lifetime risk per million (1%ile),58%ile,0.1 (1%ile),58%ile,7.1 daily vehicles/meters distance (4%ile),57%ile,,,0.0054 sites/km distance (1%ile),58%ile,0.13 facilities/km distance (20%ile),58%ile,0.091 facilities/km distance (14%ile),58%ile,,,,,0.0099 facilities/sq km area (4%ile),56%ile,118113265.0000000000,4116462.0000000000,0,0,68639.8224567451,140691933.2772550285 +20629,15009030402,8562,8562,6540,3180,3473,4778,5420,0.6330296660,1535,0.1792805419,294,0.0449541284,39,0.0122641509,397,0.0463676711,1550,0.1810324690,210,0.0439514441,264,0.0760149726,0.4061551039,0.1828214379,3477.5000000000,1565.3171513473,429.7617698603,-5.5554252167,0.0153866969,10.0000000000,0.1000000000,233.6880574427,,0.0055146115,0.6633705951,0.5914191729,,,0.4432670413,32.6683291803,6.6126141119,4297.6176986027,42.9761769860,100430.1931617688,,2.3699691830,285.0913210180,254.1693504667,,,190.4992282028,Hawaii,HI,9,75.2886221409,26.8565530362,21.4439231945,41.5097856648,28.7449442824,68.6690121278,79.3833218173,46.1798708736,30.8409993308,65.1595288992,59.9703175674,82.9721886150,59.7346670413,0.4238414396,1.8303662611,1.0748659980,46.4851154373,,1.4363834459,65.6894718646,46.0793962757,,,25.2542546354,68.9789876320,59.8548490559,63.0853618909,62.2063722143,69.2554009969,,60.7689024131,74.9199434460,69.9134569074,,,65.2067338787,8,3,3,5,3,7,8,5,4,7,6,9,6,1,1,1,5,0,1,7,5,0,0,3,7,6,7,7,7,0,7,8,7,0,0,7,63% (75%ile),18% (26%ile),4% (21%ile),1% (41%ile),5% (28%ile),18% (68%ile),4% (46%ile),41% (65%ile),0.076 = fraction pre-1960 (30%ile),68%ile,0.0154 ug/m3 (0%ile),59%ile,10 lifetime risk per million (1%ile),63%ile,0.1 (1%ile),62%ile,230 daily vehicles/meters distance (46%ile),69%ile,,,0.0055 sites/km distance (1%ile),60%ile,0.66 facilities/km distance (65%ile),74%ile,0.59 facilities/km distance (46%ile),69%ile,,,,,0.44 facilities/sq km area (25%ile),65%ile,46066876.0000000000,109238.0000000000,0,1,49929.5140313853,53123180.2369696796 +20639,15009030800,7879,7871,5174,2235,2335,4210,6161,0.7819520244,1091,0.1386100877,195,0.0376884422,3,0.0013422819,594,0.0753902780,1027,0.1303464907,163,0.0387173397,285,0.1220556745,0.4602810560,0.1942216008,3626.5544403507,1530.2719926347,821.9375850282,84.7096204381,0.0169064550,10.0000000000,0.1000000000,575.9991000531,0.0008675195,0.0061499864,1.0347888110,0.5999348163,,,0.0263640121,100.3221463525,13.8960508008,8219.3758502819,82.1937585028,473435.3092760353,0.7130468458,5.0549049971,850.5318163110,493.1089740953,,,21.6695724544,Hawaii,HI,9,83.5697361596,17.7978784355,16.7867123482,22.7576898463,75.1762179508,37.9999341618,67.3605976499,38.4952526223,39.4719140158,71.0499579469,63.9144112870,82.0845801288,66.2238170511,0.5328167647,1.8303662611,1.0748659980,70.4238972397,46.4801242962,1.6311542890,76.8934515139,46.3897571267,,,6.2894452647,76.1966079716,60.9434745816,66.6801583126,65.1083356253,81.3398307482,78.4421389238,62.8183222708,84.3474497827,73.9856189804,,,61.7713841743,9,2,2,3,8,4,7,4,4,8,7,9,7,1,1,1,8,5,1,8,5,0,0,1,8,7,7,7,9,8,7,9,8,0,0,7,78% (83%ile),14% (17%ile),4% (16%ile),0% (22%ile),8% (75%ile),13% (37%ile),4% (38%ile),46% (71%ile),0.12 = fraction pre-1960 (39%ile),76%ile,0.0169 ug/m3 (0%ile),60%ile,10 lifetime risk per million (1%ile),66%ile,0.1 (1%ile),65%ile,580 daily vehicles/meters distance (70%ile),81%ile,0.00087 toxicity-weighted concentration/meters distance (46%ile),78%ile,0.0061 sites/km distance (1%ile),62%ile,1 facilities/km distance (76%ile),84%ile,0.6 facilities/km distance (46%ile),73%ile,,,,,0.026 facilities/sq km area (6%ile),61%ile,141603534.0000000000,11781155.0000000000,0,0,80194.4536675024,176674254.7197769880 diff --git a/data/data-pipeline/data_pipeline/tests/sources/ejscreen/data/output.csv b/data/data-pipeline/data_pipeline/tests/sources/ejscreen/data/output.csv new file mode 100644 index 00000000..71421615 --- /dev/null +++ b/data/data-pipeline/data_pipeline/tests/sources/ejscreen/data/output.csv @@ -0,0 +1,16 @@ +GEOID10_TRACT,Total population,Air toxics cancer risk,Respiratory hazard index,Diesel particulate matter exposure,PM2.5 in the air,Ozone,Traffic proximity and volume,Proximity to Risk Management Plan (RMP) facilities,Proximity to hazardous waste sites,Proximity to NPL sites,Wastewater discharge,Percent of households in linguistic isolation,Poverty (Less than 200% of federal poverty line),Individuals over 64 years old,Individuals under 5 years old,Percent pre-1960s housing (lead paint indicator),Leaky underground storage tanks +06027000800,3054,20.0000000000,0.2000000000,0.0162608457,5.9332945205,59.8143830065,134.3731709435,0.0161739005,0.0231458734,0.0088169702,0.0000000476,0.0943661972,0.4021269525,0.2445972495,0.0422396857,0.3691340106,0.0271801764 +06061021322,20899,30.0000000000,0.5000000000,0.1849562857,12.1102756164,52.7832287582,12.5173455346,0.4515663958,0.2027045525,0.0687928975,0.2667203153,0.0343563903,0.1859250743,0.1406287382,0.0683764773,0.0334588644,0.0258826940 +06069000802,3049,20.0000000000,0.2000000000,0.0375346206,7.4113546849,47.0434058824,15.7944927934,0.0811927061,0.1674220356,0.0396183204,,0.0324607330,0.2453201970,0.1534929485,0.0787143326,0.3485254692,0.0102735941 +15001021010,8606,10.0000000000,0.1000000000,0.0067389217,,,0.1074143214,0.0478749209,0.0931096253,0.0027318608,,0.0109090909,0.5159562078,0.1992795724,0.0366023704,0.0112496943,0.0259838494 +15001021101,3054,10.0000000000,0.1000000000,0.0033713587,,,1.7167679255,0.2484740667,0.2746856427,0.0025910486,,0.0194426442,0.4755657593,0.2976424361,0.0301244270,0.0168539326,0.0375389154 +15001021402,3778,10.0000000000,0.1000000000,0.0131608945,,,635.9981128640,0.0225482603,0.6278707343,0.0033357209,,0.0407569141,0.1877496671,0.2469560614,0.0751720487,0.1743524953,0.5088713177 +15001021800,5998,10.0000000000,0.1000000000,0.0049503455,,,0.0743045071,0.0402733327,0.0410968274,0.0038298946,,0.0359848485,0.2698678267,0.2352450817,0.0586862287,0.1676168757,0.1071290552 +15003010201,4936,10.0000000000,0.1000000000,0.0171119880,,,1493.8870892160,0.0548137804,0.4080845621,0.0694550700,,0.0340041638,0.2999166319,0.1318881686,0.0964343598,0.2131062951,0.0995447326 +15007040603,2984,10.0000000000,0.1000000000,0.0225796264,,,255.5966484444,0.1042895043,0.5200441984,0.0065810172,,0.0311909263,0.2676292814,0.2533512064,0.0563002681,0.0935077519,0.1610354485 +15007040604,3529,10.0000000000,0.1000000000,0.0297040750,,,464.0468169721,0.1282189641,0.3810520320,0.0064334940,,0.0353833193,0.3687102371,0.1790875602,0.0943610088,0.1981538462,0.2277699060 +15007040700,9552,10.0000000000,0.1000000000,0.0120486502,,,829.6297843840,0.2776903565,0.5315584393,0.0062317499,,0.0328151986,0.2079176730,0.1920016750,0.0808207705,0.1049120679,0.8605507426 +15009030100,1405,10.0000000000,0.1000000000,0.0026846006,,,,0.0398066625,0.0329594792,0.0046765532,,0.0000000000,0.2911208151,0.2434163701,0.0882562278,0.2135678392,0.0973247551 +15009030201,2340,10.0000000000,0.1000000000,0.0063521816,,,7.0868595222,0.1292001112,0.0908033666,0.0053511202,,0.0000000000,0.2677266867,0.2367521368,0.0641025641,0.0928229665,0.0098923140 +15009030402,8562,10.0000000000,0.1000000000,0.0153866969,,,233.6880574427,0.6633705951,0.5914191729,0.0055146115,,0.0122641509,0.1792805419,0.1810324690,0.0463676711,0.0760149726,0.4432670413 +15009030800,7879,10.0000000000,0.1000000000,0.0169064550,,,575.9991000531,1.0347888110,0.5999348163,0.0061499864,0.0008675195,0.0013422819,0.1386100877,0.1303464907,0.0753902780,0.1220556745,0.0263640121 diff --git a/data/data-pipeline/data_pipeline/tests/sources/ejscreen/data/transform.csv b/data/data-pipeline/data_pipeline/tests/sources/ejscreen/data/transform.csv new file mode 100644 index 00000000..9247c547 --- /dev/null +++ b/data/data-pipeline/data_pipeline/tests/sources/ejscreen/data/transform.csv @@ -0,0 +1,16 @@ +OBJECTID,GEOID10_TRACT,Total population,ACSIPOVBAS,ACSEDUCBAS,ACSTOTHH,ACSTOTHU,ACSUNEMPBAS,MINORPOP,MINORPCT,LOWINCOME,Poverty (Less than 200% of federal poverty line),LESSHS,LESSHSPCT,LINGISO,Percent of households in linguistic isolation,UNDER5,Individuals under 5 years old,OVER64,Individuals over 64 years old,UNEMP,UNEMPPCT,PRE1960,Percent pre-1960s housing (lead paint indicator),VULEOPCT,VULSVI6PCT,VULEO,VULSVI6,DISPEO,DISPSVI6,Diesel particulate matter exposure,Air toxics cancer risk,Respiratory hazard index,Traffic proximity and volume,Wastewater discharge,Proximity to NPL sites,Proximity to Risk Management Plan (RMP) facilities,Proximity to hazardous waste sites,Ozone,PM2.5 in the air,Leaky underground storage tanks,D_LDPNT_2,D_DSLPM_2,D_CANCR_2,D_RESP_2,D_PTRAF_2,D_PWDIS_2,D_PNPL_2,D_PRMP_2,D_PTSDF_2,D_OZONE_2,D_PM25_2,D_UST_2,STATE_NAME,ST_ABBREV,REGION,P_MINORPCT,P_LWINCPCT,P_LESHSPCT,P_LNGISPCT,P_UNDR5PCT,P_OVR64PCT,P_UNEMP,P_UNEMPPCT,P_LDPNT,P_VULEOPCT,P_VULSVI6PCT,P_VULSVI6,P_DISPSVI6,P_DSLPM,P_CANCR,P_RESP,P_PTRAF,P_PWDIS,P_PNPL,P_PRMP,P_PTSDF,P_OZONE,P_PM25,P_UST,P_LDPNT_D2,P_DSLPM_D2,P_CANCR_D2,P_RESP_D2,P_PTRAF_D2,P_PWDIS_D2,P_PNPL_D2,P_PRMP_D2,P_PTSDF_D2,P_OZONE_D2,P_PM25_D2,P_UST_D2,B_MINORPCT,B_LWINCPCT,B_LESHSPCT,B_LNGISPCT,B_UNDR5PCT,B_OVR64PCT,B_UNEMP,B_UNEMPPCT,B_LDPNT,B_VULEOPCT,B_VULSVI6PCT,B_VULSVI6,B_DISPSVI6,B_DSLPM,B_CANCR,B_RESP,B_PTRAF,B_PWDIS,B_PNPL,B_PRMP,B_PTSDF,B_OZONE,B_PM25,B_UST,B_LDPNT_D2,B_DSLPM_D2,B_CANCR_D2,B_RESP_D2,B_PTRAF_D2,B_PWDIS_D2,B_PNPL_D2,B_PRMP_D2,B_PTSDF_D2,B_OZONE_D2,B_PM25_D2,B_UST_D2,T_MINORPCT,T_LWINCPCT,T_LESHSPCT,T_LNGISPCT,T_UNDR5PCT,T_OVR64PCT,T_UNEMPPCT,T_VULEOPCT,T_LDPNT,T_LDPNT_D2,T_DSLPM,T_DSLPM_D2,T_CANCR,T_CANCR_D2,T_RESP,T_RESP_D2,T_PTRAF,T_PTRAF_D2,T_PWDIS,T_PWDIS_D2,T_PNPL,T_PNPL_D2,T_PRMP,T_PRMP_D2,T_PTSDF,T_PTSDF_D2,T_OZONE,T_OZONE_D2,T_PM25,T_PM25_D2,T_UST,T_UST_D2,AREALAND,AREAWATER,NPL_CNT,TSDF_CNT,Shape_Length,Shape_Area +4529,06027000800,3054,3009,2337,1420,2067,1443,1218,0.3988212181,1210,0.4021269525,475,0.2032520325,134,0.0943661972,129,0.0422396857,747,0.2445972495,62,0.0429660430,763,0.3691340106,0.4004740853,0.2309005559,1223.0478564307,705.1702977293,135.9429095904,144.8520486255,0.0162608457,20.0000000000,0.2000000000,134.3731709435,0.0000000476,0.0088169702,0.0161739005,0.0231458734,59.8143830065,5.9332945205,0.0271801764,50.1811514356,2.2105466749,2718.8581918080,27.1885819181,18267.0798289539,0.0000064773,1.1986045786,2.1987270931,3.1465173743,8131.3412612630,806.5893205801,3.6949522625,California,CA,9,58.2565807824,70.8357682483,82.0300855712,83.4211514441,22.4791060804,91.4310072487,20.6342392033,44.8003303446,69.4492207493,64.4805710566,73.9747591523,41.2001973366,69.9936559849,0.4881982980,32.2031638835,14.4688811492,33.6358789383,2.7793036790,3.1380644255,0.3541522801,2.0598614138,97.6642425963,3.6388096802,6.3535808084,71.4956721564,59.1319320934,61.5316181718,60.9745786385,62.4689837463,62.0864910202,59.8317854029,59.0710337447,59.2599060994,64.9284478117,62.2619591744,60.9702180540,6,8,9,9,3,10,3,5,7,7,8,5,7,1,4,2,4,1,1,1,1,11,1,1,8,6,7,7,7,7,6,6,6,7,7,7,40% (58%ile),40% (70%ile),20% (82%ile),9% (83%ile),4% (22%ile),24% (91%ile),4% (44%ile),40% (64%ile),0.37 = fraction pre-1960 (69%ile),71%ile,0.0163 ug/m3 (0%ile),59%ile,20 lifetime risk per million (32%ile),61%ile,0.2 (14%ile),60%ile,130 daily vehicles/meters distance (33%ile),62%ile,0.000000048 toxicity-weighted concentration/meters distance (2%ile),62%ile,0.0088 sites/km distance (3%ile),59%ile,0.016 facilities/km distance (0%ile),59%ile,0.023 facilities/km distance (2%ile),59%ile,59.8 ppb (97%ile),64%ile,5.93 ug/m3 (3%ile),62%ile,0.027 facilities/sq km area (6%ile),60%ile,17743852489.0000000000,41257887.0000000000,0,1,969231.5231135677,27404749177.8422279358 +8028,06061021322,20899,20874,13290,6549,6904,9172,9199,0.4401646012,3881,0.1859250743,825,0.0620767494,225,0.0343563903,1429,0.0683764773,2939,0.1406287382,312,0.0340165722,231,0.0334588644,0.3130448377,0.1552546718,6542.3240634282,3244.6673856589,-896.9052371663,-589.6780917541,0.1849562857,30.0000000000,0.5000000000,12.5173455346,0.2667203153,0.0687928975,0.4515663958,0.2027045525,52.7832287582,12.1102756164,0.0258826940,-30.0094307337,-165.8882612555,-26907.1571149896,-448.4526185832,-11226.8727654026,-239.2228476257,-61.7007100657,-405.0122653138,-181.8067747336,-47341.5543077505,-10861.7696239112,-23.2143238368,California,CA,9,61.7694531724,28.3124099080,32.2625612545,63.3138029183,65.9392366308,44.1611446180,92.1063805127,31.2336817151,19.3531578232,52.0599864076,48.1147912182,98.1253263672,8.5598852754,35.4160437794,83.7767623034,95.2520218071,6.7786023570,88.6613290583,53.5138135020,56.0049245976,28.8270859466,89.7745222973,94.2035706464,6.2511191138,43.0185694890,24.7769097248,17.2770098374,9.5647689629,49.9350307593,5.0850465016,20.5837755437,15.4478896201,34.6338200533,14.8104044330,10.3206402564,53.0011626680,7,3,4,7,7,5,10,4,2,6,5,11,1,4,9,11,1,9,6,6,3,9,10,1,5,3,2,1,5,1,3,2,4,2,2,6,44% (61%ile),19% (28%ile),6% (32%ile),3% (63%ile),7% (65%ile),14% (44%ile),3% (31%ile),31% (52%ile),0.033 = fraction pre-1960 (19%ile),43%ile,0.185 ug/m3 (35%ile),24%ile,30 lifetime risk per million (83%ile),17%ile,0.5 (95%ile),9%ile,13 daily vehicles/meters distance (6%ile),49%ile,0.27 toxicity-weighted concentration/meters distance (88%ile),5%ile,0.069 sites/km distance (53%ile),20%ile,0.45 facilities/km distance (56%ile),15%ile,0.2 facilities/km distance (28%ile),34%ile,52.8 ppb (89%ile),14%ile,12.1 ug/m3 (94%ile),10%ile,0.026 facilities/sq km area (6%ile),53%ile,258653359.0000000000,119890.0000000000,0,0,124755.3452199987,427225089.6229769588 +8849,06069000802,3049,3045,2076,955,1119,1493,1247,0.4089865530,747,0.2453201970,307,0.1478805395,31,0.0324607330,240,0.0787143326,468,0.1534929485,93,0.0622906899,390,0.3485254692,0.3271533750,0.1778092173,997.4906403941,542.1403034316,-87.8345013597,-17.2605942492,0.0375346206,20.0000000000,0.2000000000,15.7944927934,,0.0396183204,0.0811927061,0.1674220356,47.0434058824,7.4113546849,0.0102735941,-30.6125607956,-3.2968346872,-1756.6900271942,-17.5669002719,-1387.3013987358,,-3.4798554127,-7.1315208575,-14.7054310128,-4132.0340979390,-650.9726431509,-0.9023760119,California,CA,9,59.1858457424,41.3904741949,69.9513617378,62.0187896062,79.0518001240,52.1216510370,37.3180569516,68.3483551403,67.5701406274,54.3994266601,57.9926859232,26.1831217492,58.7612911558,2.0014414700,32.2031638835,14.4688811492,8.1570460385,,34.5749415665,10.3739430074,25.1131375379,84.5333172848,19.2864164585,4.9410824602,42.8621394303,58.0471933934,56.5430390950,57.0023528116,55.7266348497,,54.6373148803,57.1359685902,54.8116596007,56.2167239668,56.9568759225,56.2801621878,6,5,7,7,8,6,4,7,7,6,6,3,6,1,4,2,1,0,4,2,3,9,2,1,5,6,6,6,6,0,6,6,6,6,6,6,41% (59%ile),25% (41%ile),15% (69%ile),3% (62%ile),8% (79%ile),15% (52%ile),6% (68%ile),33% (54%ile),0.35 = fraction pre-1960 (67%ile),42%ile,0.0375 ug/m3 (2%ile),58%ile,20 lifetime risk per million (32%ile),56%ile,0.2 (14%ile),57%ile,16 daily vehicles/meters distance (8%ile),55%ile,,,0.04 sites/km distance (34%ile),54%ile,0.081 facilities/km distance (10%ile),57%ile,0.17 facilities/km distance (25%ile),54%ile,47 ppb (84%ile),56%ile,7.41 ug/m3 (19%ile),56%ile,0.01 facilities/sq km area (4%ile),56%ile,2987635876.0000000000,3272257.0000000000,1,0,422237.6856758550,4643687820.1565904617 +20324,15001021010,8606,8586,6124,3300,4089,3602,5362,0.6230536835,4430,0.5159562078,425,0.0693990856,36,0.0109090909,315,0.0366023704,1715,0.1992795724,502,0.1393670183,46,0.0112496943,0.5695049456,0.2425333351,4901.1595620778,2087.2418818153,1837.7590471768,508.2966127298,0.0067389217,10.0000000000,0.1000000000,0.1074143214,,0.0027318608,0.0478749209,0.0931096253,,,0.0259838494,20.6742274811,12.3845143014,18377.5904717679,183.7759047177,197.4016409694,,5.0205019537,87.9825690670,171.1130563323,,,47.7520542990,Hawaii,HI,9,74.7108013633,85.0291087110,36.7675143964,39.4832933303,15.2054293702,77.9602931979,98.1974410889,95.8100562593,9.2273848439,81.1726508957,76.5777942789,91.7961653862,85.2496673015,0.0699884723,1.8303662611,1.0748659980,0.5930748980,,0.1022787768,3.9663081684,14.5954101870,,,6.2654376121,66.8695670869,60.7245800447,74.1372134844,71.3832220072,58.5855777989,,62.7945832024,66.7236133386,67.8259227785,,,62.6039374599,8,9,4,4,2,8,11,11,1,9,8,10,9,1,1,1,1,0,1,1,2,0,0,1,7,7,8,8,6,0,7,7,7,0,0,7,62% (74%ile),52% (85%ile),7% (36%ile),1% (39%ile),4% (15%ile),20% (77%ile),14% (95%ile),57% (81%ile),0.011 = fraction pre-1960 (9%ile),66%ile,0.00674 ug/m3 (0%ile),60%ile,10 lifetime risk per million (1%ile),74%ile,0.1 (1%ile),71%ile,0.11 daily vehicles/meters distance (0%ile),58%ile,,,0.0027 sites/km distance (0%ile),62%ile,0.048 facilities/km distance (3%ile),66%ile,0.093 facilities/km distance (14%ile),67%ile,,,,,0.026 facilities/sq km area (6%ile),62%ile,151184621.0000000000,0.0000000000,0,0,71817.3979516648,171030272.0024483502 +20327,15001021101,3054,3049,2569,1543,1958,1227,1086,0.3555992141,1450,0.4755657593,159,0.0618917867,30,0.0194426442,92,0.0301244270,909,0.2976424361,144,0.1173594132,33,0.0168539326,0.4155824867,0.2067110446,1269.1889143982,631.2955301209,182.0839675579,70.9772810171,0.0033713587,10.0000000000,0.1000000000,1.7167679255,,0.0025910486,0.2484740667,0.2746856427,,,0.0375389154,3.0688309139,0.6138703733,1820.8396755785,18.2083967558,312.5959152482,,0.4717884163,45.2431438983,50.0158516497,,,6.8352346500,Hawaii,HI,9,53.9485559986,80.7725127831,32.1386664749,50.6977998974,9.0084816593,96.6414513706,60.5643076662,92.9519087655,12.2637725970,66.3100091245,67.7573127614,34.5728724616,65.2982705045,0.0304238852,1.8303662611,1.0748659980,2.1438079687,,0.0856704529,41.7496844562,33.9762800526,,,7.1437776548,61.7192931562,58.8385531222,60.6418036749,60.2753222588,58.6611251941,,59.1844884925,63.7189045047,63.4569851575,,,61.1560978740,6,9,4,6,1,11,7,10,2,7,7,4,7,1,1,1,1,0,1,5,4,0,0,1,7,6,7,7,6,0,6,7,7,0,0,7,36% (53%ile),48% (80%ile),6% (32%ile),2% (50%ile),3% (9%ile),30% (96%ile),12% (92%ile),42% (66%ile),0.017 = fraction pre-1960 (12%ile),61%ile,0.00337 ug/m3 (0%ile),58%ile,10 lifetime risk per million (1%ile),60%ile,0.1 (1%ile),60%ile,1.7 daily vehicles/meters distance (2%ile),58%ile,,,0.0026 sites/km distance (0%ile),59%ile,0.25 facilities/km distance (41%ile),63%ile,0.27 facilities/km distance (33%ile),63%ile,,,,,0.038 facilities/sq km area (7%ile),61%ile,106332317.0000000000,11164968.0000000000,0,1,61396.6485753379,132838116.6897320002 +20331,15001021402,3778,3755,2731,1374,1583,1803,3034,0.8030704076,705,0.1877496671,214,0.0783595752,56,0.0407569141,284,0.0751720487,933,0.2469560614,23,0.0127565169,276,0.1743524953,0.4954100374,0.2386774457,1871.6591211718,901.7233898617,526.8383978048,208.5726547229,0.0131608945,10.0000000000,0.1000000000,635.9981128640,,0.0033357209,0.0225482603,0.6278707343,,,0.5088713177,91.8555892572,6.9336645713,5268.3839780481,52.6838397805,335068.2267881447,,1.7573858477,11.8792893273,330.7864116735,,,268.0929496982,Hawaii,HI,9,84.7051677398,28.7308989413,41.8348284107,67.0398022547,74.9080519616,91.8116448026,3.9836481634,4.5613171192,47.5766504879,74.5975822746,75.7504514060,55.7976616902,73.4870488517,0.2827998785,1.8303662611,1.0748659980,72.8760884284,,0.2741900811,0.7727230166,47.2090589343,,,27.1111550629,75.5651255220,59.8939075539,64.0364159199,62.9860278691,78.3054903833,,60.2827950818,60.4356420863,71.4747506314,,,66.1787674526,9,3,5,7,8,10,1,1,5,8,8,6,8,1,1,1,8,0,1,1,5,0,0,3,8,6,7,7,8,0,7,7,8,0,0,7,80% (84%ile),19% (28%ile),8% (41%ile),4% (67%ile),8% (74%ile),25% (91%ile),1% (4%ile),50% (74%ile),0.17 = fraction pre-1960 (47%ile),75%ile,0.0132 ug/m3 (0%ile),59%ile,10 lifetime risk per million (1%ile),64%ile,0.1 (1%ile),62%ile,640 daily vehicles/meters distance (72%ile),78%ile,,,0.0033 sites/km distance (0%ile),60%ile,0.023 facilities/km distance (0%ile),60%ile,0.63 facilities/km distance (47%ile),71%ile,,,,,0.51 facilities/sq km area (27%ile),66%ile,41940841.0000000000,6313950.0000000000,0,1,49320.0395726063,54601507.7207057551 +20340,15001021800,5998,5977,4357,2112,2631,3179,4020,0.6702234078,1613,0.2698678267,180,0.0413128299,76,0.0359848485,352,0.0586862287,1411,0.2352450817,241,0.0758100031,441,0.1676168757,0.4700456172,0.2185533706,2819.3336121800,1310.8831165809,684.2794304026,210.4283496771,0.0049503455,10.0000000000,0.1000000000,0.0743045071,,0.0038298946,0.0402733327,0.0410968274,,,0.1071290552,114.6967802385,3.3874195708,6842.7943040262,68.4279430403,50.8450457862,,2.6207181028,27.5582131707,28.1217136206,,,73.3062089025,Hawaii,HI,9,77.3411360526,46.5995830170,19.2008132329,64.3649112351,50.1501995746,89.5998226210,84.7007475319,78.6950985799,46.5822171086,72.0610072717,71.0200691830,75.5958544882,73.5619777633,0.0461211856,1.8303662611,1.0748659980,0.4752886632,,0.3337575583,2.8011103792,5.4042726335,,,11.7520153164,77.1764003076,59.3164619569,65.4523902797,64.1879137098,58.4409328484,,60.9620163260,62.2019174282,62.1288554973,,,63.2369902585,8,5,2,7,6,9,9,8,5,8,8,8,8,1,1,1,1,0,1,1,1,0,0,2,8,6,7,7,6,0,7,7,7,0,0,7,67% (77%ile),27% (46%ile),4% (19%ile),4% (64%ile),6% (50%ile),24% (89%ile),8% (78%ile),47% (72%ile),0.17 = fraction pre-1960 (46%ile),77%ile,0.00495 ug/m3 (0%ile),59%ile,10 lifetime risk per million (1%ile),65%ile,0.1 (1%ile),64%ile,0.074 daily vehicles/meters distance (0%ile),58%ile,,,0.0038 sites/km distance (0%ile),60%ile,0.04 facilities/km distance (2%ile),62%ile,0.041 facilities/km distance (5%ile),62%ile,,,,,0.11 facilities/sq km area (11%ile),63%ile,365110254.0000000000,37900489.0000000000,0,0,92961.9049100969,459707845.8010936975 +20560,15003010201,4936,4798,3182,1441,1938,2266,3695,0.7485818476,1439,0.2999166319,231,0.0725958517,49,0.0340041638,476,0.0964343598,651,0.1318881686,115,0.0507502207,413,0.2131062951,0.5242492398,0.2305701706,2587.6942476032,1138.0943619037,830.6706662005,232.4850372225,0.0171119880,10.0000000000,0.1000000000,1493.8870892160,,0.0694550700,0.0548137804,0.4080845621,,,0.0995447326,177.0211481635,14.2144264416,8306.7066620049,83.0670666200,1240928.1836273719,,57.6942892272,45.5321994534,338.9838750865,,,82.6888893711,Hawaii,HI,9,81.7629198374,52.7224313484,38.5909198362,63.0702607098,91.9817357703,38.9013315993,48.2935747432,55.4563410918,53.0116816593,77.2470730276,73.8991238733,68.7030659293,74.5973100320,0.5552831600,1.8303662611,1.0748659980,88.8972054263,,53.8623224639,5.1063675682,39.9537688137,,,11.2751492958,80.6790478991,60.9966060167,66.7493132622,65.1696166202,90.1940360178,,78.6444132849,63.7434121326,71.6211763075,,,63.4227438652,9,6,4,7,10,4,5,6,6,8,8,7,8,1,1,1,9,0,6,1,4,0,0,2,9,7,7,7,10,0,8,7,8,0,0,7,75% (81%ile),30% (52%ile),7% (38%ile),3% (63%ile),10% (91%ile),13% (38%ile),5% (55%ile),52% (77%ile),0.21 = fraction pre-1960 (53%ile),80%ile,0.0171 ug/m3 (0%ile),60%ile,10 lifetime risk per million (1%ile),66%ile,0.1 (1%ile),65%ile,1500 daily vehicles/meters distance (88%ile),90%ile,,,0.069 sites/km distance (53%ile),78%ile,0.055 facilities/km distance (5%ile),63%ile,0.41 facilities/km distance (39%ile),71%ile,,,,,0.1 facilities/sq km area (11%ile),63%ile,66256288.0000000000,7249455.0000000000,0,0,42997.4044651793,85395519.3857139796 +20614,15007040603,2984,2978,2104,1058,2064,1468,2011,0.6739276139,797,0.2676292814,138,0.0655893536,33,0.0311909263,168,0.0563002681,756,0.2533512064,64,0.0435967302,193,0.0935077519,0.4707784477,0.2246647750,1404.8028878442,670.3996884791,342.6152122150,122.9243592959,0.0225796264,10.0000000000,0.1000000000,255.5966484444,,0.0065810172,0.1042895043,0.5200441984,,,0.1610354485,32.0371782740,7.7361234992,3426.1521221502,34.2615212215,87571.2999482384,,2.2547565999,35.7311706322,178.1750534077,,,55.1731943631,Hawaii,HI,9,77.5456369017,46.0970850340,34.4418368771,61.1045870101,46.0008635792,92.7630589707,21.6211577108,45.6650954498,34.4080455626,72.1367523014,72.5017628853,38.1901457794,68.6478207991,0.9188048692,1.8303662611,1.0748659980,48.7907692784,,1.7474241413,15.4267424965,43.8004140051,,,14.6634418792,68.8698351102,60.0337698662,62.2468364816,61.5558275260,68.4712288534,,60.6727330898,62.8928664453,68.0134997705,,,62.8256530053,8,5,4,7,5,10,3,5,4,8,8,4,7,1,1,1,5,0,1,2,5,0,0,2,7,7,7,7,7,0,7,7,7,0,0,7,67% (77%ile),27% (46%ile),7% (34%ile),3% (61%ile),6% (46%ile),25% (92%ile),4% (45%ile),47% (72%ile),0.094 = fraction pre-1960 (34%ile),68%ile,0.0226 ug/m3 (0%ile),60%ile,10 lifetime risk per million (1%ile),62%ile,0.1 (1%ile),61%ile,260 daily vehicles/meters distance (48%ile),68%ile,,,0.0066 sites/km distance (1%ile),60%ile,0.1 facilities/km distance (15%ile),62%ile,0.52 facilities/km distance (43%ile),68%ile,,,,,0.16 facilities/sq km area (14%ile),62%ile,41255867.0000000000,7041518.0000000000,0,0,36855.9892981643,56378891.3118786365 +20615,15007040604,3529,3458,2370,1187,1625,1757,2203,0.6242561632,1275,0.3687102371,133,0.0561181435,42,0.0353833193,333,0.0943610088,632,0.1790875602,109,0.0620375640,322,0.1981538462,0.4964832002,0.2263194054,1752.0892134182,798.6811814815,495.9027833594,151.2145472028,0.0297040750,10.0000000000,0.1000000000,464.0468169721,,0.0064334940,0.1282189641,0.3810520320,,,0.2277699060,98.2650438411,14.7303334730,4959.0278335940,49.5902783359,230122.1081455294,,3.1903875793,63.5841411863,188.9647632974,,,112.9517303753,Hawaii,HI,9,74.7796778307,65.5138546463,28.4761545436,63.9820945208,90.9928060818,67.6211097498,45.4287763592,68.1087856009,51.0102487547,74.6953085329,72.8443904311,48.6363888652,70.3699602168,1.3729134893,1.8303662611,1.0748659980,64.8054389268,,1.7014251995,20.6071512154,38.9237463430,,,17.5743663328,76.0259182532,61.0699450431,63.7308514802,62.7533582560,75.0094643983,,61.3999056181,65.1045697304,68.3212418487,,,64.0124628341,8,7,3,7,10,7,5,7,6,8,8,5,8,1,1,1,7,0,1,3,4,0,0,2,8,7,7,7,8,0,7,7,7,0,0,7,62% (74%ile),37% (65%ile),6% (28%ile),4% (63%ile),9% (90%ile),18% (67%ile),6% (68%ile),50% (74%ile),0.2 = fraction pre-1960 (51%ile),76%ile,0.0297 ug/m3 (1%ile),61%ile,10 lifetime risk per million (1%ile),63%ile,0.1 (1%ile),62%ile,460 daily vehicles/meters distance (64%ile),75%ile,,,0.0064 sites/km distance (1%ile),61%ile,0.13 facilities/km distance (20%ile),65%ile,0.38 facilities/km distance (38%ile),68%ile,,,,,0.23 facilities/sq km area (17%ile),64%ile,21724894.0000000000,2371158.0000000000,0,1,27760.3117775823,28129042.7970332205 +20616,15007040700,9552,9523,6234,2895,3298,4974,7071,0.7402638191,1980,0.2079176730,309,0.0495668912,95,0.0328151986,772,0.0808207705,1834,0.1920016750,205,0.0412143144,346,0.1049120679,0.4740907460,0.2172310046,4528.5148062585,2074.9905558098,1128.3751689898,322.4823975128,0.0120486502,10.0000000000,0.1000000000,829.6297843840,,0.0062317499,0.2776903565,0.5315584393,,,0.8605507426,118.3801723682,13.5953976825,11283.7516898982,112.8375168990,936133.6481533048,,7.0317517976,313.3389029609,599.7973437979,,,971.0240895638,Hawaii,HI,9,81.2804870193,33.1925490446,24.3539720817,62.2442189921,81.2507708079,74.5290795692,78.3337372056,42.2880946853,36.4589583341,72.4690666385,70.6843205648,91.6638490724,78.6800785920,0.2182262554,1.8303662611,1.0748659980,78.8807149861,,1.6540510210,44.6852775053,44.1677595343,,,35.8025350464,77.4223719690,60.9007569981,69.0968995957,67.2008685709,87.5969776098,,64.0977043448,75.6532891441,75.2184280558,,,72.0462363568,9,4,3,7,9,8,8,5,4,8,8,10,8,1,1,1,8,0,1,5,5,0,0,4,8,7,7,7,9,0,7,8,8,0,0,8,74% (81%ile),21% (33%ile),5% (24%ile),3% (62%ile),8% (81%ile),19% (74%ile),4% (42%ile),47% (72%ile),0.1 = fraction pre-1960 (36%ile),77%ile,0.012 ug/m3 (0%ile),60%ile,10 lifetime risk per million (1%ile),69%ile,0.1 (1%ile),67%ile,830 daily vehicles/meters distance (78%ile),87%ile,,,0.0062 sites/km distance (1%ile),64%ile,0.28 facilities/km distance (44%ile),75%ile,0.53 facilities/km distance (44%ile),75%ile,,,,,0.86 facilities/sq km area (35%ile),72%ile,93005151.0000000000,5658877.0000000000,0,1,70950.4293149945,115233329.7073323578 +20624,15009030100,1405,1374,980,467,796,729,1060,0.7544483986,400,0.2911208151,44,0.0448979592,0,0.0000000000,124,0.0882562278,342,0.2434163701,41,0.0562414266,170,0.2135678392,0.5227846069,0.2370232951,734.5123726346,333.0177296537,234.3871433253,75.2419798205,0.0026846006,10.0000000000,0.1000000000,,,0.0046765532,0.0398066625,0.0329594792,,,0.0973247551,50.0575557353,0.6292358547,2343.8714332533,23.4387143325,,,1.0961239415,9.3301699117,7.7252781836,,,22.8116713113,Hawaii,HI,9,82.0728020535,51.0042892937,21.4097431797,22.2149776961,87.3264635475,91.2196103997,10.4895772204,61.9590696975,53.0589655984,77.1174117652,75.3619764353,7.5930221772,65.5506156308,0.0292580509,1.8303662611,1.0748659980,,,0.7827594098,2.7444494654,3.8365691660,,,11.1452377410,71.4770935032,58.8413451490,61.1806956436,60.6714580607,,,59.7579323526,60.0938330974,59.9756316335,,,61.8121208766,9,6,3,3,9,10,2,7,6,8,8,1,7,1,1,1,0,0,1,1,1,0,0,2,8,6,7,7,0,0,6,7,6,0,0,7,75% (82%ile),29% (51%ile),4% (21%ile),0% (22%ile),9% (87%ile),24% (91%ile),6% (61%ile),52% (77%ile),0.21 = fraction pre-1960 (53%ile),71%ile,0.00268 ug/m3 (0%ile),58%ile,10 lifetime risk per million (1%ile),61%ile,0.1 (1%ile),60%ile,,,,,0.0047 sites/km distance (0%ile),59%ile,0.04 facilities/km distance (2%ile),60%ile,0.033 facilities/km distance (3%ile),59%ile,,,,,0.097 facilities/sq km area (11%ile),61%ile,555262221.0000000000,25398369.0000000000,0,0,165450.9181509207,667169893.1947253942 +20625,15009030201,2340,2327,1879,842,1045,1395,992,0.4239316239,623,0.2677266867,62,0.0329962746,0,0.0000000000,150,0.0641025641,554,0.2367521368,133,0.0953405018,97,0.0928229665,0.3458291553,0.1709182144,809.2402234637,399.9486215874,-23.7085570230,-29.3718443271,0.0063521816,10.0000000000,0.1000000000,7.0868595222,,0.0053511202,0.1292001112,0.0908033666,,,0.0098923140,-2.2006985945,-0.1506010605,-237.0855702299,-2.3708557023,-168.0192130960,,-0.1268673373,-3.0631482031,-2.1528167944,,,-0.2345324900,Hawaii,HI,9,60.4734810662,46.1223337837,13.8610651481,22.2149776961,59.2951174656,89.9006101655,56.2295738392,87.6116027815,34.2663836750,57.2620275919,55.1869446757,12.9269845729,57.7176364334,0.0651806253,1.8303662611,1.0748659980,4.6368984700,,1.3707744996,20.8229399202,14.1722671160,,,4.9075810703,55.1341860078,58.6779670685,58.4263245996,58.4806520514,57.3658121936,,58.5702573228,58.1201119303,58.2216584263,,,56.4332033900,7,5,2,3,6,9,6,9,4,6,6,2,6,1,1,1,1,0,1,3,2,0,0,1,6,6,6,6,6,0,6,6,6,0,0,6,42% (60%ile),27% (46%ile),3% (13%ile),0% (22%ile),6% (59%ile),24% (89%ile),10% (87%ile),35% (57%ile),0.093 = fraction pre-1960 (34%ile),55%ile,0.00635 ug/m3 (0%ile),58%ile,10 lifetime risk per million (1%ile),58%ile,0.1 (1%ile),58%ile,7.1 daily vehicles/meters distance (4%ile),57%ile,,,0.0054 sites/km distance (1%ile),58%ile,0.13 facilities/km distance (20%ile),58%ile,0.091 facilities/km distance (14%ile),58%ile,,,,,0.0099 facilities/sq km area (4%ile),56%ile,118113265.0000000000,4116462.0000000000,0,0,68639.8224567451,140691933.2772550285 +20629,15009030402,8562,8562,6540,3180,3473,4778,5420,0.6330296660,1535,0.1792805419,294,0.0449541284,39,0.0122641509,397,0.0463676711,1550,0.1810324690,210,0.0439514441,264,0.0760149726,0.4061551039,0.1828214379,3477.5000000000,1565.3171513473,429.7617698603,-5.5554252167,0.0153866969,10.0000000000,0.1000000000,233.6880574427,,0.0055146115,0.6633705951,0.5914191729,,,0.4432670413,32.6683291803,6.6126141119,4297.6176986027,42.9761769860,100430.1931617688,,2.3699691830,285.0913210180,254.1693504667,,,190.4992282028,Hawaii,HI,9,75.2886221409,26.8565530362,21.4439231945,41.5097856648,28.7449442824,68.6690121278,79.3833218173,46.1798708736,30.8409993308,65.1595288992,59.9703175674,82.9721886150,59.7346670413,0.4238414396,1.8303662611,1.0748659980,46.4851154373,,1.4363834459,65.6894718646,46.0793962757,,,25.2542546354,68.9789876320,59.8548490559,63.0853618909,62.2063722143,69.2554009969,,60.7689024131,74.9199434460,69.9134569074,,,65.2067338787,8,3,3,5,3,7,8,5,4,7,6,9,6,1,1,1,5,0,1,7,5,0,0,3,7,6,7,7,7,0,7,8,7,0,0,7,63% (75%ile),18% (26%ile),4% (21%ile),1% (41%ile),5% (28%ile),18% (68%ile),4% (46%ile),41% (65%ile),0.076 = fraction pre-1960 (30%ile),68%ile,0.0154 ug/m3 (0%ile),59%ile,10 lifetime risk per million (1%ile),63%ile,0.1 (1%ile),62%ile,230 daily vehicles/meters distance (46%ile),69%ile,,,0.0055 sites/km distance (1%ile),60%ile,0.66 facilities/km distance (65%ile),74%ile,0.59 facilities/km distance (46%ile),69%ile,,,,,0.44 facilities/sq km area (25%ile),65%ile,46066876.0000000000,109238.0000000000,0,1,49929.5140313853,53123180.2369696796 +20639,15009030800,7879,7871,5174,2235,2335,4210,6161,0.7819520244,1091,0.1386100877,195,0.0376884422,3,0.0013422819,594,0.0753902780,1027,0.1303464907,163,0.0387173397,285,0.1220556745,0.4602810560,0.1942216008,3626.5544403507,1530.2719926347,821.9375850282,84.7096204381,0.0169064550,10.0000000000,0.1000000000,575.9991000531,0.0008675195,0.0061499864,1.0347888110,0.5999348163,,,0.0263640121,100.3221463525,13.8960508008,8219.3758502819,82.1937585028,473435.3092760353,0.7130468458,5.0549049971,850.5318163110,493.1089740953,,,21.6695724544,Hawaii,HI,9,83.5697361596,17.7978784355,16.7867123482,22.7576898463,75.1762179508,37.9999341618,67.3605976499,38.4952526223,39.4719140158,71.0499579469,63.9144112870,82.0845801288,66.2238170511,0.5328167647,1.8303662611,1.0748659980,70.4238972397,46.4801242962,1.6311542890,76.8934515139,46.3897571267,,,6.2894452647,76.1966079716,60.9434745816,66.6801583126,65.1083356253,81.3398307482,78.4421389238,62.8183222708,84.3474497827,73.9856189804,,,61.7713841743,9,2,2,3,8,4,7,4,4,8,7,9,7,1,1,1,8,5,1,8,5,0,0,1,8,7,7,7,9,8,7,9,8,0,0,7,78% (83%ile),14% (17%ile),4% (16%ile),0% (22%ile),8% (75%ile),13% (37%ile),4% (38%ile),46% (71%ile),0.12 = fraction pre-1960 (39%ile),76%ile,0.0169 ug/m3 (0%ile),60%ile,10 lifetime risk per million (1%ile),66%ile,0.1 (1%ile),65%ile,580 daily vehicles/meters distance (70%ile),81%ile,0.00087 toxicity-weighted concentration/meters distance (46%ile),78%ile,0.0061 sites/km distance (1%ile),62%ile,1 facilities/km distance (76%ile),84%ile,0.6 facilities/km distance (46%ile),73%ile,,,,,0.026 facilities/sq km area (6%ile),61%ile,141603534.0000000000,11781155.0000000000,0,0,80194.4536675024,176674254.7197769880 diff --git a/data/data-pipeline/data_pipeline/tests/sources/ejscreen/test_etl.py b/data/data-pipeline/data_pipeline/tests/sources/ejscreen/test_etl.py new file mode 100644 index 00000000..1ab2b6bc --- /dev/null +++ b/data/data-pipeline/data_pipeline/tests/sources/ejscreen/test_etl.py @@ -0,0 +1,19 @@ +import pathlib +from data_pipeline.tests.sources.example.test_etl import TestETL +from data_pipeline.etl.sources.ejscreen.etl import EJSCREENETL + + +class TestEJSCREENETL(TestETL): + _ETL_CLASS = EJSCREENETL + + _SAMPLE_DATA_PATH = pathlib.Path(__file__).parents[0] / "data" + _SAMPLE_DATA_FILE_NAME = "EJSCREEN_2021_USPR_Tracts.csv" + _SAMPLE_DATA_ZIP_FILE_NAME = "EJSCREEN_2021_USPR_Tracts.csv.zip" + _EXTRACT_TMP_FOLDER_NAME = "EJSCREENETL" + + def setup_method(self, _method, filename=__file__): + """Invoke `setup_method` from Parent, but using the current file name. + + This code can be copied identically between all child classes. + """ + super().setup_method(_method=_method, filename=filename) diff --git a/data/data-pipeline/data_pipeline/tests/sources/example/etl.py b/data/data-pipeline/data_pipeline/tests/sources/example/etl.py index 1f7c5dc5..2e578c7a 100644 --- a/data/data-pipeline/data_pipeline/tests/sources/example/etl.py +++ b/data/data-pipeline/data_pipeline/tests/sources/example/etl.py @@ -21,6 +21,7 @@ class ExampleETL(ExtractTransformLoad): LAST_UPDATED_YEAR = 2017 SOURCE_URL = "https://www.example.com/example.zip" GEO_LEVEL = ValidGeoLevel.CENSUS_TRACT + LOAD_YAML_CONFIG: bool = True def __init__(self): self.COLUMNS_TO_KEEP = [ diff --git a/data/data-pipeline/data_pipeline/tests/sources/example/test_etl.py b/data/data-pipeline/data_pipeline/tests/sources/example/test_etl.py index 8855baad..34a56083 100644 --- a/data/data-pipeline/data_pipeline/tests/sources/example/test_etl.py +++ b/data/data-pipeline/data_pipeline/tests/sources/example/test_etl.py @@ -2,7 +2,7 @@ import copy import os import pathlib -from typing import Type +from typing import Type, Optional from unittest import mock import pytest @@ -45,7 +45,7 @@ class TestETL: # so that we do not have to manually copy the "sample data" when we run the tests. _SAMPLE_DATA_PATH = pathlib.Path(__file__).parents[0] / "data" _SAMPLE_DATA_FILE_NAME = "input.csv" - _SAMPLE_DATA_ZIP_FILE_NAME = "input.zip" + _SAMPLE_DATA_ZIP_FILE_NAME: Optional[str] = "input.zip" _EXTRACT_TMP_FOLDER_NAME = "ExampleETL" # Note: We used shared census tract IDs so that later our tests can join all the @@ -124,22 +124,37 @@ class TestETL: used to retrieve data in order to force that method to retrieve the fixture data. A basic version of that patching is included here for classes that can use it. """ - with mock.patch("data_pipeline.utils.requests") as requests_mock: - zip_file_fixture_src = ( - self._DATA_DIRECTORY_FOR_TEST / self._SAMPLE_DATA_ZIP_FILE_NAME - ) - tmp_path = mock_paths[1] - # Create mock response. - with open(zip_file_fixture_src, mode="rb") as file: - file_contents = file.read() + with mock.patch( + "data_pipeline.utils.requests" + ) as requests_mock, mock.patch( + "data_pipeline.etl.score.etl_utils.get_state_fips_codes" + ) as mock_get_state_fips_codes: + tmp_path = mock_paths[1] + if self._SAMPLE_DATA_ZIP_FILE_NAME is not None: + zip_file_fixture_src = ( + self._DATA_DIRECTORY_FOR_TEST + / self._SAMPLE_DATA_ZIP_FILE_NAME + ) + + # Create mock response. + with open(zip_file_fixture_src, mode="rb") as file: + file_contents = file.read() + else: + with open( + self._DATA_DIRECTORY_FOR_TEST / self._SAMPLE_DATA_FILE_NAME, + "rb", + ) as file: + file_contents = file.read() response_mock = requests.Response() response_mock.status_code = 200 # pylint: disable=protected-access response_mock._content = file_contents # Return text fixture: requests_mock.get = mock.MagicMock(return_value=response_mock) - + mock_get_state_fips_codes.return_value = [ + x[0:2] for x in self._FIXTURES_SHARED_TRACT_IDS + ] # Instantiate the ETL class. etl = self._get_instance_of_etl_class() @@ -225,9 +240,14 @@ class TestETL: """This will test that the sample data exists where it's supposed to as it's supposed to As per conversation with Jorge, here we can *just* test that the zip file exists. """ - assert ( - self._SAMPLE_DATA_PATH / self._SAMPLE_DATA_ZIP_FILE_NAME - ).exists() + if self._SAMPLE_DATA_ZIP_FILE_NAME is not None: + assert ( + self._SAMPLE_DATA_PATH / self._SAMPLE_DATA_ZIP_FILE_NAME + ).exists() + else: + assert ( + self._SAMPLE_DATA_PATH / self._SAMPLE_DATA_FILE_NAME + ).exists() def test_extract_unzips_base(self, mock_etl, mock_paths): """Tests the extract method. @@ -235,17 +255,18 @@ class TestETL: As per conversation with Jorge, no longer includes snapshot. Instead, verifies that the file was unzipped from a "fake" downloaded zip (located in data) in a temporary path. """ - tmp_path = mock_paths[1] + if self._SAMPLE_DATA_ZIP_FILE_NAME is not None: + tmp_path = mock_paths[1] - _ = self._setup_etl_instance_and_run_extract( - mock_etl=mock_etl, - mock_paths=mock_paths, - ) - assert ( - tmp_path - / self._EXTRACT_TMP_FOLDER_NAME - / self._SAMPLE_DATA_FILE_NAME - ).exists() + _ = self._setup_etl_instance_and_run_extract( + mock_etl=mock_etl, + mock_paths=mock_paths, + ) + assert ( + tmp_path + / self._EXTRACT_TMP_FOLDER_NAME + / self._SAMPLE_DATA_FILE_NAME + ).exists() def test_extract_produces_valid_data(self, snapshot, mock_etl, mock_paths): """Tests the extract method. diff --git a/data/data-pipeline/data_pipeline/tests/sources/fsf_flood_risk/__init__.py b/data/data-pipeline/data_pipeline/tests/sources/fsf_flood_risk/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/data/data-pipeline/data_pipeline/tests/sources/fsf_flood_risk/data/extract.csv b/data/data-pipeline/data_pipeline/tests/sources/fsf_flood_risk/data/extract.csv new file mode 100644 index 00000000..7a37fcd1 --- /dev/null +++ b/data/data-pipeline/data_pipeline/tests/sources/fsf_flood_risk/data/extract.csv @@ -0,0 +1,17 @@ +GEOID,count_properties,mid_depth_100_year00,mid_depth_100_year30 +6027000800,942,214,215 +6069000802,1131,283,292 +6061021322,1483,100,108 +15001021010,1888,179,186 +15001021101,3463,130,137 +15007040603,1557,152,181 +15007040700,1533,177,191 +15009030100,1658,232,242 +15009030201,6144,431,447 +15001021402,4118,321,329 +15001021800,2813,350,356 +15009030402,3374,852,888 +15009030800,4847,1003,1019 +15003010201,2335,220,227 +15007040604,5364,630,641 +2290000400,1,1,1 diff --git a/data/data-pipeline/data_pipeline/tests/sources/fsf_flood_risk/data/fsf_flood.zip b/data/data-pipeline/data_pipeline/tests/sources/fsf_flood_risk/data/fsf_flood.zip new file mode 100644 index 0000000000000000000000000000000000000000..c605c141073d53bc828fdacb2d16b144721079a5 GIT binary patch literal 602 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+06027000800,942,214,215,0.2271762208,0.2282377919 +06069000802,1131,283,292,0.2502210433,0.2581786030 +06061021322,1483,100,108,0.0674308833,0.0728253540 +15001021010,1888,179,186,0.0948093220,0.0985169492 +15001021101,3463,130,137,0.0375397055,0.0395610742 +15007040603,1557,152,181,0.0976236352,0.1162491972 +15007040700,1533,177,191,0.1154598826,0.1245923027 +15009030100,1658,232,242,0.1399276236,0.1459589867 +15009030201,6144,431,447,0.0701497396,0.0727539062 +15001021402,4118,321,329,0.0779504614,0.0798931520 +15001021800,2813,350,356,0.1244223249,0.1265552791 +15009030402,3374,852,888,0.2525192650,0.2631890931 +15009030800,4847,1003,1019,0.2069321230,0.2102331339 +15003010201,2335,220,227,0.0942184154,0.0972162741 +15007040604,5364,630,641,0.1174496644,0.1195003729 +02290000400,250,1,1,0.0040000000,0.0040000000 diff --git a/data/data-pipeline/data_pipeline/tests/sources/fsf_flood_risk/data/transform.csv b/data/data-pipeline/data_pipeline/tests/sources/fsf_flood_risk/data/transform.csv new file mode 100644 index 00000000..f3381403 --- /dev/null +++ b/data/data-pipeline/data_pipeline/tests/sources/fsf_flood_risk/data/transform.csv @@ -0,0 +1,17 @@ +GEOID,count_properties,Count of properties at risk of flood today,Count of properties at risk of flood in 30 years,GEOID10_TRACT,Count of properties eligible for flood risk calculation within tract (floor of 250),Share of properties at risk of flood today,Share of properties at risk of flood in 30 years +06027000800,942,214,215,06027000800,942,0.2271762208,0.2282377919 +06069000802,1131,283,292,06069000802,1131,0.2502210433,0.2581786030 +06061021322,1483,100,108,06061021322,1483,0.0674308833,0.0728253540 +15001021010,1888,179,186,15001021010,1888,0.0948093220,0.0985169492 +15001021101,3463,130,137,15001021101,3463,0.0375397055,0.0395610742 +15007040603,1557,152,181,15007040603,1557,0.0976236352,0.1162491972 +15007040700,1533,177,191,15007040700,1533,0.1154598826,0.1245923027 +15009030100,1658,232,242,15009030100,1658,0.1399276236,0.1459589867 +15009030201,6144,431,447,15009030201,6144,0.0701497396,0.0727539062 +15001021402,4118,321,329,15001021402,4118,0.0779504614,0.0798931520 +15001021800,2813,350,356,15001021800,2813,0.1244223249,0.1265552791 +15009030402,3374,852,888,15009030402,3374,0.2525192650,0.2631890931 +15009030800,4847,1003,1019,15009030800,4847,0.2069321230,0.2102331339 +15003010201,2335,220,227,15003010201,2335,0.0942184154,0.0972162741 +15007040604,5364,630,641,15007040604,5364,0.1174496644,0.1195003729 +2290000400,1,1,1,02290000400,250,0.0040000000,0.0040000000 diff --git a/data/data-pipeline/data_pipeline/tests/sources/fsf_flood_risk/test_etl.py b/data/data-pipeline/data_pipeline/tests/sources/fsf_flood_risk/test_etl.py new file mode 100644 index 00000000..bc7219b5 --- /dev/null +++ b/data/data-pipeline/data_pipeline/tests/sources/fsf_flood_risk/test_etl.py @@ -0,0 +1,22 @@ +import pathlib +from data_pipeline.tests.sources.example.test_etl import TestETL +from data_pipeline.etl.sources.fsf_flood_risk.etl import FloodRiskETL + + +class TestFloodRiskETL(TestETL): + _ETL_CLASS = FloodRiskETL + + _SAMPLE_DATA_PATH = pathlib.Path(__file__).parents[0] / "data" + _SAMPLE_DATA_FILE_NAME = "fsf_flood/flood-tract2010.csv" + _SAMPLE_DATA_ZIP_FILE_NAME = "fsf_flood.zip" + _EXTRACT_TMP_FOLDER_NAME = "FloodRiskETL" + _FIXTURES_SHARED_TRACT_IDS = TestETL._FIXTURES_SHARED_TRACT_IDS + [ + "02290000400" # A tract with 1 property + ] + + def setup_method(self, _method, filename=__file__): + """Invoke `setup_method` from Parent, but using the current file name. + + This code can be copied identically between all child classes. + """ + super().setup_method(_method=_method, filename=filename) diff --git a/data/data-pipeline/data_pipeline/tests/sources/fsf_wildfire_risk/__init__.py b/data/data-pipeline/data_pipeline/tests/sources/fsf_wildfire_risk/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/data/data-pipeline/data_pipeline/tests/sources/fsf_wildfire_risk/data/extract.csv b/data/data-pipeline/data_pipeline/tests/sources/fsf_wildfire_risk/data/extract.csv new file mode 100644 index 00000000..11a310d2 --- /dev/null +++ b/data/data-pipeline/data_pipeline/tests/sources/fsf_wildfire_risk/data/extract.csv @@ -0,0 +1,17 @@ +GEOID,count_properties,burnprob_year00_flag,burnprob_year30_flag +6027000800,942,31,634 +6069000802,1131,0,264 +6061021322,1483,13,478 +15001021010,1888,62,550 +15001021101,3463,18,192 +15007040603,1557,0,509 +15007040700,1535,0,43 +15009030100,1660,177,968 +15009030201,6144,173,2856 +15001021402,4118,20,329 +15001021800,2814,111,770 +15009030402,3375,7,437 +15009030800,4847,3268,3529 +15003010201,2335,1949,2005 +15007040604,5365,3984,4439 +4003001402,1,1,1 diff --git a/data/data-pipeline/data_pipeline/tests/sources/fsf_wildfire_risk/data/fsf_fire.zip 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+15003010201,1 +15007040603,1 +15007040604,1 +15007040700,1 +15009030100,0 +15009030201,0 +15009030402,1 +15009030800,1 diff --git a/data/data-pipeline/data_pipeline/tests/sources/geocorr/data/transform.csv b/data/data-pipeline/data_pipeline/tests/sources/geocorr/data/transform.csv new file mode 100644 index 00000000..2c12f718 --- /dev/null +++ b/data/data-pipeline/data_pipeline/tests/sources/geocorr/data/transform.csv @@ -0,0 +1,16 @@ +GEOID10_TRACT,population_in_rural_areas,population_in_urban_areas,perc_population_in_rural_areas,perc_population_in_urban_areas,Urban Heuristic Flag +06027000800,3378.0000000000,,1.0000000000,,0 +06061021322,2252.0000000000,6510.0000000000,0.2570189454,0.7429810546,1 +06069000802,2007.0000000000,527.0000000000,0.7920284136,0.2079715864,0 +15001021010,7884.0000000000,,1.0000000000,,0 +15001021101,3312.0000000000,219.0000000000,0.9379779099,0.0620220901,0 +15001021402,1532.0000000000,2493.0000000000,0.3806211180,0.6193788820,1 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TestETL +from data_pipeline.etl.sources.geocorr.etl import GeoCorrETL + + +class TestGeoCorrETL(TestETL): + _ETL_CLASS = GeoCorrETL + + _SAMPLE_DATA_PATH = pathlib.Path(__file__).parents[0] / "data" + _SAMPLE_DATA_FILE_NAME = "geocorr_urban_rural.csv" + _SAMPLE_DATA_ZIP_FILE_NAME = "geocorr_urban_rural.csv.zip" + _EXTRACT_TMP_FOLDER_NAME = "GeoCorrETL" + + def setup_method(self, _method, filename=__file__): + """Invoke `setup_method` from Parent, but using the current file name. + + This code can be copied identically between all child classes. + """ + super().setup_method(_method=_method, filename=filename) diff --git a/data/data-pipeline/data_pipeline/tests/sources/historic_redlining/__init__.py b/data/data-pipeline/data_pipeline/tests/sources/historic_redlining/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/data/data-pipeline/data_pipeline/tests/sources/historic_redlining/data/HRS_2010.zip 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+06061021322,3.9900000000,True,True,True +06069000802,3.7800000000,True,True,True +15001021010,4.0000000000,True,True,True +15001021101,4.0000000000,True,True,True +15001021402,3.8600000000,True,True,True +15001021800,4.0000000000,True,True,True +15003010201,3.9600000000,True,True,True +15007040603,3.9700000000,True,True,True +15007040604,3.9400000000,True,True,True +15007040700,3.2000000000,False,False,False +15009030100,3.7700000000,True,True,True +15009030201,3.2300000000,False,False,False +15009030402,3.0000000000,False,False,False +15009030800,3.4000000000,True,False,False diff --git a/data/data-pipeline/data_pipeline/tests/sources/historic_redlining/data/transform.csv b/data/data-pipeline/data_pipeline/tests/sources/historic_redlining/data/transform.csv new file mode 100644 index 00000000..5c49681c --- /dev/null +++ b/data/data-pipeline/data_pipeline/tests/sources/historic_redlining/data/transform.csv @@ -0,0 +1,16 @@ +GEOID10,CBSA_NAME,CBSA_NUM,EQINTERVAL2010,Tract-level redlining score,GEOID10_TRACT,Tract-level redlining score meets or exceeds 3.25,Tract-level redlining score meets or exceeds 3.5,Tract-level redlining score meets or exceeds 3.75 +6027000800,"Birmingham-Hoover, AL",13820,4,3.3000000000,06027000800,True,False,False +6061021322,"Birmingham-Hoover, AL",13820,4,3.9900000000,06061021322,True,True,True +6069000802,"Birmingham-Hoover, AL",13820,4,3.7800000000,06069000802,True,True,True +15001021010,"Birmingham-Hoover, AL",13820,4,4.0000000000,15001021010,True,True,True +15001021101,"Birmingham-Hoover, AL",13820,4,4.0000000000,15001021101,True,True,True +15001021402,"Birmingham-Hoover, AL",13820,4,3.8600000000,15001021402,True,True,True +15001021800,"Birmingham-Hoover, AL",13820,4,4.0000000000,15001021800,True,True,True +15003010201,"Birmingham-Hoover, AL",13820,4,3.9600000000,15003010201,True,True,True +15007040603,"Birmingham-Hoover, AL",13820,4,3.9700000000,15007040603,True,True,True +15007040604,"Birmingham-Hoover, AL",13820,4,3.9400000000,15007040604,True,True,True +15007040700,"Birmingham-Hoover, AL",13820,3,3.2000000000,15007040700,False,False,False +15009030100,"Birmingham-Hoover, AL",13820,4,3.7700000000,15009030100,True,True,True +15009030201,"Birmingham-Hoover, AL",13820,3,3.2300000000,15009030201,False,False,False +15009030402,"Birmingham-Hoover, AL",13820,3,3.0000000000,15009030402,False,False,False +15009030800,"Birmingham-Hoover, AL",13820,4,3.4000000000,15009030800,True,False,False diff --git a/data/data-pipeline/data_pipeline/tests/sources/historic_redlining/test_etl.py b/data/data-pipeline/data_pipeline/tests/sources/historic_redlining/test_etl.py new file mode 100644 index 00000000..06dd8b14 --- /dev/null +++ b/data/data-pipeline/data_pipeline/tests/sources/historic_redlining/test_etl.py @@ -0,0 +1,66 @@ +# pylint: disable=protected-access +import pathlib +import pandas as pd +from data_pipeline.tests.sources.example.test_etl import TestETL +from data_pipeline.etl.sources.historic_redlining.etl import ( + HistoricRedliningETL, +) + + +class TestHistoricRedliningETL(TestETL): + _ETL_CLASS = HistoricRedliningETL + + _SAMPLE_DATA_PATH = pathlib.Path(__file__).parents[0] / "data" + _SAMPLE_DATA_FILE_NAME = "HRS_2010.xlsx" + _SAMPLE_DATA_ZIP_FILE_NAME = "HRS_2010.zip" + _EXTRACT_TMP_FOLDER_NAME = "HistoricRedliningETL" + + def setup_method(self, _method, filename=__file__): + """Invoke `setup_method` from Parent, but using the current file name. + + This code can be copied identically between all child classes. + """ + super().setup_method(_method=_method, filename=filename) + + def test_extract_produces_valid_data(self, snapshot, mock_etl, mock_paths): + etl = self._setup_etl_instance_and_run_extract( + mock_etl=mock_etl, + mock_paths=mock_paths, + ) + tmp_df = pd.read_excel( + etl.get_tmp_path() / self._SAMPLE_DATA_FILE_NAME, + dtype={etl.GEOID_TRACT_FIELD_NAME: str}, + ) + assert tmp_df.shape == (15, 5) + + def test_load_base(self, snapshot, mock_etl, mock_paths): + """Test load method. + We need to run transform here for real to add + the dynamic cols to keep + """ + # setup - input variables + etl = self._setup_etl_instance_and_run_extract( + mock_etl=mock_etl, + mock_paths=mock_paths, + ) + etl.transform() + etl.load() + + # Make sure it creates the file. + actual_output_path = etl._get_output_file_path() + assert actual_output_path.exists() + + # Check COLUMNS_TO_KEEP remain + actual_output = pd.read_csv( + actual_output_path, dtype={etl.GEOID_TRACT_FIELD_NAME: str} + ) + + for col in etl.COLUMNS_TO_KEEP: + assert col in actual_output.columns, f"{col} is missing from output" + + # Check the snapshots + snapshot.snapshot_dir = self._DATA_DIRECTORY_FOR_TEST + snapshot.assert_match( + actual_output.to_csv(index=False, float_format=self._FLOAT_FORMAT), + self._OUTPUT_CSV_FILE_NAME, + ) diff --git a/data/data-pipeline/data_pipeline/tests/sources/hud_housing/__init__.py 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+source,sumlevel,geoid,name,st,cnty,tract,T3_est1,T3_est2,T3_est3,T3_est4,T3_est5,T3_est6,T3_est7,T3_est8,T3_est9,T3_est10,T3_est11,T3_est12,T3_est13,T3_est14,T3_est15,T3_est16,T3_est17,T3_est18,T3_est19,T3_est20,T3_est21,T3_est22,T3_est23,T3_est24,T3_est25,T3_est26,T3_est27,T3_est28,T3_est29,T3_est30,T3_est31,T3_est32,T3_est33,T3_est34,T3_est35,T3_est36,T3_est37,T3_est38,T3_est39,T3_est40,T3_est41,T3_est42,T3_est43,T3_est44,T3_est45,T3_est46,T3_est47,T3_est48,T3_est49,T3_est50,T3_est51,T3_est52,T3_est53,T3_est54,T3_est55,T3_est56,T3_est57,T3_est58,T3_est59,T3_est60,T3_est61,T3_est62,T3_est63,T3_est64,T3_est65,T3_est66,T3_est67,T3_est68,T3_est69,T3_est70,T3_est71,T3_est72,T3_est73,T3_est74,T3_est75,T3_est76,T3_est77,T3_est78,T3_est79,T3_est80,T3_est81,T3_est82,T3_est83,T3_est84,T3_est85,T3_est86,T3_est87,T3_moe1,T3_moe2,T3_moe3,T3_moe4,T3_moe5,T3_moe6,T3_moe7,T3_moe8,T3_moe9,T3_moe10,T3_moe11,T3_moe12,T3_moe13,T3_moe14,T3_moe15,T3_moe16,T3_moe17,T3_moe18,T3_moe19,T3_moe20,T3_moe21,T3_moe22,T3_moe23,T3_moe24,T3_moe25,T3_moe26,T3_moe27,T3_moe28,T3_moe29,T3_moe30,T3_moe31,T3_moe32,T3_moe33,T3_moe34,T3_moe35,T3_moe36,T3_moe37,T3_moe38,T3_moe39,T3_moe40,T3_moe41,T3_moe42,T3_moe43,T3_moe44,T3_moe45,T3_moe46,T3_moe47,T3_moe48,T3_moe49,T3_moe50,T3_moe51,T3_moe52,T3_moe53,T3_moe54,T3_moe55,T3_moe56,T3_moe57,T3_moe58,T3_moe59,T3_moe60,T3_moe61,T3_moe62,T3_moe63,T3_moe64,T3_moe65,T3_moe66,T3_moe67,T3_moe68,T3_moe69,T3_moe70,T3_moe71,T3_moe72,T3_moe73,T3_moe74,T3_moe75,T3_moe76,T3_moe77,T3_moe78,T3_moe79,T3_moe80,T3_moe81,T3_moe82,T3_moe83,T3_moe84,T3_moe85,T3_moe86,T3_moe87 +2014thru2018,140,14000US06027000800,"Census Tract 8, Inyo County, California",6,27,800,1375,800,30,30,0,0,0,0,0,0,0,0,0,0,10,0,0,0,10,0,115,35,30,50,0,4,115,15,35,10,15,35,0,0,0,0,0,0,530,30,65,70,50,320,580,35,25,10,0,0,0,0,0,0,0,0,0,65,0,10,0,55,0,90,70,20,0,0,0,95,10,30,40,0,10,0,0,0,0,0,0,300,10,40,85,0,165,133,101,31,31,12,12,12,12,12,12,12,12,12,12,15,12,12,12,15,12,57,26,32,41,12,5,56,15,34,13,22,35,12,12,12,12,12,12,90,31,34,41,25,79,122,34,30,16,12,12,12,12,12,12,12,12,12,67,12,18,12,64,12,83,79,32,12,12,12,52,20,32,35,12,14,12,12,12,12,12,12,89,14,30,50,12,64 +2014thru2018,140,14000US06061021322,"Census Tract 213.22, Placer County, California",6,61,21322,5395,4250,0,0,0,0,0,0,15,0,0,0,0,15,120,0,0,0,55,65,250,70,45,105,35,0,630,30,75,130,105,290,65,65,0,0,0,0,3170,15,45,140,60,2905,1145,0,0,0,0,0,0,50,50,0,0,0,0,45,0,45,0,0,0,240,160,30,50,0,0,270,0,160,85,30,0,15,15,0,0,0,0,520,65,0,135,105,215,179,212,19,19,19,19,19,19,26,19,19,19,19,26,108,19,19,19,65,84,122,57,39,84,39,19,151,37,88,78,67,116,87,87,19,19,19,19,238,20,38,92,56,249,187,19,19,19,19,19,19,81,81,19,19,19,19,73,19,73,19,19,19,100,95,35,52,19,19,115,19,93,90,30,19,26,26,19,19,19,19,170,83,19,105,87,99 +2014thru2018,140,14000US06069000802,"Census Tract 8.02, San Benito County, California",6,69,802,885,615,4,4,0,4,0,4,4,0,0,0,0,4,0,0,0,0,0,0,70,4,20,20,4,25,90,4,10,10,10,55,4,4,0,0,0,0,440,10,25,35,35,335,265,4,0,0,4,0,0,4,0,0,0,4,0,20,4,15,0,0,0,25,4,20,0,0,0,25,4,4,15,0,0,4,4,0,0,0,0,190,20,35,20,35,80,69,70,11,5,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,25,12,16,12,1,20,28,3,11,11,12,23,10,10,12,12,12,12,62,10,31,20,19,46,45,4,12,12,4,12,12,11,12,12,12,11,12,19,12,17,12,12,12,17,11,18,12,12,12,14,2,7,14,12,12,3,3,12,12,12,12,43,14,19,14,18,28 +2014thru2018,140,14000US15001021010,"Census Tract 210.10, Hawaii County, Hawaii",15,1,21010,3130,2515,230,85,70,4,20,50,55,10,35,0,0,10,110,10,15,50,0,30,245,155,85,4,0,0,275,30,60,135,40,4,55,55,0,0,0,0,1550,170,290,140,190,760,615,40,20,4,0,4,10,65,20,0,0,0,45,50,20,0,0,25,4,55,40,4,0,10,0,100,0,50,45,0,0,70,70,0,0,0,0,235,30,20,40,35,105,176,219,100,44,81,10,32,43,37,13,35,15,15,14,56,23,22,41,15,32,121,112,62,19,15,15,136,38,40,118,34,15,40,40,15,15,15,15,212,104,145,72,107,189,168,26,18,9,15,10,13,77,30,15,15,15,70,40,22,15,15,33,2,35,31,14,15,15,15,55,15,40,44,15,15,86,86,15,15,15,15,111,25,27,36,30,91 +2014thru2018,140,14000US15001021101,"Census Tract 211.01, Hawaii County, Hawaii",15,1,21101,1690,1385,125,75,30,0,0,20,35,4,10,10,0,10,4,4,0,0,0,0,255,140,80,35,0,0,125,50,4,20,20,30,30,30,0,0,0,0,810,85,115,115,170,320,300,40,4,0,35,0,0,25,25,0,0,0,0,4,0,0,0,0,4,70,70,0,0,0,0,35,15,10,4,10,0,4,4,0,0,0,0,125,30,10,20,15,50,157,136,73,58,29,11,11,36,29,8,20,15,11,13,3,3,11,11,11,11,78,69,46,34,11,11,40,27,10,14,22,24,23,23,11,11,11,11,117,39,55,61,65,96,98,52,12,11,54,11,11,38,38,11,11,11,11,2,11,11,11,11,2,60,60,11,11,11,11,28,22,13,11,15,11,11,11,11,11,11,11,51,26,13,23,21,36 +2014thru2018,140,14000US15001021402,"Census Tract 214.02, Hawaii County, Hawaii",15,1,21402,1340,830,4,0,0,4,0,0,4,0,0,0,0,4,70,0,0,0,10,60,70,30,4,25,4,0,130,10,10,55,0,55,0,0,0,0,0,0,550,10,4,50,30,455,510,30,30,0,0,0,0,10,0,0,10,0,0,15,0,10,0,0,4,115,100,4,4,0,0,85,20,20,15,25,4,0,0,0,0,0,0,250,40,15,35,45,120,103,132,1,11,11,1,11,11,13,11,11,11,11,13,61,11,11,11,19,59,53,46,14,21,2,11,68,16,20,52,11,32,11,11,11,11,11,11,122,17,15,31,30,107,102,50,50,11,11,11,11,13,11,11,13,11,11,21,11,16,11,11,12,74,70,10,13,11,11,51,21,31,22,40,7,11,11,11,11,11,11,92,22,22,33,47,58 +2014thru2018,140,14000US15001021800,"Census Tract 218, Hawaii County, Hawaii",15,1,21800,2015,1375,30,25,0,0,0,4,10,0,4,4,4,4,65,0,4,4,4,55,100,35,15,30,0,20,285,25,20,20,15,205,15,15,0,0,0,0,870,15,40,105,95,615,640,55,55,0,4,0,0,15,10,0,0,0,4,25,10,4,0,0,10,55,40,15,0,0,0,100,25,25,35,10,4,0,0,0,0,0,0,395,25,0,50,25,290,201,188,41,40,15,15,15,15,13,15,9,4,5,3,41,15,3,5,1,41,53,34,21,28,15,26,118,25,20,16,20,115,21,21,15,15,15,15,159,16,27,51,40,140,159,80,79,15,15,15,15,17,14,15,15,15,11,24,14,10,15,15,14,37,31,18,15,15,15,53,30,20,42,14,3,15,15,15,15,15,15,162,27,15,34,24,155 +2014thru2018,140,14000US15003010201,"Census Tract 102.01, Honolulu County, Hawaii",15,3,10201,1515,785,15,4,0,10,0,0,35,4,0,15,10,4,85,0,4,40,10,30,95,50,10,15,4,10,125,4,15,50,25,35,4,4,0,0,0,0,425,15,4,45,55,305,730,50,10,15,20,0,4,95,25,4,40,15,10,75,0,15,35,4,20,150,55,85,10,0,0,95,10,10,40,25,15,30,30,0,0,0,0,230,4,10,50,45,120,66,73,16,10,15,13,15,15,27,10,15,25,12,9,46,15,9,33,13,22,31,30,13,12,3,17,33,15,13,18,16,15,11,11,15,15,15,15,69,15,11,22,31,52,72,23,11,20,17,15,15,42,21,11,32,14,13,36,15,23,25,15,18,48,28,42,13,15,15,43,14,13,32,22,15,25,25,15,15,15,15,53,10,17,28,23,48 +2014thru2018,140,14000US15007040603,"Census Tract 406.03, Kauai County, Hawaii",15,7,40603,1035,595,4,0,0,0,0,4,4,0,0,0,0,4,30,0,0,4,4,25,100,15,20,20,30,20,80,0,15,4,4,55,4,4,0,0,0,0,370,10,20,55,10,275,440,4,0,0,0,0,4,10,10,0,0,0,0,30,4,4,4,10,10,105,55,25,25,0,4,80,4,15,30,15,10,10,10,0,0,0,0,195,15,0,35,35,115,84,70,11,11,11,11,11,11,18,11,11,11,11,18,25,11,11,4,11,26,34,15,16,12,22,13,31,11,14,11,11,29,11,11,11,11,11,11,66,15,16,29,12,55,75,11,11,11,11,11,11,15,15,11,11,11,11,27,3,3,16,13,14,45,26,20,23,11,17,35,15,21,20,13,14,13,13,11,11,11,11,52,16,11,24,25,45 +2014thru2018,140,14000US15007040604,"Census Tract 406.04, Kauai County, Hawaii",15,7,40604,1235,655,10,0,0,0,0,10,10,0,0,0,4,4,15,0,4,0,0,10,75,45,4,15,4,0,120,4,15,20,25,55,15,15,0,0,0,0,410,15,30,35,15,315,580,4,4,0,0,0,0,30,0,20,0,0,10,25,4,4,0,4,15,90,15,50,20,0,0,165,35,70,15,20,30,0,0,0,0,0,0,260,30,40,20,25,150,79,96,14,11,11,11,11,14,13,11,11,11,10,1,18,11,10,11,11,13,36,33,10,14,9,11,55,11,22,23,28,32,15,15,11,11,11,11,80,28,30,18,18,74,101,11,11,11,11,11,11,31,11,29,11,11,13,25,7,3,11,10,21,45,18,35,24,11,11,56,28,44,15,23,24,11,11,11,11,11,11,77,27,26,16,22,63 +2014thru2018,140,14000US15007040700,"Census Tract 407, Kauai County, Hawaii",15,7,40700,2875,1930,0,0,0,0,0,0,0,0,0,0,0,0,115,0,0,4,0,110,245,80,10,115,15,25,380,80,20,45,70,170,25,25,0,0,0,0,1165,40,20,110,75,920,950,25,0,0,0,4,15,80,0,10,0,10,60,45,0,10,4,0,30,205,90,60,55,0,0,105,20,15,25,0,45,15,15,0,0,0,0,470,25,20,45,100,285,162,199,15,15,15,15,15,15,15,15,15,15,15,15,51,15,15,10,15,52,98,62,17,62,21,30,128,74,29,32,53,83,27,27,15,15,15,15,171,34,17,46,53,179,157,29,15,15,15,13,25,67,15,19,15,21,61,48,15,17,15,15,44,94,64,48,43,15,15,75,23,23,41,15,53,19,19,15,15,15,15,107,18,24,35,72,115 +2014thru2018,140,14000US15009030100,"Census Tract 301, Maui County, Hawaii",15,9,30100,500,320,20,10,0,0,0,10,10,0,0,4,0,4,40,4,0,15,0,20,30,25,0,10,0,0,15,0,0,4,0,10,0,0,0,0,0,0,200,4,65,15,20,95,175,4,4,0,0,0,0,10,0,0,0,0,10,20,0,0,0,20,0,20,20,0,0,0,0,4,0,4,0,0,0,0,0,0,0,0,0,120,25,4,10,0,80,71,75,27,18,11,11,11,18,15,11,11,14,11,10,23,11,11,23,11,14,25,19,11,15,11,11,20,11,11,10,11,18,11,11,11,11,11,11,62,11,57,15,23,53,69,11,11,11,11,11,11,16,11,11,11,11,16,25,11,11,11,25,11,24,24,11,11,11,11,9,11,9,11,11,11,11,11,11,11,11,11,62,30,9,12,11,59 +2014thru2018,140,14000US15009030201,"Census Tract 302.01, Maui County, Hawaii",15,9,30201,820,605,0,0,0,0,0,0,15,0,0,0,0,15,10,0,0,0,10,0,70,25,35,0,0,10,160,10,10,65,15,60,0,0,0,0,0,0,350,40,40,35,10,225,215,0,0,0,0,0,0,0,0,0,0,0,0,10,0,0,10,0,0,4,0,4,0,0,0,45,0,0,45,0,0,0,0,0,0,0,0,155,0,10,55,0,90,99,113,11,11,11,11,11,11,23,11,11,11,11,23,24,11,11,11,24,11,47,30,31,11,11,17,81,15,18,73,21,31,11,11,11,11,11,11,91,36,34,34,21,80,100,11,11,11,11,11,11,11,11,11,11,11,11,20,11,11,20,11,11,20,11,20,11,11,11,49,11,11,49,11,11,11,11,11,11,11,11,85,11,21,49,11,78 +2014thru2018,140,14000US15009030402,"Census Tract 304.02, Maui County, Hawaii",15,9,30402,3140,2205,0,0,0,0,0,0,15,0,0,0,0,15,70,25,0,0,0,45,360,100,85,75,85,20,275,10,0,10,10,245,45,45,0,0,0,0,1435,0,90,150,90,1105,935,0,0,0,0,0,0,0,0,0,0,0,0,75,0,15,15,0,45,95,30,30,40,0,0,220,0,45,90,0,80,0,0,0,0,0,0,540,35,0,85,155,270,167,236,15,15,15,15,15,15,30,15,15,15,15,30,55,39,15,15,15,43,150,74,71,65,62,32,130,19,15,21,21,128,70,70,15,15,15,15,227,15,59,122,67,235,244,15,15,15,15,15,15,15,15,15,15,15,15,83,15,29,36,15,72,67,36,34,48,15,15,141,15,44,98,15,97,15,15,15,15,15,15,182,49,15,97,105,127 +2014thru2018,140,14000US15009030800,"Census Tract 308, Maui County, Hawaii",15,9,30800,2250,1810,20,0,0,0,0,20,25,0,4,4,0,20,65,4,0,4,4,60,185,70,30,60,15,15,365,30,15,45,80,195,4,4,0,0,0,0,1140,4,80,50,110,895,445,0,0,0,0,0,0,25,0,10,4,0,10,30,0,0,20,0,10,65,20,15,30,0,0,65,0,10,25,4,25,0,0,0,0,0,0,260,20,35,115,10,85,110,134,27,15,15,15,15,27,19,15,3,8,15,17,38,3,15,4,4,36,70,45,28,36,18,23,120,33,18,31,65,110,11,11,15,15,15,15,166,15,82,41,49,167,132,15,15,15,15,15,15,22,15,17,15,15,15,29,15,15,28,15,17,38,24,19,29,15,15,34,15,17,21,10,32,15,15,15,15,15,15,114,29,31,108,13,40 diff --git a/data/data-pipeline/data_pipeline/tests/sources/hud_housing/data/output.csv b/data/data-pipeline/data_pipeline/tests/sources/hud_housing/data/output.csv new file mode 100644 index 00000000..acd00572 --- /dev/null +++ b/data/data-pipeline/data_pipeline/tests/sources/hud_housing/data/output.csv @@ -0,0 +1,16 @@ +GEOID10_TRACT,HOUSING_BURDEN_NUMERATOR,HOUSING_BURDEN_DENOMINATOR,Housing burden (percent),Share of homes with no kitchen or indoor plumbing (percent),DENOM INCL NOT COMPUTED +06027000800,370,1376,0.2688953488,0.0471014493,1380 +06061021322,985,5265,0.1870845204,0.0000000000,5395 +06069000802,136,872,0.1559633028,0.0090909091,880 +15001021010,723,2970,0.2434343434,0.0862619808,3130 +15001021101,449,1610,0.2788819876,0.0979228487,1685 +15001021402,354,1340,0.2641791045,0.0253731343,1340 +15001021800,355,2000,0.1775000000,0.0421836228,2015 +15003010201,504,1471,0.3426240653,0.0429042904,1515 +15007040603,238,1021,0.2331047992,0.0077294686,1035 +15007040604,328,1220,0.2688524590,0.0113360324,1235 +15007040700,635,2840,0.2235915493,0.0086805556,2880 +15009030100,84,491,0.1710794297,0.0484848485,495 +15009030201,194,820,0.2365853659,0.0000000000,820 +15009030402,555,3095,0.1793214863,0.0000000000,3140 +15009030800,385,2251,0.1710350955,0.0088691796,2255 diff --git a/data/data-pipeline/data_pipeline/tests/sources/hud_housing/data/transform.csv b/data/data-pipeline/data_pipeline/tests/sources/hud_housing/data/transform.csv new file mode 100644 index 00000000..1eb4b626 --- /dev/null +++ b/data/data-pipeline/data_pipeline/tests/sources/hud_housing/data/transform.csv @@ -0,0 +1,16 @@ +source_x,sumlevel_x,geoid_x,name_x,st_x,cnty_x,tract_x,T8_est1,T8_est2,T8_est3,T8_est4,T8_est5,T8_est6,T8_est7,T8_est8,T8_est9,T8_est10,T8_est11,T8_est12,T8_est13,T8_est14,T8_est15,T8_est16,T8_est17,T8_est18,T8_est19,T8_est20,T8_est21,T8_est22,T8_est23,T8_est24,T8_est25,T8_est26,T8_est27,T8_est28,T8_est29,T8_est30,T8_est31,T8_est32,T8_est33,T8_est34,T8_est35,T8_est36,T8_est37,T8_est38,T8_est39,T8_est40,T8_est41,T8_est42,T8_est43,T8_est44,T8_est45,T8_est46,T8_est47,T8_est48,T8_est49,T8_est50,T8_est51,T8_est52,T8_est53,T8_est54,T8_est55,T8_est56,T8_est57,T8_est58,T8_est59,T8_est60,T8_est61,T8_est62,T8_est63,T8_est64,T8_est65,T8_est66,T8_est67,T8_est68,T8_est69,T8_est70,T8_est71,T8_est72,T8_est73,T8_est74,T8_est75,T8_est76,T8_est77,T8_est78,T8_est79,T8_est80,T8_est81,T8_est82,T8_est83,T8_est84,T8_est85,T8_est86,T8_est87,T8_est88,T8_est89,T8_est90,T8_est91,T8_est92,T8_est93,T8_est94,T8_est95,T8_est96,T8_est97,T8_est98,T8_est99,T8_est100,T8_est101,T8_est102,T8_est103,T8_est104,T8_est105,T8_est106,T8_est107,T8_est108,T8_est109,T8_est110,T8_est111,T8_est112,T8_est113,T8_est114,T8_est115,T8_est116,T8_est117,T8_est118,T8_est119,T8_est120,T8_est121,T8_est122,T8_est123,T8_est124,T8_est125,T8_est126,T8_est127,T8_est128,T8_est129,T8_est130,T8_est131,T8_est132,T8_est133,T8_moe1,T8_moe2,T8_moe3,T8_moe4,T8_moe5,T8_moe6,T8_moe7,T8_moe8,T8_moe9,T8_moe10,T8_moe11,T8_moe12,T8_moe13,T8_moe14,T8_moe15,T8_moe16,T8_moe17,T8_moe18,T8_moe19,T8_moe20,T8_moe21,T8_moe22,T8_moe23,T8_moe24,T8_moe25,T8_moe26,T8_moe27,T8_moe28,T8_moe29,T8_moe30,T8_moe31,T8_moe32,T8_moe33,T8_moe34,T8_moe35,T8_moe36,T8_moe37,T8_moe38,T8_moe39,T8_moe40,T8_moe41,T8_moe42,T8_moe43,T8_moe44,T8_moe45,T8_moe46,T8_moe47,T8_moe48,T8_moe49,T8_moe50,T8_moe51,T8_moe52,T8_moe53,T8_moe54,T8_moe55,T8_moe56,T8_moe57,T8_moe58,T8_moe59,T8_moe60,T8_moe61,T8_moe62,T8_moe63,T8_moe64,T8_moe65,T8_moe66,T8_moe67,T8_moe68,T8_moe69,T8_moe70,T8_moe71,T8_moe72,T8_moe73,T8_moe74,T8_moe75,T8_moe76,T8_moe77,T8_moe78,T8_moe79,T8_moe80,T8_moe81,T8_moe82,T8_moe83,T8_moe84,T8_moe85,T8_moe86,T8_moe87,T8_moe88,T8_moe89,T8_moe90,T8_moe91,T8_moe92,T8_moe93,T8_moe94,T8_moe95,T8_moe96,T8_moe97,T8_moe98,T8_moe99,T8_moe100,T8_moe101,T8_moe102,T8_moe103,T8_moe104,T8_moe105,T8_moe106,T8_moe107,T8_moe108,T8_moe109,T8_moe110,T8_moe111,T8_moe112,T8_moe113,T8_moe114,T8_moe115,T8_moe116,T8_moe117,T8_moe118,T8_moe119,T8_moe120,T8_moe121,T8_moe122,T8_moe123,T8_moe124,T8_moe125,T8_moe126,T8_moe127,T8_moe128,T8_moe129,T8_moe130,T8_moe131,T8_moe132,T8_moe133,GEOID10_TRACT,source_y,sumlevel_y,geoid_y,name_y,st_y,cnty_y,tract_y,T3_est1,T3_est2,T3_est3,T3_est4,T3_est5,T3_est6,T3_est7,T3_est8,T3_est9,T3_est10,T3_est11,T3_est12,T3_est13,T3_est14,T3_est15,T3_est16,T3_est17,T3_est18,T3_est19,T3_est20,T3_est21,T3_est22,T3_est23,T3_est24,T3_est25,T3_est26,T3_est27,T3_est28,T3_est29,T3_est30,T3_est31,T3_est32,T3_est33,T3_est34,T3_est35,T3_est36,T3_est37,T3_est38,T3_est39,T3_est40,T3_est41,T3_est42,T3_est43,T3_est44,T3_est45,T3_est46,T3_est47,T3_est48,T3_est49,T3_est50,T3_est51,T3_est52,T3_est53,T3_est54,T3_est55,T3_est56,T3_est57,T3_est58,T3_est59,T3_est60,T3_est61,T3_est62,T3_est63,T3_est64,T3_est65,T3_est66,T3_est67,T3_est68,T3_est69,T3_est70,T3_est71,T3_est72,T3_est73,T3_est74,T3_est75,T3_est76,T3_est77,T3_est78,T3_est79,T3_est80,T3_est81,T3_est82,T3_est83,T3_est84,T3_est85,T3_est86,T3_est87,T3_moe1,T3_moe2,T3_moe3,T3_moe4,T3_moe5,T3_moe6,T3_moe7,T3_moe8,T3_moe9,T3_moe10,T3_moe11,T3_moe12,T3_moe13,T3_moe14,T3_moe15,T3_moe16,T3_moe17,T3_moe18,T3_moe19,T3_moe20,T3_moe21,T3_moe22,T3_moe23,T3_moe24,T3_moe25,T3_moe26,T3_moe27,T3_moe28,T3_moe29,T3_moe30,T3_moe31,T3_moe32,T3_moe33,T3_moe34,T3_moe35,T3_moe36,T3_moe37,T3_moe38,T3_moe39,T3_moe40,T3_moe41,T3_moe42,T3_moe43,T3_moe44,T3_moe45,T3_moe46,T3_moe47,T3_moe48,T3_moe49,T3_moe50,T3_moe51,T3_moe52,T3_moe53,T3_moe54,T3_moe55,T3_moe56,T3_moe57,T3_moe58,T3_moe59,T3_moe60,T3_moe61,T3_moe62,T3_moe63,T3_moe64,T3_moe65,T3_moe66,T3_moe67,T3_moe68,T3_moe69,T3_moe70,T3_moe71,T3_moe72,T3_moe73,T3_moe74,T3_moe75,T3_moe76,T3_moe77,T3_moe78,T3_moe79,T3_moe80,T3_moe81,T3_moe82,T3_moe83,T3_moe84,T3_moe85,T3_moe86,T3_moe87,Share of homes with no kitchen or indoor plumbing (percent),HOUSING_BURDEN_NUMERATOR,HOUSING_BURDEN_DENOMINATOR,DENOM INCL NOT COMPUTED,Housing burden (percent) +2014thru2018,140,14000US06027000800,"Census Tract 8, Inyo County, California",6,27,800,1375,800,105,40,15,30,15,0,15,50,15,35,0,0,0,130,65,0,65,35,0,35,30,0,30,0,0,0,130,70,0,70,10,0,10,50,0,50,0,0,0,70,55,0,55,15,0,15,0,0,0,0,0,0,360,320,0,320,35,0,35,4,0,4,0,0,0,580,120,30,20,10,10,0,10,70,0,70,4,4,0,110,50,10,40,40,0,40,20,0,20,0,0,0,125,85,0,85,40,0,40,0,0,0,0,0,0,55,0,0,0,55,0,55,0,0,0,0,0,0,170,165,0,165,10,0,10,0,0,0,0,0,0,133,101,51,30,15,31,15,12,15,37,31,26,12,12,12,48,34,12,34,34,12,34,32,12,32,12,12,12,59,41,12,41,13,12,13,41,12,41,12,12,12,38,33,12,33,22,12,22,12,12,12,12,12,12,87,79,12,79,35,12,35,5,12,5,12,12,12,122,84,29,28,14,20,12,20,79,12,79,12,12,12,54,34,16,30,37,12,37,32,12,32,12,12,12,56,50,12,50,35,12,35,12,12,12,12,12,12,64,12,12,12,64,12,64,12,12,12,12,12,12,68,64,12,64,14,12,14,12,12,12,12,12,12,06027000800,2014thru2018,140,14000US06027000800,"Census Tract 8, Inyo County, California",6,27,800,1375,800,30,30,0,0,0,0,0,0,0,0,0,0,10,0,0,0,10,0,115,35,30,50,0,4,115,15,35,10,15,35,0,0,0,0,0,0,530,30,65,70,50,320,580,35,25,10,0,0,0,0,0,0,0,0,0,65,0,10,0,55,0,90,70,20,0,0,0,95,10,30,40,0,10,0,0,0,0,0,0,300,10,40,85,0,165,133,101,31,31,12,12,12,12,12,12,12,12,12,12,15,12,12,12,15,12,57,26,32,41,12,5,56,15,34,13,22,35,12,12,12,12,12,12,90,31,34,41,25,79,122,34,30,16,12,12,12,12,12,12,12,12,12,67,12,18,12,64,12,83,79,32,12,12,12,52,20,32,35,12,14,12,12,12,12,12,12,89,14,30,50,12,64,0.0471014493,370,1376,1380,0.2688953488 +2014thru2018,140,14000US06061021322,"Census Tract 213.22, Placer County, California",6,61,21322,5395,4250,185,15,0,15,30,0,30,70,0,70,65,0,65,165,45,0,45,75,0,75,45,0,45,0,0,0,375,140,0,140,130,0,130,105,0,105,0,0,0,255,60,0,60,160,0,160,35,0,35,0,0,0,3275,2990,0,2990,290,0,290,0,0,0,0,0,0,1145,295,65,0,65,0,0,0,160,0,160,65,0,65,235,0,0,0,205,0,205,30,0,30,0,0,0,270,135,0,135,85,0,85,50,0,50,0,0,0,130,105,0,105,30,0,30,0,0,0,0,0,0,215,215,0,215,0,0,0,0,0,0,0,0,0,179,212,100,20,19,20,37,19,37,57,19,57,87,19,87,100,38,19,38,88,19,88,39,19,39,19,19,19,164,92,19,92,78,19,78,84,19,84,19,19,19,109,56,19,56,88,19,88,39,19,39,19,19,19,232,232,19,232,116,19,116,19,19,19,19,19,19,187,139,83,19,83,19,19,19,95,19,95,94,19,94,115,19,19,19,100,19,100,35,19,35,19,19,19,146,105,19,105,90,19,90,52,19,52,19,19,19,91,87,19,87,30,19,30,19,19,19,19,19,19,99,99,19,99,19,19,19,19,19,19,19,19,19,06061021322,2014thru2018,140,14000US06061021322,"Census Tract 213.22, Placer County, California",6,61,21322,5395,4250,0,0,0,0,0,0,15,0,0,0,0,15,120,0,0,0,55,65,250,70,45,105,35,0,630,30,75,130,105,290,65,65,0,0,0,0,3170,15,45,140,60,2905,1145,0,0,0,0,0,0,50,50,0,0,0,0,45,0,45,0,0,0,240,160,30,50,0,0,270,0,160,85,30,0,15,15,0,0,0,0,520,65,0,135,105,215,179,212,19,19,19,19,19,19,26,19,19,19,19,26,108,19,19,19,65,84,122,57,39,84,39,19,151,37,88,78,67,116,87,87,19,19,19,19,238,20,38,92,56,249,187,19,19,19,19,19,19,81,81,19,19,19,19,73,19,73,19,19,19,100,95,35,52,19,19,115,19,93,90,30,19,26,26,19,19,19,19,170,83,19,105,87,99,0.0000000000,985,5265,5395,0.1870845204 +2014thru2018,140,14000US06069000802,"Census Tract 8.02, San Benito County, California",6,69,802,885,615,25,10,4,10,4,0,4,4,0,4,4,0,4,55,25,0,25,10,0,10,20,0,20,0,0,0,70,40,4,35,10,0,10,20,0,20,0,0,0,45,35,0,35,10,0,10,4,0,4,0,0,0,420,340,4,335,55,0,55,25,0,25,0,0,0,265,35,20,0,20,4,0,4,10,0,10,4,0,4,75,35,0,35,4,0,4,35,0,35,0,0,0,35,25,4,20,15,0,15,0,0,0,0,0,0,40,40,0,40,0,0,0,0,0,0,0,0,0,80,80,0,80,0,0,0,0,0,0,0,0,0,69,70,14,12,5,10,3,12,3,12,12,12,10,12,10,35,31,12,31,11,12,11,16,12,16,12,12,12,22,18,12,20,11,12,11,12,12,12,12,12,12,20,19,12,19,12,12,12,1,12,1,12,12,12,49,47,12,47,23,12,23,20,12,20,12,12,12,45,15,14,12,14,2,12,2,9,12,9,3,12,3,28,19,12,19,7,12,7,23,12,23,12,12,12,21,15,4,14,14,12,14,12,12,12,12,12,12,20,20,12,20,12,12,12,12,12,12,12,12,12,28,28,12,28,12,12,12,12,12,12,12,12,12,06069000802,2014thru2018,140,14000US06069000802,"Census Tract 8.02, San Benito County, California",6,69,802,885,615,4,4,0,4,0,4,4,0,0,0,0,4,0,0,0,0,0,0,70,4,20,20,4,25,90,4,10,10,10,55,4,4,0,0,0,0,440,10,25,35,35,335,265,4,0,0,4,0,0,4,0,0,0,4,0,20,4,15,0,0,0,25,4,20,0,0,0,25,4,4,15,0,0,4,4,0,0,0,0,190,20,35,20,35,80,69,70,11,5,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,12,25,12,16,12,1,20,28,3,11,11,12,23,10,10,12,12,12,12,62,10,31,20,19,46,45,4,12,12,4,12,12,11,12,12,12,11,12,19,12,17,12,12,12,17,11,18,12,12,12,14,2,7,14,12,12,3,3,12,12,12,12,43,14,19,14,18,28,0.0090909091,136,872,880,0.1559633028 +2014thru2018,140,14000US15001021010,"Census Tract 210.10, Hawaii County, Hawaii",15,1,21010,3130,2515,520,210,25,190,40,10,30,190,30,155,80,20,60,555,385,70,310,90,0,90,85,0,85,0,0,0,345,200,4,190,135,0,135,4,0,4,0,0,0,245,205,20,190,40,0,40,0,0,0,0,0,0,855,840,40,800,15,10,4,0,0,0,0,0,0,615,200,40,10,30,0,0,0,80,10,70,80,0,80,85,25,4,20,50,0,50,4,0,4,0,0,0,90,40,0,40,45,0,45,0,0,0,0,0,0,80,70,4,65,0,0,0,10,0,10,0,0,0,160,160,10,155,0,0,0,0,0,0,0,0,0,176,219,147,112,21,107,40,13,38,116,33,112,43,21,40,166,161,81,146,48,15,48,62,15,62,15,15,15,143,80,10,81,118,15,118,19,15,19,15,15,15,100,99,32,107,34,15,34,15,15,15,15,15,15,196,199,39,189,22,13,15,15,15,15,15,15,15,168,111,30,13,25,15,15,15,50,17,44,92,15,92,51,29,9,27,40,15,40,14,15,14,15,15,15,53,36,15,36,44,15,44,15,15,15,15,15,15,43,43,10,42,15,15,15,15,15,15,15,15,15,114,114,13,114,15,15,15,15,15,15,15,15,15,15001021010,2014thru2018,140,14000US15001021010,"Census Tract 210.10, Hawaii County, Hawaii",15,1,21010,3130,2515,230,85,70,4,20,50,55,10,35,0,0,10,110,10,15,50,0,30,245,155,85,4,0,0,275,30,60,135,40,4,55,55,0,0,0,0,1550,170,290,140,190,760,615,40,20,4,0,4,10,65,20,0,0,0,45,50,20,0,0,25,4,55,40,4,0,10,0,100,0,50,45,0,0,70,70,0,0,0,0,235,30,20,40,35,105,176,219,100,44,81,10,32,43,37,13,35,15,15,14,56,23,22,41,15,32,121,112,62,19,15,15,136,38,40,118,34,15,40,40,15,15,15,15,212,104,145,72,107,189,168,26,18,9,15,10,13,77,30,15,15,15,70,40,22,15,15,33,2,35,31,14,15,15,15,55,15,40,44,15,15,86,86,15,15,15,15,111,25,27,36,30,91,0.0862619808,723,2970,3130,0.2434343434 +2014thru2018,140,14000US15001021101,"Census Tract 211.01, Hawaii County, Hawaii",15,1,21101,1690,1385,390,145,60,85,50,0,50,145,0,145,50,15,30,240,140,15,125,20,15,4,80,0,80,0,0,0,185,130,0,130,20,0,20,35,0,35,0,0,0,190,170,0,170,20,0,20,0,0,0,0,0,0,380,350,20,330,30,0,30,0,0,0,0,0,0,300,150,35,4,30,15,0,15,70,0,70,25,0,25,15,10,0,10,10,0,10,0,0,0,0,0,0,55,55,35,20,4,0,4,0,0,0,0,0,0,25,15,0,15,10,0,10,0,0,0,0,0,0,55,55,0,55,0,0,0,0,0,0,0,0,0,157,136,98,63,54,39,27,11,27,71,11,71,33,24,25,79,63,22,59,23,24,10,46,11,46,11,11,11,74,63,11,63,14,11,14,34,11,34,11,11,11,68,65,11,65,22,11,22,11,11,11,11,11,11,101,97,36,97,24,11,24,11,11,11,11,11,11,98,76,29,12,26,22,11,22,60,11,60,36,11,36,21,13,11,13,13,11,13,11,11,11,11,11,11,58,57,54,23,11,11,11,11,11,11,11,11,11,22,21,11,21,15,11,15,11,11,11,11,11,11,33,33,11,33,11,11,11,11,11,11,11,11,11,15001021101,2014thru2018,140,14000US15001021101,"Census Tract 211.01, Hawaii County, Hawaii",15,1,21101,1690,1385,125,75,30,0,0,20,35,4,10,10,0,10,4,4,0,0,0,0,255,140,80,35,0,0,125,50,4,20,20,30,30,30,0,0,0,0,810,85,115,115,170,320,300,40,4,0,35,0,0,25,25,0,0,0,0,4,0,0,0,0,4,70,70,0,0,0,0,35,15,10,4,10,0,4,4,0,0,0,0,125,30,10,20,15,50,157,136,73,58,29,11,11,36,29,8,20,15,11,13,3,3,11,11,11,11,78,69,46,34,11,11,40,27,10,14,22,24,23,23,11,11,11,11,117,39,55,61,65,96,98,52,12,11,54,11,11,38,38,11,11,11,11,2,11,11,11,11,2,60,60,11,11,11,11,28,22,13,11,15,11,11,11,11,11,11,11,51,26,13,23,21,36,0.0979228487,449,1610,1685,0.2788819876 +2014thru2018,140,14000US15001021402,"Census Tract 214.02, Hawaii County, Hawaii",15,1,21402,1340,830,55,10,0,10,10,0,10,30,0,30,0,0,0,25,4,0,4,10,0,10,4,0,4,0,0,0,130,50,0,50,55,0,55,30,4,25,0,0,0,45,40,0,40,0,0,0,4,0,4,0,0,0,575,520,0,520,55,0,55,0,0,0,0,0,0,510,195,40,0,40,20,0,20,130,30,100,0,0,0,50,15,0,15,20,0,20,15,0,15,0,0,0,65,35,0,35,15,0,15,15,0,15,0,0,0,70,45,0,45,25,0,25,0,0,0,0,0,0,130,125,0,125,4,0,4,0,0,0,0,0,0,103,132,51,17,11,17,16,11,16,46,11,46,11,11,11,21,15,11,15,20,11,20,14,11,14,11,11,11,58,31,11,31,52,11,52,24,1,21,11,11,11,34,34,11,34,11,11,11,2,11,2,11,11,11,119,121,11,121,32,11,32,11,11,11,11,11,11,102,83,22,11,22,21,11,21,84,50,70,11,11,11,39,22,11,22,31,11,31,19,11,19,11,11,11,41,33,11,33,22,11,22,20,11,20,11,11,11,59,47,11,47,40,11,40,11,11,11,11,11,11,60,60,11,60,7,11,7,11,11,11,11,11,11,15001021402,2014thru2018,140,14000US15001021402,"Census Tract 214.02, Hawaii County, Hawaii",15,1,21402,1340,830,4,0,0,4,0,0,4,0,0,0,0,4,70,0,0,0,10,60,70,30,4,25,4,0,130,10,10,55,0,55,0,0,0,0,0,0,550,10,4,50,30,455,510,30,30,0,0,0,0,10,0,0,10,0,0,15,0,10,0,0,4,115,100,4,4,0,0,85,20,20,15,25,4,0,0,0,0,0,0,250,40,15,35,45,120,103,132,1,11,11,1,11,11,13,11,11,11,11,13,61,11,11,11,19,59,53,46,14,21,2,11,68,16,20,52,11,32,11,11,11,11,11,11,122,17,15,31,30,107,102,50,50,11,11,11,11,13,11,11,13,11,11,21,11,16,11,11,12,74,70,10,13,11,11,51,21,31,22,40,7,11,11,11,11,11,11,92,22,22,33,47,58,0.0253731343,354,1340,1340,0.2641791045 +2014thru2018,140,14000US15001021800,"Census Tract 218, Hawaii County, Hawaii",15,1,21800,2015,1375,120,40,25,15,25,0,25,35,0,35,15,0,15,80,45,0,45,20,0,20,15,0,15,0,0,0,155,110,0,110,20,0,20,30,0,30,0,0,0,110,100,0,100,15,0,15,0,0,0,0,0,0,905,680,4,675,205,0,205,20,0,20,0,0,0,640,160,25,0,25,25,0,25,110,55,55,0,0,0,45,4,0,4,25,0,25,15,0,15,0,0,0,85,55,4,50,35,0,35,0,0,0,0,0,0,35,25,0,25,10,0,10,0,0,0,0,0,0,310,310,0,310,4,0,4,0,0,0,0,0,0,201,188,62,43,40,16,25,15,25,34,15,34,21,15,21,39,31,15,31,20,15,20,21,15,21,15,15,15,58,49,15,49,16,15,16,28,15,28,15,15,15,45,40,15,40,20,15,20,15,15,15,15,15,15,170,149,15,148,115,15,115,26,15,26,15,15,15,159,91,27,15,27,30,15,30,86,79,37,15,15,15,29,10,15,10,20,15,20,18,15,18,15,15,15,50,36,15,34,42,15,42,15,15,15,15,15,15,27,24,15,24,14,15,14,15,15,15,15,15,15,155,155,15,155,3,15,3,15,15,15,15,15,15,15001021800,2014thru2018,140,14000US15001021800,"Census Tract 218, Hawaii County, Hawaii",15,1,21800,2015,1375,30,25,0,0,0,4,10,0,4,4,4,4,65,0,4,4,4,55,100,35,15,30,0,20,285,25,20,20,15,205,15,15,0,0,0,0,870,15,40,105,95,615,640,55,55,0,4,0,0,15,10,0,0,0,4,25,10,4,0,0,10,55,40,15,0,0,0,100,25,25,35,10,4,0,0,0,0,0,0,395,25,0,50,25,290,201,188,41,40,15,15,15,15,13,15,9,4,5,3,41,15,3,5,1,41,53,34,21,28,15,26,118,25,20,16,20,115,21,21,15,15,15,15,159,16,27,51,40,140,159,80,79,15,15,15,15,17,14,15,15,15,11,24,14,10,15,15,14,37,31,18,15,15,15,53,30,20,42,14,3,15,15,15,15,15,15,162,27,15,34,24,155,0.0421836228,355,2000,2015,0.1775000000 +2014thru2018,140,14000US15003010201,"Census Tract 102.01, Honolulu County, Hawaii",15,3,10201,1515,785,85,15,0,15,4,0,4,65,4,55,4,0,4,35,4,0,4,15,0,15,15,0,15,0,0,0,180,75,4,70,75,10,65,30,0,30,0,0,0,100,70,0,70,25,0,25,4,0,4,0,0,0,385,340,0,340,35,0,35,10,0,10,0,0,0,730,135,10,4,4,10,0,10,75,4,70,40,0,40,150,10,0,10,50,15,35,85,0,85,0,0,0,195,110,10,100,65,4,65,15,4,10,0,0,0,90,65,0,65,25,0,25,0,0,0,0,0,0,165,145,4,140,20,0,20,0,0,0,0,0,0,66,73,36,15,15,15,15,15,15,31,10,31,11,15,11,18,11,15,11,13,15,13,14,15,14,15,15,15,50,35,15,35,26,15,28,24,15,24,15,15,15,35,33,15,33,16,15,16,3,15,3,15,15,15,54,50,15,50,15,15,15,17,15,17,15,15,15,72,46,14,15,10,14,15,14,33,10,34,28,15,28,55,17,15,17,35,20,31,42,15,42,15,15,15,57,43,13,41,34,15,33,22,13,13,15,15,15,34,29,15,29,22,15,22,15,15,15,15,15,15,57,54,15,56,18,15,18,15,15,15,15,15,15,15003010201,2014thru2018,140,14000US15003010201,"Census Tract 102.01, Honolulu County, Hawaii",15,3,10201,1515,785,15,4,0,10,0,0,35,4,0,15,10,4,85,0,4,40,10,30,95,50,10,15,4,10,125,4,15,50,25,35,4,4,0,0,0,0,425,15,4,45,55,305,730,50,10,15,20,0,4,95,25,4,40,15,10,75,0,15,35,4,20,150,55,85,10,0,0,95,10,10,40,25,15,30,30,0,0,0,0,230,4,10,50,45,120,66,73,16,10,15,13,15,15,27,10,15,25,12,9,46,15,9,33,13,22,31,30,13,12,3,17,33,15,13,18,16,15,11,11,15,15,15,15,69,15,11,22,31,52,72,23,11,20,17,15,15,42,21,11,32,14,13,36,15,23,25,15,18,48,28,42,13,15,15,43,14,13,32,22,15,25,25,15,15,15,15,53,10,17,28,23,48,0.0429042904,504,1471,1515,0.3426240653 +2014thru2018,140,14000US15007040603,"Census Tract 406.03, Kauai County, Hawaii",15,7,40603,1035,595,25,10,0,10,0,0,0,15,0,15,4,0,4,55,20,0,20,15,0,15,20,0,20,0,0,0,80,55,0,55,4,0,4,20,0,20,0,0,0,50,10,0,10,10,0,10,30,0,30,0,0,0,385,310,4,305,55,0,55,20,0,20,0,0,0,440,100,25,0,25,4,0,4,55,0,55,10,0,10,40,4,0,4,15,0,15,25,0,25,0,0,0,100,35,0,35,40,0,40,25,0,25,0,0,0,60,40,0,40,15,0,15,0,0,0,0,0,0,145,125,4,125,10,0,10,4,0,4,0,0,0,84,70,15,15,11,15,11,11,11,15,11,15,11,11,11,24,16,11,16,14,11,14,16,11,16,11,11,11,33,29,11,29,11,11,11,14,11,14,11,11,11,21,12,11,12,10,11,10,22,11,22,11,11,11,67,60,11,61,29,11,29,13,11,13,11,11,11,75,34,21,11,21,15,11,15,28,11,28,13,11,13,29,3,11,3,21,11,21,20,11,20,11,11,11,43,24,11,24,23,11,23,23,11,23,11,11,11,32,30,11,30,13,11,13,11,11,11,11,11,11,53,49,11,49,14,11,14,17,11,17,11,11,11,15007040603,2014thru2018,140,14000US15007040603,"Census Tract 406.03, Kauai County, Hawaii",15,7,40603,1035,595,4,0,0,0,0,4,4,0,0,0,0,4,30,0,0,4,4,25,100,15,20,20,30,20,80,0,15,4,4,55,4,4,0,0,0,0,370,10,20,55,10,275,440,4,0,0,0,0,4,10,10,0,0,0,0,30,4,4,4,10,10,105,55,25,25,0,4,80,4,15,30,15,10,10,10,0,0,0,0,195,15,0,35,35,115,84,70,11,11,11,11,11,11,18,11,11,11,11,18,25,11,11,4,11,26,34,15,16,12,22,13,31,11,14,11,11,29,11,11,11,11,11,11,66,15,16,29,12,55,75,11,11,11,11,11,11,15,15,11,11,11,11,27,3,3,16,13,14,45,26,20,23,11,17,35,15,21,20,13,14,13,13,11,11,11,11,52,16,11,24,25,45,0.0077294686,238,1021,1035,0.2331047992 +2014thru2018,140,14000US15007040604,"Census Tract 406.04, Kauai County, Hawaii",15,7,40604,1235,655,85,15,0,15,4,0,4,45,0,45,15,0,15,55,35,0,35,15,0,15,4,0,4,0,0,0,70,35,0,35,20,0,20,15,0,15,0,0,0,50,20,0,20,25,0,25,4,0,4,0,0,0,395,335,4,330,60,4,55,0,0,0,0,0,0,580,95,35,4,30,40,0,40,15,0,15,0,0,0,180,45,0,45,85,0,85,50,0,50,0,0,0,55,20,0,20,15,0,15,20,0,20,0,0,0,45,25,0,25,20,0,20,0,0,0,0,0,0,205,175,0,175,30,0,30,0,0,0,0,0,0,79,96,46,28,11,28,11,11,11,33,11,33,15,11,15,35,30,11,30,22,11,22,10,11,10,11,11,11,31,18,11,18,23,11,23,14,11,14,11,11,11,36,19,11,19,28,11,28,9,11,9,11,11,11,80,74,13,76,33,5,32,11,11,11,11,11,11,101,44,28,11,27,30,11,30,18,11,18,11,11,11,65,29,11,29,50,11,50,35,11,35,11,11,11,28,16,11,16,15,11,15,24,11,24,11,11,11,29,21,11,21,23,11,23,11,11,11,11,11,11,72,69,11,69,24,11,24,11,11,11,11,11,11,15007040604,2014thru2018,140,14000US15007040604,"Census Tract 406.04, Kauai County, Hawaii",15,7,40604,1235,655,10,0,0,0,0,10,10,0,0,0,4,4,15,0,4,0,0,10,75,45,4,15,4,0,120,4,15,20,25,55,15,15,0,0,0,0,410,15,30,35,15,315,580,4,4,0,0,0,0,30,0,20,0,0,10,25,4,4,0,4,15,90,15,50,20,0,0,165,35,70,15,20,30,0,0,0,0,0,0,260,30,40,20,25,150,79,96,14,11,11,11,11,14,13,11,11,11,10,1,18,11,10,11,11,13,36,33,10,14,9,11,55,11,22,23,28,32,15,15,11,11,11,11,80,28,30,18,18,74,101,11,11,11,11,11,11,31,11,29,11,11,13,25,7,3,11,10,21,45,18,35,24,11,11,56,28,44,15,23,24,11,11,11,11,11,11,77,27,26,16,22,63,0.0113360324,328,1220,1235,0.2688524590 +2014thru2018,140,14000US15007040700,"Census Tract 407, Kauai County, Hawaii",15,7,40700,2875,1930,220,40,0,40,80,0,80,80,0,80,25,0,25,50,20,0,20,20,0,20,10,0,10,0,0,0,275,115,0,115,45,0,45,115,0,115,0,0,0,160,75,0,75,70,0,70,15,0,15,0,0,0,1225,1030,0,1030,170,0,170,25,0,25,0,0,0,950,150,25,0,25,20,0,20,90,0,90,15,0,15,120,35,0,35,25,0,25,60,0,60,0,0,0,135,45,0,45,35,0,35,55,0,55,0,0,0,115,115,4,110,0,0,0,0,0,0,0,0,0,430,370,0,370,60,15,45,0,0,0,0,0,0,162,199,93,34,15,34,74,15,74,62,15,62,27,15,27,38,17,15,17,29,15,29,17,15,17,15,15,15,82,46,15,46,32,15,32,62,15,62,15,15,15,73,53,15,53,53,15,53,21,15,21,15,15,15,187,173,15,173,83,15,83,30,15,30,15,15,15,157,73,18,15,18,23,15,23,64,15,64,19,15,19,68,32,15,32,33,15,33,48,15,48,15,15,15,66,35,15,35,44,15,44,43,15,43,15,15,15,79,79,13,74,15,15,15,15,15,15,15,15,15,141,134,15,134,57,25,53,15,15,15,15,15,15,15007040700,2014thru2018,140,14000US15007040700,"Census Tract 407, Kauai County, Hawaii",15,7,40700,2875,1930,0,0,0,0,0,0,0,0,0,0,0,0,115,0,0,4,0,110,245,80,10,115,15,25,380,80,20,45,70,170,25,25,0,0,0,0,1165,40,20,110,75,920,950,25,0,0,0,4,15,80,0,10,0,10,60,45,0,10,4,0,30,205,90,60,55,0,0,105,20,15,25,0,45,15,15,0,0,0,0,470,25,20,45,100,285,162,199,15,15,15,15,15,15,15,15,15,15,15,15,51,15,15,10,15,52,98,62,17,62,21,30,128,74,29,32,53,83,27,27,15,15,15,15,171,34,17,46,53,179,157,29,15,15,15,13,25,67,15,19,15,21,61,48,15,17,15,15,44,94,64,48,43,15,15,75,23,23,41,15,53,19,19,15,15,15,15,107,18,24,35,72,115,0.0086805556,635,2840,2880,0.2235915493 +2014thru2018,140,14000US15009030100,"Census Tract 301, Maui County, Hawaii",15,9,30100,500,320,45,4,0,4,10,10,0,25,0,25,4,0,4,65,65,0,65,0,0,0,0,0,0,0,0,0,50,35,0,35,10,0,10,10,0,10,0,0,0,20,20,0,20,0,0,0,0,0,0,0,0,0,145,135,10,120,10,0,10,0,0,0,0,0,0,175,50,25,0,25,0,0,0,25,4,20,0,0,0,10,4,0,4,4,0,4,0,0,0,0,0,0,10,10,0,10,0,0,0,0,0,0,0,0,0,20,15,0,15,4,0,4,0,0,0,0,0,0,90,90,0,90,0,0,0,0,0,0,0,0,0,71,75,25,11,11,11,18,18,11,19,11,19,11,11,11,57,57,11,57,11,11,11,11,11,11,11,11,11,32,27,11,27,14,11,14,15,11,15,11,11,11,23,23,11,23,11,11,11,11,11,11,11,11,11,58,55,18,53,18,11,18,11,11,11,11,11,11,69,41,30,11,30,11,11,11,24,11,24,11,11,11,14,9,11,9,9,11,9,11,11,11,11,11,11,12,12,11,12,11,11,11,11,11,11,11,11,11,25,25,11,25,10,11,10,11,11,11,11,11,11,59,59,11,59,11,11,11,11,11,11,11,11,11,15009030100,2014thru2018,140,14000US15009030100,"Census Tract 301, Maui County, Hawaii",15,9,30100,500,320,20,10,0,0,0,10,10,0,0,4,0,4,40,4,0,15,0,20,30,25,0,10,0,0,15,0,0,4,0,10,0,0,0,0,0,0,200,4,65,15,20,95,175,4,4,0,0,0,0,10,0,0,0,0,10,20,0,0,0,20,0,20,20,0,0,0,0,4,0,4,0,0,0,0,0,0,0,0,0,120,25,4,10,0,80,71,75,27,18,11,11,11,18,15,11,11,14,11,10,23,11,11,23,11,14,25,19,11,15,11,11,20,11,11,10,11,18,11,11,11,11,11,11,62,11,57,15,23,53,69,11,11,11,11,11,11,16,11,11,11,11,16,25,11,11,11,25,11,24,24,11,11,11,11,9,11,9,11,11,11,11,11,11,11,11,11,62,30,9,12,11,59,0.0484848485,84,491,495,0.1710794297 +2014thru2018,140,14000US15009030201,"Census Tract 302.01, Maui County, Hawaii",15,9,30201,820,605,75,40,0,40,10,0,10,25,0,25,0,0,0,85,40,0,40,10,0,10,35,0,35,0,0,0,100,35,0,35,65,0,65,0,0,0,0,0,0,40,10,0,10,15,0,15,10,0,10,0,0,0,310,240,0,240,60,0,60,10,0,10,0,0,0,215,0,0,0,0,0,0,0,0,0,0,0,0,0,15,10,0,10,0,0,0,4,0,4,0,0,0,110,65,0,65,45,0,45,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,90,90,0,90,0,0,0,0,0,0,0,0,0,99,113,49,36,11,36,15,11,15,30,11,30,11,11,11,48,34,11,34,18,11,18,31,11,31,11,11,11,79,34,11,34,73,11,73,11,11,11,11,11,11,35,21,11,21,21,11,21,24,11,24,11,11,11,88,84,11,84,31,11,31,17,11,17,11,11,11,100,11,11,11,11,11,11,11,11,11,11,11,11,11,25,21,11,21,11,11,11,20,11,20,11,11,11,67,45,11,45,49,11,49,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,11,78,78,11,78,11,11,11,11,11,11,11,11,11,15009030201,2014thru2018,140,14000US15009030201,"Census Tract 302.01, Maui County, Hawaii",15,9,30201,820,605,0,0,0,0,0,0,15,0,0,0,0,15,10,0,0,0,10,0,70,25,35,0,0,10,160,10,10,65,15,60,0,0,0,0,0,0,350,40,40,35,10,225,215,0,0,0,0,0,0,0,0,0,0,0,0,10,0,0,10,0,0,4,0,4,0,0,0,45,0,0,45,0,0,0,0,0,0,0,0,155,0,10,55,0,90,99,113,11,11,11,11,11,11,23,11,11,11,11,23,24,11,11,11,24,11,47,30,31,11,11,17,81,15,18,73,21,31,11,11,11,11,11,11,91,36,34,34,21,80,100,11,11,11,11,11,11,11,11,11,11,11,11,20,11,11,20,11,11,20,11,20,11,11,11,49,11,11,49,11,11,11,11,11,11,11,11,85,11,21,49,11,78,0.0000000000,194,820,820,0.2365853659 +2014thru2018,140,14000US15009030402,"Census Tract 304.02, Maui County, Hawaii",15,9,30402,3140,2205,185,0,0,0,10,0,10,125,0,125,45,0,45,175,90,0,90,0,0,0,85,0,85,0,0,0,235,150,0,150,10,0,10,75,0,75,0,0,0,185,90,0,90,10,0,10,85,0,85,0,0,0,1425,1165,0,1165,245,0,245,20,0,20,0,0,0,935,65,35,0,35,0,0,0,30,0,30,0,0,0,90,0,0,0,45,0,45,45,0,45,0,0,0,230,100,0,100,90,0,90,40,0,40,0,0,0,155,155,0,155,0,0,0,0,0,0,0,0,0,395,315,0,315,80,0,80,0,0,0,0,0,0,167,236,102,15,15,15,19,15,19,84,15,84,70,15,70,88,59,15,59,15,15,15,71,15,71,15,15,15,139,122,15,122,21,15,21,65,15,65,15,15,15,93,67,15,67,21,15,21,62,15,62,15,15,15,218,231,15,231,128,15,128,32,15,32,15,15,15,244,62,49,15,49,15,15,15,36,15,36,15,15,15,57,15,15,15,44,15,44,46,15,46,15,15,15,155,100,15,100,98,15,98,48,15,48,15,15,15,105,105,15,105,15,15,15,15,15,15,15,15,15,167,148,15,148,97,15,97,15,15,15,15,15,15,15009030402,2014thru2018,140,14000US15009030402,"Census Tract 304.02, Maui County, Hawaii",15,9,30402,3140,2205,0,0,0,0,0,0,15,0,0,0,0,15,70,25,0,0,0,45,360,100,85,75,85,20,275,10,0,10,10,245,45,45,0,0,0,0,1435,0,90,150,90,1105,935,0,0,0,0,0,0,0,0,0,0,0,0,75,0,15,15,0,45,95,30,30,40,0,0,220,0,45,90,0,80,0,0,0,0,0,0,540,35,0,85,155,270,167,236,15,15,15,15,15,15,30,15,15,15,15,30,55,39,15,15,15,43,150,74,71,65,62,32,130,19,15,21,21,128,70,70,15,15,15,15,227,15,59,122,67,235,244,15,15,15,15,15,15,15,15,15,15,15,15,83,15,29,36,15,72,67,36,34,48,15,15,141,15,44,98,15,97,15,15,15,15,15,15,182,49,15,97,105,127,0.0000000000,555,3095,3140,0.1793214863 +2014thru2018,140,14000US15009030800,"Census Tract 308, Maui County, Hawaii",15,9,30800,2250,1810,110,4,0,4,30,0,30,75,0,75,4,0,4,125,80,0,80,15,0,15,30,0,30,0,0,0,160,55,0,55,45,0,45,60,0,60,0,0,0,210,115,0,115,80,0,80,15,0,15,0,0,0,1205,995,20,980,195,0,195,15,0,15,0,0,0,445,40,20,0,20,0,0,0,20,0,20,0,0,0,70,35,0,35,10,0,10,25,0,25,0,0,0,190,115,0,115,45,0,45,30,0,30,0,0,0,15,10,0,10,4,0,4,0,0,0,0,0,0,130,105,0,105,25,0,25,0,0,0,0,0,0,110,134,56,15,15,15,33,15,33,47,15,47,11,15,11,87,82,15,82,15,15,15,28,15,28,15,15,15,61,40,15,40,31,15,31,36,15,36,15,15,15,82,47,15,47,65,15,65,18,15,18,15,15,15,159,175,27,168,110,15,110,23,15,23,15,15,15,132,34,29,15,29,15,15,15,24,15,24,15,15,15,42,31,15,31,17,15,17,23,15,23,15,15,15,120,106,15,106,30,15,30,29,15,29,15,15,15,16,13,15,13,10,15,10,15,15,15,15,15,15,57,48,15,48,32,15,32,15,15,15,15,15,15,15009030800,2014thru2018,140,14000US15009030800,"Census Tract 308, Maui County, Hawaii",15,9,30800,2250,1810,20,0,0,0,0,20,25,0,4,4,0,20,65,4,0,4,4,60,185,70,30,60,15,15,365,30,15,45,80,195,4,4,0,0,0,0,1140,4,80,50,110,895,445,0,0,0,0,0,0,25,0,10,4,0,10,30,0,0,20,0,10,65,20,15,30,0,0,65,0,10,25,4,25,0,0,0,0,0,0,260,20,35,115,10,85,110,134,27,15,15,15,15,27,19,15,3,8,15,17,38,3,15,4,4,36,70,45,28,36,18,23,120,33,18,31,65,110,11,11,15,15,15,15,166,15,82,41,49,167,132,15,15,15,15,15,15,22,15,17,15,15,15,29,15,15,28,15,17,38,24,19,29,15,15,34,15,17,21,10,32,15,15,15,15,15,15,114,29,31,108,13,40,0.0088691796,385,2251,2255,0.1710350955 diff --git a/data/data-pipeline/data_pipeline/tests/sources/hud_housing/test_etl.py b/data/data-pipeline/data_pipeline/tests/sources/hud_housing/test_etl.py new file mode 100644 index 00000000..934cd84e --- /dev/null +++ b/data/data-pipeline/data_pipeline/tests/sources/hud_housing/test_etl.py @@ -0,0 +1,19 @@ +import pathlib +from data_pipeline.tests.sources.example.test_etl import TestETL +from data_pipeline.etl.sources.hud_housing.etl import HudHousingETL + + +class TestHudHousingETL(TestETL): + _ETL_CLASS = HudHousingETL + + _SAMPLE_DATA_PATH = pathlib.Path(__file__).parents[0] / "data" + _SAMPLE_DATA_FILE_NAME = "140/Table3.csv" + _SAMPLE_DATA_ZIP_FILE_NAME = "2014thru2018-140-csv.zip" + _EXTRACT_TMP_FOLDER_NAME = "HudHousingETL" + + def setup_method(self, _method, filename=__file__): + """Invoke `setup_method` from Parent, but using the current file name. + + This code can be copied identically between all child classes. + """ + super().setup_method(_method=_method, filename=filename) diff --git a/data/data-pipeline/data_pipeline/tests/sources/nlcd_nature_deprived/__init__.py b/data/data-pipeline/data_pipeline/tests/sources/nlcd_nature_deprived/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/data/data-pipeline/data_pipeline/tests/sources/nlcd_nature_deprived/data/extract.csv b/data/data-pipeline/data_pipeline/tests/sources/nlcd_nature_deprived/data/extract.csv new file mode 100644 index 00000000..9af12e04 --- /dev/null +++ b/data/data-pipeline/data_pipeline/tests/sources/nlcd_nature_deprived/data/extract.csv @@ -0,0 +1,16 @@ +GEOID10,SF,CF,P200_PFS,CA_LT20,TractAcres,AcresCrops,PctCrops,PctImperv,PctNatural,PctNat90,ImpOrCrop,LowInAndEd,NatureDep,GEOID10_TRACT +6027000800,California,Inyo County,0.6700000000,1,4374150.0000000000,833.5350000000,0.0190559000,0.2995360000,99.6814000000,0,0,1,0,06027000800 +6069000802,California,San Benito County,0.3700000000,1,738261.0000000000,4498.8200000000,0.6093810000,0.1123740000,99.2782000000,0,0,0,0,06069000802 +6061021322,California,Placer County,0.2500000000,1,63993.3000000000,25309.6000000000,39.5504000000,2.9274100000,57.5222000000,0,1,0,0,06061021322 +15001021010,Hawaii,Hawaii County,0.3700000000,1,63993.3000000000,25309.6000000000,39.5504000000,2.9274100000,57.5222000000,0,1,0,0,15001021010 +15001021101,Hawaii,Hawaii County,0.3700000000,1,63993.3000000000,25309.6000000000,39.5504000000,2.9274100000,57.5222000000,0,1,0,0,15001021101 +15007040603,Hawaii,Hawaii County,0.3700000000,1,63993.3000000000,25309.6000000000,39.5504000000,2.9274100000,57.5222000000,0,1,0,0,15007040603 +15007040700,Hawaii,Hawaii County,0.3700000000,1,63993.3000000000,25309.6000000000,39.5504000000,2.9274100000,57.5222000000,0,1,0,0,15007040700 +15009030100,Hawaii,Hawaii County,0.3700000000,1,63993.3000000000,25309.6000000000,39.5504000000,2.9274100000,57.5222000000,0,1,0,0,15009030100 +15009030201,Hawaii,Hawaii County,0.3700000000,1,63993.3000000000,25309.6000000000,39.5504000000,2.9274100000,57.5222000000,0,1,0,0,15009030201 +15001021402,Hawaii,Hawaii County,0.3700000000,1,63993.3000000000,25309.6000000000,39.5504000000,2.9274100000,57.5222000000,0,1,0,0,15001021402 +15001021800,Hawaii,Hawaii County,0.3700000000,1,63993.3000000000,25309.6000000000,39.5504000000,2.9274100000,57.5222000000,0,1,0,0,15001021800 +15009030402,Hawaii,Hawaii County,0.3700000000,1,63993.3000000000,25309.6000000000,39.5504000000,2.9274100000,57.5222000000,0,1,0,0,15009030402 +15009030800,Hawaii,Hawaii County,0.3700000000,1,63993.3000000000,25309.6000000000,39.5504000000,2.9274100000,57.5222000000,0,1,0,0,15009030800 +15003010201,Hawaii,Hawaii County,0.3700000000,1,63993.3000000000,25309.6000000000,39.5504000000,2.9274100000,57.5222000000,0,1,0,0,15003010201 +15007040604,Hawaii,Hawaii County,0.3700000000,1,63993.3000000000,25309.6000000000,39.5504000000,2.9274100000,57.5222000000,0,1,0,0,15007040604 diff --git a/data/data-pipeline/data_pipeline/tests/sources/nlcd_nature_deprived/data/output.csv b/data/data-pipeline/data_pipeline/tests/sources/nlcd_nature_deprived/data/output.csv new file mode 100644 index 00000000..2a38807c --- /dev/null +++ b/data/data-pipeline/data_pipeline/tests/sources/nlcd_nature_deprived/data/output.csv @@ -0,0 +1,16 @@ +GEOID10_TRACT,Does the tract have at least 35 acres in it?,Share of the tract's land area that is covered by impervious surface as a percent,Share of the tract's land area that is covered by impervious surface or cropland as a percent,Share of the tract's land area that is covered by cropland as a percent +06027000800,True,0.2995360000,0.3186000000,0.0190559000 +06069000802,True,0.1123740000,0.7218000000,0.6093810000 +06061021322,True,2.9274100000,42.4778000000,39.5504000000 +15001021010,True,2.9274100000,42.4778000000,39.5504000000 +15001021101,True,2.9274100000,42.4778000000,39.5504000000 +15007040603,True,2.9274100000,42.4778000000,39.5504000000 +15007040700,True,2.9274100000,42.4778000000,39.5504000000 +15009030100,True,2.9274100000,42.4778000000,39.5504000000 +15009030201,True,2.9274100000,42.4778000000,39.5504000000 +15001021402,True,2.9274100000,42.4778000000,39.5504000000 +15001021800,True,2.9274100000,42.4778000000,39.5504000000 +15009030402,True,2.9274100000,42.4778000000,39.5504000000 +15009030800,True,2.9274100000,42.4778000000,39.5504000000 +15003010201,True,2.9274100000,42.4778000000,39.5504000000 +15007040604,True,2.9274100000,42.4778000000,39.5504000000 diff --git a/data/data-pipeline/data_pipeline/tests/sources/nlcd_nature_deprived/data/transform.csv b/data/data-pipeline/data_pipeline/tests/sources/nlcd_nature_deprived/data/transform.csv new file mode 100644 index 00000000..9cb2e054 --- /dev/null +++ b/data/data-pipeline/data_pipeline/tests/sources/nlcd_nature_deprived/data/transform.csv @@ -0,0 +1,16 @@ +GEOID10,SF,CF,P200_PFS,CA_LT20,TractAcres,AcresCrops,Share of the tract's land area that is covered by cropland as a percent,Share of the tract's land area that is covered by impervious surface as a percent,PctNatural,PctNat90,ImpOrCrop,LowInAndEd,NatureDep,GEOID10_TRACT,Does the tract have at least 35 acres in it?,Share of the tract's land area that is covered by impervious surface or cropland as a percent +6027000800,California,Inyo County,0.6700000000,1,4374150.0000000000,833.5350000000,0.0190559000,0.2995360000,99.6814000000,0,0,1,0,06027000800,True,0.3186000000 +6069000802,California,San Benito County,0.3700000000,1,738261.0000000000,4498.8200000000,0.6093810000,0.1123740000,99.2782000000,0,0,0,0,06069000802,True,0.7218000000 +6061021322,California,Placer County,0.2500000000,1,63993.3000000000,25309.6000000000,39.5504000000,2.9274100000,57.5222000000,0,1,0,0,06061021322,True,42.4778000000 +15001021010,Hawaii,Hawaii County,0.3700000000,1,63993.3000000000,25309.6000000000,39.5504000000,2.9274100000,57.5222000000,0,1,0,0,15001021010,True,42.4778000000 +15001021101,Hawaii,Hawaii County,0.3700000000,1,63993.3000000000,25309.6000000000,39.5504000000,2.9274100000,57.5222000000,0,1,0,0,15001021101,True,42.4778000000 +15007040603,Hawaii,Hawaii County,0.3700000000,1,63993.3000000000,25309.6000000000,39.5504000000,2.9274100000,57.5222000000,0,1,0,0,15007040603,True,42.4778000000 +15007040700,Hawaii,Hawaii County,0.3700000000,1,63993.3000000000,25309.6000000000,39.5504000000,2.9274100000,57.5222000000,0,1,0,0,15007040700,True,42.4778000000 +15009030100,Hawaii,Hawaii County,0.3700000000,1,63993.3000000000,25309.6000000000,39.5504000000,2.9274100000,57.5222000000,0,1,0,0,15009030100,True,42.4778000000 +15009030201,Hawaii,Hawaii County,0.3700000000,1,63993.3000000000,25309.6000000000,39.5504000000,2.9274100000,57.5222000000,0,1,0,0,15009030201,True,42.4778000000 +15001021402,Hawaii,Hawaii County,0.3700000000,1,63993.3000000000,25309.6000000000,39.5504000000,2.9274100000,57.5222000000,0,1,0,0,15001021402,True,42.4778000000 +15001021800,Hawaii,Hawaii County,0.3700000000,1,63993.3000000000,25309.6000000000,39.5504000000,2.9274100000,57.5222000000,0,1,0,0,15001021800,True,42.4778000000 +15009030402,Hawaii,Hawaii County,0.3700000000,1,63993.3000000000,25309.6000000000,39.5504000000,2.9274100000,57.5222000000,0,1,0,0,15009030402,True,42.4778000000 +15009030800,Hawaii,Hawaii 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b/data/data-pipeline/data_pipeline/tests/sources/persistent_poverty/data/extract.csv @@ -0,0 +1,16 @@ +TRTID10,state,county,tract,placefp10,cbsa10,metdiv10,ccflag10,FAMILY90,FHH90,POP90SF3,POP90SF4,RUANC90,ITANC90,GEANC90,IRANC90,SCANC90,RUFB90,ITFB90,GEFB90,IRFB90,SCFB90,FB90,NAT90,N10IMM90,AG5UP90,OLANG90,LEP90,AG25UP90,HS90,COL90,CLF90,UNEMP90,DFLABF90,FLABF90,EMPCLF90,PROF90,MANUF90,SEMP90,AG16CV90,VET90,CNI16U90,DIS90,DPOV90,NPOV90,N65POV90,DFMPOV90,NFMPOV90,DWPOV90,NWPOV90,DBPOV90,NBPOV90,DNAPOV90,NNAPOV90,DHPOV90,NHPOV90,DAPOV90,NAPOV90,INCPC90,HU90SP,H30OLD90,OHU90SP,H10YRS90,DMULTI90,MULTI90,HINC90,HINCW90,HINCB90,HINCH90,HINCA90,HH90,HHW90,HHB90,HHH90,HHA90 +6027000800,CA,Inyo County,Census Tract 8,42580,13860,99999,0,947.0000232000,74.0000021900,3473.0000650000,3473.0000650000,37.0000004100,55.0000036300,622.0000106000,362.0000078000,85.0000038300,0.0000000000,2.0000001030,6.0000003420,2.0000000000,6.0000000000,114.0000027000,62.0000010600,34.0000009600,3275.0000600000,283.0000059000,58.0000006200,2628.0000430000,1752.0000340000,226.0000070000,1572.0000260000,128.0000016000,1429.0000340000,692.0000155000,1444.0000240000,269.0000081000,28.0000005800,118.0000038000,2840.0000470000,691.0000141000,2804.0000450000,524.0000085000,3419.0000630000,485.0000113000,114.0000012000,947.0000232000,352.0000117000,3019.0000460000,386.0000059000,15.0000000000,8.0000000000,258.0000152000,87.0000044100,376.0000049000,55.0000017100,34.0000018500,0.0000009240,11282.9890600000,2029.0000420000,765.0000165000,1510.0000340000,960.0000195000,2029.0000420000,164.0000004000,18802.8290100000,19238.2409400000, 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213.22,62938,40900,99999,1,413.8015522000,36.9504473300,1437.7500650000,1437.7500650000,5.7998258990,51.4048475500,323.0300046000,123.0183659000,58.9943210200,0.0000000000,0.0069602750,12.2533182200,0.0000000000,0.0000000000,75.9738778900,37.8148188500,12.5473476100,1364.6021230000,168.7317567000,17.6124934500,929.3841615000,516.7910383000,76.2496196600,700.1886827000,28.5566857300,533.7939340000,287.1322093000,671.6319973000,103.8683825000,80.0058629500,126.2160115000,1089.1046050000,219.7375810000,1081.3416300000,146.6018800000,1429.0499390000,95.1913104400,16.2047431600,413.8015522000,225.9298114000,1319.3738320000,87.5246539400,7.5682855450,0.0000000000,26.4881058700,0.9588502050,148.2951801000,11.1419056600,24.8093601300,6.7078032490,15350.9551100000,532.3409888000,122.7573153000,504.1733706000,330.6022812000,532.3409888000,37.2781012100,37057.6325400000,37560.5137700000,20087.15489,39771.1722300000,42048.1950700000,492.8297963000,457.7224989000,6.7315500680,48.6243697900,0.0646896060 +6069000802,CA,San Benito County,Census Tract 8.02,99999,41940,99999,0,566.0214233000,12.1359653500,1951.9486760000,1951.9486760000,9.2233333590,158.2529907000,270.8747559000,162.6219330000,84.4663162200,0.0000000000,4.8543863300,0.0000000000,0.0000000000,2.9126317500,173.3015900000,55.8254394500,83.4954452500,1864.5697250000,333.9817810000,66.0196533200,1427.6749700000,640.2935181000,294.6612549000,935.9256520000,61.6507034300,741.7501831000,351.4575500000,874.2749478000,259.7096558000,121.3596573000,123.3014069000,1577.1900640000,384.4673767000,1498.5490080000,130.0975494000,1866.5114790000,66.9905319200,6.3107018470,566.0214233000,209.2240448000,1679.1321730000,49.5147399900,24.2719306900,0.0000000000,39.3205261200,0.0000000000,256.7970276000,18.9321060200,35.9224586500,0.0000000000,22612.9993700000,791.7503662000,190.2919312000,710.1967163000,505.8270264000,791.7503662000,5.3398246770,43749.9997700000,46405.6612800000, 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210.10,12450,25900,99999,0,771.8966675000,86.6812591600,3130.9272040000,3130.9272040000,26.0043792700,45.0742569000,242.2741241000,138.6900177000,51.1419448900,0.0000000000,0.0000000000,4.3340630530,3.4672505860,0.0000000000,211.9356842000,116.5862961000,49.4083213800,2843.1454120000,355.8265686000,75.4126968400,1971.5653170000,1099.1184080000,332.4226379000,1311.4875030000,83.2140121500,1057.0780030000,588.1323853000,1228.2734910000,259.1769714000,74.5458831800,155.5928650000,2196.9366000000,371.4291992000,2196.9366000000,353.6595459000,3093.6542610000,698.2175903000,46.3744735700,771.8966675000,452.9096069000,1413.3379860000,390.9324951000,15.6026268000,2.1670315270,9.9683456420,7.3679075240,357.9936218000,99.6834487900,1573.2648930000,287.3483887000,10019.0003000000,1394.2680660000,256.5765381000,1128.5900880000,832.1401367000,1394.2680660000,12.1353769300,22281.0001400000,22568.4923400000,15000.00026,22678.5718600000,24593.3011400000,1116.8880620000,606.7688599000,4.3340630530,119.6201401000,455.5100403000 +15001021101,HI,Hawaii County,Census Tract 211.01,99999,25900,99999,0,490.9738464000,84.5782699600,2022.9801700000,2022.9801700000,10.4973030100,48.2875900300,143.0632324000,113.6707916000,53.6862030000,0.0000000000,0.0000000000,10.7972259500,2.3993835450,5.3986129760,280.4279480000,180.5536041000,118.4695587000,1845.4257950000,389.8998108000,63.8835830700,1185.8952700000,673.3269653000,161.6584625000,795.6955362000,69.2821960400,712.0170288000,351.8096008000,726.4133391000,128.9668579000,37.7902908300,146.9622345000,1389.5429400000,210.5458984000,1389.5429400000,255.8342590000,2009.1837150000,644.2344360000,38.6900596600,490.9738464000,303.5220032000,909.0664041000,289.4256287000,6.5983042720,0.0000000000,47.9876670800,34.1912155200,234.5397339000,111.5713272000,1009.2406620000,317.3184509000,8204.9999510000,822.6885986000,87.2775726300,681.1250000000,481.6762390000,822.6885986000,24.2937564800,18360.0001000000,18140.0966400000, ,13000.0004000000,19794.8707300000,669.4279785000,370.7047424000,0.0000000000,53.9861259500,273.8296509000 +15001021402,HI,Hawaii County,Census Tract 214.02,3850,25900,99999,0,816.2897568000,46.7089718600,2908.3037620000,2908.3037620000,9.3842508490,34.3563535200,85.1050386400,49.6372810000,42.1222216500,0.4366300400,0.0000000000,0.0507011120,0.0000000000,0.0000000000,154.3531404000,35.6412766000,100.6419504000,2718.7213800000,400.5848715000,42.0668509000,2029.9287820000,1120.4883020000,389.2195942000,1513.2753420000,90.2473753700,1164.6149540000,732.8806071000,1423.0279690000,398.0990586000,38.5432898100,148.3798876000,2264.6563310000,400.7801185000,2260.1444870000,204.3723986000,2896.0170940000,164.3064289000,42.8245659800,816.2897568000,369.2554536000,823.5514248000,42.8368821100,9.1951896550,0.2450553770,0.6089137490,0.0000000000,231.1957982000,25.7701943800,2046.6653000000,121.1737876000,15523.0524500000,1168.2462790000,424.7533337000,1042.7740610000,613.2432083000,1168.2462790000,233.6237568000,36432.4851700000,35023.9123700000,2500.00003,19986.2886800000,38377.4267300000,1038.3984520000,350.9512153000,0.0591512960,100.2060835000,672.1332075000 +15001021800,HI,Hawaii County,Census Tract 218,99999,25900,99999,0,1029.1142040000,113.6784696000,4267.4235680000,4267.4235680000,6.2395850530,53.9177365400,216.6516714000,128.9012836000,50.9871561900,6.0000000000,0.1063158070,0.1550576070,0.0000000000,5.0000000000,447.7201072000,220.2602014000,108.4859570000,3916.0639380000,732.8903613000,141.4261076000,2614.6461610000,1725.1835680000,326.8660370000,2059.8485030000,80.4418689800,1526.3987780000,994.8898024000,1979.4066340000,257.4833096000,26.3705349100,165.9507309000,3139.4967370000,446.5797471000,3112.4800730000,479.9428353000,4196.1618470000,304.9946221000,35.1246452800,1029.1142040000,599.1987248000,1353.4594670000,88.4248028000,12.0483253700,0.0000000000,29.1300475600,0.0200242130,796.1207250000,80.2921846500,2650.0290710000,198.5235648000,11456.1517100000,1564.6212330000,673.4738505000,1367.9637000000,704.9298707000,1564.6212330000,108.9002704000,31093.0763300000,34891.6831500000, ,36029.4523900000,28993.2036700000,1309.0228300000,454.9163605000,0.0000000000,162.9089448000,789.8354598000 +15003010201,HI,Honolulu County,Census Tract 102.01,12400,26180,99999,0,1066.0000000000,123.0000000000,4656.0000160000,4656.0000160000,27.0000000000,106.0000000000,304.0000000000,147.0000000000,83.0000000000,0.0000000000,0.0000000000,20.0000000000,0.0000000000,0.0000000000,309.0000000000,104.0000000000,102.0000000000,4143.0000140000,719.0000000000,44.0000000000,2719.0000090000,1527.0000000000,528.0000000000,2131.0000070000,117.0000000000,1555.0000000000,895.0000000000,2014.0000070000,545.0000000000,67.0000000000,127.0000000000,3303.0000110000,535.0000000000,3267.0000110000,453.0000000000,4587.0000150000,729.0000000000,52.0000000000,1066.0000000000,633.0000000000,1662.0000060000,204.0000000000,42.0000000000,0.0000000000,43.0000000000,28.0000000000,261.0000000000,18.0000000000,2743.0000000000,497.0000000000,14178.0000500000,1876.0000000000,634.0000000000,1494.0000000000,1037.0000000000,1876.0000000000,447.0000000000,35151.0001200000,36678.3218000000,30000.0001,44583.3334800000,36453.4884900000,1462.0000000000,656.0000000000,9.0000000000,23.0000000000,763.0000000000 +15007040603,HI,Kauai County,Census Tract 406.03,39200,28180,99999,0,588.9533691000,14.8749074900,2288.4115600000,2288.4115600000,4.1835675240,41.8356781000,141.7764587000,88.7846069300,45.5544052100,0.0000000000,4.1835675240,5.1132493020,0.0000000000,0.0000000000,315.6269531000,189.6550751000,98.0814209000,2138.7328030000,491.8016357000,87.8549194300,1520.0296170000,716.3197632000,270.5373840000,1197.8949000000,18.1287937200,877.6195679000,518.2975464000,1179.7661060000,306.7949829000,46.4840850800,99.0111007700,1745.9422750000,233.3501129000,1714.7979380000,137.1280518000,2260.0562680000,153.8623199000,23.7068843800,588.9533691000,321.6698608000,921.7794281000,104.1243515000,0.0000000000,0.0000000000,5.1132493020,0.0000000000,238.9282074000,29.2849750500,1265.2968750000,33.0037002600,14882.0000400000,836.7135620000,238.4633636000,753.0421753000,469.9541016000,836.7135620000,116.6750565000,38942.0014900000,37622.3762200000, ,34318.1815500000,38219.1778500000,761.4093018000,345.8416138000,0.0000000000,55.3160629300,393.2553711000 +15007040604,HI,Kauai County,Census Tract 406.04,57800,28180,99999,0,678.0467656000,17.1250984800,2634.5889580000,2634.5889580000,4.8164320010,48.1643366100,163.2235751000,102.2154066000,52.4456040000,0.0000000000,4.8164319990,5.8867523410,0.0000000000,0.0000000000,363.3731129000,218.3449656000,112.9185931000,2462.2676790000,566.1984698000,101.1450919000,1749.9707180000,824.6804260000,311.4626748000,1379.1053670000,20.8712132900,1010.3806350000,596.7025865000,1358.2341530000,353.2050822000,53.5159359900,113.9889124000,2010.0581130000,268.6499275000,1974.2024500000,157.8719865000,2601.9442500000,177.1377060000,27.2931263100,678.0467656000,370.3301829000,1061.2207750000,119.8756585000,0.0000014800,0.0000009040,5.8867517830,0.0000000055,275.0718744000,33.7150284500,1456.7034330000,37.9963147800,14881.9998700000,963.2866173000,274.5367055000,866.9579964000,541.0460023000,963.2866173000,134.3249464000,38942.0019300000,37622.3794400000,2708.021126,34318.1816500000,38219.1788500000,876.5908071000,398.1584626000,0.0000004130,63.6839504200,452.7447185000 +15007040700,HI,Kauai County,Census Tract 407,24950,28180,99999,0,1639.5124120000,84.3661893400,6497.4160920000,6497.4160920000,0.0261385850,179.0821346000,411.3031126000,164.1830575000,112.0709766000,0.0000000000,0.0000000000,19.9999980900,0.0000000000,0.0000000000,803.9005321000,495.4262798000,170.9395295000,6045.6744670000,1284.0984150000,230.2267010000,4202.3582790000,2302.9328280000,716.3603780000,3341.4274500000,85.2877002200,2471.2365200000,1616.8583390000,3256.1397500000,792.3786741000,256.5087146000,160.2696385000,4873.0529120000,677.1858005000,4858.6607240000,465.8659478000,6483.8447060000,315.4761577000,130.1721194000,1639.5124120000,903.8070090000,2542.4494830000,121.2988364000,18.0560093400,10.9999990500,13.2091077300,0.0672135060,809.4964124000,19.4300633300,3747.7267120000,182.9719543000,14847.6424500000,2182.2980030000,656.0313461000,2089.1407680000,1264.6352900000,2182.2980030000,127.4408560000,42756.8743900000,41907.5748200000,2708.021223,33400.6344500000,42210.6869300000,2068.2044520000,930.0281326000,5.0261381080,162.4411862000,1090.9635380000 +15009030100,HI,Maui County,Census Tract 301,11350,27980,99999,0,383.3068000000,27.2305791200,1910.5015780000,1910.5015780000,6.2592231110,27.4607778700,91.6621148600,27.1932371400,35.6747865500,0.0000000000,0.0035966930,0.0898007360,0.0000000000,8.1204749640,37.2918099000,6.5858333300,8.6301826290,1675.7861590000,177.6883274000,0.2490721380,1213.4786900000,793.3024273000,173.2832380000,864.0971374000,12.1702029000,576.9324621000,348.0594975000,851.9269343000,157.0642045000,28.2470664600,176.1750701000,1347.1787240000,314.6769609000,1347.1232320000,153.7392875000,1890.3938120000,392.8893720000,49.0661406900,383.3068000000,238.4715474000,807.4789838000,151.7785114000,0.0395477110,0.0000000000,20.0771960800,19.9998645800,82.6743149300,0.0311889900,1052.6142140000,211.1110742000,10415.9855900000,772.7004868000,261.4311255000,595.1029364000,362.3008839000,772.7004868000,23.2643179700,26030.6739600000,26529.6239500000,62499.99972,10011.7479600000,27536.6825200000,634.0477989000,310.2452140000,0.0041105070,14.1661638200,316.7386816000 +15009030201,HI,Maui County,Census Tract 302.01,99999,27980,99999,0,370.6344604000,44.2015914900,1578.6282440000,1578.6282440000,14.5508346600,25.8071403500,170.2173004000,68.9105529800,56.8306160000,0.0000000000,0.0000000000,1.9218082430,0.0000000000,0.0000000000,74.4014358500,42.8288688700,32.6707420300,1447.3961910000,143.5865326000,10.1581296900,1008.1257240000,471.1175842000,209.7516479000,834.6138888000,21.9635238600,543.5972290000,359.1036072000,812.6503654000,172.4136658000,95.5413284300,161.4319000000,1135.7887030000,143.0374451000,1135.7887030000,111.7394257000,1568.4701140000,158.1373749000,6.8636012080,370.6344604000,243.5205688000,939.4897130000,125.1920853000,3.0199844840,0.0000000000,20.3162593800,8.5108652110,190.2590179000,18.6689949000,572.1497803000,21.1398906700,13734.0000100000,569.6788940000,110.0921631000,526.5755005000,365.9671936000,569.6788940000,19.2180824300,36271.9983800000,36752.7165200000,29999.99935,38274.6483700000,41103.4484300000,537.2827148000,379.9689636000,1.3727202420,52.9869995100,135.0756683000 +15009030402,HI,Maui County,Census Tract 304.02,65900,27980,99999,0,1576.0000000000,169.0000000000,6064.0000020000,6064.0000020000,14.0000000000,21.0000000000,388.0000000000,130.0000000000,116.0000000000,0.0000000000,6.0000000000,12.0000000000,0.0000000000,0.0000000000,384.0000000000,204.0000000000,142.0000000000,5577.0000020000,733.0000000000,120.0000000000,3752.0000010000,1692.0000000000,664.0000000000,3218.0000010000,94.0000000000,2147.0000000000,1431.0000000000,3124.0000010000,752.0000000000,197.0000000000,253.0000000000,4402.0000010000,742.0000000000,4402.0000010000,325.0000000000,6036.0000020000,397.0000000000,22.0000000000,1576.0000000000,970.0000000000,2603.0000010000,182.0000000000,0.0000000000,0.0000000000,65.0000000000,0.0000000000,496.0000000000,63.0000000000,3305.0000000000,215.0000000000,16362.0000100000,1995.0000000000,159.0000000000,1914.0000000000,1194.0000000000,1995.0000000000,22.0000000000,43032.0000100000,41381.5789600000, ,33000.0000100000,43802.6315900000,1919.0000000000,922.0000000000,0.0000000000,152.0000000000,973.0000000000 +15009030800,HI,Maui County,Census Tract 308,75950,27980,99999,0,566.0331867000,29.0024671100,2380.1187810000,2380.1187810000,17.9996613400,10.0023785700,58.0117214400,83.0014279100,6.0035655440,0.0000246000,0.0000492000,0.0000246000,0.0000000000,0.0000871000,275.9955389000,160.9963087000,63.0011419100,2200.1173260000,563.9967822000,125.9982522000,1366.1198620000,676.0562812000,278.0319260000,1075.1001440000,14.0013287000,774.0661832000,485.0454395000,1061.0988150000,250.0390396000,61.0059311800,72.0103169500,1604.1293560000,165.0293852000,1604.1262590000,126.0096916000,2373.1152960000,158.9950915000,16.9988727100,566.0331867000,349.0062412000,543.0424034000,75.9953928000,19.9996131500,0.0000000000,0.0005046810,0.0000416000,204.9941585000,6.0003820650,1744.0754940000,82.9987536900,13742.8599100000,725.0676672000,157.0406731000,678.0611339000,390.0359396000,725.0676672000,15.0405894400,43750.2909800000,37947.0558900000,56651.51814,40624.8900800000,47291.6931700000,623.0686622000,167.0220174000,0.0003813110,27.9999225900,438.0467044000 diff --git a/data/data-pipeline/data_pipeline/tests/sources/persistent_poverty/data/output.csv b/data/data-pipeline/data_pipeline/tests/sources/persistent_poverty/data/output.csv new file mode 100644 index 00000000..ec83b70e --- /dev/null +++ b/data/data-pipeline/data_pipeline/tests/sources/persistent_poverty/data/output.csv @@ -0,0 +1,16 @@ +GEOID10_TRACT,Individuals in Poverty (percent) (1990),Individuals in Poverty (percent) (2000),Individuals in Poverty (percent) (2010),Persistent Poverty Census Tract +06027000800,0.1418543441,0.1857745998,0.1475304682,False +06061021322,0.0666116053,0.0607935935,0.0947959404,False +06069000802,0.0358907688,0.0632887874,0.0613686534,False +15001021010,0.2256934781,0.2156182598,0.3112706837,True +15001021101,0.3206448625,0.2732381146,0.2703479094,True +15001021402,0.0567353105,0.1106101497,0.0918238994,False +15001021800,0.0726841893,0.1204888825,0.1765701710,False +15003010201,0.1589274030,0.1844549763,0.1866137266,False +15007040603,0.0680789775,0.1103461130,0.0346287033,False +15007040604,0.0680789782,0.1103461036,0.1002400960,False +15007040700,0.0486557239,0.0635433492,0.1525797252,False +15009030100,0.2078346689,0.1729195461,0.0979676915,False +15009030201,0.1008226892,0.1610559432,0.1041162228,False +15009030402,0.0657720344,0.0643312931,0.0270912548,False +15009030800,0.0669984690,0.0322814539,0.0189065700,False diff --git a/data/data-pipeline/data_pipeline/tests/sources/persistent_poverty/data/transform.csv b/data/data-pipeline/data_pipeline/tests/sources/persistent_poverty/data/transform.csv new file mode 100644 index 00000000..9fdb1238 --- /dev/null +++ b/data/data-pipeline/data_pipeline/tests/sources/persistent_poverty/data/transform.csv @@ -0,0 +1,16 @@ +GEOID10_TRACT,state_x,county_x,tract_x,placefp10_x,cbsa10_x,metdiv10_x,ccflag10_x,FAMILY90,FHH90,POP90SF3,POP90SF4,RUANC90,ITANC90,GEANC90,IRANC90,SCANC90,RUFB90,ITFB90,GEFB90,IRFB90,SCFB90,FB90,NAT90,N10IMM90,AG5UP90,OLANG90,LEP90,AG25UP90,HS90,COL90,CLF90,UNEMP90,DFLABF90,FLABF90,EMPCLF90,PROF90,MANUF90,SEMP90,AG16CV90,VET90,CNI16U90,DIS90,DPOV90,NPOV90,N65POV90,DFMPOV90,NFMPOV90,DWPOV90,NWPOV90,DBPOV90,NBPOV90,DNAPOV90,NNAPOV90,DHPOV90,NHPOV90,DAPOV90,NAPOV90,INCPC90,HU90SP,H30OLD90,OHU90SP,H10YRS90,DMULTI90,MULTI90,HINC90,HINCW90,HINCB90,HINCH90,HINCA90,HH90,HHW90,HHB90,HHH90,HHA90,state_y,county_y,tract_y,placefp10_y,cbsa10_y,metdiv10_y,ccflag10_y,POP00SF3,RUANC00,ITANC00,GEANC00,IRANC00,SCANC00,RUFB00,ITFB00,GEFB00,IRFB00,SCFB00,FB00,NAT00,N10IMM00,AG5UP00,OLANG00,LEP00,AG25UP00,HS00,COL00,AG15UP00,Mar-00,WDS00,CLF00,UNEMP00,DFLABF00,FLABF00,EMPCLF00,PROF00,MANUF00,SEMP00,AG18CV00,VET00,CNI16U00,DIS00,DPOV00,NPOV00,N65POV00,DFMPOV00,NFMPOV00,DWPOV00,NWPOV00,DBPOV00,NBPOV00,DNAPOV00,NNAPOV00,DHPOV00,NHPOV00,DAPOV00,NAPOV00,INCPC00,HU00SP,H30OLD00,OHU00SP,H10YRS00,DMULTI00,MULTI00,HINC00,HINCW00,HINCB00,HINCH00,HINCA00,MHMVAL00,MRENT00,HH00,HHW00,HHB00,HHH00,HHA00,statea,countya,tracta,pnhwht12,pnhblk12,phisp12,pntv12,pasian12,phaw12,pindia12,pchina12,pfilip12,pjapan12,pkorea12,pviet12,p15wht12,p65wht12,p15blk12,p65blk12,p15hsp12,p65hsp12,p15ntv12,p65ntv12,p15asn12,p65asn12,pmex12,pcuban12,ppr12,pruanc12,pitanc12,pgeanc12,piranc12,pscanc12,pfb12,pnat12,p10imm12,prufb12,pitfb12,pgefb12,pirfb12,pscfb12,polang12,plep12,phs12,pcol12,punemp12,pflabf12,pprof12,pmanuf12,psemp12,pvet12,p65pov12,ppov12,pwpov12,pnapov12,pfmpov12,pbpov12,phpov12,papov12,pvac12,pown12,pmulti12,p30old12,p18und12,p60up12,p75up12,pmar12,pwds12,pfhh12,p10yrs12,ageblk12,agentv12,agewht12,agehsp12,india12,filip12,japan12,korea12,viet12,pop12,nhwht12,nhblk12,ntv12,hisp12,asian12,haw12,china12,a15wht12,a65wht12,a15blk12,a65blk12,a15hsp12,a65hsp12,a15ntv12,a65ntv12,ageasn12,a15asn12,a65asn12,mex12,pr12,cuban12,geanc12,iranc12,itanc12,ruanc12,fb12,nat12,itfb12,rufb12,ag5up12,irfb12,gefb12,scanc12,n10imm12,olang12,lep12,scfb12,ag25up12,dfmpov12,hh12,hinc12,hincb12,hincw12,hinch12,incpc12,ag18cv12,vet12,empclf12,dpov12,npov12,dbpov12,nbpov12,dnapov12,nnapov12,dwpov12,nwpov12,dhpov12,nhpov12,hhb12,hhw12,hhh12,hs12,col12,clf12,unemp12,dflabf12,flabf12,prof12,manuf12,semp12,hha12,hinca12,n65pov12,nfmpov12,napov12,dapov12,family12,hu12,vac12,ohu12,own12,rent12,dmulti12,mrent12,mhmval12,multi12,h30old12,h10yrs12,a18und12,a60up12,a75up12,ag15up12,12-Mar,wds12,fhh12,Individuals in Poverty (percent) (1990),Individuals in Poverty (percent) (2000),Individuals in Poverty (percent) (2010),Persistent Poverty Census Tract +06027000800,CA,Inyo County,Census Tract 8,42580,13860,99999,0,947.0000232000,74.0000021900,3473.0000650000,3473.0000650000,37.0000004100,55.0000036300,622.0000106000,362.0000078000,85.0000038300,0.0000000000,2.0000001030,6.0000003420,2.0000000000,6.0000000000,114.0000027000,62.0000010600,34.0000009600,3275.0000600000,283.0000059000,58.0000006200,2628.0000430000,1752.0000340000,226.0000070000,1572.0000260000,128.0000016000,1429.0000340000,692.0000155000,1444.0000240000,269.0000081000,28.0000005800,118.0000038000,2840.0000470000,691.0000141000,2804.0000450000,524.0000085000,3419.0000630000,485.0000113000,114.0000012000,947.0000232000,352.0000117000,3019.0000460000,386.0000059000,15.0000000000,8.0000000000,258.0000152000,87.0000044100,376.0000049000,55.0000017100,34.0000018500,0.0000009240,11282.9890600000,2029.0000420000,765.0000165000,1510.0000340000,960.0000195000,2029.0000420000,164.0000004000,18802.8290100000,19238.2409400000, ,15749.9998600000,15000.0018300000,1520.0000330000,1406.0000270000,0.0000000000,135.0000015000,6.0000008560,CA,Inyo County,Census Tract 8,42580,13860,99999,0,3117.0000890000,26.0000002400,47.0000032500,431.0000158000,294.0000076000,74.0000032200,0.0000000000,0.0000000000,8.0000003420,2.0000000000,0.0000002050,334.0000029000,79.0000007500,191.0000008000,2944.0000850000,503.0000064000,133.0000008000,2216.0000650000,1187.0000340000,363.0000082000,2558.0000730000,1369.0000420000,597.0000171000,1408.0000410000,108.0000034000,1275.0000380000,657.0000202000,1300.0000380000,314.0000119000,76.0000009200,129.0000042000,2410.0000690000,460.0000123000,1930.0000520000,382.0000085000,3079.0000870000,572.0000091000,56.0000019200,820.0000247000,94.0000010300,2107.0000620000,313.0000046000,3.0000001370,3.0000001370,242.0000129000,88.0000031100,629.0000075000,168.0000011000,22.0000008600,0.0000000000,16938.9166400000,1931.0000460000,1129.0000250000,1426.0000380000,965.0000213000,1931.0000460000,149.0000013000,28218.7649100000,28242.2954200000, ,31174.2175600000,33787.87882,69875.9413600000,313.8598744000,1411.0000370000,1084.0000290000,0.0000000000,184.0000021000,11.0000002700,6,27,800,66.5400009200,0.8999999760,20.4699993100,7.3000001910,2.0699999330,0.0000000000,0.0000000000,0.3100000020,1.6100000140,0.1500000060,0.0000000000,0.0000000000,13.2399997700,24.0200004600,55.1699981700,0.0000000000,31.4200000800,4.8299999240,20.9899997700,15.6499996200,32.8400001500,14.9300003100,17.9699993100,0.7400000100,0.5899999740,0.0000000000,1.4500000480,14.5600004200,12.0299997300,3.2799999710,7.9200000760,2.4400000570,3.1500000950,0.0000000000,0.0000000000,0.1500000060,0.0000000000,0.0000000000,10.9899997700,2.9100000860,48.5699996900,15.5799999200,9.0500001910,55.3100013700,25.6700000800,8.8999996190,10.3100004200,11.5600004200,3.1800000670,14.7500000000,13.4399995800,29.0100002300,5.7600002290,0.0000000000,16.4899997700,0.0000000000,28.1800003100,55.1599998500,8.0000000000,69.1699981700,22.9400005300,23.5000000000,9.2799997330,45.7099990800,24.7800006900,16.3600006100,64.4599990800,29,262,2152,662,0,52,5,0,0,3234,2152,29,236,662,67,0,10,285,517,16,0,208,32,55,41,67,22,10,581,19,24,471,389,47,0,256,79,0,0,3020,0,5,106,102,332,88,0,2304,764,1463,35995,-999,35750,36250,21842,2492,288,1348,3118,460,29,0,262,76,2053,276,655,108,10,1093,215,1119,359,1459,132,1206,667,346,120,139,35,26458.0000000000,99,44,0,57,764,2037,574,1463,807,656,2037,546,139000,163,1409,943,742,760,300,2623,1199,650,125,0.1418543441,0.1857745998,0.1475304682,False +06061021322,CA,Placer County,Census Tract 213.22,62938,40900,99999,1,413.8015522000,36.9504473300,1437.7500650000,1437.7500650000,5.7998258990,51.4048475500,323.0300046000,123.0183659000,58.9943210200,0.0000000000,0.0069602750,12.2533182200,0.0000000000,0.0000000000,75.9738778900,37.8148188500,12.5473476100,1364.6021230000,168.7317567000,17.6124934500,929.3841615000,516.7910383000,76.2496196600,700.1886827000,28.5566857300,533.7939340000,287.1322093000,671.6319973000,103.8683825000,80.0058629500,126.2160115000,1089.1046050000,219.7375810000,1081.3416300000,146.6018800000,1429.0499390000,95.1913104400,16.2047431600,413.8015522000,225.9298114000,1319.3738320000,87.5246539400,7.5682855450,0.0000000000,26.4881058700,0.9588502050,148.2951801000,11.1419056600,24.8093601300,6.7078032490,15350.9551100000,532.3409888000,122.7573153000,504.1733706000,330.6022812000,532.3409888000,37.2781012100,37057.6325400000,37560.5137700000,20087.15489,39771.1722300000,42048.1950700000,492.8297963000,457.7224989000,6.7315500680,48.6243697900,0.0646896060,CA,Placer County,Census Tract 213.22,62938,40900,99999,1,1540.0000810000,10.6516208600,76.2431793200,249.4721680000,91.3796920800,28.0305805200,0.0000000000,0.0000000000,6.7273392680,0.0000000000,0.0000000000,75.6825637800,14.0152902600,24.1062984500,1426.1959230000,149.6833038000,26.9093570700,1021.4343260000,398.5948486000,110.4404831000,1182.3298340000,848.7659912000,123.8951645000,778.6895142000,31.3942489600,550.5205688000,334.1245117000,747.2952881000,228.7295380000,90.2584686300,126.1376114000,1113.9353030000,209.6687469000,1009.1008910000,222.5628052000,1540.0000810000,93.6221389800,6.7273392680,462.5045776000,9.5303974150,1286.6036380000,65.0309448200,20.7426300000,0.0000000000,9.5303974150,1.1212232110,167.6228638000,22.4244651800,0.0000000000,0.0000000000,23335.9993700000,572.3844604000,165.9410400000,546.5963135000,365.5187683000,572.3844604000,2.2424464230,56432.0022500000,55077.9998500000,76598.99674,59249.9976100000, ,172800.0014000000,763.9999878000,558.9297485000,490.5351563000,11.2122325900,44.2883186300,0.0000000000,6,61,21322,67.5899963400,2.2000000480,8.9300003050,0.5699999930,15.1099996600,0.0599999990,2.9700000290,1.2999999520,9.1999998090,0.1299999950,1.0199999810,0.2800000010,22.3700008400,12.1599998500,25.4899997700,4.4099998470,48.7900009200,8.3299999240,75.4700012200,0.0000000000,19.0100002300,5.1799998280,7.6900000570,0.0000000000,0.1299999950,2.8099999430,5.3800001140,6.2199997900,6.4299998280,3.5699999330,18.3299999200,8.6599998470,11.0299997300,0.1899999980,0.0000000000,0.3499999940,0.0000000000,0.0900000040,24.3600006100,4.2500000000,21.3999996200,40.7400016800,9.2399997710,56.3199996900,49.7299995400,4.9200000760,9.5900001530,11.2899999600,0.0000000000,9.4799995420,9.3500003810,0.0000000000,7.5599999430,40.2000007600,10.6300001100,2.2699999810,6.1399998660,81.3499984700,8.9200000760,11.0299997300,29.7399997700,14.6099996600,3.5000000000,69.6999969500,12.6800003100,9.2799997330,84.7099990800,204,53,6268,828,275,853,12,95,26,9274,6268,204,53,828,1401,6,121,1402,762,52,9,404,69,40,0,1410,268,73,713,12,0,577,596,499,261,1700,803,0,18,8173,0,32,331,1023,1991,347,8,6004,2381,3029,92617,37021,84167,67404,32077,6499,734,3859,9262,878,204,82,53,0,6256,585,828,88,108,2324,173,1285,2446,4187,387,3386,1907,1919,190,370,343,125840.0938000000,0,180,32,1410,2381,3227,198,3029,2464,565,3227,972,357100,288,356,2566,2758,1355,325,6829,4760,866,221,0.0666116053,0.0607935935,0.0947959404,False +06069000802,CA,San Benito County,Census Tract 8.02,99999,41940,99999,0,566.0214233000,12.1359653500,1951.9486760000,1951.9486760000,9.2233333590,158.2529907000,270.8747559000,162.6219330000,84.4663162200,0.0000000000,4.8543863300,0.0000000000,0.0000000000,2.9126317500,173.3015900000,55.8254394500,83.4954452500,1864.5697250000,333.9817810000,66.0196533200,1427.6749700000,640.2935181000,294.6612549000,935.9256520000,61.6507034300,741.7501831000,351.4575500000,874.2749478000,259.7096558000,121.3596573000,123.3014069000,1577.1900640000,384.4673767000,1498.5490080000,130.0975494000,1866.5114790000,66.9905319200,6.3107018470,566.0214233000,209.2240448000,1679.1321730000,49.5147399900,24.2719306900,0.0000000000,39.3205261200,0.0000000000,256.7970276000,18.9321060200,35.9224586500,0.0000000000,22612.9993700000,791.7503662000,190.2919312000,710.1967163000,505.8270264000,791.7503662000,5.3398246770,43749.9997700000,46405.6612800000, ,32115.3843600000,87500.0022700000,711.6530151000,653.8858032000,0.0000000000,89.8061447100,12.6214036900,CA,San Benito County,Census Tract 8.02,99999,41940,99999,0,2657.7764180000,6.3107018470,240.2921143000,224.2726440000,208.2531586000,73.3012313800,2.4271931650,0.0000000000,7.2815790180,4.8543863300,0.0000000000,252.4280853000,110.6800079000,82.0391235400,2500.4943850000,363.5935364000,83.4954452500,1862.6279300000,609.7108765000,485.9240417000,2128.6484380000,1458.2575680000,298.5447388000,1287.3831790000,50.4856147800,1056.7998050000,536.8950806000,1236.8975830000,491.2638855000,173.7870178000,257.7679138000,2013.1138920000,316.0205383000,1667.9670410000,258.7387695000,2615.5432580000,165.5345612000,15.0485973400,757.7696533000,20.3884220100,1988.8420410000,95.1459655800,12.6214036900,0.0000000000,23.3010540000,1.4563158750,508.2542419000,60.1943893400,39.8059654200,5.8252635000,31026.0005900000,1050.4891360000,239.3212433000,971.8480835000,678.1577148000,1050.4891360000,21.3592987100,70101.9979700000,71048.0004500000,150900.9987,56500.0015600000,73750.00078,403799.9956000000,850.0000062000,970.8772583000,796.1193237000,6.7961406710,127.1849136000,14.0777196900,6,69,802,63.1399993900,0.3499999940,29.7999992400,0.2599999900,0.4799999890,0.0000000000,0.0000000000,0.0900000040,0.3100000020,0.1299999950,0.0000000000,0.0000000000,16.4899997700,17.1800003100,0.0000000000,37.5000000000,18.8600006100,8.6300001140,0.0000000000,0.0000000000,0.0000000000,0.0000000000,27.2299995400,0.0000000000,0.0000000000,0.1299999950,6.1900000570,13.6800003100,8.6300001140,3.0099999900,12.8999996200,2.7899999620,2.0899999140,0.0000000000,0.0000000000,0.0000000000,0.0900000040,0.0000000000,22.0400009200,9.3000001910,42.0299987800,20.2500000000,12.1599998500,55.5699996900,24.9500007600,9.7700004580,21.6599998500,8.0000000000,0.8399999740,6.1399998660,5.3299999240,15.3800001100,2.7599999900,25.0000000000,8.6999998090,16.6700000800,16.3400001500,74.9400024400,0.0000000000,47.1699981700,25.3999996200,22.2199993100,5.9699997900,65.1399993900,14.8599996600,5.0399999620,50.3699989300,8,13,1449,684,0,7,3,0,0,2295,1449,8,6,684,11,0,2,239,249,0,3,129,59,0,0,12,0,0,625,0,0,314,198,142,3,296,64,0,0,2151,2,0,69,48,474,200,0,1580,615,814,78333,-999,86553,44009,33400,1712,137,1034,2265,139,8,2,13,2,1425,76,678,59,3,605,195,664,320,1151,140,916,509,258,101,224,0,-999.0000000000,19,17,2,12,615,973,159,814,610,204,973,1135,521600,0,459,410,583,510,137,1830,1192,272,31,0.0358907688,0.0632887874,0.0613686534,False +15001021010,HI,Hawaii County,Census Tract 210.10,12450,25900,99999,0,771.8966675000,86.6812591600,3130.9272040000,3130.9272040000,26.0043792700,45.0742569000,242.2741241000,138.6900177000,51.1419448900,0.0000000000,0.0000000000,4.3340630530,3.4672505860,0.0000000000,211.9356842000,116.5862961000,49.4083213800,2843.1454120000,355.8265686000,75.4126968400,1971.5653170000,1099.1184080000,332.4226379000,1311.4875030000,83.2140121500,1057.0780030000,588.1323853000,1228.2734910000,259.1769714000,74.5458831800,155.5928650000,2196.9366000000,371.4291992000,2196.9366000000,353.6595459000,3093.6542610000,698.2175903000,46.3744735700,771.8966675000,452.9096069000,1413.3379860000,390.9324951000,15.6026268000,2.1670315270,9.9683456420,7.3679075240,357.9936218000,99.6834487900,1573.2648930000,287.3483887000,10019.0003000000,1394.2680660000,256.5765381000,1128.5900880000,832.1401367000,1394.2680660000,12.1353769300,22281.0001400000,22568.4923400000,15000.00026,22678.5718600000,24593.3011400000,1116.8880620000,606.7688599000,4.3340630530,119.6201401000,455.5100403000,HI,Hawaii County,Census Tract 210.10,12450,25900,99999,0,4751.0001150000,12.1353769300,56.3428230300,321.1540833000,149.0917816000,93.1823577900,0.0000000000,0.0000000000,3.4672505860,0.0000000000,0.0000000000,413.9030457000,235.7730408000,144.7577057000,4499.6245120000,796.1674194000,91.8821411100,3127.8935550000,1404.2364500000,636.2404785000,3694.7888180000,1919.1231690000,733.7568970000,2136.6931150000,183.3308716000,1747.0609130000,1036.2745360000,1953.3623050000,659.2109985000,107.9181747000,421.2709351000,3432.1447750000,563.4282227000,3017.3747560000,751.5265503000,4623.1452500000,996.8345337000,29.9050369300,1164.9962160000,161.6605530000,1540.7595210000,302.0841980000,13.4355964700,0.0000000000,16.0360336300,3.9006569390,531.7895508000,219.7369995000,1502.1862790000,195.8996582000,15289.0003700000,2121.5239260000,377.4969177000,1747.4942630000,1156.3281250000,2121.5239260000,34.6725044300,31499.9995000000,31948.9999800000,28750,18512.0010300000,35000.00137,107099.9963000000,511.9999843000,1753.5620120000,783.5986328000,3.4672505860,133.9225464000,530.4893188000,15,1,21010,35.2299995400,2.0199999810,21.8299999200,0.8600000140,21.4599990800,15.4799995400,0.0000000000,0.8000000120,7.3600001340,1.6399999860,0.0799999980,0.4499999880,3.0699999330,15.8599996600,64.1200027500,0.0000000000,38.2599983200,7.2100000380,32.2599983200,16.1299991600,24.3500003800,8.2799997330,1.9299999480,0.0000000000,14.6300001100,0.9399999980,2.8900001050,5.9899997710,4.7699999810,2.6900000570,5.2500000000,3.3800001140,1.3999999760,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,10.2299995400,0.4399999980,39.6599998500,19.5499992400,12.9399995800,61.9799995400,31.4799995400,5.4899997710,18.2099990800,10.5600004200,1.5000000000,31.1299991600,21.7600002300,64.5199966400,17.5499992400,72.5199966400,51.4900016800,33.1699981700,25.0900001500,70.6600036600,1.9800000190,20.9099998500,25.1800003100,19.1599998500,4.5399999620,45.7000007600,22.7600002300,19.0100002300,63.7400016800,131,62,2282,1414,0,477,106,5,29,6478,2282,131,56,1414,1390,1003,52,70,362,84,0,541,102,20,10,1848,450,153,125,948,0,388,309,187,61,340,219,0,0,6178,0,0,174,91,632,27,0,4511,1510,2457,36901,102083,34554,34167,18671,4847,512,2570,6406,1994,131,95,62,40,2270,494,1408,725,15,1237,408,1789,882,2929,379,2470,1531,809,141,468,594,37287.7929700000,96,265,609,1836,1510,3280,823,2457,1736,721,3280,737,195800,65,686,1566,1631,1241,294,5109,2335,1163,287,0.2256934781,0.2156182598,0.3112706837,True +15001021101,HI,Hawaii County,Census Tract 211.01,99999,25900,99999,0,490.9738464000,84.5782699600,2022.9801700000,2022.9801700000,10.4973030100,48.2875900300,143.0632324000,113.6707916000,53.6862030000,0.0000000000,0.0000000000,10.7972259500,2.3993835450,5.3986129760,280.4279480000,180.5536041000,118.4695587000,1845.4257950000,389.8998108000,63.8835830700,1185.8952700000,673.3269653000,161.6584625000,795.6955362000,69.2821960400,712.0170288000,351.8096008000,726.4133391000,128.9668579000,37.7902908300,146.9622345000,1389.5429400000,210.5458984000,1389.5429400000,255.8342590000,2009.1837150000,644.2344360000,38.6900596600,490.9738464000,303.5220032000,909.0664041000,289.4256287000,6.5983042720,0.0000000000,47.9876670800,34.1912155200,234.5397339000,111.5713272000,1009.2406620000,317.3184509000,8204.9999510000,822.6885986000,87.2775726300,681.1250000000,481.6762390000,822.6885986000,24.2937564800,18360.0001000000,18140.0966400000, ,13000.0004000000,19794.8707300000,669.4279785000,370.7047424000,0.0000000000,53.9861259500,273.8296509000,HI,Hawaii County,Census Tract 211.01,99999,25900,99999,0,2578.4375420000,21.8943748500,50.0871315000,155.6600037000,101.0740356000,16.7956848100,0.0000000000,0.0000000000,10.4973030100,0.0000000000,0.0000000000,257.9337158000,148.7617798000,76.4803466800,2405.3820800000,462.1812439000,77.0802002000,1641.7781980000,845.4827881000,278.3284912000,1978.5916750000,962.4526978000,400.6970520000,1090.8197020000,182.3531494000,957.6539307000,537.1619873000,908.4666138000,233.0401306000,20.6946830700,163.1580811000,1836.7281490000,312.2197876000,1648.6763920000,428.8898010000,2561.9417800000,700.0201416000,36.8905220000,622.6400146000,108.5721054000,908.7665405000,230.3408203000,11.0971489000,11.0971489000,11.0971489000,2.3993835450,310.1203308000,142.1634827000,821.7888794000,188.0516815000,12554.0002000000,1113.9138180000,144.8627777000,934.2599487000,647.2337036000,1113.9138180000,35.6908302300,27919.9990900000,24655.0000000000,8333,18233.0009200000,35092.59367,86700.0020800000,501.0000064000,933.3602295000,460.6816406000,5.9984588620,75.2806549100,248.9360352000,15,1,21101,54.7400016800,0.1299999950,13.7899999600,0.2599999900,17.4899997700,4.2800002100,0.0000000000,1.4700000290,7.4400000570,1.3999999760,0.1299999950,0.0000000000,7.7600002290,27.3500003800,80.0000000000,0.0000000000,32.1800003100,14.8100004200,0.0000000000,0.0000000000,9.0900001530,16.7600002300,2.2699999810,0.0000000000,5.3299999240,0.6700000170,2.9700000290,12.7399997700,12.1899995800,2.9400000570,15.1300001100,9.1599998470,1.3099999430,0.0000000000,0.0000000000,2.7100000380,0.0000000000,0.0000000000,9.7299995420,2.0399999620,29.2900009200,32.0200004600,10.3299999200,42.8600006100,34.1899986300,3.1099998950,38.2799987800,15.5900001500,2.1099998950,27.0300006900,24.8999996200,68.0000000000,11.6700000800,0.0000000000,30.5599994700,23.7099990800,27.2399997700,74.3499984700,0.0000000000,28.4899997700,17.0799999200,38.5900001500,6.3800001140,48.5800018300,23.3500003800,9.4499998090,59.8100013700,5,25,1715,432,0,233,44,4,0,3133,1715,4,8,432,548,134,46,133,469,4,0,139,64,0,0,561,51,94,71,167,0,399,382,93,21,474,287,0,0,2940,0,85,92,41,286,60,0,2414,677,1341,35457,-999,43060,33500,21491,2598,405,1126,3133,847,5,0,25,17,1715,427,432,132,0,943,134,707,773,1230,127,1316,564,385,35,431,146,50551.3710900000,66,79,133,561,677,1843,502,1341,997,344,1843,699,222000,0,525,802,535,1209,200,2672,1298,624,64,0.3206448625,0.2732381146,0.2703479094,True +15001021402,HI,Hawaii County,Census Tract 214.02,3850,25900,99999,0,816.2897568000,46.7089718600,2908.3037620000,2908.3037620000,9.3842508490,34.3563535200,85.1050386400,49.6372810000,42.1222216500,0.4366300400,0.0000000000,0.0507011120,0.0000000000,0.0000000000,154.3531404000,35.6412766000,100.6419504000,2718.7213800000,400.5848715000,42.0668509000,2029.9287820000,1120.4883020000,389.2195942000,1513.2753420000,90.2473753700,1164.6149540000,732.8806071000,1423.0279690000,398.0990586000,38.5432898100,148.3798876000,2264.6563310000,400.7801185000,2260.1444870000,204.3723986000,2896.0170940000,164.3064289000,42.8245659800,816.2897568000,369.2554536000,823.5514248000,42.8368821100,9.1951896550,0.2450553770,0.6089137490,0.0000000000,231.1957982000,25.7701943800,2046.6653000000,121.1737876000,15523.0524500000,1168.2462790000,424.7533337000,1042.7740610000,613.2432083000,1168.2462790000,233.6237568000,36432.4851700000,35023.9123700000,2500.00003,19986.2886800000,38377.4267300000,1038.3984520000,350.9512153000,0.0591512960,100.2060835000,672.1332075000,HI,Hawaii County,Census Tract 214.02,3850,25900,99999,0,3381.8505830000,1.9577749070,48.3774284700,115.8866985000,93.2828779200,36.4310612700,0.0000000000,0.3638583720,5.7721619310,0.0000000000,0.0253505560,282.6050465000,125.8827071000,96.1817516100,3159.6427310000,587.9516835000,95.3819903100,2343.0448720000,1163.6868530000,542.8995190000,2748.0508690000,1427.7453750000,544.9161553000,1707.5037210000,80.2293815600,1373.5711330000,811.6988583000,1627.2742840000,501.5004673000,31.7776957200,320.6619287000,2598.9638020000,399.0852509000,2084.6770590000,316.4326797000,3369.1762350000,372.6650877000,36.2398244600,879.1825342000,42.0022880400,834.3656845000,104.8442249000,9.6353598830,4.8270140890,3.2579476980,0.0000000000,257.8107343000,54.3398767400,1558.9517870000,107.9598858000,22057.7679900000,1474.9353560000,461.2082181000,1293.6710150000,711.8759928000,1474.9353560000,358.6005371000,44010.1419300000,43324.4107200000,55794.0467,43510.7687300000,46409.70582,223090.1061000000,447.0194726000,1277.6085930000,391.2560530000,3.4560823290,53.3338078900,627.7848501000,15,1,21402,20.2800006900,0.1700000020,11.1800003100,0.0000000000,42.0200004600,5.7699999810,0.0000000000,0.2000000030,10.0399999600,21.7900009200,0.0000000000,0.0000000000,6.2500000000,20.8299999200,50.0000000000,0.0000000000,30.0000000000,12.8900003400,0.0000000000,0.0000000000,12.9300003100,29.6700000800,2.8299999240,0.0000000000,3.8499999050,0.2199999990,0.3499999940,4.5700001720,3.1300001140,0.8700000050,11.2299995400,3.5799999240,5.7199997900,0.1000000010,0.0000000000,0.0000000000,0.0000000000,0.1199999970,20.3299999200,2.5000000000,37.2500000000,25.1399993900,5.9899997710,66.2900009200,32.6699981700,2.2000000480,17.5799999200,10.1300001100,2.1400001050,9.1800003050,11.5000000000,0.0000000000,1.7500000000,0.0000000000,5.5799999240,11.1099996600,9.6700000760,53.1800003100,17.5799999200,60.9799995400,23.0900001500,25.5499992400,9.0699996950,45.8300018300,20.2500000000,16.2099990800,51.1599998500,14,7,816,450,0,404,877,0,0,4024,816,7,0,450,1691,232,8,51,170,7,0,135,58,0,0,1702,220,505,114,155,0,184,126,14,9,452,144,0,4,3846,0,0,35,230,782,96,5,2765,913,1382,49063,-999,53500,45294,25021,3090,313,2002,3975,365,14,0,7,0,809,93,448,25,0,420,106,1030,695,2055,123,1667,1105,654,44,352,610,52648.5000000000,85,16,187,1683,913,1530,148,1382,735,647,1530,827,460400,269,933,707,929,1028,365,3229,1480,654,148,0.0567353105,0.1106101497,0.0918238994,False +15001021800,HI,Hawaii County,Census Tract 218,99999,25900,99999,0,1029.1142040000,113.6784696000,4267.4235680000,4267.4235680000,6.2395850530,53.9177365400,216.6516714000,128.9012836000,50.9871561900,6.0000000000,0.1063158070,0.1550576070,0.0000000000,5.0000000000,447.7201072000,220.2602014000,108.4859570000,3916.0639380000,732.8903613000,141.4261076000,2614.6461610000,1725.1835680000,326.8660370000,2059.8485030000,80.4418689800,1526.3987780000,994.8898024000,1979.4066340000,257.4833096000,26.3705349100,165.9507309000,3139.4967370000,446.5797471000,3112.4800730000,479.9428353000,4196.1618470000,304.9946221000,35.1246452800,1029.1142040000,599.1987248000,1353.4594670000,88.4248028000,12.0483253700,0.0000000000,29.1300475600,0.0200242130,796.1207250000,80.2921846500,2650.0290710000,198.5235648000,11456.1517100000,1564.6212330000,673.4738505000,1367.9637000000,704.9298707000,1564.6212330000,108.9002704000,31093.0763300000,34891.6831500000, ,36029.4523900000,28993.2036700000,1309.0228300000,454.9163605000,0.0000000000,162.9089448000,789.8354598000,HI,Hawaii County,Census Tract 218,99999,25900,99999,0,6083.7582190000,42.5401219700,100.2628204000,291.9288304000,171.6701803000,157.2704277000,0.0000000000,2.0988955720,28.2434352500,0.0000000000,20.0000000000,700.2193766000,355.5028186000,246.8417125000,5743.3729480000,1009.5126270000,168.4758261000,3991.0794680000,2101.9089420000,797.0409708000,4842.6058430000,2504.2846360000,926.1391325000,2558.9977570000,121.9585263000,2419.9872150000,1230.3273470000,2437.0392300000,619.1433029000,30.3347234700,415.5754552000,4589.7876850000,591.1840434000,3872.4750500000,680.1646583000,5362.4843550000,646.1197476000,69.0912882200,1290.5175300000,78.9204895500,1384.6919960000,170.5595070000,13.1141102800,0.0532514600,22.1521470200,3.0000000000,824.9101787000,120.8063793000,1800.7153220000,184.9550893000,20169.3576300000,1946.0392300000,826.0422071000,1768.0480750000,1032.9408390000,1946.0392300000,64.1862440100,47766.7945700000,52099.9003000000,28574.27751,48066.4015300000,46427.75726,183195.4901000000,640.1530363000,1748.9796090000,584.2699242000,9.0912882160,174.9509189000,606.5754552000,15,1,21800,19.7700004600,0.3899999860,12.9399995800,0.0799999980,28.2299995400,11.1099996600,0.0000000000,1.1299999950,9.3699998860,7.0100002290,0.0000000000,0.0200000000,10.6599998500,13.9799995400,0.0000000000,0.0000000000,27.7299995400,13.0200004600,100.0000000000,0.0000000000,11.6800003100,22.7600002300,3.6700000760,0.0700000000,5.6799998280,0.0000000000,0.5400000210,3.2000000480,2.2400000100,0.7599999900,7.3800001140,4.1599998470,2.1400001050,0.0000000000,0.0000000000,0.0000000000,0.0000000000,0.0000000000,16.2700004600,0.9900000100,48.9399986300,19.7800006900,10.6700000800,55.4000015300,21.2099990800,1.8799999950,11.8999996200,11.8699998900,2.0299999710,17.6599998500,16.9699993100,0.0000000000,9.9399995800,0.0000000000,14.9700002700,10.3000001900,23.0300006900,73.8300018300,9.0799999240,44.4900016800,29.4599990800,20.9300003100,6.6700000760,49.5699996900,21.6599998500,15.1899995800,43.8600006100,23,5,1173,768,0,556,416,0,1,5934,1173,23,5,768,1675,659,67,125,164,0,0,213,100,5,0,1823,213,415,218,337,4,190,133,32,0,438,247,0,0,5647,0,0,45,127,919,56,0,3978,1238,1865,49659,-999,41579,44700,22489,4186,497,2504,5907,1043,23,0,5,0,1167,198,768,115,0,596,204,1947,787,2764,295,2166,1200,531,47,298,728,53624.0312500000,120,123,186,1805,1238,2423,558,1865,1377,488,2423,883,378200,220,1078,818,1748,1242,396,4543,2252,984,188,0.0726841893,0.1204888825,0.1765701710,False +15003010201,HI,Honolulu County,Census Tract 102.01,12400,26180,99999,0,1066.0000000000,123.0000000000,4656.0000160000,4656.0000160000,27.0000000000,106.0000000000,304.0000000000,147.0000000000,83.0000000000,0.0000000000,0.0000000000,20.0000000000,0.0000000000,0.0000000000,309.0000000000,104.0000000000,102.0000000000,4143.0000140000,719.0000000000,44.0000000000,2719.0000090000,1527.0000000000,528.0000000000,2131.0000070000,117.0000000000,1555.0000000000,895.0000000000,2014.0000070000,545.0000000000,67.0000000000,127.0000000000,3303.0000110000,535.0000000000,3267.0000110000,453.0000000000,4587.0000150000,729.0000000000,52.0000000000,1066.0000000000,633.0000000000,1662.0000060000,204.0000000000,42.0000000000,0.0000000000,43.0000000000,28.0000000000,261.0000000000,18.0000000000,2743.0000000000,497.0000000000,14178.0000500000,1876.0000000000,634.0000000000,1494.0000000000,1037.0000000000,1876.0000000000,447.0000000000,35151.0001200000,36678.3218000000,30000.0001,44583.3334800000,36453.4884900000,1462.0000000000,656.0000000000,9.0000000000,23.0000000000,763.0000000000,HI,Honolulu County,Census Tract 102.01,12400,26180,99999,0,5312.0000000000,42.0000000000,65.0000000000,199.0000000000,127.0000000000,75.0000000000,6.0000000000,0.0000000000,2.0000000000,5.0000000000,2.0000000000,464.0000000000,281.0000000000,141.0000000000,4859.0000000000,1025.0000000000,123.0000000000,3126.0000000000,1506.0000000000,689.0000000000,3917.0000000000,2107.0000000000,652.0000000000,2315.0000000000,262.0000000000,1960.0000000000,1101.0000000000,2053.0000000000,673.0000000000,51.0000000000,250.0000000000,3646.0000000000,430.0000000000,3270.0000000000,655.0000000000,5275.0000000000,973.0000000000,39.0000000000,1186.0000000000,142.0000000000,1243.0000000000,105.0000000000,36.0000000000,7.0000000000,9.0000000000,2.0000000000,381.0000000000,74.0000000000,2149.0000000000,564.0000000000,16156.0000000000,1932.0000000000,876.0000000000,1571.0000000000,1032.0000000000,1932.0000000000,500.0000000000,40300.0000000000,48214.0000000000,61667,37250.0000000000,39419.64286,262400.0000000000,709.0000000000,1584.0000000000,540.0000000000,20.0000000000,70.0000000000,609.0000000000,15,3,10201,26.5300006900,1.3700000050,10.6800003100,0.0000000000,26.7399997700,14.1999998100,0.1299999950,1.4099999670,2.9800000190,2.9600000380,0.3600000140,0.1299999950,13.9799995400,15.1800003100,0.0000000000,1.3700000050,43.1599998500,0.0000000000,-999.0000000000,-999.0000000000,14.6000003800,14.2600002300,1.7400000100,0.0900000040,2.7000000480,0.4699999990,1.3500000240,5.4200000760,3.4900000100,1.6100000140,7.4400000570,3.2200000290,2.4700000290,0.0000000000,0.0599999990,0.0900000040,0.0399999990,0.2399999950,16.8500003800,1.2000000480,38.2999992400,24.8299999200,9.0799999240,55.0999984700,38.9099998500,4.1300001140,13.5600004200,12.3500003800,0.6100000140,18.6599998500,6.8400001530,-999.0000000000,7.9800000190,23.2900009200,5.6100001340,27.2099990800,18.8400001500,52.2700004600,23.2199993100,65.0400009200,30.1499996200,14.4700002700,5.1500000950,48.9599990800,16.4599990800,13.2100000400,67.6600036600,73,0,1416,570,7,159,158,19,7,5337,1416,73,0,570,1427,758,75,198,215,0,1,246,0,0,0,1466,214,209,93,144,5,289,186,72,25,397,172,3,0,4915,2,5,86,132,828,59,13,3290,1166,1611,65469,30417,67500,70865,24202,3708,458,2205,5289,987,73,17,0,0,1404,96,570,32,25,630,147,1260,817,2411,219,2138,1178,858,91,299,454,66324.0859400000,32,93,391,1437,1166,1985,374,1611,842,769,1985,1225,611600,461,1291,1090,1609,772,275,3985,1951,656,154,0.1589274030,0.1844549763,0.1866137266,False +15007040603,HI,Kauai County,Census Tract 406.03,39200,28180,99999,0,588.9533691000,14.8749074900,2288.4115600000,2288.4115600000,4.1835675240,41.8356781000,141.7764587000,88.7846069300,45.5544052100,0.0000000000,4.1835675240,5.1132493020,0.0000000000,0.0000000000,315.6269531000,189.6550751000,98.0814209000,2138.7328030000,491.8016357000,87.8549194300,1520.0296170000,716.3197632000,270.5373840000,1197.8949000000,18.1287937200,877.6195679000,518.2975464000,1179.7661060000,306.7949829000,46.4840850800,99.0111007700,1745.9422750000,233.3501129000,1714.7979380000,137.1280518000,2260.0562680000,153.8623199000,23.7068843800,588.9533691000,321.6698608000,921.7794281000,104.1243515000,0.0000000000,0.0000000000,5.1132493020,0.0000000000,238.9282074000,29.2849750500,1265.2968750000,33.0037002600,14882.0000400000,836.7135620000,238.4633636000,753.0421753000,469.9541016000,836.7135620000,116.6750565000,38942.0014900000,37622.3762200000, ,34318.1815500000,38219.1778500000,761.4093018000,345.8416138000,0.0000000000,55.3160629300,393.2553711000,HI,Kauai County,Census Tract 406.03,39200,28180,99999,0,2512.0000030000,12.5507030500,38.1169509900,121.7883072000,77.1635818500,70.1909713700,0.0000000000,0.0000000000,7.9022946360,0.0000000000,0.0000000000,323.5292358000,237.0688324000,74.8393783600,2365.1103520000,422.5403442000,69.7261276200,1722.2353520000,746.0695801000,355.1384277000,2035.0732420000,1124.4500730000,359.7868347000,1264.3671880000,76.2339019800,1000.8023680000,612.1954346000,1188.1331790000,341.1931763000,19.9881572700,207.3190308000,1902.1287840000,257.5218506000,1584.6424560000,271.9319153000,2498.0547770000,275.6506348000,25.1014061000,661.9334106000,43.2302017200,864.6040039000,92.0384903000,3.7187268730,0.0000000000,11.6210212700,0.9296817180,250.5492249000,43.2302017200,1018.0014650000,79.0229492200,22782.0000300000,1669.2435300000,386.2827454000,917.1310425000,572.2191162000,1669.2435300000,817.1902466000,48053.0011600000,49650.0007600000,43125.00166,38438.0005900000,48353.65708,250600.0029000000,624.9999952000,918.9903564000,396.5092468000,2.3242042060,59.9644699100,364.9000854000,15,7,40603,34.2400016800,0.5000000000,7.1500000950,0.1199999970,42.3899993900,2.5000000000,0.1500000060,1.1900000570,29.2500000000,7.1900000570,0.1500000060,0.0000000000,9.6499996190,32.2099990800,0.0000000000,0.0000000000,13.4399995800,9.6800003050,0.0000000000,0.0000000000,11.8599996600,18.8899993900,0.3100000020,0.0000000000,2.0000000000,1.5000000000,2.9600000380,3.4600000380,4.3800001140,2.1099998950,20.1000003800,10.4200000800,5.6900000570,0.0000000000,0.2300000040,0.0000000000,0.1500000060,0.0000000000,30.1200008400,6.3800001140,29.7399997700,32.7299995400,6.6500000950,63.3300018300,31.6000003800,1.6100000140,13.5600004200,8.6300001140,1.0399999620,3.4600000380,2.0199999810,0.0000000000,2.3299999240,0.0000000000,15.0500001900,1.4099999670,53.7000007600,65.8700027500,52.2099990800,55.1899986300,17.4899997700,30.0499992400,10.8000001900,53.8899993900,18.1299991600,8.1499996190,53.0099983200,13,3,891,186,4,761,187,4,0,2602,891,13,3,186,1103,65,31,86,287,0,0,25,18,0,0,1138,135,215,8,52,0,90,114,77,39,523,271,6,0,2460,4,0,55,148,741,157,0,1974,687,964,67500,-999,82308,45833,35475,2143,185,1364,2599,90,13,0,3,0,889,18,186,28,5,442,58,587,646,1444,96,1178,746,431,22,185,369,62339.4296900000,27,16,16,1137,687,2082,1118,964,635,329,2082,950,650400,1087,1149,511,455,782,281,2251,1213,408,56,0.0680789775,0.1103461130,0.0346287033,False +15007040604,HI,Kauai County,Census Tract 406.04,57800,28180,99999,0,678.0467656000,17.1250984800,2634.5889580000,2634.5889580000,4.8164320010,48.1643366100,163.2235751000,102.2154066000,52.4456040000,0.0000000000,4.8164319990,5.8867523410,0.0000000000,0.0000000000,363.3731129000,218.3449656000,112.9185931000,2462.2676790000,566.1984698000,101.1450919000,1749.9707180000,824.6804260000,311.4626748000,1379.1053670000,20.8712132900,1010.3806350000,596.7025865000,1358.2341530000,353.2050822000,53.5159359900,113.9889124000,2010.0581130000,268.6499275000,1974.2024500000,157.8719865000,2601.9442500000,177.1377060000,27.2931263100,678.0467656000,370.3301829000,1061.2207750000,119.8756585000,0.0000014800,0.0000009040,5.8867517830,0.0000000055,275.0718744000,33.7150284500,1456.7034330000,37.9963147800,14881.9998700000,963.2866173000,274.5367055000,866.9579964000,541.0460023000,963.2866173000,134.3249464000,38942.0019300000,37622.3794400000,2708.021126,34318.1816500000,38219.1788500000,876.5908071000,398.1584626000,0.0000004130,63.6839504200,452.7447185000,HI,Kauai County,Census Tract 406.04,57800,28180,99999,0,2892.0007690000,14.4492981500,43.8830701600,140.2117278000,88.8364418900,80.8090412700,0.0000000000,0.0000001640,9.0977060880,0.0000009040,0.0000001640,372.4708665000,272.9312307000,86.1606489900,2722.8904680000,486.4597859000,80.2738927500,1982.7651850000,858.9306725000,408.8616791000,2342.9272420000,1294.5503190000,414.2132735000,1455.6332330000,87.7661221700,1152.1979950000,704.8047703000,1367.8671030000,392.8069129000,23.0118530200,238.6810128000,2189.8717920000,296.4782494000,1824.3580440000,313.0681749000,2875.9459930000,317.3494345000,28.8985995100,762.0668105000,49.7698076000,995.3962359000,105.9615271000,4.2812743510,0.0000001640,13.3789809200,1.0703183410,288.4508700000,49.7698064500,1171.9987960000,90.9770727900,22781.9989000000,1921.7568180000,444.7173368000,1055.8692320000,658.7810654000,1921.7568180000,940.8098295000,48053.0024700000,49650.0006500000,43124.99753,38437.9995900000,48353.65972,250600.0011000000,624.9999948000,1058.0098580000,456.4908656000,2.6757964510,69.0355465200,420.1000380000,15,7,40604,37.3300018300,0.0000000000,14.1000003800,0.0000000000,29.2700004600,5.7800002100,0.0000000000,0.5899999740,9.2399997710,9.7500000000,0.1500000060,0.0000000000,8.9700002670,15.9499998100,-999.0000000000,-999.0000000000,30.2500000000,14.5000000000,0.0000000000,0.0000000000,15.8000001900,21.1000003800,2.2500000000,0.0000000000,7.5900001530,0.1199999970,2.9300000670,5.5999999050,4.8299999240,3.0799999240,7.5599999430,4.7699999810,1.6000000240,0.0000000000,0.2099999930,0.0000000000,0.1199999970,0.0000000000,12.4099998500,0.9800000190,37.6500015300,24.5799999200,8.5500001910,63.1899986300,33.0600013700,3.5699999330,18.5499992400,11.3999996200,1.0800000430,10.0200004600,7.7899999620,0.0000000000,5.4200000760,-999.0000000000,19.9200000800,9.2899999620,27.3099994700,63.2000007600,29.4699993100,45.0200004600,21.6900005300,22.3700008400,7.3800001140,54.2099990800,19.1299991600,8.1300001140,53.5800018300,0,7,1260,476,0,312,329,5,0,3375,1260,0,0,476,988,195,20,113,201,0,0,144,69,0,0,1057,167,223,76,256,0,189,163,99,4,255,161,7,0,3160,4,0,104,54,392,31,0,2388,849,1174,62065,-999,66250,50179,30569,2640,301,1736,3332,334,0,0,7,0,1245,97,472,94,0,570,119,899,587,1848,158,1440,910,574,62,322,396,64327.2617200000,36,46,96,1033,849,1615,441,1174,742,432,1615,1084,627500,476,727,629,732,755,249,2745,1488,525,69,0.0680789782,0.1103461036,0.1002400960,False +15007040700,HI,Kauai County,Census Tract 407,24950,28180,99999,0,1639.5124120000,84.3661893400,6497.4160920000,6497.4160920000,0.0261385850,179.0821346000,411.3031126000,164.1830575000,112.0709766000,0.0000000000,0.0000000000,19.9999980900,0.0000000000,0.0000000000,803.9005321000,495.4262798000,170.9395295000,6045.6744670000,1284.0984150000,230.2267010000,4202.3582790000,2302.9328280000,716.3603780000,3341.4274500000,85.2877002200,2471.2365200000,1616.8583390000,3256.1397500000,792.3786741000,256.5087146000,160.2696385000,4873.0529120000,677.1858005000,4858.6607240000,465.8659478000,6483.8447060000,315.4761577000,130.1721194000,1639.5124120000,903.8070090000,2542.4494830000,121.2988364000,18.0560093400,10.9999990500,13.2091077300,0.0672135060,809.4964124000,19.4300633300,3747.7267120000,182.9719543000,14847.6424500000,2182.2980030000,656.0313461000,2089.1407680000,1264.6352900000,2182.2980030000,127.4408560000,42756.8743900000,41907.5748200000,2708.021223,33400.6344500000,42210.6869300000,2068.2044520000,930.0281326000,5.0261381080,162.4411862000,1090.9635380000,HI,Kauai County,Census Tract 407,24950,28180,99999,0,7440.9995560000,2.9999997620,210.9999847000,332.9999695000,195.9999847000,60.9999961900,0.0000000000,1.9999998810,2.9999997620,10.9999990500,1.9999998810,873.9999390000,581.9999390000,239.9999847000,7007.9995120000,1212.9998780000,247.9999847000,5041.9995120000,2330.9997560000,928.9999390000,5895.9995120000,3286.9997560000,945.9999390000,3634.9997560000,200.9999847000,2936.9997560000,1749.9998780000,3433.9997560000,1084.9998780000,101.9999924000,344.9999695000,5525.9995120000,844.9999390000,4595.9995120000,725.9999390000,7427.9995570000,471.9999695000,44.9999961900,1947.9998780000,66.9999923700,2175.9997560000,118.9999924000,11.9999990500,1.9999998810,14.9999990500,0.0000000000,782.9999390000,52.9999961900,3172.9997560000,174.9999847000,20804.0001300000,2749.9997560000,628.9999390000,2599.9997560000,1467.9998780000,2749.9997560000,182.9999847000,52005.0017700000,60036.0001000000,29167,29896.0005000000,51689.65767,208200.0016000000,615.0000111000,2605.9997560000,996.9999390000,7.9999995230,199.9999847000,1130.9998780000,15,7,40700,22.9200000800,0.0000000000,15.6499996200,0.0000000000,39.6800003100,4.5000000000,0.9399999980,1.2500000000,16.1599998500,12.4200000800,1.5299999710,0.0000000000,11.1700000800,19.4599990800,-999.0000000000,-999.0000000000,43.8499984700,5.2800002100,-999.0000000000,-999.0000000000,11.2299995400,19.5699996900,1.3400000330,1.1499999760,6.7199997900,0.1400000010,0.9700000290,3.5799999240,1.2100000380,1.0199999810,12.4200000800,8.2200002670,3.0999999050,0.0500000010,0.0000000000,0.0599999990,0.0000000000,0.0000000000,17.7999992400,3.0299999710,37.4900016800,25.4799995400,5.1799998280,62.7799987800,30.3799991600,2.6500000950,11.9300003100,9.5600004200,1.5599999430,15.2600002300,12.5500001900,-999.0000000000,7.4800000190,-999.0000000000,16.2700004600,11.0500001900,16.9099998500,66.0999984700,9.9099998470,41.5299987800,25.9899997700,22.1299991600,6.2800002100,49.0000000000,19.9099998500,9.3100004200,47.7200012200,0,0,1773,1211,73,1250,961,118,0,7737,1773,0,0,1211,3070,348,97,198,345,0,0,531,64,0,0,3331,374,652,104,520,89,277,94,75,11,961,636,0,4,7107,0,5,79,240,1265,215,0,5271,1966,2649,64050,-999,61146,34483,25835,5670,542,3654,7714,1177,0,0,0,0,1769,222,1211,197,0,800,336,1976,1343,3806,197,2931,1840,1110,97,436,1212,82946.1484400000,120,147,366,3312,1966,3188,539,2649,1751,898,3188,954,486600,316,1324,1264,2011,1712,486,5943,2912,1183,183,0.0486557239,0.0635433492,0.1525797252,False +15009030100,HI,Maui County,Census Tract 301,11350,27980,99999,0,383.3068000000,27.2305791200,1910.5015780000,1910.5015780000,6.2592231110,27.4607778700,91.6621148600,27.1932371400,35.6747865500,0.0000000000,0.0035966930,0.0898007360,0.0000000000,8.1204749640,37.2918099000,6.5858333300,8.6301826290,1675.7861590000,177.6883274000,0.2490721380,1213.4786900000,793.3024273000,173.2832380000,864.0971374000,12.1702029000,576.9324621000,348.0594975000,851.9269343000,157.0642045000,28.2470664600,176.1750701000,1347.1787240000,314.6769609000,1347.1232320000,153.7392875000,1890.3938120000,392.8893720000,49.0661406900,383.3068000000,238.4715474000,807.4789838000,151.7785114000,0.0395477110,0.0000000000,20.0771960800,19.9998645800,82.6743149300,0.0311889900,1052.6142140000,211.1110742000,10415.9855900000,772.7004868000,261.4311255000,595.1029364000,362.3008839000,772.7004868000,23.2643179700,26030.6739600000,26529.6239500000,62499.99972,10011.7479600000,27536.6825200000,634.0477989000,310.2452140000,0.0041105070,14.1661638200,316.7386816000,HI,Maui County,Census Tract 301,11350,27980,99999,0,1866.9362440000,6.0427683000,47.7465012100,49.6951799400,40.5015555000,14.6726287000,0.0000000000,0.0233281630,11.2371696700,0.0000000000,6.0544323810,45.4074658200,20.7581653000,0.2993780970,1737.6018730000,257.5202186000,18.1205288500,1144.9066930000,659.1384151000,168.3234863000,1416.6104270000,672.4774532000,267.8818052000,916.8195996000,57.2021774200,684.1633000000,426.9665649000,859.6174226000,247.6905148000,8.0583204110,242.1889594000,1309.3110490000,183.3297054000,1187.5264430000,234.0342152000,1860.8740350000,321.7814935000,11.1399689800,404.3709197000,48.0699844900,482.2535048000,57.6687406900,0.0622084360,0.0000000000,5.0816485730,0.0000000000,76.4937794500,8.0000000000,784.1197518000,187.1127528000,16594.5856500000,882.8553724000,300.2488337000,597.4743423000,289.1446371000,882.8553724000,50.5171108200,38601.3524400000,49277.4119600000,27082.99935,33789.8790400000,32796.112,282034.9806000000,597.3191359000,598.5054464000,230.7045131000,0.0388802740,25.1516330700,222.5093316000,15,9,30100,34.4799995400,0.0000000000,3.5399999620,0.0000000000,20.8999996200,17.7800006900,0.0000000000,0.0000000000,0.6200000050,1.9199999570,0.0000000000,0.0000000000,14.1800003100,23.5300006900,-999.0000000000,-999.0000000000,27.9400005300,0.0000000000,-999.0000000000,-999.0000000000,12.9399995800,15.4200000800,3.1700000760,0.0000000000,0.3600000140,0.2599999900,1.8700000050,3.9500000480,5.2500000000,1.9199999570,5.3600001340,4.1100001340,2.0799999240,0.0000000000,0.0000000000,0.6200000050,0.0000000000,0.0000000000,10.5500001900,0.1099999990,45.0800018300,21.3199996900,10.4399995800,56.5299987800,23.3099994700,3.7899999620,42.5900001500,10.6599998500,0.5699999930,9.8000001910,8.8999996190,-999.0000000000,7.2600002290,-999.0000000000,89.7099990800,16.1700000800,33.9399986300,62.1500015300,10.6700000800,53.5600013700,27.8199996900,28.2399997700,2.5499999520,52.4000015300,15.3500003800,10.6899995800,50.0800018300,0,0,663,68,0,12,37,0,0,1923,663,0,0,68,402,342,0,94,156,0,0,19,0,0,0,402,52,62,61,7,0,76,101,36,5,103,79,0,0,1792,0,12,37,40,189,2,0,1271,496,613,58983,-999,66667,-999,21861,1388,148,871,1919,188,0,0,0,0,663,59,68,61,0,332,15,573,271,910,95,697,394,203,33,371,112,53658.6250000000,11,36,65,402,496,928,315,613,381,232,928,1005,609400,99,497,307,535,543,49,1479,775,227,53,0.2078346689,0.1729195461,0.0979676915,False +15009030201,HI,Maui County,Census Tract 302.01,99999,27980,99999,0,370.6344604000,44.2015914900,1578.6282440000,1578.6282440000,14.5508346600,25.8071403500,170.2173004000,68.9105529800,56.8306160000,0.0000000000,0.0000000000,1.9218082430,0.0000000000,0.0000000000,74.4014358500,42.8288688700,32.6707420300,1447.3961910000,143.5865326000,10.1581296900,1008.1257240000,471.1175842000,209.7516479000,834.6138888000,21.9635238600,543.5972290000,359.1036072000,812.6503654000,172.4136658000,95.5413284300,161.4319000000,1135.7887030000,143.0374451000,1135.7887030000,111.7394257000,1568.4701140000,158.1373749000,6.8636012080,370.6344604000,243.5205688000,939.4897130000,125.1920853000,3.0199844840,0.0000000000,20.3162593800,8.5108652110,190.2590179000,18.6689949000,572.1497803000,21.1398906700,13734.0000100000,569.6788940000,110.0921631000,526.5755005000,365.9671936000,569.6788940000,19.2180824300,36271.9983800000,36752.7165200000,29999.99935,38274.6483700000,41103.4484300000,537.2827148000,379.9689636000,1.3727202420,52.9869995100,135.0756683000,HI,Maui County,Census Tract 302.01,99999,27980,99999,0,2299.8553430000,30.4743881200,48.0452041600,193.2789917000,112.2885056000,88.9522628800,3.2945284840,2.7454402450,22.2380657200,0.0000000000,0.0000000000,154.0191956000,79.8923111000,51.8888206500,2135.1289060000,263.0131836000,2.7454402450,1551.9974370000,586.7006226000,406.0506287000,1811.9906010000,897.4844360000,344.8273010000,1165.4394530000,64.2433013900,904.6225586000,484.8447571000,1101.1961670000,393.4216003000,54.6342620800,271.7985840000,1706.0166020000,161.1573486000,1664.2858890000,289.6439514000,2267.1846030000,365.1435547000,8.5108652110,544.4208374000,40.3579711900,1261.2552490000,189.1608429000,7.6872329710,2.4708962440,9.0599527360,0.0000000000,187.2390289000,21.9635219600,364.8690186000,53.5360870400,22104.0011400000,877.1681519000,139.4683685000,816.2194214000,560.6188965000,877.1681519000,23.3362426800,43381.9986900000,41483.9988900000, 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diff --git a/data/data-pipeline/data_pipeline/tests/sources/persistent_poverty/test_etl.py b/data/data-pipeline/data_pipeline/tests/sources/persistent_poverty/test_etl.py new file mode 100644 index 00000000..f5331ff0 --- /dev/null +++ b/data/data-pipeline/data_pipeline/tests/sources/persistent_poverty/test_etl.py @@ -0,0 +1,19 @@ +import pathlib +from data_pipeline.tests.sources.example.test_etl import TestETL +from data_pipeline.etl.sources.persistent_poverty.etl import PersistentPovertyETL + + +class TestPersistentPovertyETL(TestETL): + _ETL_CLASS = PersistentPovertyETL + + _SAMPLE_DATA_PATH = pathlib.Path(__file__).parents[0] / "data" + _SAMPLE_DATA_FILE_NAME = "ltdb_std_all_sample/ltdb_std_1990_sample.csv" + _SAMPLE_DATA_ZIP_FILE_NAME = "LTDB_Std_All_Sample.zip" + _EXTRACT_TMP_FOLDER_NAME = "PersistentPovertyETL" + + def setup_method(self, _method, filename=__file__): + """Invoke `setup_method` from Parent, but using the current file name. + + This code can be copied identically between all child classes. + """ + super().setup_method(_method=_method, filename=filename) From aca226165c739aef543b8c6d9b2cce5875b87f0e Mon Sep 17 00:00:00 2001 From: Lucas Merrill Brown Date: Tue, 20 Sep 2022 14:53:12 -0400 Subject: [PATCH 07/11] Issue 1900: Tribal overlap with Census tracts (#1903) * working notebook * updating notebook * wip * fixing broken tests * adding tribal overlap files * WIP * WIP * WIP, calculated count and names * working * partial cleanup * partial cleanup * updating field names * fixing bug * removing pyogrio * removing unused imports * updating test fixtures to be more realistic * cleaning up notebook * fixing black * fixing flake8 errors * adding tox instructions * updating etl_score * suppressing warning * Use projected CRSes, ignore geom types (#1900) I looked into this a bit, and in general the geometry type mismatch changes very little about the calculation; we have a mix of multipolygons and polygons. The fastest thing to do is just not keep geom type; I did some runs with it set to both True and False, and they're the same within 9 digits of precision. Logically we just want to overlaps, regardless of how the actual geometries are encoded between the frames, so we can in this case ignore the geom types and feel OKAY. I also moved to projected CRSes, since we are actually trying to do area calculations and so like, we should. Again, the change is small in magnitude but logically more sound. * Readd CDC dataset config (#1900) * adding comments to fips code * delete unnecessary loggers Co-authored-by: matt bowen --- data/data-pipeline/README.md | 9 + .../data_pipeline/etl/constants.py | 6 + .../data-pipeline/data_pipeline/etl/runner.py | 2 + .../etl/score/config/datasets.yml | 29 +- .../data_pipeline/etl/score/etl_score.py | 9 + .../etl/score/tests/test_etl_utils.py | 22 + .../data_pipeline/etl/sources/geo_utils.py | 57 +- .../data_pipeline/etl/sources/tribal/etl.py | 20 +- .../etl/sources/tribal_overlap/README.md | 0 .../etl/sources/tribal_overlap/__init__.py | 0 .../etl/sources/tribal_overlap/etl.py | 208 +++ .../ipython/explore_eamlis.ipynb | 2 +- .../ipython/geopandas_speed_test.ipynb | 128 ++ .../ipython/tribal_and_tracts_overlap.ipynb | 1420 +++++++++++++++++ .../data_pipeline/score/field_names.py | 11 + .../data_pipeline/score/score_narwhal.py | 2 +- .../tests/score/test_utils/data/us.geojson | 18 +- .../tests/score/test_utils/test_adjacency.py | 6 +- .../tests/sources/test_geo_utils.py | 8 +- 19 files changed, 1921 insertions(+), 36 deletions(-) create mode 100644 data/data-pipeline/data_pipeline/etl/sources/tribal_overlap/README.md create mode 100644 data/data-pipeline/data_pipeline/etl/sources/tribal_overlap/__init__.py create mode 100644 data/data-pipeline/data_pipeline/etl/sources/tribal_overlap/etl.py create mode 100644 data/data-pipeline/data_pipeline/ipython/geopandas_speed_test.ipynb create mode 100644 data/data-pipeline/data_pipeline/ipython/tribal_and_tracts_overlap.ipynb diff --git a/data/data-pipeline/README.md b/data/data-pipeline/README.md index 3f46b22a..d61b1713 100644 --- a/data/data-pipeline/README.md +++ b/data/data-pipeline/README.md @@ -234,6 +234,15 @@ If you want to run tile generation, please install TippeCanoe [following these i - We use [Poetry](https://python-poetry.org/) for managing dependencies and building the application. Please follow the instructions on their site to download. - Install Poetry requirements with `poetry install` +### Running tox + +Our full test and check suite is run using tox. This can be run using commands such +as `poetry run tox`. + +Each run can take a while to build the whole environment. If you'd like to save time, +you can use the previously built environment by running `poetry run tox -e lint` +which will drastically speed up the process. + ### The Application entrypoint After installing the poetry dependencies, you can see a list of commands with the following steps: diff --git a/data/data-pipeline/data_pipeline/etl/constants.py b/data/data-pipeline/data_pipeline/etl/constants.py index 1223ddb1..f0d5b171 100644 --- a/data/data-pipeline/data_pipeline/etl/constants.py +++ b/data/data-pipeline/data_pipeline/etl/constants.py @@ -186,6 +186,12 @@ DATASET_LIST = [ "class_name": "AbandonedMineETL", "is_memory_intensive": True, }, + { + "name": "tribal_overlap", + "module_dir": "tribal_overlap", + "class_name": "TribalOverlapETL", + "is_memory_intensive": True, + }, ] CENSUS_INFO = { diff --git a/data/data-pipeline/data_pipeline/etl/runner.py b/data/data-pipeline/data_pipeline/etl/runner.py index b4df63a9..0b814159 100644 --- a/data/data-pipeline/data_pipeline/etl/runner.py +++ b/data/data-pipeline/data_pipeline/etl/runner.py @@ -106,6 +106,8 @@ def etl_runner(dataset_to_run: str = None) -> None: # Otherwise, the exceptions are silently ignored. fut.result() + # Note: these high-memory datasets also usually require the Census geojson to be + # generated, and one of them requires the Tribal geojson to be generated. if high_memory_datasets: logger.info("Running high-memory jobs") for dataset in high_memory_datasets: diff --git a/data/data-pipeline/data_pipeline/etl/score/config/datasets.yml b/data/data-pipeline/data_pipeline/etl/score/config/datasets.yml index 25ed4ccd..bcd72a97 100644 --- a/data/data-pipeline/data_pipeline/etl/score/config/datasets.yml +++ b/data/data-pipeline/data_pipeline/etl/score/config/datasets.yml @@ -290,6 +290,32 @@ datasets: include_in_tiles: true include_in_downloadable_files: true create_percentile: true + - long_name: "Overlap between Census tract boundaries and Tribal area boundaries." + short_name: "tribal_overlap" + module_name: "tribal_overlap" + input_geoid_tract_field_name: "GEOID10_TRACT" + load_fields: + - short_name: "tribal_count" + df_field_name: "COUNT_OF_TRIBAL_AREAS_IN_TRACT" + long_name: "Number of Tribal areas within Census tract" + field_type: int64 + include_in_tiles: true + include_in_downloadable_files: true + create_percentile: false + - short_name: "tribal_percent" + df_field_name: "PERCENT_OF_TRIBAL_AREA_IN_TRACT" + long_name: "Percent of the Census tract that is within Tribal areas" + field_type: float + include_in_tiles: true + include_in_downloadable_files: true + create_percentile: false + number_of_decimals_in_output: 6 + - short_name: "tribal_names" + df_field_name: "NAMES_OF_TRIBAL_AREAS_IN_TRACT" + long_name: "Names of Tribal areas within Census tract" + field_type: string + include_in_tiles: true + include_in_downloadable_files: true - long_name: "CDC Life Expeectancy" short_name: "cdc_life_expectancy" module_name: "cdc_life_expectancy" @@ -302,5 +328,4 @@ datasets: include_in_tiles: false include_in_downloadable_files: true create_percentile: false - create_reverse_percentile: true - \ No newline at end of file + create_reverse_percentile: true \ No newline at end of file diff --git a/data/data-pipeline/data_pipeline/etl/score/etl_score.py b/data/data-pipeline/data_pipeline/etl/score/etl_score.py index 3cdedf8d..53f7d260 100644 --- a/data/data-pipeline/data_pipeline/etl/score/etl_score.py +++ b/data/data-pipeline/data_pipeline/etl/score/etl_score.py @@ -15,6 +15,7 @@ from data_pipeline.etl.sources.fsf_flood_risk.etl import ( FloodRiskETL, ) from data_pipeline.etl.sources.eamlis.etl import AbandonedMineETL +from data_pipeline.etl.sources.tribal_overlap.etl import TribalOverlapETL from data_pipeline.etl.sources.us_army_fuds.etl import USArmyFUDS from data_pipeline.etl.sources.nlcd_nature_deprived.etl import NatureDeprivedETL from data_pipeline.etl.sources.fsf_wildfire_risk.etl import WildfireRiskETL @@ -52,6 +53,7 @@ class ScoreETL(ExtractTransformLoad): self.nature_deprived_df: pd.DataFrame self.eamlis_df: pd.DataFrame self.fuds_df: pd.DataFrame + self.tribal_overlap_df: pd.DataFrame def extract(self) -> None: logger.info("Loading data sets from disk.") @@ -148,6 +150,9 @@ class ScoreETL(ExtractTransformLoad): # Load FUDS dataset self.fuds_df = USArmyFUDS.get_data_frame() + # Load Tribal overlap dataset + self.tribal_overlap_df = TribalOverlapETL.get_data_frame() + # Load GeoCorr Urban Rural Map geocorr_urban_rural_csv = ( constants.DATA_PATH / "dataset" / "geocorr" / "usa.csv" @@ -359,6 +364,7 @@ class ScoreETL(ExtractTransformLoad): self.nature_deprived_df, self.eamlis_df, self.fuds_df, + self.tribal_overlap_df ] # Sanity check each data frame before merging. @@ -469,12 +475,15 @@ class ScoreETL(ExtractTransformLoad): field_names.PERCENT_AGE_UNDER_10, field_names.PERCENT_AGE_10_TO_64, field_names.PERCENT_AGE_OVER_64, + field_names.PERCENT_OF_TRIBAL_AREA_IN_TRACT, + field_names.COUNT_OF_TRIBAL_AREAS_IN_TRACT, ] non_numeric_columns = [ self.GEOID_TRACT_FIELD_NAME, field_names.TRACT_ELIGIBLE_FOR_NONNATURAL_THRESHOLD, field_names.AGRICULTURAL_VALUE_BOOL_FIELD, + field_names.NAMES_OF_TRIBAL_AREAS_IN_TRACT, ] boolean_columns = [ diff --git a/data/data-pipeline/data_pipeline/etl/score/tests/test_etl_utils.py b/data/data-pipeline/data_pipeline/etl/score/tests/test_etl_utils.py index ed33c63e..5d0a1e53 100644 --- a/data/data-pipeline/data_pipeline/etl/score/tests/test_etl_utils.py +++ b/data/data-pipeline/data_pipeline/etl/score/tests/test_etl_utils.py @@ -229,3 +229,25 @@ def test_compare_to_list_of_expected_state_fips_codes(): continental_us_expected=False, alaska_and_hawaii_expected=False, ) + + # Missing Hawaii but not Alaska + fips_codes_test_5 = [x for x in fips_codes_test_1 if x not in ["15"]] + + # Should raise error because both Hawaii and Alaska are expected + with pytest.raises(ValueError) as exception_info: + compare_to_list_of_expected_state_fips_codes( + actual_state_fips_codes=fips_codes_test_5, + alaska_and_hawaii_expected=True, + ) + partial_expected_error_message = ( + "FIPS state codes expected that are not present in the data:\n" + "['15']\n" + ) + assert partial_expected_error_message in str(exception_info.value) + + # Should work as expected + compare_to_list_of_expected_state_fips_codes( + actual_state_fips_codes=fips_codes_test_5, + alaska_and_hawaii_expected=True, + additional_fips_codes_not_expected=["15"], + ) diff --git a/data/data-pipeline/data_pipeline/etl/sources/geo_utils.py b/data/data-pipeline/data_pipeline/etl/sources/geo_utils.py index 87ce119f..7120aa45 100644 --- a/data/data-pipeline/data_pipeline/etl/sources/geo_utils.py +++ b/data/data-pipeline/data_pipeline/etl/sources/geo_utils.py @@ -4,6 +4,7 @@ from pathlib import Path from typing import Optional from functools import lru_cache import geopandas as gpd +from data_pipeline.etl.sources.tribal.etl import TribalETL from data_pipeline.utils import get_module_logger from .census.etl import CensusETL @@ -18,21 +19,44 @@ def get_tract_geojson( GEOJSON_PATH = _tract_data_path if GEOJSON_PATH is None: GEOJSON_PATH = CensusETL.NATIONAL_TRACT_JSON_PATH - if not GEOJSON_PATH.exists(): - logger.debug("Census data has not been computed, running") - census_etl = CensusETL() - census_etl.extract() - census_etl.transform() - census_etl.load() - else: - logger.debug("Loading existing tract geojson") - tract_data = gpd.read_file(GEOJSON_PATH, include_fields=["GEOID10"]) - tract_data.rename(columns={"GEOID10": "GEOID10_TRACT"}, inplace=True) + if not GEOJSON_PATH.exists(): + logger.debug("Census data has not been computed, running") + census_etl = CensusETL() + census_etl.extract() + census_etl.transform() + census_etl.load() + tract_data = gpd.read_file( + GEOJSON_PATH, + include_fields=["GEOID10"], + ) + tract_data = tract_data.rename( + columns={"GEOID10": "GEOID10_TRACT"}, errors="raise" + ) return tract_data +@lru_cache() +def get_tribal_geojson( + _tribal_data_path: Optional[Path] = None, +) -> gpd.GeoDataFrame: + logger.info("Loading Tribal geometry data from Tribal ETL") + GEOJSON_PATH = _tribal_data_path + if GEOJSON_PATH is None: + GEOJSON_PATH = TribalETL().NATIONAL_TRIBAL_GEOJSON_PATH + if not GEOJSON_PATH.exists(): + logger.debug("Tribal data has not been computed, running") + tribal_etl = TribalETL() + tribal_etl.extract() + tribal_etl.transform() + tribal_etl.load() + tribal_data = gpd.read_file( + GEOJSON_PATH, + ) + return tribal_data + + def add_tracts_for_geometries( - df: gpd.GeoDataFrame, _tract_data_path: Optional[Path] = None + df: gpd.GeoDataFrame, tract_data: Optional[gpd.GeoDataFrame] = None ) -> gpd.GeoDataFrame: """Adds tract-geoids to dataframe df that contains spatial geometries @@ -40,8 +64,8 @@ def add_tracts_for_geometries( Args: df (GeoDataFrame): a geopandas GeoDataFrame with a point geometry column - _tract_data_path (Path): an override to directly pass a GEOJSON file of - tracts->Geometries, to simplify testing. + tract_data (GeoDataFrame): optional override to directly pass a + geodataframe of the tract boundaries. Also helps simplify testing. Returns: GeoDataFrame: the above dataframe, with an additional GEOID10_TRACT column that @@ -49,7 +73,12 @@ def add_tracts_for_geometries( spatial analysis """ logger.debug("Appending tract data to dataframe") - tract_data = get_tract_geojson(_tract_data_path) + + if tract_data is None: + tract_data = get_tract_geojson() + else: + logger.debug("Using existing tract data.") + assert ( tract_data.crs == df.crs ), f"Dataframe must be projected to {tract_data.crs}" diff --git a/data/data-pipeline/data_pipeline/etl/sources/tribal/etl.py b/data/data-pipeline/data_pipeline/etl/sources/tribal/etl.py index 48258268..82142774 100644 --- a/data/data-pipeline/data_pipeline/etl/sources/tribal/etl.py +++ b/data/data-pipeline/data_pipeline/etl/sources/tribal/etl.py @@ -3,6 +3,7 @@ import geopandas as gpd import pandas as pd from data_pipeline.etl.base import ExtractTransformLoad +from data_pipeline.score import field_names from data_pipeline.utils import get_module_logger, unzip_file_from_url logger = get_module_logger(__name__) @@ -59,7 +60,10 @@ class TribalETL(ExtractTransformLoad): ) bia_national_lar_df.rename( - columns={"LARID": "tribalId", "LARName": "landAreaName"}, + columns={ + "LARID": field_names.TRIBAL_ID, + "LARName": field_names.TRIBAL_LAND_AREA_NAME, + }, inplace=True, ) @@ -87,7 +91,10 @@ class TribalETL(ExtractTransformLoad): ) bia_aian_supplemental_df.rename( - columns={"OBJECTID": "tribalId", "Land_Area_": "landAreaName"}, + columns={ + "OBJECTID": field_names.TRIBAL_ID, + "Land_Area_": field_names.TRIBAL_LAND_AREA_NAME, + }, inplace=True, ) @@ -113,7 +120,10 @@ class TribalETL(ExtractTransformLoad): ) bia_tsa_df.rename( - columns={"TSAID": "tribalId", "LARName": "landAreaName"}, + columns={ + "TSAID": field_names.TRIBAL_ID, + "LARName": field_names.TRIBAL_LAND_AREA_NAME, + }, inplace=True, ) @@ -136,8 +146,8 @@ class TribalETL(ExtractTransformLoad): alaska_native_villages_df.rename( columns={ - "GlobalID": "tribalId", - "TRIBALOFFICENAME": "landAreaName", + "GlobalID": field_names.TRIBAL_ID, + "TRIBALOFFICENAME": field_names.TRIBAL_LAND_AREA_NAME, }, inplace=True, ) diff --git a/data/data-pipeline/data_pipeline/etl/sources/tribal_overlap/README.md b/data/data-pipeline/data_pipeline/etl/sources/tribal_overlap/README.md new file mode 100644 index 00000000..e69de29b diff --git a/data/data-pipeline/data_pipeline/etl/sources/tribal_overlap/__init__.py b/data/data-pipeline/data_pipeline/etl/sources/tribal_overlap/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/data/data-pipeline/data_pipeline/etl/sources/tribal_overlap/etl.py b/data/data-pipeline/data_pipeline/etl/sources/tribal_overlap/etl.py new file mode 100644 index 00000000..cf7ec805 --- /dev/null +++ b/data/data-pipeline/data_pipeline/etl/sources/tribal_overlap/etl.py @@ -0,0 +1,208 @@ +import geopandas as gpd +import numpy as np +import pandas as pd +from data_pipeline.etl.base import ExtractTransformLoad, ValidGeoLevel +from data_pipeline.etl.sources.geo_utils import ( + add_tracts_for_geometries, + get_tribal_geojson, + get_tract_geojson, +) +from data_pipeline.score import field_names +from data_pipeline.utils import get_module_logger + +logger = get_module_logger(__name__) + + +class TribalOverlapETL(ExtractTransformLoad): + """Calculates the overlap between Census tracts and Tribal boundaries.""" + + # Metadata for the baseclass + NAME = "tribal_overlap" + GEO_LEVEL = ValidGeoLevel.CENSUS_TRACT + + PUERTO_RICO_EXPECTED_IN_DATA = False + ALASKA_AND_HAWAII_EXPECTED_IN_DATA = True + EXPECTED_MISSING_STATES = [ + # 15 is Hawaii, which has Hawaiian Home Lands, but they are not included in + # this dataset. + "15", + # The following states do not have any federally recognized Tribes in this + # dataset. + "10", + "11", + "13", + "17", + "18", + "21", + "24", + "33", + "34", + "39", + "50", + "51", + "54", + ] + + # A Tribal area that requires some special processing. + ANNETTE_ISLAND_TRIBAL_NAME = "Annette Island LAR" + + CRS_INTEGER = 3857 + + # Define these for easy code completion + def __init__(self): + self.COLUMNS_TO_KEEP = [ + self.GEOID_TRACT_FIELD_NAME, + field_names.COUNT_OF_TRIBAL_AREAS_IN_TRACT, + field_names.PERCENT_OF_TRIBAL_AREA_IN_TRACT, + field_names.NAMES_OF_TRIBAL_AREAS_IN_TRACT, + ] + + self.output_df: pd.DataFrame + self.census_tract_gdf: gpd.GeoDataFrame + self.tribal_gdf: gpd.GeoDataFrame + + @staticmethod + def _create_string_from_list(series: pd.Series) -> str: + """Helper method that creates a sorted string list (for tribal names).""" + str_list = series.tolist() + str_list = sorted(str_list) + return ", ".join(str_list) + + def extract(self) -> None: + self.census_tract_gdf = get_tract_geojson() + self.tribal_gdf = get_tribal_geojson() + + def transform(self) -> None: + logger.info("Starting tribal overlap transforms.") + + # First, calculate whether tracts include any areas from the Tribal areas, + # for both the points in AK and the polygons in the continental US (CONUS). + tribal_overlap_with_tracts = add_tracts_for_geometries( + df=self.tribal_gdf, tract_data=self.census_tract_gdf + ) + + tribal_overlap_with_tracts = tribal_overlap_with_tracts.groupby( + [self.GEOID_TRACT_FIELD_NAME] + ).agg( + { + field_names.TRIBAL_ID: "count", + field_names.TRIBAL_LAND_AREA_NAME: self._create_string_from_list, + } + ) + + tribal_overlap_with_tracts = tribal_overlap_with_tracts.reset_index() + + tribal_overlap_with_tracts = tribal_overlap_with_tracts.rename( + columns={ + field_names.TRIBAL_ID: field_names.COUNT_OF_TRIBAL_AREAS_IN_TRACT, + field_names.TRIBAL_LAND_AREA_NAME: field_names.NAMES_OF_TRIBAL_AREAS_IN_TRACT, + } + ) + + # Second, calculate percentage overlap. + # Drop the points from the Tribal data (because these cannot be joined to a + # (Multi)Polygon tract data frame) + tribal_gdf_without_points = self.tribal_gdf[ + self.tribal_gdf.geom_type.isin(["Polygon", "MultiPolygon"]) + ] + + # Switch from geographic to projected CRSes + # because logically that's right + self.census_tract_gdf = self.census_tract_gdf.to_crs(crs=self.CRS_INTEGER) + tribal_gdf_without_points = tribal_gdf_without_points.to_crs(crs=self.CRS_INTEGER) + + # Create a measure for the entire census tract area + self.census_tract_gdf["area_tract"] = self.census_tract_gdf.area + + # Performing overlay funcion + # We have a mix of polygons and multipolygons, and we just want the overlaps + # without caring a ton about the specific types, so we ignore geom type. + # Realistically, this changes almost nothing in the calculation; True and False + # are the same within 9 digits of precision + gdf_joined = gpd.overlay( + self.census_tract_gdf, + tribal_gdf_without_points, + how="intersection", + keep_geom_type=False, + ) + + # Calculating the areas of the newly-created overlapping geometries + gdf_joined["area_joined"] = gdf_joined.area + + # Calculating the areas of the newly-created geometries in relation + # to the original tract geometries + gdf_joined[field_names.PERCENT_OF_TRIBAL_AREA_IN_TRACT] = ( + gdf_joined["area_joined"] / gdf_joined["area_tract"] + ) + + # Aggregate the results + percentage_results = gdf_joined.groupby( + [self.GEOID_TRACT_FIELD_NAME] + ).agg({field_names.PERCENT_OF_TRIBAL_AREA_IN_TRACT: "sum"}) + + percentage_results = percentage_results.reset_index() + + # Merge the two results. + merged_output_df = tribal_overlap_with_tracts.merge( + right=percentage_results, + how="outer", + on=self.GEOID_TRACT_FIELD_NAME, + ) + + # Finally, fix one unique error. + # There is one unique Tribal area (self.ANNETTE_ISLAND_TRIBAL_NAME) that is a polygon in + # Alaska. All other Tribal areas in Alaska are points. + # For tracts that *only* contain that Tribal area, leave percentage as is. + # For tracts that include that Tribal area AND Alaska Native villages, + # null the percentage, because we cannot calculate the percent of the tract + # this is within Tribal areas. + + # Create state FIPS codes. + merged_output_df_state_fips_code = merged_output_df[ + self.GEOID_TRACT_FIELD_NAME + ].str[0:2] + + # Start by testing for Annette Island exception, to make sure data is as + # expected + alaskan_non_annette_matches = ( + # Data from Alaska + (merged_output_df_state_fips_code == "02") + # Where the Tribal areas do *not* include Annette + & ( + ~merged_output_df[ + field_names.NAMES_OF_TRIBAL_AREAS_IN_TRACT + ].str.contains(self.ANNETTE_ISLAND_TRIBAL_NAME) + ) + # But somehow percentage is greater than zero. + & ( + merged_output_df[field_names.PERCENT_OF_TRIBAL_AREA_IN_TRACT] + > 0 + ) + ) + + # There should be none of these matches. + if sum(alaskan_non_annette_matches) > 0: + raise ValueError( + "Data has changed. More than one Alaskan Tribal Area has polygon " + "boundaries. You'll need to refactor this ETL. \n" + f"Data:\n{merged_output_df[alaskan_non_annette_matches]}" + ) + + # Now, fix the exception that is already known. + merged_output_df[ + field_names.PERCENT_OF_TRIBAL_AREA_IN_TRACT + ] = np.where( + # For tracts inside Alaska + (merged_output_df_state_fips_code == "02") + # That are not only represented by Annette Island + & ( + merged_output_df[field_names.NAMES_OF_TRIBAL_AREAS_IN_TRACT] + != self.ANNETTE_ISLAND_TRIBAL_NAME + ), + # Set the value to `None` for tracts with more than just Annette. + None, + # Otherwise, set the value to what it was. + merged_output_df[field_names.PERCENT_OF_TRIBAL_AREA_IN_TRACT], + ) + + self.output_df = merged_output_df diff --git a/data/data-pipeline/data_pipeline/ipython/explore_eamlis.ipynb b/data/data-pipeline/data_pipeline/ipython/explore_eamlis.ipynb index 41085325..87107be2 100644 --- a/data/data-pipeline/data_pipeline/ipython/explore_eamlis.ipynb +++ b/data/data-pipeline/data_pipeline/ipython/explore_eamlis.ipynb @@ -2435,7 +2435,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.4" + "version": "3.9.6" } }, "nbformat": 4, diff --git a/data/data-pipeline/data_pipeline/ipython/geopandas_speed_test.ipynb b/data/data-pipeline/data_pipeline/ipython/geopandas_speed_test.ipynb new file mode 100644 index 00000000..568e0a49 --- /dev/null +++ b/data/data-pipeline/data_pipeline/ipython/geopandas_speed_test.ipynb @@ -0,0 +1,128 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "e0b801f9", + "metadata": {}, + "outputs": [], + "source": [ + "import geopandas as gpd\n", + "import pyogrio\n", + "from data_pipeline.etl.sources.census.etl import CensusETL\n", + "\n", + "import time" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "c4cbab25", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Time taken to execute the function using pyogrio is 63.07696199417114\n" + ] + } + ], + "source": [ + "begin = time.time()\n", + "census_tract_gdf = gpd.read_file(\n", + " CensusETL.NATIONAL_TRACT_JSON_PATH,\n", + " # Use `pyogrio` because it's vectorized and faster.\n", + " engine=\"pyogrio\",\n", + ")\n", + "end = time.time()\n", + " \n", + "print(\"Time taken to execute the function using pyogrio is\", end-begin)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "372ab939", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Time taken to execute the function using include fields is 67.33577013015747\n" + ] + } + ], + "source": [ + "begin2 = time.time()\n", + "census_tract_gdf = gpd.read_file(\n", + " CensusETL.NATIONAL_TRACT_JSON_PATH,\n", + " engine=\"fiona\",\n", + " include_fields=[\"GEOID10\"]\n", + ")\n", + "end2 = time.time()\n", + " \n", + "print(\"Time taken to execute the function using include fields is\", end2-begin2)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "32fb7d4b", + "metadata": {}, + "outputs": [ + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m/var/folders/lx/xmq8p65j71v9xq2bhsd2j5w40000gp/T/ipykernel_21074/2531126572.py\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mbegin2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m census_tract_gdf = gpd.read_file(\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mCensusETL\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mNATIONAL_TRACT_JSON_PATH\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mengine\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"fiona\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0minclude_fields\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"GEOID10\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/.virtualenvs/scoring2/lib/python3.9/site-packages/geopandas/io/file.py\u001b[0m in \u001b[0;36m_read_file\u001b[0;34m(filename, bbox, mask, rows, engine, **kwargs)\u001b[0m\n\u001b[1;32m 251\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 252\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"fiona\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 253\u001b[0;31m return _read_file_fiona(\n\u001b[0m\u001b[1;32m 254\u001b[0m \u001b[0mpath_or_bytes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfrom_bytes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbbox\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbbox\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmask\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrows\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrows\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 255\u001b[0m )\n", + "\u001b[0;32m~/.virtualenvs/scoring2/lib/python3.9/site-packages/geopandas/io/file.py\u001b[0m in \u001b[0;36m_read_file_fiona\u001b[0;34m(path_or_bytes, from_bytes, bbox, mask, rows, **kwargs)\u001b[0m\n\u001b[1;32m 338\u001b[0m )\n\u001b[1;32m 339\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 340\u001b[0;31m df = GeoDataFrame.from_features(\n\u001b[0m\u001b[1;32m 341\u001b[0m \u001b[0mf_filt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcrs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcrs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcolumns\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m\"geometry\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 342\u001b[0m )\n", + "\u001b[0;32m~/.virtualenvs/scoring2/lib/python3.9/site-packages/geopandas/geodataframe.py\u001b[0m in \u001b[0;36mfrom_features\u001b[0;34m(cls, features, crs, columns)\u001b[0m\n\u001b[1;32m 641\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 642\u001b[0m \u001b[0mrows\u001b[0m \u001b[0;34m=\u001b[0m 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"\u001b[0;32m~/.pyenv/versions/3.9.6/lib/python3.9/logging/__init__.py\u001b[0m in \u001b[0;36mdebug\u001b[0;34m(self, msg, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1422\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmanager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_clear_cache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1423\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1424\u001b[0;31m \u001b[0;32mdef\u001b[0m \u001b[0mdebug\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmsg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1425\u001b[0m \"\"\"\n\u001b[1;32m 1426\u001b[0m \u001b[0mLog\u001b[0m \u001b[0;34m'msg % args'\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mseverity\u001b[0m \u001b[0;34m'DEBUG'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + ] + } + ], + "source": [ + "begin2 = time.time()\n", + "census_tract_gdf = gpd.read_file(\n", + " CensusETL.NATIONAL_TRACT_JSON_PATH,\n", + " engine=\"fiona\",\n", + " include_fields=[\"GEOID10\"],\n", + " rows=slice(0, 76322, 100),\n", + ")\n", + "end2 = time.time()\n", + "\n", + "print(\"Time taken to execute the function using slice is\", end2 - begin2)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/data/data-pipeline/data_pipeline/ipython/tribal_and_tracts_overlap.ipynb b/data/data-pipeline/data_pipeline/ipython/tribal_and_tracts_overlap.ipynb new file mode 100644 index 00000000..effeed69 --- /dev/null +++ b/data/data-pipeline/data_pipeline/ipython/tribal_and_tracts_overlap.ipynb @@ -0,0 +1,1420 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "id": "f0b6f7e2", + "metadata": {}, + "outputs": [], + "source": [ + "import geopandas as gpd\n", + "import pyogrio\n", + "from data_pipeline.etl.sources.census.etl import CensusETL\n", + "from data_pipeline.etl.sources.tribal.etl import TribalETL\n", + "\n", + "import time\n", + "\n", + "begin = time.time()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "1e3e65af", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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tribalIdlandAreaNameClassificationgeometry
0LAR0001Cheyenne River LAR1MULTIPOLYGON (((-100.49935 45.47125, -100.4993...
1LAR0002Crow Creek LAR1POLYGON ((-99.42137 44.27733, -99.42138 44.273...
2LAR0003Flandreau LAR1MULTIPOLYGON (((-96.56655 44.08786, -96.57165 ...
3LAR0004Fort Berthold LAR1POLYGON ((-102.78362 47.99900, -102.78192 47.9...
4LAR0005Lake Traverse (Sisseton) LAR1MULTIPOLYGON (((-97.28946 45.76084, -97.28955 ...
...............
592{0886416F-643E-497E-89D3-E9CC0240158D}ChilkatNonePOINT (-135.88440 59.40390)
593{2029C35B-86D7-4751-A946-EA0772C81A80}ChilkootNonePOINT (-135.44500 59.23580)
594{24DF6536-95CB-4964-94DF-16E440ABCA92}CraigNonePOINT (-133.14830 55.47640)
595{ACDE097A-9BDA-4FCA-9DB7-297DA6B73F88}DouglasNonePOINT (-134.41970 58.30190)
596{5E1D1895-FF41-4B11-9EDB-0C1254A360C4}AgdaaguxNonePOINT (-162.31030 55.06170)
\n", + "

597 rows × 4 columns

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" + ], + "text/plain": [ + " tribalId landAreaName \\\n", + "0 LAR0001 Cheyenne River LAR \n", + "1 LAR0002 Crow Creek LAR \n", + "2 LAR0003 Flandreau LAR \n", + "3 LAR0004 Fort Berthold LAR \n", + "4 LAR0005 Lake Traverse (Sisseton) LAR \n", + ".. ... ... \n", + "592 {0886416F-643E-497E-89D3-E9CC0240158D} Chilkat \n", + "593 {2029C35B-86D7-4751-A946-EA0772C81A80} Chilkoot \n", + "594 {24DF6536-95CB-4964-94DF-16E440ABCA92} Craig \n", + "595 {ACDE097A-9BDA-4FCA-9DB7-297DA6B73F88} Douglas \n", + "596 {5E1D1895-FF41-4B11-9EDB-0C1254A360C4} Agdaagux \n", + "\n", + " Classification geometry \n", + "0 1 MULTIPOLYGON (((-100.49935 45.47125, -100.4993... \n", + "1 1 POLYGON ((-99.42137 44.27733, -99.42138 44.273... \n", + "2 1 MULTIPOLYGON (((-96.56655 44.08786, -96.57165 ... \n", + "3 1 POLYGON ((-102.78362 47.99900, -102.78192 47.9... \n", + "4 1 MULTIPOLYGON (((-97.28946 45.76084, -97.28955 ... \n", + ".. ... ... \n", + "592 None POINT (-135.88440 59.40390) \n", + "593 None POINT (-135.44500 59.23580) \n", + "594 None POINT (-133.14830 55.47640) \n", + "595 None POINT (-134.41970 58.30190) \n", + "596 None POINT (-162.31030 55.06170) \n", + "\n", + "[597 rows x 4 columns]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Load Tribal geojson\n", + "tribal_gdf = gpd.read_file(\n", + " TribalETL().NATIONAL_TRIBAL_GEOJSON_PATH,\n", + " # Use `pyogrio` because it's vectorized and faster.\n", + " engine=\"pyogrio\",\n", + ")\n", + "\n", + "tribal_gdf" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "89fedd44", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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tribalIdlandAreaNameClassificationgeometry
0LAR0001Cheyenne River LAR1MULTIPOLYGON (((-100.49935 45.47125, -100.4993...
1LAR0002Crow Creek LAR1POLYGON ((-99.42137 44.27733, -99.42138 44.273...
2LAR0003Flandreau LAR1MULTIPOLYGON (((-96.56655 44.08786, -96.57165 ...
3LAR0004Fort Berthold LAR1POLYGON ((-102.78362 47.99900, -102.78192 47.9...
4LAR0005Lake Traverse (Sisseton) LAR1MULTIPOLYGON (((-97.28946 45.76084, -97.28955 ...
...............
365TSA0354Seminole TSANonePOLYGON ((-96.49048 34.90423, -96.49146 34.903...
366TSA0355Seneca Cayuga TSANonePOLYGON ((-94.61803 36.62531, -94.62083 36.625...
367TSA0356Tonkawa TSANonePOLYGON ((-97.24698 36.68082, -97.24697 36.677...
368TSA0357Wichita Caddo and Delaware TSANonePOLYGON ((-97.99931 35.36425, -97.99948 35.360...
369TSA0358Wyandotte TSANonePOLYGON ((-94.61820 36.82030, -94.61821 36.816...
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370 rows × 4 columns

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" + ], + "text/plain": [ + " tribalId landAreaName Classification \\\n", + "0 LAR0001 Cheyenne River LAR 1 \n", + "1 LAR0002 Crow Creek LAR 1 \n", + "2 LAR0003 Flandreau LAR 1 \n", + "3 LAR0004 Fort Berthold LAR 1 \n", + "4 LAR0005 Lake Traverse (Sisseton) LAR 1 \n", + ".. ... ... ... \n", + "365 TSA0354 Seminole TSA None \n", + "366 TSA0355 Seneca Cayuga TSA None \n", + "367 TSA0356 Tonkawa TSA None \n", + "368 TSA0357 Wichita Caddo and Delaware TSA None \n", + "369 TSA0358 Wyandotte TSA None \n", + "\n", + " geometry \n", + "0 MULTIPOLYGON (((-100.49935 45.47125, -100.4993... \n", + "1 POLYGON ((-99.42137 44.27733, -99.42138 44.273... \n", + "2 MULTIPOLYGON (((-96.56655 44.08786, -96.57165 ... \n", + "3 POLYGON ((-102.78362 47.99900, -102.78192 47.9... \n", + "4 MULTIPOLYGON (((-97.28946 45.76084, -97.28955 ... \n", + ".. ... \n", + "365 POLYGON ((-96.49048 34.90423, -96.49146 34.903... \n", + "366 POLYGON ((-94.61803 36.62531, -94.62083 36.625... \n", + "367 POLYGON ((-97.24698 36.68082, -97.24697 36.677... \n", + "368 POLYGON ((-97.99931 35.36425, -97.99948 35.360... \n", + "369 POLYGON ((-94.61820 36.82030, -94.61821 36.816... \n", + "\n", + "[370 rows x 4 columns]" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Drop the points from the Tribal data (because these cannot be joined to a (Multi)Polygon tract data frame)\n", + "tribal_gdf = tribal_gdf[tribal_gdf.geom_type != \"Point\"]\n", + "tribal_gdf" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "5940556f", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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STATEFP10COUNTYFP10TRACTCE10GEOID10NAME10NAMELSAD10MTFCC10FUNCSTAT10ALAND10AWATER10INTPTLAT10INTPTLON10geometry
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220175965700201759657009657Census Tract 9657G5020S9466451358282+37.0625361-100.9131437POLYGON ((-100.94250 37.06497, -100.94251 37.0...
32004302030020043020300203Census Tract 203G5020S2115932067045771+39.7881238-094.9734666POLYGON ((-94.95518 39.90129, -94.95475 39.901...
42004302020020043020200202Census Tract 202G5020S3526870262968059+39.7540484-095.1060098POLYGON ((-95.02575 39.88295, -95.02585 39.883...
..........................................
7412935049000600350490006006Census Tract 6G5020S16294710+35.6758519-105.9446097POLYGON ((-105.95207 35.67367, -105.95215 35.6...
7413035049000700350490007007Census Tract 7G5020S12855970+35.6802004-105.9558818POLYGON ((-105.96221 35.67223, -105.96245 35.6...
7413135049000800350490008008Census Tract 8G5020S19167970+35.6805095-105.9703558POLYGON ((-105.98159 35.67739, -105.98143 35.6...
7413235049000900350490009009Census Tract 9G5020S25455630+35.6692966-105.9755351POLYGON ((-105.96362 35.67616, -105.96365 35.6...
74133350490010013504900100110.01Census Tract 10.01G5020S26172810+35.6647341-105.9468629POLYGON ((-105.94510 35.65705, -105.94563 35.6...
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74134 rows × 13 columns

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" + ], + "text/plain": [ + " STATEFP10 COUNTYFP10 TRACTCE10 GEOID10 NAME10 NAMELSAD10 \\\n", + "0 20 071 958100 20071958100 9581 Census Tract 9581 \n", + "1 20 175 965600 20175965600 9656 Census Tract 9656 \n", + "2 20 175 965700 20175965700 9657 Census Tract 9657 \n", + "3 20 043 020300 20043020300 203 Census Tract 203 \n", + "4 20 043 020200 20043020200 202 Census Tract 202 \n", + "... ... ... ... ... ... ... \n", + "74129 35 049 000600 35049000600 6 Census Tract 6 \n", + "74130 35 049 000700 35049000700 7 Census Tract 7 \n", + "74131 35 049 000800 35049000800 8 Census Tract 8 \n", + "74132 35 049 000900 35049000900 9 Census Tract 9 \n", + "74133 35 049 001001 35049001001 10.01 Census Tract 10.01 \n", + "\n", + " MTFCC10 FUNCSTAT10 ALAND10 AWATER10 INTPTLAT10 INTPTLON10 \\\n", + "0 G5020 S 2016176814 0 +38.4804076 -101.8059837 \n", + "1 G5020 S 1603575701 2204351 +37.1805849 -100.8547406 \n", + "2 G5020 S 9466451 358282 +37.0625361 -100.9131437 \n", + "3 G5020 S 211593206 7045771 +39.7881238 -094.9734666 \n", + "4 G5020 S 352687026 2968059 +39.7540484 -095.1060098 \n", + "... ... ... ... ... ... ... \n", + "74129 G5020 S 1629471 0 +35.6758519 -105.9446097 \n", + "74130 G5020 S 1285597 0 +35.6802004 -105.9558818 \n", + "74131 G5020 S 1916797 0 +35.6805095 -105.9703558 \n", + "74132 G5020 S 2545563 0 +35.6692966 -105.9755351 \n", + "74133 G5020 S 2617281 0 +35.6647341 -105.9468629 \n", + "\n", + " geometry \n", + "0 POLYGON ((-101.79971 38.69806, -101.79097 38.6... \n", + "1 POLYGON ((-101.06766 37.20440, -101.06768 37.2... \n", + "2 POLYGON ((-100.94250 37.06497, -100.94251 37.0... \n", + "3 POLYGON ((-94.95518 39.90129, -94.95475 39.901... \n", + "4 POLYGON ((-95.02575 39.88295, -95.02585 39.883... \n", + "... ... \n", + "74129 POLYGON ((-105.95207 35.67367, -105.95215 35.6... \n", + "74130 POLYGON ((-105.96221 35.67223, -105.96245 35.6... \n", + "74131 POLYGON ((-105.98159 35.67739, -105.98143 35.6... \n", + "74132 POLYGON ((-105.96362 35.67616, -105.96365 35.6... \n", + "74133 POLYGON ((-105.94510 35.65705, -105.94563 35.6... \n", + "\n", + "[74134 rows x 13 columns]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Load Census tracts geojson\n", + "census_tract_gdf = gpd.read_file(\n", + " CensusETL.NATIONAL_TRACT_JSON_PATH,\n", + " # Use `pyogrio` because it's vectorized and faster.\n", + " engine=\"pyogrio\",\n", + ")\n", + "\n", + "census_tract_gdf" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "595b2a2a", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/lx/xmq8p65j71v9xq2bhsd2j5w40000gp/T/ipykernel_768/2956500515.py:2: UserWarning: Geometry is in a geographic CRS. Results from 'area' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation.\n", + "\n", + " census_tract_gdf[\"area_tract\"] = census_tract_gdf.area\n" + ] + }, + { + "data": { + "text/html": [ + "
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020071958100200719581009581Census Tract 9581G5020S20161768140+38.4804076-101.8059837POLYGON ((-101.79971 38.69806, -101.79097 38.6...0.208156
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220175965700201759657009657Census Tract 9657G5020S9466451358282+37.0625361-100.9131437POLYGON ((-100.94250 37.06497, -100.94251 37.0...0.000995
32004302030020043020300203Census Tract 203G5020S2115932067045771+39.7881238-094.9734666POLYGON ((-94.95518 39.90129, -94.95475 39.901...0.022990
42004302020020043020200202Census Tract 202G5020S3526870262968059+39.7540484-095.1060098POLYGON ((-95.02575 39.88295, -95.02585 39.883...0.037373
.............................................
7412935049000600350490006006Census Tract 6G5020S16294710+35.6758519-105.9446097POLYGON ((-105.95207 35.67367, -105.95215 35.6...0.000162
7413035049000700350490007007Census Tract 7G5020S12855970+35.6802004-105.9558818POLYGON ((-105.96221 35.67223, -105.96245 35.6...0.000128
7413135049000800350490008008Census Tract 8G5020S19167970+35.6805095-105.9703558POLYGON ((-105.98159 35.67739, -105.98143 35.6...0.000191
7413235049000900350490009009Census Tract 9G5020S25455630+35.6692966-105.9755351POLYGON ((-105.96362 35.67616, -105.96365 35.6...0.000253
74133350490010013504900100110.01Census Tract 10.01G5020S26172810+35.6647341-105.9468629POLYGON ((-105.94510 35.65705, -105.94563 35.6...0.000261
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74134 rows × 14 columns

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" + ], + "text/plain": [ + " STATEFP10 COUNTYFP10 TRACTCE10 GEOID10 NAME10 NAMELSAD10 \\\n", + "0 20 071 958100 20071958100 9581 Census Tract 9581 \n", + "1 20 175 965600 20175965600 9656 Census Tract 9656 \n", + "2 20 175 965700 20175965700 9657 Census Tract 9657 \n", + "3 20 043 020300 20043020300 203 Census Tract 203 \n", + "4 20 043 020200 20043020200 202 Census Tract 202 \n", + "... ... ... ... ... ... ... \n", + "74129 35 049 000600 35049000600 6 Census Tract 6 \n", + "74130 35 049 000700 35049000700 7 Census Tract 7 \n", + "74131 35 049 000800 35049000800 8 Census Tract 8 \n", + "74132 35 049 000900 35049000900 9 Census Tract 9 \n", + "74133 35 049 001001 35049001001 10.01 Census Tract 10.01 \n", + "\n", + " MTFCC10 FUNCSTAT10 ALAND10 AWATER10 INTPTLAT10 INTPTLON10 \\\n", + "0 G5020 S 2016176814 0 +38.4804076 -101.8059837 \n", + "1 G5020 S 1603575701 2204351 +37.1805849 -100.8547406 \n", + "2 G5020 S 9466451 358282 +37.0625361 -100.9131437 \n", + "3 G5020 S 211593206 7045771 +39.7881238 -094.9734666 \n", + "4 G5020 S 352687026 2968059 +39.7540484 -095.1060098 \n", + "... ... ... ... ... ... ... \n", + "74129 G5020 S 1629471 0 +35.6758519 -105.9446097 \n", + "74130 G5020 S 1285597 0 +35.6802004 -105.9558818 \n", + "74131 G5020 S 1916797 0 +35.6805095 -105.9703558 \n", + "74132 G5020 S 2545563 0 +35.6692966 -105.9755351 \n", + "74133 G5020 S 2617281 0 +35.6647341 -105.9468629 \n", + "\n", + " geometry area_tract \n", + "0 POLYGON ((-101.79971 38.69806, -101.79097 38.6... 0.208156 \n", + "1 POLYGON ((-101.06766 37.20440, -101.06768 37.2... 0.162976 \n", + "2 POLYGON ((-100.94250 37.06497, -100.94251 37.0... 0.000995 \n", + "3 POLYGON ((-94.95518 39.90129, -94.95475 39.901... 0.022990 \n", + "4 POLYGON ((-95.02575 39.88295, -95.02585 39.883... 0.037373 \n", + "... ... ... \n", + "74129 POLYGON ((-105.95207 35.67367, -105.95215 35.6... 0.000162 \n", + "74130 POLYGON ((-105.96221 35.67223, -105.96245 35.6... 0.000128 \n", + "74131 POLYGON ((-105.98159 35.67739, -105.98143 35.6... 0.000191 \n", + "74132 POLYGON ((-105.96362 35.67616, -105.96365 35.6... 0.000253 \n", + "74133 POLYGON ((-105.94510 35.65705, -105.94563 35.6... 0.000261 \n", + "\n", + "[74134 rows x 14 columns]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Create a measure for the entire census tract area\n", + "census_tract_gdf[\"area_tract\"] = census_tract_gdf.area\n", + "census_tract_gdf" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "0ea396ed", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/lx/xmq8p65j71v9xq2bhsd2j5w40000gp/T/ipykernel_768/1353983773.py:2: UserWarning: `keep_geom_type=True` in overlay resulted in 1123 dropped geometries of different geometry types than df1 has. Set `keep_geom_type=False` to retain all geometries\n", + " gdf_joined = gpd.overlay(census_tract_gdf, tribal_gdf, how=\"union\")\n" + ] + } + ], + "source": [ + "# Performing overlay funcion\n", + "gdf_joined = gpd.overlay(census_tract_gdf, tribal_gdf, how=\"union\")" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "7fb3ef69", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/lx/xmq8p65j71v9xq2bhsd2j5w40000gp/T/ipykernel_768/2727120487.py:3: UserWarning: Geometry is in a geographic CRS. Results from 'area' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation.\n", + "\n", + " gdf_joined['area_joined'] = gdf_joined.area\n" + ] + }, + { + "data": { + "text/html": [ + "
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STATEFP10COUNTYFP10TRACTCE10GEOID10NAME10NAMELSAD10MTFCC10FUNCSTAT10ALAND10AWATER10INTPTLAT10INTPTLON10area_tracttribalIdlandAreaNameClassificationgeometryarea_joinedtribal_area_as_a_share_of_tract_area
02004302010020043020100201Census Tract 201G5020S454634616.02601186.0+39.8206800-095.25672790.048098LAR0210Iowa LAR1POLYGON ((-95.33994 39.97506, -95.33994 39.975...4.998139e-040.010391
120013480600200134806004806Census Tract 4806G5020S882293538.01376818.0+39.8596443-095.62551870.093019LAR0210Iowa LAR1POLYGON ((-95.45656 40.00025, -95.45528 40.000...3.209294e-030.034502
231147964500311479645009645Census Tract 9645G5020S677848509.06076731.0+40.1522236-095.58588700.072289LAR0210Iowa LAR1MULTIPOLYGON (((-95.38162 40.02744, -95.38119 ...1.476624e-030.020427
329087960300290879603009603Census Tract 9603G5020S412869716.06745159.0+39.9730230-095.14797010.044239LAR0210Iowa LAR1POLYGON ((-95.38119 40.02755, -95.38162 40.027...1.965514e-070.000004
42008508260020085082600826Census Tract 826G5020S690868809.0947758.0+39.4553966-095.67314040.072404LAR0211Kickapoo (Kansas) LAR1POLYGON ((-95.71031 39.65308, -95.69902 39.653...5.285627e-060.000073
............................................................
76317NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNTSA0353Sac and Fox TSANoneMULTIPOLYGON (((-96.62002 35.75143, -96.62001 ...6.560647e-17NaN
76318NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNTSA0354Seminole TSANoneMULTIPOLYGON (((-96.77536 35.03300, -96.77536 ...7.207055e-18NaN
76319NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNTSA0355Seneca Cayuga TSANonePOLYGON ((-94.61836 36.74340, -94.61836 36.743...7.016721e-18NaN
76320NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNTSA0356Tonkawa TSANoneMULTIPOLYGON (((-97.24698 36.69942, -97.24692 ...2.612218e-17NaN
76321NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNTSA0358Wyandotte TSANonePOLYGON ((-94.61828 36.78970, -94.61834 36.795...1.555259e-18NaN
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76322 rows × 19 columns

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" + ], + "text/plain": [ + " STATEFP10 COUNTYFP10 TRACTCE10 GEOID10 NAME10 NAMELSAD10 \\\n", + "0 20 043 020100 20043020100 201 Census Tract 201 \n", + "1 20 013 480600 20013480600 4806 Census Tract 4806 \n", + "2 31 147 964500 31147964500 9645 Census Tract 9645 \n", + "3 29 087 960300 29087960300 9603 Census Tract 9603 \n", + "4 20 085 082600 20085082600 826 Census Tract 826 \n", + "... ... ... ... ... ... ... \n", + "76317 NaN NaN NaN NaN NaN NaN \n", + "76318 NaN NaN NaN NaN NaN NaN \n", + "76319 NaN NaN NaN NaN NaN NaN \n", + "76320 NaN NaN NaN NaN NaN NaN \n", + "76321 NaN NaN NaN NaN NaN NaN \n", + "\n", + " MTFCC10 FUNCSTAT10 ALAND10 AWATER10 INTPTLAT10 INTPTLON10 \\\n", + "0 G5020 S 454634616.0 2601186.0 +39.8206800 -095.2567279 \n", + "1 G5020 S 882293538.0 1376818.0 +39.8596443 -095.6255187 \n", + "2 G5020 S 677848509.0 6076731.0 +40.1522236 -095.5858870 \n", + "3 G5020 S 412869716.0 6745159.0 +39.9730230 -095.1479701 \n", + "4 G5020 S 690868809.0 947758.0 +39.4553966 -095.6731404 \n", + "... ... ... ... ... ... ... \n", + "76317 NaN NaN NaN NaN NaN NaN \n", + "76318 NaN NaN NaN NaN NaN NaN \n", + "76319 NaN NaN NaN NaN NaN NaN \n", + "76320 NaN NaN NaN NaN NaN NaN \n", + "76321 NaN NaN NaN NaN NaN NaN \n", + "\n", + " area_tract tribalId landAreaName Classification \\\n", + "0 0.048098 LAR0210 Iowa LAR 1 \n", + "1 0.093019 LAR0210 Iowa LAR 1 \n", + "2 0.072289 LAR0210 Iowa LAR 1 \n", + "3 0.044239 LAR0210 Iowa LAR 1 \n", + "4 0.072404 LAR0211 Kickapoo (Kansas) LAR 1 \n", + "... ... ... ... ... \n", + "76317 NaN TSA0353 Sac and Fox TSA None \n", + "76318 NaN TSA0354 Seminole TSA None \n", + "76319 NaN TSA0355 Seneca Cayuga TSA None \n", + "76320 NaN TSA0356 Tonkawa TSA None \n", + "76321 NaN TSA0358 Wyandotte TSA None \n", + "\n", + " geometry area_joined \\\n", + "0 POLYGON ((-95.33994 39.97506, -95.33994 39.975... 4.998139e-04 \n", + "1 POLYGON ((-95.45656 40.00025, -95.45528 40.000... 3.209294e-03 \n", + "2 MULTIPOLYGON (((-95.38162 40.02744, -95.38119 ... 1.476624e-03 \n", + "3 POLYGON ((-95.38119 40.02755, -95.38162 40.027... 1.965514e-07 \n", + "4 POLYGON ((-95.71031 39.65308, -95.69902 39.653... 5.285627e-06 \n", + "... ... ... \n", + "76317 MULTIPOLYGON (((-96.62002 35.75143, -96.62001 ... 6.560647e-17 \n", + "76318 MULTIPOLYGON (((-96.77536 35.03300, -96.77536 ... 7.207055e-18 \n", + "76319 POLYGON ((-94.61836 36.74340, -94.61836 36.743... 7.016721e-18 \n", + "76320 MULTIPOLYGON (((-97.24698 36.69942, -97.24692 ... 2.612218e-17 \n", + "76321 POLYGON ((-94.61828 36.78970, -94.61834 36.795... 1.555259e-18 \n", + "\n", + " tribal_area_as_a_share_of_tract_area \n", + "0 0.010391 \n", + "1 0.034502 \n", + "2 0.020427 \n", + "3 0.000004 \n", + "4 0.000073 \n", + "... ... \n", + "76317 NaN \n", + "76318 NaN \n", + "76319 NaN \n", + "76320 NaN \n", + "76321 NaN \n", + "\n", + "[76322 rows x 19 columns]" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Calculate overlap\n", + "# Calculating the areas of the newly-created geometries\n", + "gdf_joined[\"area_joined\"] = gdf_joined.area\n", + "\n", + "# Calculating the areas of the newly-created geometries in relation\n", + "# to the original grid cells\n", + "gdf_joined[\"tribal_area_as_a_share_of_tract_area\"] = (\n", + " gdf_joined[\"area_joined\"] / gdf_joined[\"area_tract\"]\n", + ")\n", + "gdf_joined" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "042da05e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " GEOID10 landAreaName tribal_area_as_a_share_of_tract_area\n", + "0 01051030800 Poarch Creek LAR 0.002467\n", + "1 01053970400 Poarch Creek LAR 0.002367\n", + "2 01053970500 Poarch Creek LAR 0.000682\n", + "3 01101005408 Poarch Creek LAR 0.001391\n", + "4 02130000100 Annette Island LAR 0.000038\n", + "... ... ... ...\n", + "2585 56013940300 Wind River LAR 0.204039\n", + "2586 56013940400 Wind River LAR 0.053289\n", + "2587 56017967900 Wind River LAR 0.191189\n", + "2588 56033000600 Crow LAR 0.000565\n", + "2589 56035000102 Wind River LAR 0.000140\n", + "\n", + "[2590 rows x 3 columns]\n" + ] + } + ], + "source": [ + "# Aggregating the results\n", + "results = gdf_joined.groupby([\"GEOID10\", \"landAreaName\"]).agg(\n", + " {\"tribal_area_as_a_share_of_tract_area\": \"sum\"}\n", + ")\n", + "\n", + "results = results.reset_index()\n", + "\n", + "results.to_csv(\"~/Downloads/tribal_area_as_a_share_of_tract_area.csv\", index=False)\n", + "\n", + "# Printing results\n", + "print(results)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "34524a94", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Time taken to execute the function is 140.10310292243958\n" + ] + } + ], + "source": [ + "end = time.time()\n", + "\n", + "print(\"Time taken to execute the ETL is\", end - begin)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/data/data-pipeline/data_pipeline/score/field_names.py b/data/data-pipeline/data_pipeline/score/field_names.py index 7aaf376c..570dae88 100644 --- a/data/data-pipeline/data_pipeline/score/field_names.py +++ b/data/data-pipeline/data_pipeline/score/field_names.py @@ -349,6 +349,17 @@ ELIGIBLE_FUDS_BINARY_FIELD_NAME = ( ) ELIGIBLE_FUDS_FILLED_IN_FIELD_NAME = "Is there at least one Formerly Used Defense Site (FUDS) in the tract, where missing data is treated as False?" +# Tribal variables +TRIBAL_ID = "tribalId" +TRIBAL_LAND_AREA_NAME = "landAreaName" + +# Tribal overlap variables +COUNT_OF_TRIBAL_AREAS_IN_TRACT = "Number of Tribal areas within Census tract" +NAMES_OF_TRIBAL_AREAS_IN_TRACT = "Names of Tribal areas within Census tract" +PERCENT_OF_TRIBAL_AREA_IN_TRACT = ( + "Percent of the Census tract that is within Tribal areas" +) + ##### # Names for individual factors being exceeded diff --git a/data/data-pipeline/data_pipeline/score/score_narwhal.py b/data/data-pipeline/data_pipeline/score/score_narwhal.py index 66fb3251..fd7129ff 100644 --- a/data/data-pipeline/data_pipeline/score/score_narwhal.py +++ b/data/data-pipeline/data_pipeline/score/score_narwhal.py @@ -12,7 +12,7 @@ logger = get_module_logger(__name__) class ScoreNarwhal(Score): - """Very similar to Score M, at present.""" + """Score N, aka Narwhal.""" LOW_INCOME_THRESHOLD: float = 0.65 MAX_COLLEGE_ATTENDANCE_THRESHOLD: float = 0.20 diff --git a/data/data-pipeline/data_pipeline/tests/score/test_utils/data/us.geojson b/data/data-pipeline/data_pipeline/tests/score/test_utils/data/us.geojson index 13a4d5b9..51b54c74 100644 --- a/data/data-pipeline/data_pipeline/tests/score/test_utils/data/us.geojson +++ b/data/data-pipeline/data_pipeline/tests/score/test_utils/data/us.geojson @@ -2,14 +2,14 @@ "type": "FeatureCollection", "crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:OGC:1.3:CRS84" } }, "features": [ -{ "type": "Feature", "properties": { "STATEFP10": "24", "COUNTYFP10": "027", "TRACTCE10": "602100", "GEOID10_TRACT": "24027602100", "NAME10": "6021", "NAMELSAD10": "Census Tract 6021", "MTFCC10": "G5020", "FUNCSTAT10": "S", "ALAND10": 13769934, "AWATER10": 3674, "INTPTLAT10": "+39.3076905", "INTPTLON10": "-076.8349752" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -76.863049, 39.31484 ], [ -76.863078, 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[ -76.877693, 39.302711 ], [ -76.877569, 39.3028 ], [ -76.877496, 39.302901 ] ] ] } } ] } diff --git a/data/data-pipeline/data_pipeline/tests/score/test_utils/test_adjacency.py b/data/data-pipeline/data_pipeline/tests/score/test_utils/test_adjacency.py index fa18611a..5f80aaee 100644 --- a/data/data-pipeline/data_pipeline/tests/score/test_utils/test_adjacency.py +++ b/data/data-pipeline/data_pipeline/tests/score/test_utils/test_adjacency.py @@ -16,9 +16,11 @@ from data_pipeline.score import field_names @contextmanager def patch_calculate_tract_adjacency_scores(): - tract_data = Path(__file__).parent / "data" / "us.geojson" + # Use fixtures for tract data. + tract_data_path = Path(__file__).parent / "data" / "us.geojson" + get_tract_geojson_mock = partial( - get_tract_geojson, _tract_data_path=tract_data + get_tract_geojson, _tract_data_path=tract_data_path ) with mock.patch( "data_pipeline.score.utils.get_tract_geojson", diff --git a/data/data-pipeline/data_pipeline/tests/sources/test_geo_utils.py b/data/data-pipeline/data_pipeline/tests/sources/test_geo_utils.py index 5b7a5d06..dcd42ba0 100644 --- a/data/data-pipeline/data_pipeline/tests/sources/test_geo_utils.py +++ b/data/data-pipeline/data_pipeline/tests/sources/test_geo_utils.py @@ -23,6 +23,10 @@ def test_add_tracts_for_geometries(): ), crs="epsg:4326", ) - tract_data = Path(__file__).parent / "data" / "us.geojson" - enriched_df = add_tracts_for_geometries(df, _tract_data_path=tract_data) + + # Use fixtures for tract data. + tract_data_path = Path(__file__).parent / "data" / "us.geojson" + tract_data = gpd.read_file(tract_data_path) + + enriched_df = add_tracts_for_geometries(df, tract_data=tract_data) assert (df["expected_geoid"] == enriched_df["GEOID10_TRACT"]).all() From f70f30d610df2479b8a06cc064343acadfc490c2 Mon Sep 17 00:00:00 2001 From: Matt Bowen <83967628+mattbowen-usds@users.noreply.github.com> Date: Fri, 23 Sep 2022 13:18:15 -0400 Subject: [PATCH 08/11] Improve score test documentation based on Lucas's feedback (#1835) (#1914) * Better document base on Lucas's feedback (#1835) * Fix typo (#1835) * Add test to verify GEOJSON matches tiles (#1835) * Remove NOOP line (#1835) * Move GEOJSON generation up for new smoketest (#1835) * Fixup code format (#1835) * Update readme for new somketest (#1835) --- .github/workflows/deploy_be_staging.yml | 8 +- data/data-pipeline/README.md | 29 +++++-- .../data_pipeline/etl/score/etl_score_geo.py | 1 - .../data_pipeline/tests/score/test_output.py | 18 ++++- .../tests/score/test_tiles_smoketests.py | 75 ++++++++++++++++--- 5 files changed, 108 insertions(+), 23 deletions(-) diff --git a/.github/workflows/deploy_be_staging.yml b/.github/workflows/deploy_be_staging.yml index 8a10cf38..d33fe9e3 100644 --- a/.github/workflows/deploy_be_staging.yml +++ b/.github/workflows/deploy_be_staging.yml @@ -61,7 +61,10 @@ jobs: poetry run python3 data_pipeline/application.py score-full-run - name: Generate Score Post run: | - poetry run python3 data_pipeline/application.py generate-score-post -s aws + poetry run python3 data_pipeline/application.py generate-score-post + - name: Generate Score Geo + run: | + poetry run python3 data_pipeline/application.py geo-score - name: Run Smoketests run: | poetry run pytest data_pipeline/ -m smoketest @@ -100,9 +103,6 @@ jobs: mkdir -p /usr/local/bin cp tippecanoe /usr/local/bin/tippecanoe tippecanoe -v - - name: Generate Score Geo - run: | - poetry run python3 data_pipeline/application.py geo-score - name: Generate Tiles run: | poetry run python3 data_pipeline/application.py generate-map-tiles diff --git a/data/data-pipeline/README.md b/data/data-pipeline/README.md index d61b1713..d553f3b9 100644 --- a/data/data-pipeline/README.md +++ b/data/data-pipeline/README.md @@ -12,11 +12,14 @@ - [2. Extract-Transform-Load (ETL) the data](#2-extract-transform-load-etl-the-data) - [3. Combined dataset](#3-combined-dataset) - [4. Tileset](#4-tileset) + - [5. Shapefiles](#5-shapefiles) - [Score generation and comparison workflow](#score-generation-and-comparison-workflow) - [Workflow Diagram](#workflow-diagram) - [Step 0: Set up your environment](#step-0-set-up-your-environment) - [Step 1: Run the script to download census data or download from the Justice40 S3 URL](#step-1-run-the-script-to-download-census-data-or-download-from-the-justice40-s3-url) - [Step 2: Run the ETL script for each data source](#step-2-run-the-etl-script-for-each-data-source) + - [Table of commands](#table-of-commands) + - [ETL steps](#etl-steps) - [Step 3: Calculate the Justice40 score experiments](#step-3-calculate-the-justice40-score-experiments) - [Step 4: Compare the Justice40 score experiments to other indices](#step-4-compare-the-justice40-score-experiments-to-other-indices) - [Data Sources](#data-sources) @@ -26,21 +29,27 @@ - [MacOS](#macos) - [Windows Users](#windows-users) - [Setting up Poetry](#setting-up-poetry) - - [Downloading Census Block Groups GeoJSON and Generating CBG CSVs](#downloading-census-block-groups-geojson-and-generating-cbg-csvs) + - [Running tox](#running-tox) + - [The Application entrypoint](#the-application-entrypoint) + - [Downloading Census Block Groups GeoJSON and Generating CBG CSVs (not normally required)](#downloading-census-block-groups-geojson-and-generating-cbg-csvs-not-normally-required) + - [Run all ETL, score and map generation processes](#run-all-etl-score-and-map-generation-processes) + - [Run both ETL and score generation processes](#run-both-etl-and-score-generation-processes) + - [Run all ETL processes](#run-all-etl-processes) - [Generating Map Tiles](#generating-map-tiles) - [Serve the map locally](#serve-the-map-locally) - [Running Jupyter notebooks](#running-jupyter-notebooks) - [Activating variable-enabled Markdown for Jupyter notebooks](#activating-variable-enabled-markdown-for-jupyter-notebooks) - - [Miscellaneous](#miscellaneous) - [Testing](#testing) - [Background](#background) - - [Configuration / Fixtures](#configuration--fixtures) + - [Score and post-processing tests](#score-and-post-processing-tests) - [Updating Pickles](#updating-pickles) - - [Future Enchancements](#future-enchancements) - - [ETL Unit Tests](#etl-unit-tests) + - [Future Enhancements](#future-enhancements) + - [Fixtures used in ETL "snapshot tests"](#fixtures-used-in-etl-snapshot-tests) + - [Other ETL Unit Tests](#other-etl-unit-tests) - [Extract Tests](#extract-tests) - [Transform Tests](#transform-tests) - [Load Tests](#load-tests) + - [Smoketests](#smoketests) @@ -496,3 +505,13 @@ See above [Fixtures](#configuration--fixtures) section for information about whe These make use of [tmp_path_factory](https://docs.pytest.org/en/latest/how-to/tmp_path.html) to create a file-system located under `temp_dir`, and validate whether the correct files are written to the correct locations. Additional future modifications could include the use of Pandera and/or other schema validation tools, and or a more explicit test that the data written to file can be read back in and yield the same dataframe. + +### Smoketests + +To ensure the score and tiles process correctly, there is a suite of "smoke tests" that can be run after the ETL and score data have been run, and outputs like the frontend GEOJSON have been created. +These tests are implemented as pytest test, but are skipped by default. To run them. + +1. Generate a full score with `poetry run python3 data_pipeline/application.py score-full-run` +2. Generate the tile data with `poetry run python3 data_pipeline/application.py generate-score-post` +3. Generate the frontend GEOJSON with `poetry run python3 data_pipeline/application.py geo-score` +4. Select the smoke tests for pytest with `poetry run pytest data_pipeline/tests -k smoketest` \ No newline at end of file diff --git a/data/data-pipeline/data_pipeline/etl/score/etl_score_geo.py b/data/data-pipeline/data_pipeline/etl/score/etl_score_geo.py index 31eacbe1..48320678 100644 --- a/data/data-pipeline/data_pipeline/etl/score/etl_score_geo.py +++ b/data/data-pipeline/data_pipeline/etl/score/etl_score_geo.py @@ -41,7 +41,6 @@ class GeoScoreETL(ExtractTransformLoad): self.SCORE_CSV_PATH = self.DATA_PATH / "score" / "csv" self.TILE_SCORE_CSV = self.SCORE_CSV_PATH / "tiles" / "usa.csv" - self.DATA_SOURCE = data_source self.CENSUS_USA_GEOJSON = ( self.DATA_PATH / "census" / "geojson" / "us.json" ) diff --git a/data/data-pipeline/data_pipeline/tests/score/test_output.py b/data/data-pipeline/data_pipeline/tests/score/test_output.py index 0945fb9e..f88011bc 100644 --- a/data/data-pipeline/data_pipeline/tests/score/test_output.py +++ b/data/data-pipeline/data_pipeline/tests/score/test_output.py @@ -31,7 +31,7 @@ from .fixtures import ( pytestmark = pytest.mark.smoketest -UNMATCHED_TRACK_THRESHOLD = 1000 +UNMATCHED_TRACT_THRESHOLD = 1000 def _helper_test_count_exceeding_threshold(df, col, error_check=1000): @@ -254,6 +254,15 @@ def test_data_sources( key: value for key, value in locals().items() if key != "final_score_df" } + # For each data source that's injected via the fixtures, do the following: + # * Ensure at least one column from the source shows up in the score + # * Ensure any tracts NOT in the data source are NA/null in the score + # * Ensure the data source doesn't have a large number of tract IDs that are not + # included in the final score, since that implies the source is using 2020 + # tract IDs + # * Verify that the data from the source that's in the final score output + # is the "equal" to the data from the ETL, allowing for the minor + # differences that come from floating point comparisons for data_source_name, data_source in data_sources.items(): final = "final_" df: pd.DataFrame = final_score_df.merge( @@ -275,12 +284,12 @@ def test_data_sources( ), f"No columns from data source show up in final score in source {data_source_name}" # Make sure we have NAs for any tracts in the final data that aren't - # covered in the final data + # included in the data source assert np.all(df[df.MERGE == "left_only"][final_columns].isna()) # Make sure the datasource doesn't have a ton of unmatched tracts, implying it # has moved to 2020 tracts - assert len(df[df.MERGE == "right_only"]) < UNMATCHED_TRACK_THRESHOLD + assert len(df[df.MERGE == "right_only"]) < UNMATCHED_TRACT_THRESHOLD df = df[df.MERGE == "both"] @@ -293,6 +302,7 @@ def test_data_sources( f"Column {final_column} not equal " f"between {data_source_name} and final score" ) + # For non-numeric types, we can use the built-in equals from pandas if df[final_column].dtype in [ np.dtype(object), np.dtype(bool), @@ -301,6 +311,8 @@ def test_data_sources( assert df[final_column].equals( df[data_source_column] ), error_message + # For numeric sources, use np.close so we don't get harmed by + # float equaity weirdness else: assert np.allclose( df[final_column], diff --git a/data/data-pipeline/data_pipeline/tests/score/test_tiles_smoketests.py b/data/data-pipeline/data_pipeline/tests/score/test_tiles_smoketests.py index 4bc84c4f..3f662f71 100644 --- a/data/data-pipeline/data_pipeline/tests/score/test_tiles_smoketests.py +++ b/data/data-pipeline/data_pipeline/tests/score/test_tiles_smoketests.py @@ -2,6 +2,7 @@ from dataclasses import dataclass from typing import Optional import pandas as pd +import geopandas as gpd import numpy as np import pytest from data_pipeline.config import settings @@ -26,6 +27,13 @@ def tiles_df(scope="session"): ) +@pytest.fixture() +def tiles_geojson_df(): + return gpd.read_file( + settings.APP_ROOT / "data" / "score" / "geojson" / "usa-high.json" + ) + + PERCENTILE_FIELDS = [ "DF_PFS", "AF_PFS", @@ -102,6 +110,19 @@ def test_tract_equality(tiles_df, final_score_df): assert tiles_df.shape[0] == final_score_df.shape[0] +def is_col_fake_bool(col) -> bool: + if col.dtype == np.dtype("float64"): + fake_bool = {1.0, 0.0, None} + # Replace the nans in the column values with None for + # so we can just use issubset below + col_values = set( + not np.isnan(val) and val or None + for val in col.value_counts(dropna=False).index + ) + return len(col_values) <= 3 and col_values.issubset(fake_bool) + return False + + @dataclass class ColumnValueComparison: final_score_column: pd.Series @@ -110,16 +131,7 @@ class ColumnValueComparison: @property def _is_tiles_column_fake_bool(self) -> bool: - if self.tiles_column.dtype == np.dtype("float64"): - fake_bool = {1.0, 0.0, None} - # Replace the nans in the column values with None for - # so we can just use issubset below - col_values = set( - not np.isnan(val) and val or None - for val in self.tiles_column.value_counts(dropna=False).index - ) - return len(col_values) <= 3 and col_values.issubset(fake_bool) - return False + return is_col_fake_bool(self.tiles_column) @property def _is_dtype_ok(self) -> bool: @@ -215,6 +227,49 @@ def test_for_column_fidelitiy_from_score(tiles_df, final_score_df): assert not errors, error_message +def test_for_geojson_fidelity_from_tiles_csv(tiles_df, tiles_geojson_df): + tiles_geojson_df = tiles_geojson_df.drop(columns=["geometry"]).rename( + columns={"GEOID10": "GTF"} + ) + assert tiles_df.shape == tiles_geojson_df.shape + assert tiles_df["GTF"].equals(tiles_geojson_df["GTF"]) + assert sorted(tiles_df.columns) == sorted(tiles_geojson_df.columns) + + # Are all the dtypes and values the same? + for col_name in tiles_geojson_df.columns: + if is_col_fake_bool(tiles_df[col_name]): + tiles_df[col_name] = ( + tiles_df[col_name] + .astype("float64") + .replace({0.0: False, 1.0: True}) + ) + if is_col_fake_bool(tiles_geojson_df[col_name]): + tiles_geojson_df[col_name] = ( + tiles_geojson_df[col_name] + .astype("float64") + .replace({0.0: False, 1.0: True}) + ) + tiles_geojson_df[col_name] = tiles_df[col_name].replace({None: np.nan}) + error_message = f"Column {col_name} not equal " + # For non-numeric types, we can use the built-in equals from pandas + if tiles_df[col_name].dtype in [ + np.dtype(object), + np.dtype(bool), + np.dtype(str), + ]: + assert tiles_df[col_name].equals( + tiles_geojson_df[col_name] + ), error_message + # For numeric sources, use np.close so we don't get harmed by + # float equaity weirdness + else: + assert np.allclose( + tiles_df[col_name], + tiles_geojson_df[col_name], + equal_nan=True, + ), error_message + + def test_for_state_names(tiles_df): states = tiles_df["SF"].value_counts(dropna=False).index assert np.nan not in states From d8dd4cf047ed6ffba95a614410f55ea14eaa3f12 Mon Sep 17 00:00:00 2001 From: Matt Bowen <83967628+mattbowen-usds@users.noreply.github.com> Date: Fri, 23 Sep 2022 13:20:23 -0400 Subject: [PATCH 09/11] Cleanup source tests (#1912) * Move test to base for broader coverage (#1848) * Remove duplicate line (#1848) * FUDS needed an extra mock (#1848) --- data/data-pipeline/data_pipeline/etl/base.py | 3 --- .../tests/sources/doe_energy_burden/test_etl.py | 10 ---------- .../data_pipeline/tests/sources/eamlis/test_etl.py | 7 +++++++ .../data_pipeline/tests/sources/example/test_etl.py | 10 ++++++++++ .../tests/sources/us_army_fuds/test_etl.py | 7 +++++++ 5 files changed, 24 insertions(+), 13 deletions(-) diff --git a/data/data-pipeline/data_pipeline/etl/base.py b/data/data-pipeline/data_pipeline/etl/base.py index 5ebc8a55..9dee3915 100644 --- a/data/data-pipeline/data_pipeline/etl/base.py +++ b/data/data-pipeline/data_pipeline/etl/base.py @@ -164,9 +164,6 @@ class ExtractTransformLoad: for field in dataset_config["load_fields"]: cls.COLUMNS_TO_KEEP.append(field["long_name"]) setattr(cls, field["df_field_name"], field["long_name"]) - - # set the constants for the class - setattr(cls, field["df_field_name"], field["long_name"]) return dataset_config # This is a classmethod so it can be used by `get_data_frame` without diff --git a/data/data-pipeline/data_pipeline/tests/sources/doe_energy_burden/test_etl.py b/data/data-pipeline/data_pipeline/tests/sources/doe_energy_burden/test_etl.py index 70b8a285..bb24ba3e 100644 --- a/data/data-pipeline/data_pipeline/tests/sources/doe_energy_burden/test_etl.py +++ b/data/data-pipeline/data_pipeline/tests/sources/doe_energy_burden/test_etl.py @@ -59,13 +59,3 @@ class TestDOEEnergyBurdenETL(TestETL): data_path / "dataset" / "doe_energy_burden" / "usa.csv" ) assert output_file_path == expected_output_file_path - - def test_tract_id_lengths(self, mock_etl, mock_paths): - etl = self._setup_etl_instance_and_run_extract( - mock_etl=mock_etl, mock_paths=mock_paths - ) - etl.transform() - etl.validate() - etl.load() - df = etl.get_data_frame() - assert (df[etl.GEOID_TRACT_FIELD_NAME].str.len() == 11).all() diff --git a/data/data-pipeline/data_pipeline/tests/sources/eamlis/test_etl.py b/data/data-pipeline/data_pipeline/tests/sources/eamlis/test_etl.py index b2a5f44b..37b15f65 100644 --- a/data/data-pipeline/data_pipeline/tests/sources/eamlis/test_etl.py +++ b/data/data-pipeline/data_pipeline/tests/sources/eamlis/test_etl.py @@ -150,3 +150,10 @@ class TestAbandondedLandMineETL(TestETL): assert len(df[etl.GEOID_TRACT_FIELD_NAME]) == len( self._FIXTURES_SHARED_TRACT_IDS ) + + def test_tract_id_lengths(self, mock_etl, mock_paths): + with mock.patch( + "data_pipeline.etl.sources.eamlis.etl.add_tracts_for_geometries", + new=_fake_add_tracts_for_geometries, + ): + super().test_tract_id_lengths(mock_etl, mock_paths) \ No newline at end of file diff --git a/data/data-pipeline/data_pipeline/tests/sources/example/test_etl.py b/data/data-pipeline/data_pipeline/tests/sources/example/test_etl.py index 34a56083..f0d1e920 100644 --- a/data/data-pipeline/data_pipeline/tests/sources/example/test_etl.py +++ b/data/data-pipeline/data_pipeline/tests/sources/example/test_etl.py @@ -209,6 +209,16 @@ class TestETL: assert actual_file_path == expected_file_path + def test_tract_id_lengths(self, mock_etl, mock_paths): + etl = self._setup_etl_instance_and_run_extract( + mock_etl=mock_etl, mock_paths=mock_paths + ) + etl.transform() + etl.validate() + etl.load() + df = etl.get_data_frame() + assert (df[etl.GEOID_TRACT_FIELD_NAME].str.len() == 11).all() + def test_fixtures_contain_shared_tract_ids_base(self, mock_etl, mock_paths): """Check presence of necessary shared tract IDs. Note: We used shared census tract IDs so that later our tests can join all the diff --git a/data/data-pipeline/data_pipeline/tests/sources/us_army_fuds/test_etl.py b/data/data-pipeline/data_pipeline/tests/sources/us_army_fuds/test_etl.py index ce2b63c4..61e4ed2e 100644 --- a/data/data-pipeline/data_pipeline/tests/sources/us_army_fuds/test_etl.py +++ b/data/data-pipeline/data_pipeline/tests/sources/us_army_fuds/test_etl.py @@ -185,3 +185,10 @@ class TestUSArmyFUDSETL(TestETL): assert len(df[etl.GEOID_TRACT_FIELD_NAME]) == len( self._FIXTURES_SHARED_TRACT_IDS ) + + def test_tract_id_lengths(self, mock_etl, mock_paths): + with mock.patch( + "data_pipeline.etl.sources.us_army_fuds.etl.add_tracts_for_geometries", + new=_fake_add_tracts_for_geometries, + ): + return super().test_tract_id_lengths(mock_etl, mock_paths) From 6e0ef33d81104364ef32268a6a70b55fc6a6b4d3 Mon Sep 17 00:00:00 2001 From: Matt Bowen <83967628+mattbowen-usds@users.noreply.github.com> Date: Fri, 23 Sep 2022 14:33:15 -0400 Subject: [PATCH 10/11] Add tribal count notebook (#1917) (#1919) * Add tribal count notebook (#1917) * test without caching * added comment Co-authored-by: lucasmbrown-usds --- .github/workflows/data-checks.yml | 3 +- .../ipython/check_tribal_count.ipynb | 170 ++++++++++++++++++ 2 files changed, 172 insertions(+), 1 deletion(-) create mode 100644 data/data-pipeline/data_pipeline/ipython/check_tribal_count.ipynb diff --git a/.github/workflows/data-checks.yml b/.github/workflows/data-checks.yml index 41438384..ea4cfd4c 100644 --- a/.github/workflows/data-checks.yml +++ b/.github/workflows/data-checks.yml @@ -39,6 +39,7 @@ jobs: run: poetry show -v - name: Install dependencies run: poetry install - if: steps.cached-poetry-dependencies.outputs.cache-hit != 'true' + # TODO: investigate why caching layer started failing. + # if: steps.cached-poetry-dependencies.outputs.cache-hit != 'true' - name: Run tox run: poetry run tox diff --git a/data/data-pipeline/data_pipeline/ipython/check_tribal_count.ipynb b/data/data-pipeline/data_pipeline/ipython/check_tribal_count.ipynb new file mode 100644 index 00000000..3451f325 --- /dev/null +++ b/data/data-pipeline/data_pipeline/ipython/check_tribal_count.ipynb @@ -0,0 +1,170 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "8f03fec9-e4a0-4621-b94c-ff6459a42032", + "metadata": {}, + "source": [ + "This code is to do the one time check described in [ticket 1917]( Run a one-time check on count of federal Indian reservations)\n", + "\n", + "> We should do a one-time check on the BIA data we import, such that after import that have (for now in our staging environment) 326 federal Indian reservations on our map. The number 326 from https://www.bia.gov/faqs/what-federal-indian-reservation.\n", + ">\n", + "> If that one-time check to make sure we have all reservations fails, there's a list of federal Indian reservation names that we can use to track down which reservations are not shown on our map and troubleshoot." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "76c8eeac-21e0-4ff1-886d-b9cdd6199411", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import sys\n", + "module_path = os.path.abspath(os.path.join(\"../..\"))\n", + "if module_path not in sys.path:\n", + " sys.path.append(module_path)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "aaf46946-4fa7-4744-8cd6-723ce86a0213", + "metadata": {}, + "outputs": [], + "source": [ + "from data_pipeline.etl.sources.tribal.etl import TribalETL\n", + "from data_pipeline.etl.sources.geo_utils import get_tribal_geojson\n", + "import geopandas as gpd" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "039d244f-6c97-4b1b-af04-6f879cd2cd86", + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext lab_black" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "0f0b3597-844e-4328-b6c9-ac760301383e", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2022-09-23 11:56:25,551 [data_pipeline.etl.sources.tribal.etl] INFO Downloading Tribal Data\n", + "2022-09-23 11:56:25,552 [data_pipeline.utils] INFO Downloading https://justice40-data.s3.amazonaws.com/data-sources/BIA_National_LAR_json.zip\n", + "2022-09-23 11:56:26,068 [data_pipeline.utils] INFO Extracting /home/matt/active/justice40-tool/data/data-pipeline/data_pipeline/data/tmp/downloaded-47ce415c-cc72-4e6f-9cbc-7ad833e08813.zip\n", + "2022-09-23 11:56:26,190 [data_pipeline.utils] INFO Downloading https://justice40-data.s3.amazonaws.com/data-sources/Alaska_Native_Villages_json.zip\n", + "2022-09-23 11:56:26,290 [data_pipeline.utils] INFO Extracting /home/matt/active/justice40-tool/data/data-pipeline/data_pipeline/data/tmp/downloaded-7c700e59-83cd-4752-889c-159e58c71154.zip\n" + ] + } + ], + "source": [ + "etl = TribalETL()\n", + "etl.extract()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "51a5a5c7-c02b-4a6f-9f3d-19868e45d7b1", + "metadata": {}, + "outputs": [], + "source": [ + "GEOJSON_BASE_PATH = etl.GEOJSON_BASE_PATH" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "c4224aeb-6aa5-47fa-9c6f-e2cd9354720d", + "metadata": {}, + "outputs": [], + "source": [ + "bia_national_lar_geojson = (\n", + " GEOJSON_BASE_PATH / \"bia_national_lar\" / \"BIA_National_LAR.json\"\n", + ")\n", + "bia_aian_supplemental_geojson = (\n", + " GEOJSON_BASE_PATH / \"bia_national_lar\" / \"BIA_AIAN_Supplemental.json\"\n", + ")\n", + "bia_tsa_geojson_geojson = GEOJSON_BASE_PATH / \"bia_national_lar\" / \"BIA_TSA.json\"\n", + "alaska_native_villages_geojson = (\n", + " GEOJSON_BASE_PATH / \"alaska_native_villages\" / \"AlaskaNativeVillages.gdb.geojson\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "7dd74d24-4f55-45db-bdd9-f4ac945d6a78", + "metadata": {}, + "outputs": [], + "source": [ + "bia_national_lar_df = gpd.read_file(bia_national_lar_geojson)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "ae8dafb7-f997-4977-87da-53f0b4f98a98", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "326" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(\n", + " sorted(\n", + " list(\n", + " bia_national_lar_df.LARName.str.replace(r\"\\(.*\\) \", \"\", regex=True).unique()\n", + " )\n", + " )\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "40849cc4-345b-4658-94ac-498154107e9f", + "metadata": {}, + "source": [ + "Looking at the main BIA LAR data file, and removing paranthecials (so that `'Acoma (Red Lake) LAR','Acoma LAR'` are counted as a single tribal entry), **we have 326 tribal areas**, which is the number we expected." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.4" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 9e85375d9b1c3fe264d2baa08b871c27e3f82df8 Mon Sep 17 00:00:00 2001 From: Matt Bowen <83967628+mattbowen-usds@users.noreply.github.com> Date: Fri, 23 Sep 2022 15:05:45 -0400 Subject: [PATCH 11/11] Add tribal overlap to downloads (#1907) * Add tribal data to downloads (#1904) * Update test pickle with current cols (#1904) * Remove text of tribe names from GeoJSON (#1904) * Update test data (#1904) * Add tribal overlap to smoketests (#1904) --- .../data_pipeline/content/config/csv.yml | 9 +++++++++ .../data_pipeline/content/config/excel.yml | 9 +++++++++ .../data_pipeline/etl/score/constants.py | 5 +++++ .../tests/sample_data/score_data_initial.csv | 6 +++--- .../snapshots/downloadable_data_expected.pkl | Bin 16750 -> 17029 bytes .../tests/snapshots/score_data_expected.pkl | Bin 21790 -> 20591 bytes .../snapshots/score_transformed_expected.pkl | Bin 21441 -> 21281 bytes .../tests/snapshots/tile_data_expected.pkl | Bin 4735 -> 4790 bytes .../data_pipeline/tests/score/fixtures.py | 13 +++++++++++++ .../data_pipeline/tests/score/test_output.py | 2 ++ 10 files changed, 41 insertions(+), 3 deletions(-) diff --git a/data/data-pipeline/data_pipeline/content/config/csv.yml b/data/data-pipeline/data_pipeline/content/config/csv.yml index 14d5b570..41fb7981 100644 --- a/data/data-pipeline/data_pipeline/content/config/csv.yml +++ b/data/data-pipeline/data_pipeline/content/config/csv.yml @@ -380,3 +380,12 @@ fields: - score_name: Income data has been estimated based on neighbor income label: Income data has been estimated based on geographic neighbor income format: bool +- score_name: Number of Tribal areas within Census tract + label: Number of Tribal areas within Census tract + format: int64 +- score_name: Names of Tribal areas within Census tract + label: Names of Tribal areas within Census tract + format: string +- score_name: Percent of the Census tract that is within Tribal areas + label: Percent of the Census tract that is within Tribal areas + format: percentage diff --git a/data/data-pipeline/data_pipeline/content/config/excel.yml b/data/data-pipeline/data_pipeline/content/config/excel.yml index 18847687..2b69cef3 100644 --- a/data/data-pipeline/data_pipeline/content/config/excel.yml +++ b/data/data-pipeline/data_pipeline/content/config/excel.yml @@ -384,3 +384,12 @@ sheets: - score_name: Income data has been estimated based on neighbor income label: Income data has been estimated based on geographic neighbor income format: bool + - score_name: Number of Tribal areas within Census tract + label: Number of Tribal areas within Census tract + format: int64 + - score_name: Names of Tribal areas within Census tract + label: Names of Tribal areas within Census tract + format: string + - score_name: Percent of the Census tract that is within Tribal areas + label: Percent of the Census tract that is within Tribal areas + format: percentage diff --git a/data/data-pipeline/data_pipeline/etl/score/constants.py b/data/data-pipeline/data_pipeline/etl/score/constants.py index c112eec0..46adab52 100644 --- a/data/data-pipeline/data_pipeline/etl/score/constants.py +++ b/data/data-pipeline/data_pipeline/etl/score/constants.py @@ -379,6 +379,10 @@ TILES_SCORE_COLUMNS = { 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", + field_names.COUNT_OF_TRIBAL_AREAS_IN_TRACT: "TA_COUNT", + field_names.PERCENT_OF_TRIBAL_AREA_IN_TRACT: "TA_PERC", + + } # columns to round floats to 2 decimals @@ -452,4 +456,5 @@ TILES_SCORE_FLOAT_COLUMNS = [ field_names.ELIGIBLE_FUDS_BINARY_FIELD_NAME, field_names.AML_BOOLEAN, field_names.HISTORIC_REDLINING_SCORE_EXCEEDED, + field_names.PERCENT_OF_TRIBAL_AREA_IN_TRACT ] diff --git a/data/data-pipeline/data_pipeline/etl/score/tests/sample_data/score_data_initial.csv b/data/data-pipeline/data_pipeline/etl/score/tests/sample_data/score_data_initial.csv index bf428750..65aa209e 100644 --- a/data/data-pipeline/data_pipeline/etl/score/tests/sample_data/score_data_initial.csv +++ b/data/data-pipeline/data_pipeline/etl/score/tests/sample_data/score_data_initial.csv @@ -1,3 +1,3 @@ -GEOID10_TRACT,Persistent Poverty Census Tract,Does the tract have at least 35 acres in it?,Contains agricultural value,Housing burden (percent),Share of homes with no kitchen or indoor plumbing (percent),Total population,Median household income (% of state median household income),Current asthma among adults aged greater than or equal to 18 years,Coronary heart disease among adults aged greater than or equal to 18 years,Cancer (excluding skin cancer) among adults aged greater than or equal to 18 years,Current lack of health insurance among adults aged 18-64 years,Diagnosed diabetes among adults aged greater than or equal to 18 years,Physical health not good for greater than or equal to 14 days among adults aged greater than or equal to 18 years,Percent of individuals < 100% Federal Poverty Line,Percent of individuals < 150% Federal Poverty Line,Percent of individuals below 200% Federal Poverty Line,Area Median Income (State or metropolitan),Median household income in the past 12 months,Energy burden,FEMA Risk Index Expected Annual Loss Score,Urban Heuristic Flag,Air toxics cancer risk,Respiratory hazard index,Diesel particulate matter exposure,PM2.5 in the air,Ozone,Traffic proximity and volume,Proximity to Risk Management Plan (RMP) facilities,Proximity to hazardous waste sites,Proximity to NPL sites,Wastewater discharge,Percent pre-1960s housing (lead paint indicator),Individuals under 5 years old,Individuals over 64 years old,Linguistic isolation (percent),Percent of households in linguistic isolation,Poverty (Less than 200% of federal poverty line),Percent individuals age 25 or over with less than high school degree,Unemployment (percent),Median value ($) of owner-occupied housing units,Percent enrollment in college or graduate school,Percent of population not currently enrolled in college or graduate school,Expected building loss rate (Natural Hazards Risk Index),Expected agricultural loss rate (Natural Hazards Risk Index),Expected population loss rate (Natural Hazards Risk Index),Percent individuals age 25 or over with less than high school degree in 2009,Percentage households below 100% of federal poverty line in 2009,Unemployment (percent) in 2009,Unemployment (percent) in 2010,Percent of individuals less than 100% Federal Poverty Line in 2010,Total population in 2009,Summer days above 90F,Percent low access to healthy food,Percent impenetrable surface areas,Leaky underground storage tanks,DOT Travel Barriers Score,Share of properties at risk of flood in 30 years,Share of properties at risk of fire in 30 years,Share of the tract's land area that is covered by impervious surface or cropland as a percent,"Percent of individuals below 200% Federal Poverty Line, imputed and adjusted",Percent Black or African American,Percent American Indian / Alaska Native,Percent Asian,Percent Native Hawaiian or Pacific,Percent two or more races,Percent White,Percent Hispanic or Latino,Percent other races,Percent age under 10,Percent age 10 to 64,Percent age over 64,Third grade reading proficiency,Median household income as a percent of area median income,Life expectancy (years),Median household income as a percent of territory median income in 2009,Is there at least one abandoned mine in this census tract?,Income data has been estimated based on neighbor income,Is there at least one Formerly Used Defense Site (FUDS) in the tract?,Tract-level redlining score meets or exceeds 3.25,Housing burden (percent) (percentile),Share of homes with no kitchen or indoor plumbing (percent) (percentile),Total population (percentile),Median household income (% of state median household income) (percentile),Current asthma among adults aged greater than or equal to 18 years (percentile),Coronary heart disease among adults aged greater than or equal to 18 years (percentile),Cancer (excluding skin cancer) among adults aged greater than or equal to 18 years (percentile),Current lack of health insurance among adults aged 18-64 years (percentile),Diagnosed diabetes among adults aged greater than or equal to 18 years (percentile),Physical health not good for greater than or equal to 14 days among adults aged greater than or equal to 18 years (percentile),Percent of individuals < 100% Federal Poverty Line (percentile),Percent of individuals < 150% Federal Poverty Line (percentile),Percent of individuals below 200% Federal Poverty Line (percentile),Area Median Income (State or metropolitan) (percentile),Median household income in the past 12 months (percentile),Energy burden (percentile),FEMA Risk Index Expected Annual Loss Score (percentile),Urban Heuristic Flag (percentile),Air toxics cancer risk (percentile),Respiratory hazard index (percentile),Diesel particulate matter exposure (percentile),PM2.5 in the air (percentile),Ozone (percentile),Traffic proximity and volume (percentile),Proximity to Risk Management Plan (RMP) facilities (percentile),Proximity to hazardous waste sites (percentile),Proximity to NPL sites (percentile),Wastewater discharge (percentile),Percent pre-1960s housing (lead paint indicator) (percentile),Individuals under 5 years old (percentile),Individuals over 64 years old (percentile),Linguistic isolation (percent) (percentile),Percent of households in linguistic isolation (percentile),Poverty (Less than 200% of federal poverty line) (percentile),Percent individuals age 25 or over with less than high school degree (percentile),Unemployment (percent) (percentile),Median value ($) of owner-occupied housing units (percentile),Percent enrollment in college or graduate school (percentile),Percent of population not currently enrolled in college or graduate school (percentile),Expected building loss rate (Natural Hazards Risk Index) (percentile),Expected agricultural loss rate (Natural Hazards Risk Index) (percentile),Expected population loss rate (Natural Hazards Risk Index) (percentile),Percent individuals age 25 or over with less than high school degree in 2009 (percentile),Percentage households below 100% of federal poverty line in 2009 (percentile),Unemployment (percent) in 2009 (percentile),Unemployment (percent) in 2010 (percentile),Percent of individuals less than 100% Federal Poverty Line in 2010 (percentile),Total population in 2009 (percentile),Summer days above 90F (percentile),Percent low access to healthy food (percentile),Percent impenetrable surface areas (percentile),Leaky underground storage tanks (percentile),DOT Travel Barriers Score (percentile),Share of properties at risk of flood in 30 years (percentile),Share of properties at risk of fire in 30 years (percentile),Share of the tract's land area that is covered by impervious surface or cropland as a percent (percentile),"Percent of individuals below 200% Federal Poverty Line, imputed and adjusted (percentile)",Percent Black or African American (percentile),Percent American Indian / Alaska Native (percentile),Percent Asian (percentile),Percent Native Hawaiian or Pacific (percentile),Percent two or more races (percentile),Percent White (percentile),Percent Hispanic or Latino (percentile),Percent other races (percentile),Percent age under 10 (percentile),Percent age 10 to 64 (percentile),Percent age over 64 (percentile),Low third grade reading proficiency (percentile),Low median household income as a percent of area median income (percentile),Low life expectancy (percentile),Low median household income as a percent of territory median income in 2009 (percentile),Total population in 2009 (island areas) and 2019 (states and PR),Total threshold criteria exceeded,Is low income (imputed and adjusted)?,Greater than or equal to the 90th percentile for expected population loss,Greater than or equal to the 90th percentile for expected agricultural loss,Greater than or equal to the 90th percentile for expected building loss,Greater than or equal to the 90th percentile for share of properties at risk of flood in 30 years,Greater than or equal to the 90th percentile for share of properties at risk of fire in 30 years,At least one climate threshold exceeded,Greater than or equal to the 90th percentile for expected population loss rate and is low income?,Greater than or equal to the 90th percentile for expected agriculture loss rate and is low income?,Greater than or equal to the 90th percentile for expected building loss rate and is low income?,Greater than or equal to the 90th percentile for share of properties at risk of flood in 30 years and is low income?,Greater than or equal to the 90th percentile for share of properties at risk of fire in 30 years and is low income?,Climate Factor (Definition N),Greater than or equal to the 90th percentile for energy burden,Greater than or equal to the 90th percentile for pm2.5 exposure,At least one energy threshold exceeded,Greater than or equal to the 90th percentile for PM2.5 exposure and is low income?,Greater than or equal to the 90th percentile for energy burden and is low income?,Energy Factor (Definition N),Greater than or equal to the 90th percentile for diesel particulate matter,Greater than or equal to the 90th percentile for DOT travel barriers,Greater than or equal to the 90th percentile for traffic proximity,At least one traffic threshold exceeded,Greater than or equal to the 90th percentile for diesel particulate matter and is low income?,Greater than or equal to the 90th percentile for traffic proximity and is low income?,Greater than or equal to the 90th percentile for DOT transit barriers and is low income?,Transportation Factor (Definition N),Tract-level redlining score meets or exceeds 3.25 and is low income,Greater than or equal to the 90th percentile for share of homes without indoor plumbing or a kitchen,Greater than or equal to the 90th percentile for share of homes with no kitchen or indoor plumbing and is low income?,Greater than or equal to the 90th percentile for lead paint and the median house value is less than 90th percentile,Greater than or equal to the 90th percentile for lead paint and the median house value is less than 90th percentile and is low income?,Greater than or equal to the 90th percentile for housing burden,Greater than or equal to the 90th percentile for housing burden and is low income?,Greater than or equal to the 90th percentile for share of the tract's land area that is covered by impervious surface or cropland as a percent,Greater than or equal to the 90th percentile for share of the tract's land area that is covered by impervious surface or cropland as a percent and is low income?,At least one housing threshold exceeded,Housing Factor (Definition N),Greater than or equal to the 90th percentile for RMP proximity,Greater than or equal to the 90th percentile for NPL (superfund sites) proximity,Greater than or equal to the 90th percentile for proximity to hazardous waste sites,"Is there at least one Formerly Used Defense Site (FUDS) in the tract, where missing data is treated as False?","Is there at least one abandoned mine in this census tract, where missing data is treated as False?",At least one pollution threshold exceeded,Greater than or equal to the 90th percentile for proximity to RMP sites and is low income?,Greater than or equal to the 90th percentile for proximity to superfund sites and is low income?,Greater than or equal to the 90th percentile for proximity to hazardous waste facilities and is low income?,There is at least one abandoned mine in this census tract and the tract is low income.,There is at least one Formerly Used Defense Site (FUDS) in the tract and the tract is low income.,Pollution Factor (Definition N),Greater than or equal to the 90th percentile for wastewater discharge,Greater than or equal to the 90th percentile for leaky underwater storage tanks,Greater than or equal to the 90th percentile for wastewater discharge and is low income?,Greater than or equal to the 90th percentile for leaky underground storage tanks and is low income?,At least one water threshold exceeded,Water Factor (Definition N),Greater than or equal to the 90th percentile for diabetes,Greater than or equal to the 90th percentile for asthma,Greater than or equal to the 90th percentile for heart disease,Greater than or equal to the 90th percentile for low life expectancy,At least one health threshold exceeded,Greater than or equal to the 90th percentile for diabetes and is low income?,Greater than or equal to the 90th percentile for asthma and is low income?,Greater than or equal to the 90th percentile for heart disease and is low income?,Greater than or equal to the 90th percentile for low life expectancy and is low income?,Health Factor (Definition N),Low high school education,Greater than or equal to the 90th percentile for unemployment,Greater than or equal to the 90th percentile for low median household income as a percent of area median income,Greater than or equal to the 90th percentile for households in linguistic isolation,Greater than or equal to the 90th percentile for households at or below 100% federal poverty level,Greater than or equal to the 90th percentile for households in linguistic isolation and has low HS attainment?,Greater than or equal to the 90th percentile for households at or below 100% federal poverty level and has low HS attainment?,Greater than or equal to the 90th percentile for low median household income as a percent of area median income and has low HS attainment?,Greater than or equal to the 90th percentile for unemployment and has low HS attainment?,At least one workforce threshold exceeded,Unemployment (percent) in 2009 (island areas) and 2010 (states and PR),Unemployment (percent) in 2009 for island areas (percentile),Unemployment (percent) in 2009 exceeds 90th percentile,Percentage households below 100% of federal poverty line in 2009 (island areas) and 2010 (states and PR),Percentage households below 100% of federal poverty line in 2009 for island areas (percentile),Percentage households below 100% of federal poverty line in 2009 exceeds 90th percentile,Low median household income as a percent of territory median income in 2009 exceeds 90th percentile,Low high school education in 2009 (island areas),Greater than or equal to the 90th percentile for unemployment and has low HS education in 2009 (island areas)?,Greater than or equal to the 90th percentile for households at or below 100% federal poverty level and has low HS education in 2009 (island areas)?,Greater than or equal to the 90th percentile for low median household income as a percent of area median income and has low HS education in 2009 (island areas)?,Both workforce socioeconomic indicators exceeded,Workforce Factor (Definition N),Total categories exceeded,Definition N (communities),Definition N (communities) (percentile),Meets the less stringent low income criterion for the adjacency index?,Definition N (communities) (average of neighbors),Is the tract surrounded by disadvantaged communities?,Definition N (communities) (based on adjacency index and low income alone),"Definition N community, including adjacency index tracts" -01073001100,True,True,True,0.2752043596730245,0.0,4781.0,0.7327449738800064,11.2,7.2,6.7,16.6,19.3,15.1,0.150375939849624,0.318796992481203,0.3744360902255639,57447.0,37030.0,0.049,18.7674524286,1.0,40.0,0.5,0.467489734286576,9.8735797260274,43.056760130719,181.621925132718,2.0427358988323,0.702342755246247,0.134193041307899,4.45238981883771,0.168806466951973,0.035557414766785,0.203932231750679,0.0,0.0,0.374436090225563,0.0821917808219178,0.0092071611253196,85500.0,0.0890751899397432,0.9109248100602568,0.0004047858,5.6328e-05,2.8039e-06,,,,0.1536983669548511,0.3189099613330878,,62.666668,0.068036923,0.171,1.96440511031451,47.695227725,0.0754274220583305,0.6620851491786792,-77.7525,0.2853609002858206,0.9682074879732272,0.0121313532733737,0.0,0.0,0.0,0.0161054172767203,0.0035557414766785,0.0,0.1344906923237816,0.6615770759255386,0.2039322317506798,58.143433,0.6445941476491375,70.3,,,False,,True,0.6466760729305078,0.2159833426939357,0.6290185267766651,0.2601978513507951,0.8509696039125366,0.7264920810941454,0.4789587420739856,0.6191105803406409,0.965388552418323,0.697012994398476,0.6204255784694491,0.7319894972922707,0.6305043487774192,0.3145069836211475,0.1524256393370651,0.864954517474865,0.6038301323911519,0.5972204988211937,0.9070825388177608,0.8818509942794879,0.8407790792699537,0.8257128232087766,0.5755156814188676,0.3920895082932574,0.9007580978635424,0.4820205132363076,0.7531654977635437,0.9619599422457518,0.3979135417088958,0.1737408953933055,0.7659355954649262,0.1287706711725437,0.13169416629505,0.6347481790786611,0.4189065592792301,0.029797296373751,0.1130218397675614,0.7459773722926589,0.2540362752992234,0.7846412062513758,0.2153147384849333,0.6143028498159407,,,,0.9349594607528132,0.8950599559730369,,0.7537922665342821,0.8019598155467721,0.4126953421856217,0.521114579532709,0.4517484245644384,0.4973964722881056,0.8410893082809093,0.2685589820648203,0.607629501459933,0.9950049813710372,0.8553628212301939,0.0982626615533689,0.4219630696163662,0.0261283146588784,0.0311301570837825,0.0475755053020894,0.0977645244496608,0.6708610265718614,0.1578889904876284,0.763719241739795,0.990724418702258,0.8218135517196475,0.97046998263836,,4781.0,0,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False,True,False,False,False,False,True,False,False,False,False,False,False,True,False,False,False,False,False,True,False,False,True,True,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False,0.1536983669548511,,False,0.3189099613330878,,False,False,False,False,False,False,False,False,0.0,False,0,True,0.8571428571428571,False,False,False -01073001400,True,True,True,0.1823529411764705,0.0047058823529411,1946.0,0.7136694633528574,11.1,9.1,7.3,21.4,22.4,17.4,0.2816032887975334,0.3679342240493319,0.4835560123329907,57447.0,36066.0,0.07,17.3011023381,1.0,40.0,0.6,0.655319095139786,9.945103013698628,43.1266823529412,3260.33374354854,1.81915896353987,3.34035680534013,0.214095348702766,0.103297800913177,0.647212543554006,0.054984583761562,0.189105858170606,0.0245098039215686,0.024509803921569,0.48355601233299,0.1742543171114599,0.1150121065375302,67800.0,0.0771549125979505,0.9228450874020494,0.0008951111,5.1282e-06,2.3791e-06,,,,0.0804953560371517,0.2950894905920146,,61.666668,0.087159691,0.34900002,3.16184976454882,44.7571359825,0.2384615384615384,0.0,-56.8746,0.4064010997350401,0.9167523124357656,0.0,0.0,0.0,0.0035971223021582,0.0,0.0683453237410072,0.0775950668036999,0.0853031860226104,0.7255909558067831,0.1891058581706063,93.77919,0.6278134628440127,71.0,,,False,,True,0.3421186011150532,0.5051574635963891,0.0916001135119795,0.240302951305517,0.8385794307486707,0.9217563763541756,0.6048579715089994,0.7894025988796952,0.9878088657624612,0.8447283118655634,0.8689486351950112,0.8013648049887862,0.7892483999781194,0.3145069836211475,0.1404620788058391,0.970802270706518,0.5282998116553705,0.5972204988211937,0.9070825388177608,0.9704848815036776,0.9380686461454644,0.8391046304110233,0.5827649654828936,0.9563394697362702,0.8799745949379062,0.800259455953298,0.8653801975648978,0.8431750027766466,0.8462723476709774,0.471128768530155,0.6930041485925866,0.5867081244286861,0.5847015580870529,0.7916514641694031,0.7516347007030237,0.9067399297439892,0.0522639122516786,0.6434566620719774,0.356556985519905,0.9166162227602904,0.0865380767537716,0.558933421571466,,,,0.6917513228236646,0.8737301229199994,,0.7501654807214959,0.8647617479139218,0.6268497920495212,0.6418426778016514,0.3716517703914219,0.8850358496224203,0.3366245885930925,0.5569693544162451,0.7883908294582027,0.9840732602732248,0.2486523003016117,0.0982626615533689,0.4219630696163662,0.0924351398195788,0.0038486209108402,0.4634108061632525,0.8246557394947661,0.1930997775442523,0.5561393692083032,0.6900904835341803,0.9537899773356836,0.8364273002184828,0.959938777375042,,1946.0,9,True,False,False,True,False,False,True,False,False,True,False,False,True,True,False,True,False,True,True,True,False,True,True,True,True,False,True,True,False,False,False,False,False,False,False,False,True,True,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False,True,False,True,True,True,True,False,True,True,True,True,True,False,False,False,False,False,False,True,True,0.0804953560371517,,False,0.2950894905920146,,False,False,False,False,False,False,True,True,6.0,True,1,True,1.0,True,True,True +GEOID10_TRACT,Does the tract have at least 35 acres in it?,Contains agricultural value,Names of Tribal areas within Census tract,Housing burden (percent),Share of homes with no kitchen or indoor plumbing (percent),Total population,Median household income (% of state median household income),Current asthma among adults aged greater than or equal to 18 years,Coronary heart disease among adults aged greater than or equal to 18 years,Cancer (excluding skin cancer) among adults aged greater than or equal to 18 years,Current lack of health insurance among adults aged 18-64 years,Diagnosed diabetes among adults aged greater than or equal to 18 years,Physical health not good for greater than or equal to 14 days among adults aged greater than or equal to 18 years,Percent of individuals < 100% Federal Poverty Line,Percent of individuals < 150% Federal Poverty Line,Percent of individuals below 200% Federal Poverty Line,Area Median Income (State or metropolitan),Median household income in the past 12 months,Energy burden,FEMA Risk Index Expected Annual Loss Score,Urban Heuristic Flag,Air toxics cancer risk,Respiratory hazard index,Diesel particulate matter exposure,PM2.5 in the air,Ozone,Traffic proximity and volume,Proximity to Risk Management Plan (RMP) facilities,Proximity to hazardous waste sites,Proximity to NPL sites,Wastewater discharge,Percent pre-1960s housing (lead paint indicator),Individuals under 5 years old,Individuals over 64 years old,Linguistic isolation (percent),Percent of households in linguistic isolation,Poverty (Less than 200% of federal poverty line),Percent individuals age 25 or over with less than high school degree,Unemployment (percent),Median value ($) of owner-occupied housing units,Percent enrollment in college or graduate school,Percent of population not currently enrolled in college or graduate school,Expected building loss rate (Natural Hazards Risk Index),Expected agricultural loss rate (Natural Hazards Risk Index),Expected population loss rate (Natural Hazards Risk Index),Percent individuals age 25 or over with less than high school degree in 2009,Percentage households below 100% of federal poverty line in 2009,Unemployment (percent) in 2009,Unemployment (percent) in 2010,Percent of individuals less than 100% Federal Poverty Line in 2010,Total population in 2009,Leaky underground storage tanks,DOT Travel Barriers Score,Share of properties at risk of flood in 30 years,Share of properties at risk of fire in 30 years,Share of the tract's land area that is covered by impervious surface or cropland as a percent,"Percent of individuals below 200% Federal Poverty Line, imputed and adjusted",Percent Black or African American,Percent American Indian / Alaska Native,Percent Asian,Percent Native Hawaiian or Pacific,Percent two or more races,Percent White,Percent Hispanic or Latino,Percent other races,Percent age under 10,Percent age 10 to 64,Percent age over 64,Percent of the Census tract that is within Tribal areas,Number of Tribal areas within Census tract,Median household income as a percent of area median income,Life expectancy (years),Median household income as a percent of territory median income in 2009,Is there at least one abandoned mine in this census tract?,Income data has been estimated based on neighbor income,Is there at least one Formerly Used Defense Site (FUDS) in the tract?,Tract-level redlining score meets or exceeds 3.25,Housing burden (percent) (percentile),Share of homes with no kitchen or indoor plumbing (percent) (percentile),Total population (percentile),Median household income (% of state median household income) (percentile),Current asthma among adults aged greater than or equal to 18 years (percentile),Coronary heart disease among adults aged greater than or equal to 18 years (percentile),Cancer (excluding skin cancer) among adults aged greater than or equal to 18 years (percentile),Current lack of health insurance among adults aged 18-64 years (percentile),Diagnosed diabetes among adults aged greater than or equal to 18 years (percentile),Physical health not good for greater than or equal to 14 days among adults aged greater than or equal to 18 years (percentile),Percent of individuals < 100% Federal Poverty Line (percentile),Percent of individuals < 150% Federal Poverty Line (percentile),Percent of individuals below 200% Federal Poverty Line (percentile),Area Median Income (State or metropolitan) (percentile),Median household income in the past 12 months (percentile),Energy burden (percentile),FEMA Risk Index Expected Annual Loss Score (percentile),Urban Heuristic Flag (percentile),Air toxics cancer risk (percentile),Respiratory hazard index (percentile),Diesel particulate matter exposure (percentile),PM2.5 in the air (percentile),Ozone (percentile),Traffic proximity and volume (percentile),Proximity to Risk Management Plan (RMP) facilities (percentile),Proximity to hazardous waste sites (percentile),Proximity to NPL sites (percentile),Wastewater discharge (percentile),Percent pre-1960s housing (lead paint indicator) (percentile),Individuals under 5 years old (percentile),Individuals over 64 years old (percentile),Linguistic isolation (percent) (percentile),Percent of households in linguistic isolation (percentile),Poverty (Less than 200% of federal poverty line) (percentile),Percent individuals age 25 or over with less than high school degree (percentile),Unemployment (percent) (percentile),Median value ($) of owner-occupied housing units (percentile),Percent enrollment in college or graduate school (percentile),Percent of population not currently enrolled in college or graduate school (percentile),Expected building loss rate (Natural Hazards Risk Index) (percentile),Expected agricultural loss rate (Natural Hazards Risk Index) (percentile),Expected population loss rate (Natural Hazards Risk Index) (percentile),Percent individuals age 25 or over with less than high school degree in 2009 (percentile),Percentage households below 100% of federal poverty line in 2009 (percentile),Unemployment (percent) in 2009 (percentile),Unemployment (percent) in 2010 (percentile),Percent of individuals less than 100% Federal Poverty Line in 2010 (percentile),Total population in 2009 (percentile),Leaky underground storage tanks (percentile),DOT Travel Barriers Score (percentile),Share of properties at risk of flood in 30 years (percentile),Share of properties at risk of fire in 30 years (percentile),Share of the tract's land area that is covered by impervious surface or cropland as a percent (percentile),"Percent of individuals below 200% Federal Poverty Line, imputed and adjusted (percentile)",Percent Black or African American (percentile),Percent American Indian / Alaska Native (percentile),Percent Asian (percentile),Percent Native Hawaiian or Pacific (percentile),Percent two or more races (percentile),Percent White (percentile),Percent Hispanic or Latino (percentile),Percent other races (percentile),Percent age under 10 (percentile),Percent age 10 to 64 (percentile),Percent age over 64 (percentile),Percent of the Census tract that is within Tribal areas (percentile),Number of Tribal areas within Census tract (percentile),Low median household income as a percent of area median income (percentile),Low life expectancy (percentile),Low median household income as a percent of territory median income in 2009 (percentile),Total population in 2009 (island areas) and 2019 (states and PR),Total threshold criteria exceeded,Is low income (imputed and adjusted)?,Greater than or equal to the 90th percentile for expected population loss,Greater than or equal to the 90th percentile for expected agricultural loss,Greater than or equal to the 90th percentile for expected building loss,Greater than or equal to the 90th percentile for share of properties at risk of flood in 30 years,Greater than or equal to the 90th percentile for share of properties at risk of fire in 30 years,At least one climate threshold exceeded,Greater than or equal to the 90th percentile for expected population loss rate and is low income?,Greater than or equal to the 90th percentile for expected agriculture loss rate and is low income?,Greater than or equal to the 90th percentile for expected building loss rate and is low income?,Greater than or equal to the 90th percentile for share of properties at risk of flood in 30 years and is low income?,Greater than or equal to the 90th percentile for share of properties at risk of fire in 30 years and is low income?,Climate Factor (Definition N),Greater than or equal to the 90th percentile for energy burden,Greater than or equal to the 90th percentile for pm2.5 exposure,At least one energy threshold exceeded,Greater than or equal to the 90th percentile for PM2.5 exposure and is low income?,Greater than or equal to the 90th percentile for energy burden and is low income?,Energy Factor (Definition N),Greater than or equal to the 90th percentile for diesel particulate matter,Greater than or equal to the 90th percentile for DOT travel barriers,Greater than or equal to the 90th percentile for traffic proximity,At least one traffic threshold exceeded,Greater than or equal to the 90th percentile for diesel particulate matter and is low income?,Greater than or equal to the 90th percentile for traffic proximity and is low income?,Greater than or equal to the 90th percentile for DOT transit barriers and is low income?,Transportation Factor (Definition N),Tract-level redlining score meets or exceeds 3.25 and is low income,Greater than or equal to the 90th percentile for share of homes without indoor plumbing or a kitchen,Greater than or equal to the 90th percentile for share of homes with no kitchen or indoor plumbing and is low income?,Greater than or equal to the 90th percentile for lead paint and the median house value is less than 90th percentile,Greater than or equal to the 90th percentile for lead paint and the median house value is less than 90th percentile and is low income?,Greater than or equal to the 90th percentile for housing burden,Greater than or equal to the 90th percentile for housing burden and is low income?,Greater than or equal to the 90th percentile for share of the tract's land area that is covered by impervious surface or cropland as a percent,Greater than or equal to the 90th percentile for share of the tract's land area that is covered by impervious surface or cropland as a percent and is low income?,At least one housing threshold exceeded,Housing Factor (Definition N),Greater than or equal to the 90th percentile for RMP proximity,Greater than or equal to the 90th percentile for NPL (superfund sites) proximity,Greater than or equal to the 90th percentile for proximity to hazardous waste sites,"Is there at least one Formerly Used Defense Site (FUDS) in the tract, where missing data is treated as False?","Is there at least one abandoned mine in this census tract, where missing data is treated as False?",At least one pollution threshold exceeded,Greater than or equal to the 90th percentile for proximity to RMP sites and is low income?,Greater than or equal to the 90th percentile for proximity to superfund sites and is low income?,Greater than or equal to the 90th percentile for proximity to hazardous waste facilities and is low income?,There is at least one abandoned mine in this census tract and the tract is low income.,There is at least one Formerly Used Defense Site (FUDS) in the tract and the tract is low income.,Pollution Factor (Definition N),Greater than or equal to the 90th percentile for wastewater discharge,Greater than or equal to the 90th percentile for leaky underwater storage tanks,Greater than or equal to the 90th percentile for wastewater discharge and is low income?,Greater than or equal to the 90th percentile for leaky underground storage tanks and is low income?,At least one water threshold exceeded,Water Factor (Definition N),Greater than or equal to the 90th percentile for diabetes,Greater than or equal to the 90th percentile for asthma,Greater than or equal to the 90th percentile for heart disease,Greater than or equal to the 90th percentile for low life expectancy,At least one health threshold exceeded,Greater than or equal to the 90th percentile for diabetes and is low income?,Greater than or equal to the 90th percentile for asthma and is low income?,Greater than or equal to the 90th percentile for heart disease and is low income?,Greater than or equal to the 90th percentile for low life expectancy and is low income?,Health Factor (Definition N),Low high school education,Greater than or equal to the 90th percentile for unemployment,Greater than or equal to the 90th percentile for low median household income as a percent of area median income,Greater than or equal to the 90th percentile for households in linguistic isolation,Greater than or equal to the 90th percentile for households at or below 100% federal poverty level,Greater than or equal to the 90th percentile for households in linguistic isolation and has low HS attainment?,Greater than or equal to the 90th percentile for households at or below 100% federal poverty level and has low HS attainment?,Greater than or equal to the 90th percentile for low median household income as a percent of area median income and has low HS attainment?,Greater than or equal to the 90th percentile for unemployment and has low HS attainment?,At least one workforce threshold exceeded,Unemployment (percent) in 2009 (island areas) and 2010 (states and PR),Unemployment (percent) in 2009 for island areas (percentile),Unemployment (percent) in 2009 exceeds 90th percentile,Percentage households below 100% of federal poverty line in 2009 (island areas) and 2010 (states and PR),Percentage households below 100% of federal poverty line in 2009 for island areas (percentile),Percentage households below 100% of federal poverty line in 2009 exceeds 90th percentile,Low median household income as a percent of territory median income in 2009 exceeds 90th percentile,Low high school education in 2009 (island areas),Greater than or equal to the 90th percentile for unemployment and has low HS education in 2009 (island areas)?,Greater than or equal to the 90th percentile for households at or below 100% federal poverty level and has low HS education in 2009 (island areas)?,Greater than or equal to the 90th percentile for low median household income as a percent of area median income and has low HS education in 2009 (island areas)?,Both workforce socioeconomic indicators exceeded,Workforce Factor (Definition N),Total categories exceeded,Definition N (communities),Definition N (communities) (percentile),Meets the less stringent low income criterion for the adjacency index?,Definition N (communities) (average of neighbors),Is the tract surrounded by disadvantaged communities?,Definition N (communities) (based on adjacency index and low income alone),"Definition N community, including adjacency index tracts" +01073001100,True,True,,0.2752043596730245,0.0,4781.0,0.7327449738800064,11.2,7.2,6.7,16.6,19.3,15.1,0.150375939849624,0.318796992481203,0.3744360902255639,57447.0,37030.0,0.049,18.7674524286,1.0,40.0,0.5,0.467489734286576,9.8735797260274,43.056760130719,181.621925132718,2.0427358988323,0.702342755246247,0.134193041307899,4.45238981883771,0.168806466951973,0.035557414766785,0.203932231750679,0.0,0.0,0.374436090225563,0.0821917808219178,0.0092071611253196,85500.0,0.0890751899397432,0.9109248100602568,0.0004047858,5.6328e-05,2.8039e-06,,,,0.1536983669548511,0.3189099613330878,,1.96440511031451,47.695227725,0.0754274220583305,0.6620851491786792,-77.7525,0.2853609002858206,0.9682074879732272,0.0121313532733737,0.0,0.0,0.0,0.0161054172767203,0.0035557414766785,0.0,0.1344906923237816,0.6615770759255386,0.2039322317506798,,,0.6445941476491375,70.3,,,False,,True,0.646637250602938,0.2159888182416136,0.6290959230020547,0.2601978513507951,0.8509877263095018,0.726480167252144,0.4790223713964701,0.6190889309231579,0.9653762462133604,0.6970048212990485,0.6204255784694491,0.7319894972922707,0.6305043487774192,0.3145069836211475,0.1524256393370651,0.8649904214559387,0.6038246858588359,0.5972204988211937,0.907050889025138,0.8818107500510934,0.8407248450166905,0.8256687748238973,0.5755269239817877,0.3919915848527349,0.9007380772142316,0.4819400886774089,0.7531091164702065,0.9619657803125694,0.3979128365956526,0.1737455390937601,0.7659646371796258,0.1287706711725437,0.1316846004109441,0.6347599221369092,0.4189065592792301,0.029797296373751,0.1130218397675614,0.7459773722926589,0.2540362752992234,0.7846382434272978,0.2153147384849333,0.6144741572412268,,,,0.9349594607528132,0.8950599559730369,,0.5210203309181356,0.4533200613827713,0.4974774332542475,0.8411474612766549,0.2685589820648203,0.607629501459933,0.9950049813710372,0.8553628212301939,0.0982626615533689,0.4219630696163662,0.0261283146588784,0.0311301570837825,0.0475755053020894,0.0977645244496608,0.6708610265718614,0.1578889904876284,0.763719241739795,,,0.8218135517196475,0.970728837791148,,4781.0,0,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False,True,False,False,False,False,True,False,False,False,False,False,False,True,False,False,False,False,False,True,False,False,True,True,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False,0.1536983669548511,,False,0.3189099613330878,,False,False,False,False,False,False,False,False,0.0,False,0,True,0.8571428571428571,False,False,False 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a/data/data-pipeline/data_pipeline/tests/score/test_output.py +++ b/data/data-pipeline/data_pipeline/tests/score/test_output.py @@ -27,6 +27,7 @@ from .fixtures import ( census_2010_df, hrs_df, national_tract_df, + tribal_overlap, ) @@ -249,6 +250,7 @@ def test_data_sources( census_decennial_df, census_2010_df, hrs_df, + tribal_overlap, ): data_sources = { key: value for key, value in locals().items() if key != "final_score_df"