diff --git a/data/data-pipeline/data_pipeline/content/config/csv.yml b/data/data-pipeline/data_pipeline/content/config/csv.yml index ef2a6e6b..6451cbf9 100644 --- a/data/data-pipeline/data_pipeline/content/config/csv.yml +++ b/data/data-pipeline/data_pipeline/content/config/csv.yml @@ -260,6 +260,12 @@ fields: - score_name: Greater than or equal to the 90th percentile for leaky underground storage tanks and is low income? label: Greater than or equal to the 90th percentile for leaky underground storage tanks and is low income? format: bool + - score_name: Greater than or equal to the 90th percentile for DOT transit barriers and is low income? + label: Greater than or equal to the 90th percentile for DOT transit barriers and is low income? + format: bool + - score_name: DOT Travel Barriers Score (percentile) + label: DOT Travel Barriers Score (percentile) + format: percentage - score_name: Leaky underground storage tanks (percentile) label: Leaky underground storage tanks (percentile) 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 4820187d..94d4f55d 100644 --- a/data/data-pipeline/data_pipeline/content/config/excel.yml +++ b/data/data-pipeline/data_pipeline/content/config/excel.yml @@ -258,6 +258,12 @@ sheets: - score_name: Unemployment (percent) in 2009 (island areas) and 2010 (states and PR) label: Unemployment (percent) in 2009 (island areas) and 2010 (states and PR) format: percentage + - score_name: Greater than or equal to the 90th percentile for DOT transit barriers and is low income? + label: Greater than or equal to the 90th percentile for DOT transit barriers and is low income? + format: bool + - score_name: DOT Travel Barriers Score (percentile) + label: DOT Travel Barriers Score (percentile) + format: percentage - score_name: Percentage households below 100% of federal poverty line in 2009 (island areas) and 2010 (states and PR) label: Percentage households below 100% of federal poverty line in 2009 (island areas) and 2010 (states and PR) format: percentage diff --git a/data/data-pipeline/data_pipeline/etl/constants.py b/data/data-pipeline/data_pipeline/etl/constants.py index 72db2ce6..1ac862b8 100644 --- a/data/data-pipeline/data_pipeline/etl/constants.py +++ b/data/data-pipeline/data_pipeline/etl/constants.py @@ -9,6 +9,11 @@ DATASET_LIST = [ "module_dir": "national_risk_index", "class_name": "NationalRiskIndexETL", }, + { + "name": "travel_composite", + "module_dir": "dot_travel_composite", + "class_name": "TravelCompositeETL", + }, { "name": "tree_equity_score", "module_dir": "tree_equity_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 37b05bba..aa09e21d 100644 --- a/data/data-pipeline/data_pipeline/etl/score/config/datasets.yml +++ b/data/data-pipeline/data_pipeline/etl/score/config/datasets.yml @@ -156,3 +156,16 @@ datasets: field_type: float include_in_tiles: true include_in_downloadable_files: true + + - long_name: "DOT Travel Disadvantage Index" + short_name: "DOT" + module_name: "travel_composite" + input_geoid_tract_field_name: "GEOID10_TRACT" + load_fields: + - short_name: "travel_burden" + df_field_name: "TRAVEL_BURDEN_FIELD_NAME" + long_name: "DOT Travel Barriers Score" + field_type: float + include_in_tiles: true + include_in_downloadable_files: true + create_percentile: true diff --git a/data/data-pipeline/data_pipeline/etl/score/constants.py b/data/data-pipeline/data_pipeline/etl/score/constants.py index 17e5b435..cf3bee97 100644 --- a/data/data-pipeline/data_pipeline/etl/score/constants.py +++ b/data/data-pipeline/data_pipeline/etl/score/constants.py @@ -296,6 +296,9 @@ TILES_SCORE_COLUMNS = { field_names.FPL_200_SERIES: "FPL200S", ## Low high school for t&wd field_names.WORKFORCE_SOCIO_INDICATORS_EXCEEDED: "M_WKFC_EBSI", + field_names.DOT_BURDEN_PCTILE_THRESHOLD: "TD_ET", + field_names.DOT_TRAVEL_BURDEN_FIELD + + field_names.PERCENTILE_FIELD_SUFFIX: "TD_PFS" ## FPL 200 and low higher ed for all others should no longer be M_EBSI, but rather ## FPL_200 (there is no higher ed in narwhal) } @@ -348,4 +351,5 @@ TILES_SCORE_FLOAT_COLUMNS = [ field_names.WASTEWATER_FIELD + field_names.PERCENTILE_FIELD_SUFFIX, field_names.COLLEGE_NON_ATTENDANCE_FIELD, field_names.COLLEGE_ATTENDANCE_FIELD, + field_names.DOT_TRAVEL_BURDEN_FIELD + field_names.PERCENTILE_FIELD_SUFFIX, ] 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 0af482b9..a2e6e858 100644 --- a/data/data-pipeline/data_pipeline/etl/score/etl_score.py +++ b/data/data-pipeline/data_pipeline/etl/score/etl_score.py @@ -8,6 +8,9 @@ from data_pipeline.etl.base import ExtractTransformLoad from data_pipeline.etl.sources.national_risk_index.etl import ( NationalRiskIndexETL, ) +from data_pipeline.etl.sources.dot_travel_composite.etl import ( + TravelCompositeETL, +) from data_pipeline.score.score_runner import ScoreRunner from data_pipeline.score import field_names from data_pipeline.etl.score import constants @@ -37,6 +40,7 @@ class ScoreETL(ExtractTransformLoad): self.census_2010_df: pd.DataFrame self.child_opportunity_index_df: pd.DataFrame self.hrs_df: pd.DataFrame + self.dot_travel_disadvantage_df: pd.DataFrame def extract(self) -> None: logger.info("Loading data sets from disk.") @@ -115,6 +119,9 @@ class ScoreETL(ExtractTransformLoad): # Load FEMA national risk index data self.national_risk_index_df = NationalRiskIndexETL.get_data_frame() + # Load DOT Travel Disadvantage + self.dot_travel_disadvantage_df = TravelCompositeETL.get_data_frame() + # Load GeoCorr Urban Rural Map geocorr_urban_rural_csv = ( constants.DATA_PATH / "dataset" / "geocorr" / "usa.csv" @@ -334,6 +341,7 @@ class ScoreETL(ExtractTransformLoad): self.census_2010_df, self.child_opportunity_index_df, self.hrs_df, + self.dot_travel_disadvantage_df, ] # Sanity check each data frame before merging. @@ -416,6 +424,7 @@ class ScoreETL(ExtractTransformLoad): field_names.HEALTHY_FOOD_FIELD, field_names.IMPENETRABLE_SURFACES_FIELD, field_names.UST_FIELD, + field_names.DOT_TRAVEL_BURDEN_FIELD, field_names.AGRICULTURAL_VALUE_BOOL_FIELD, field_names.POVERTY_LESS_THAN_200_FPL_IMPUTED_FIELD, ] 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 ab73e421..f7b0ebd5 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,Tract-level redlining score meets or exceeds 3.25,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,Contains agricultural value,"Percent of individuals below 200% Federal Poverty Line, imputed and adjusted",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,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),Contains agricultural value (percentile),"Percent of individuals below 200% Federal Poverty Line, imputed and adjusted (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),Score A,Score B,Socioeconomic Factors,Sensitive populations,Environmental effects,Exposures,Pollution Burden,Population Characteristics,Score C,"Low AMI, Low HS graduation",Meets socioeconomic criteria,Meets burden criteria,Score F (communities),Score G (communities),Score G,Score G (percentile),Score H (communities),Score H,Score I (communities),Score I,Score I (percentile),NMTC (communities),Score K (communities),Total threshold criteria exceeded,Is low income?,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?,Climate Factor (Definition L),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 L),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?,Transportation Factor (Definition L),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 and is low income?,Housing Factor (Definition L),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?,Pollution Factor (Definition L),Greater than or equal to the 90th percentile for wastewater discharge and is low income?,Water Factor (Definition L),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 L),Low high school education,Greater than or equal to the 90th percentile for households in linguistic isolation and has low HS education?,Greater than or equal to the 90th percentile for households at or below 100% federal poverty level and has low HS education?,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?,Greater than or equal to the 90th percentile for unemployment and has low HS education?,Unemployment (percent) in 2009 (island areas) and 2010 (states and PR),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 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)?,Workforce Factor (Definition L),Definition L (communities),Any Non-Workforce Factor (Definition L),Definition L (percentile),Exceeds FPL200 threshold,Percent higher ed enrollment rate is less than 20%,Is low income and has a low percent of higher ed students?,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,At least one climate threshold exceeded,"Greater than or equal to the 90th percentile for expected population loss rate, is low income, and has a low percent of higher ed students?","Greater than or equal to the 90th percentile for expected agriculture loss rate, is low income, and has a low percent of higher ed students?","Greater than or equal to the 90th percentile for expected building loss rate, is low income, and has a low percent of higher ed students?",Climate Factor (Definition M),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, is low income, and has a low percent of higher ed students?","Greater than or equal to the 90th percentile for energy burden, is low income, and has a low percent of higher ed students?",Energy Factor (Definition M),Greater than or equal to the 90th percentile for diesel particulate matter,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, is low income, and has a low percent of higher ed students?","Greater than or equal to the 90th percentile for traffic proximity, is low income, and has a low percent of higher ed students?",Transportation Factor (Definition M),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 housing burden,At least one housing threshold exceeded,"Greater than or equal to the 90th percentile for lead paint, the median house value is less than 90th percentile, is low income, and has a low percent of higher ed students?","Greater than or equal to the 90th percentile for housing burden, is low income, and has a low percent of higher ed students?",Housing Factor (Definition M),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,At least one pollution threshold exceeded,"Greater than or equal to the 90th percentile for proximity to RMP sites, is low income, and has a low percent of higher ed students?","Greater than or equal to the 90th percentile for proximity to superfund sites, is low income, and has a low percent of higher ed students?","Greater than or equal to the 90th percentile for proximity to hazardous waste facilities, is low income, and has a low percent of higher ed students?",Pollution Factor (Definition M),Greater than or equal to the 90th percentile for wastewater discharge,At least one water threshold exceeded,"Greater than or equal to the 90th percentile for wastewater discharge, is low income, and has a low percent of higher ed students?",Water Factor (Definition M),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, is low income, and has a low percent of higher ed students?","Greater than or equal to the 90th percentile for asthma, is low income, and has a low percent of higher ed students?","Greater than or equal to the 90th percentile for heart disease, is low income, and has a low percent of higher ed students?","Greater than or equal to the 90th percentile for low life expectancy, is low income, and has a low percent of higher ed students?",Health Factor (Definition M),Low high school education and low percent of higher ed students,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, has low HS attainment, and has a low percent of higher ed students?","Greater than or equal to the 90th percentile for households at or below 100% federal poverty level, has low HS attainment, and has a low percent of higher ed students?","Greater than or equal to the 90th percentile for low median household income as a percent of area median income, has low HS attainment, and has a low percent of higher ed students?","Greater than or equal to the 90th percentile for unemployment, has low HS attainment, and has a low percent of higher ed students?",Unemployment (percent) in 2009 for island areas (percentile),Percentage households below 100% of federal poverty line in 2009 for island areas (percentile),At least one workforce threshold exceeded,Both workforce socioeconomic indicators exceeded,Workforce Factor (Definition M),Total categories exceeded,Definition M (communities),Any Non-Workforce Factor (Definition M),Definition M (percentile),Is low income (imputed and adjusted)?,Climate Factor (Definition N),Energy Factor (Definition N),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?,Housing Factor (Definition N),Pollution Factor (Definition N),Greater than or equal to the 90th percentile for leaky underwater storage tanks,Greater than or equal to the 90th percentile for leaky underground storage tanks and is low income?,Water Factor (Definition N),Health Factor (Definition N),Workforce Factor (Definition N),Definition N (communities),Definition N (communities) (percentile) -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,,62.666668,0.068036923,0.171,1.96440511031451,1.0,0.2853609002858206,58.143433,0.6445941476491375,70.3,,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,,0.7537922665342821,0.8019598155467721,0.4126953421856217,0.521114579532709,0.6564635202573585,0.607629501459933,0.990724418702258,0.8218135517196475,0.97046998263836,,4781.0,0.5268273691789456,0.2659001757065985,0.37236445479144,0.3561490540102585,0.6991635185636083,0.7371717708813507,0.7245023534421032,0.3642567544008492,0.2639048758205974,False,False,True,False,True,1,1,True,1,True,1,1,True,True,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,0.1536983669548511,False,0.3189099613330878,False,False,False,False,False,False,False,False,False,0,False,True,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,True,False,False,False,False,True,False,False,False,True,False,False,True,True,False,False,False,False,False,False,False,False,False,False,False,False,False,False,,,False,False,False,0.0,False,False,0,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False,0 -01073001400,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,,61.666668,0.087159691,0.34900002,3.16184976454882,1.0,0.4064010997350401,93.77919,0.6278134628440127,71.0,,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,,0.7501654807214959,0.8647617479139218,0.6268497920495212,0.6418426778016514,0.6564635202573585,0.7883908294582027,0.9537899773356836,0.8364273002184828,0.959938777375042,,1946.0,0.7716430824362135,0.5950327113320798,0.6753692507637045,0.5836136805171427,0.8470123197807451,0.8656408553495148,0.8594313434932582,0.6294914656404236,0.5410046960328895,False,True,True,True,True,1,1,True,1,True,1,1,True,True,9,True,False,False,True,True,False,True,True,True,True,True,False,False,False,False,False,False,False,False,False,True,False,True,True,True,True,False,False,False,True,0.0804953560371517,False,0.2950894905920146,False,False,False,False,False,False,True,True,True,1,True,True,True,False,False,True,True,False,False,True,True,True,False,True,False,True,True,True,True,True,True,True,True,False,False,True,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,True,True,6.0,True,True,1,True,True,True,True,True,False,False,True,False,False,False,False,True,True,True,1 +GEOID10_TRACT,Persistent Poverty Census Tract,Tract-level redlining score meets or exceeds 3.25,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,Contains agricultural value,"Percent of individuals below 200% Federal Poverty Line, imputed and adjusted",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,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),Contains agricultural value (percentile),"Percent of individuals below 200% Federal Poverty Line, imputed and adjusted (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),Score A,Score B,Socioeconomic Factors,Sensitive populations,Environmental effects,Exposures,Pollution Burden,Population Characteristics,Score C,"Low AMI, Low HS graduation",Meets socioeconomic criteria,Meets burden criteria,Score F (communities),Score G (communities),Score G,Score G (percentile),Score H (communities),Score H,Score I (communities),Score I,Score I (percentile),NMTC (communities),Score K (communities),Total threshold criteria exceeded,Is low income?,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?,Climate Factor (Definition L),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 L),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?,Transportation Factor (Definition L),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 and is low income?,Housing Factor (Definition L),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?,Pollution Factor (Definition L),Greater than or equal to the 90th percentile for wastewater discharge and is low income?,Water Factor (Definition L),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 L),Low high school education,Greater than or equal to the 90th percentile for households in linguistic isolation and has low HS education?,Greater than or equal to the 90th percentile for households at or below 100% federal poverty level and has low HS education?,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?,Greater than or equal to the 90th percentile for unemployment and has low HS education?,Unemployment (percent) in 2009 (island areas) and 2010 (states and PR),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 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)?,Workforce Factor (Definition L),Definition L (communities),Any Non-Workforce Factor (Definition L),Definition L (percentile),Exceeds FPL200 threshold,Percent higher ed enrollment rate is less than 20%,Is low income and has a low percent of higher ed students?,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,At least one climate threshold exceeded,"Greater than or equal to the 90th percentile for expected population loss rate, is low income, and has a low percent of higher ed students?","Greater than or equal to the 90th percentile for expected agriculture loss rate, is low income, and has a low percent of higher ed students?","Greater than or equal to the 90th percentile for expected building loss rate, is low income, and has a low percent of higher ed students?",Climate Factor (Definition M),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, is low income, and has a low percent of higher ed students?","Greater than or equal to the 90th percentile for energy burden, is low income, and has a low percent of higher ed students?",Energy Factor (Definition M),Greater than or equal to the 90th percentile for diesel particulate matter,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, is low income, and has a low percent of higher ed students?","Greater than or equal to the 90th percentile for traffic proximity, is low income, and has a low percent of higher ed students?",Transportation Factor (Definition M),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 housing burden,At least one housing threshold exceeded,"Greater than or equal to the 90th percentile for lead paint, the median house value is less than 90th percentile, is low income, and has a low percent of higher ed students?","Greater than or equal to the 90th percentile for housing burden, is low income, and has a low percent of higher ed students?",Housing Factor (Definition M),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,At least one pollution threshold exceeded,"Greater than or equal to the 90th percentile for proximity to RMP sites, is low income, and has a low percent of higher ed students?","Greater than or equal to the 90th percentile for proximity to superfund sites, is low income, and has a low percent of higher ed students?","Greater than or equal to the 90th percentile for proximity to hazardous waste facilities, is low income, and has a low percent of higher ed students?",Pollution Factor (Definition M),Greater than or equal to the 90th percentile for wastewater discharge,At least one water threshold exceeded,"Greater than or equal to the 90th percentile for wastewater discharge, is low income, and has a low percent of higher ed students?",Water Factor (Definition M),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, is low income, and has a low percent of higher ed students?","Greater than or equal to the 90th percentile for asthma, is low income, and has a low percent of higher ed students?","Greater than or equal to the 90th percentile for heart disease, is low income, and has a low percent of higher ed students?","Greater than or equal to the 90th percentile for low life expectancy, is low income, and has a low percent of higher ed students?",Health Factor (Definition M),Low high school education and low percent of higher ed students,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, has low HS attainment, and has a low percent of higher ed students?","Greater than or equal to the 90th percentile for households at or below 100% federal poverty level, has low HS attainment, and has a low percent of higher ed students?","Greater than or equal to the 90th percentile for low median household income as a percent of area median income, has low HS attainment, and has a low percent of higher ed students?","Greater than or equal to the 90th percentile for unemployment, has low HS attainment, and has a low percent of higher ed students?",Unemployment (percent) in 2009 for island areas (percentile),Percentage households below 100% of federal poverty line in 2009 for island areas (percentile),At least one workforce threshold exceeded,Both workforce socioeconomic indicators exceeded,Workforce Factor (Definition M),Total categories exceeded,Definition M (communities),Any Non-Workforce Factor (Definition M),Definition M (percentile),Is low income (imputed and adjusted)?,Climate Factor (Definition N),Energy Factor (Definition N),Greater than or equal to the 90th percentile for DOT travel barriers,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?,Housing Factor (Definition N),Pollution Factor (Definition N),Greater than or equal to the 90th percentile for leaky underwater storage tanks,Greater than or equal to the 90th percentile for leaky underground storage tanks and is low income?,Water Factor (Definition N),Health Factor (Definition N),Workforce Factor (Definition N),Definition N (communities),Definition N (communities) (percentile) +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,,62.666668,0.068036923,0.171,1.96440511031451,47.695227725,1.0,0.2853609002858206,58.143433,0.6445941476491375,70.3,,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,,0.7537922665342821,0.8019598155467721,0.4126953421856217,0.521114579532709,0.4517484245644384,0.6564635202573585,0.607629501459933,0.990724418702258,0.8218135517196475,0.97046998263836,,4781.0,0.5268273691789456,0.2659001757065985,0.37236445479144,0.3561490540102585,0.6991635185636083,0.7371717708813507,0.7245023534421032,0.3642567544008492,0.2639048758205974,False,False,True,False,True,1,1,True,1,True,1,1,True,True,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,0.1536983669548511,False,0.3189099613330878,False,False,False,False,False,False,False,False,False,0,False,True,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,True,False,False,False,False,True,False,False,False,True,False,False,True,True,False,False,False,False,False,False,False,False,False,False,False,False,False,False,,,False,False,False,0.0,False,False,0,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False,0 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diff --git a/data/data-pipeline/data_pipeline/etl/score/tests/snapshots/downloadable_data_expected.pkl b/data/data-pipeline/data_pipeline/etl/score/tests/snapshots/downloadable_data_expected.pkl index 424797a9..c7b18cd7 100644 Binary files a/data/data-pipeline/data_pipeline/etl/score/tests/snapshots/downloadable_data_expected.pkl and b/data/data-pipeline/data_pipeline/etl/score/tests/snapshots/downloadable_data_expected.pkl differ diff --git a/data/data-pipeline/data_pipeline/etl/score/tests/snapshots/score_data_expected.pkl b/data/data-pipeline/data_pipeline/etl/score/tests/snapshots/score_data_expected.pkl index 905e4f37..2700c78b 100644 Binary files a/data/data-pipeline/data_pipeline/etl/score/tests/snapshots/score_data_expected.pkl and b/data/data-pipeline/data_pipeline/etl/score/tests/snapshots/score_data_expected.pkl differ diff --git a/data/data-pipeline/data_pipeline/etl/score/tests/snapshots/score_transformed_expected.pkl 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b/data/data-pipeline/data_pipeline/etl/sources/dot_travel_composite/README.md @@ -0,0 +1,16 @@ +# DOT travel barriers + +The below description is taken from DOT directly: + +Consistent with OMB’s Interim Guidance for the Justice40 Initiative, DOT’s interim definition of DACs includes (a) certain qualifying census tracts, (b) any Tribal land, or (c) any territory or possession of the United States. DOT has provided a mapping tool to assist applicants in identifying whether a project is located in a Disadvantaged Community, available at Transportation Disadvantaged Census Tracts (arcgis.com). A shapefile of the geospatial data is available Transportation Disadvantaged Census Tracts shapefile (version 2 .0, posted 5/10/22). + +The DOT interim definition for DACs was developed by an internal and external collaborative research process (see recordings from November 2021 public meetings). It includes data for 22 indicators collected at the census tract level and grouped into six (6) categories of transportation disadvantage. The numbers in parenthesis show how many indicators fall in that category: + +- Transportation access disadvantage identifies communities and places that spend more, and take longer, to get where they need to go. (4) +- Health disadvantage identifies communities based on variables associated with adverse health outcomes, disability, as well as environmental exposures. (3) +- Environmental disadvantage identifies communities with disproportionately high levels of certain air pollutants and high potential presence of lead-based paint in housing units. (6) +- Economic disadvantage identifies areas and populations with high poverty, low wealth, lack of local jobs, low homeownership, low educational attainment, and high inequality. (7) +Resilience disadvantage identifies communities vulnerable to hazards caused by climate change. (1) +- Equity disadvantage identifies communities with a with a high percentile of persons (age 5+) who speak English "less than well." (1) + +The CEJST uses only Transportation Access Disadvantage. \ No newline at end of file diff --git a/data/data-pipeline/data_pipeline/etl/sources/dot_travel_composite/__init__.py b/data/data-pipeline/data_pipeline/etl/sources/dot_travel_composite/__init__.py new file mode 100644 index 00000000..e69de29b 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 new file mode 100644 index 00000000..2a99f76f --- /dev/null +++ b/data/data-pipeline/data_pipeline/etl/sources/dot_travel_composite/etl.py @@ -0,0 +1,59 @@ +# pylint: disable=unsubscriptable-object +# pylint: disable=unsupported-assignment-operation + +import pandas as pd +import geopandas as gpd + +from data_pipeline.etl.base import ExtractTransformLoad, ValidGeoLevel +from data_pipeline.utils import get_module_logger + +logger = get_module_logger(__name__) + + +class TravelCompositeETL(ExtractTransformLoad): + """ETL class for the DOT Travel Disadvantage Dataset""" + + NAME = "travel_composite" + SOURCE_URL = "https://www.transportation.gov/sites/dot.gov/files/Shapefile_and_Metadata.zip" + GEO_LEVEL = ValidGeoLevel.CENSUS_TRACT + + # Output score variables (values set on datasets.yml) for linting purposes + TRAVEL_BURDEN_FIELD_NAME: str + + def __init__(self): + # define the full path for the input CSV file + self.INPUT_SHP = ( + self.get_tmp_path() / "DOT_Disadvantage_Layer_Final_April2022.shp" + ) + + # this is the main dataframe + self.df: pd.DataFrame + + # Start dataset-specific vars here + ## Average of Transportation Indicator Percentiles (calculated) + ## Calculated: Average of (EPL_TCB+EPL_NWKI+EPL_NOVEH+EPL_COMMUTE) excluding NULLS + ## See metadata for more information + self.INPUT_TRAVEL_DISADVANTAGE_FIELD_NAME = "Transp_TH" + self.INPUT_GEOID_TRACT_FIELD_NAME = "FIPS" + + def transform(self) -> None: + """Reads the unzipped data file into memory and applies the following + transformations to prepare it for the load() method: + + - Renames the Census Tract column to match the other datasets + - Converts to CSV + """ + logger.info("Transforming DOT Travel Disadvantage Data") + + # read in the unzipped shapefile from data source + # reformat it to be standard df, remove unassigned rows, and + # then rename the Census Tract column for merging + df_dot: pd.DataFrame = gpd.read_file(self.INPUT_SHP) + df_dot = df_dot.rename( + columns={ + self.INPUT_GEOID_TRACT_FIELD_NAME: self.GEOID_TRACT_FIELD_NAME, + self.INPUT_TRAVEL_DISADVANTAGE_FIELD_NAME: self.TRAVEL_BURDEN_FIELD_NAME, + } + ).dropna(subset=[self.GEOID_TRACT_FIELD_NAME]) + # Assign the final df to the class' output_df for the load method + self.output_df = df_dot diff --git a/data/data-pipeline/data_pipeline/score/field_names.py b/data/data-pipeline/data_pipeline/score/field_names.py index 4fa0a75e..41af88da 100644 --- a/data/data-pipeline/data_pipeline/score/field_names.py +++ b/data/data-pipeline/data_pipeline/score/field_names.py @@ -344,6 +344,9 @@ CDC_SVI_INDEX_RPL_THEMES_OVERALL_FIELD: str = ( ) CDC_SVI_INDEX_THEMES_PRIORITY_COMMUNITY: str = "At or above 90 for overall percentile ranking according to Social Vulnerability Indices" +# DOT Travel Burden Data +DOT_TRAVEL_BURDEN_FIELD: str = "DOT Travel Barriers Score" + # Maryland EJSCREEN Data. MARYLAND_EJSCREEN_SCORE_FIELD: str = "Maryland Environmental Justice Score" @@ -416,6 +419,7 @@ DIESEL_PARTICULATE_MATTER_LOW_INCOME_FIELD = ( ) TRAFFIC_PROXIMITY_LOW_INCOME_FIELD = f"Greater than or equal to the {PERCENTILE}th percentile for traffic proximity and is low income?" + # Affordable and Sustainable Housing LEAD_PAINT_MEDIAN_HOUSE_VALUE_LOW_INCOME_FIELD = ( f"Greater than or equal to the {PERCENTILE}th percentile for lead paint and" @@ -494,6 +498,10 @@ TRAFFIC_PROXIMITY_LOW_INCOME_LOW_HIGHER_ED_FIELD = ( f"traffic proximity{SCORE_M_LOW_INCOME_SUFFIX}?" ) +DOT_TRAVEL_BURDEN_LOW_INCOME_FIELD = ( + f"Greater than or equal to the {PERCENTILE}th percentile " + f"for DOT transit barriers and is low income?" +) # Affordable and Sustainable Housing LEAD_PAINT_MEDIAN_HOUSE_VALUE_LOW_INCOME_LOW_HIGHER_ED_FIELD = ( f"Greater than or equal to the {PERCENTILE}th percentile for lead paint," @@ -624,6 +632,7 @@ PM25_EXCEEDS_PCTILE_THRESHOLD = ( ) DIESEL_EXCEEDS_PCTILE_THRESHOLD = f"Greater than or equal to the {PERCENTILE}th percentile for diesel particulate matter" TRAFFIC_PROXIMITY_PCTILE_THRESHOLD = f"Greater than or equal to the {PERCENTILE}th percentile for traffic proximity" +DOT_BURDEN_PCTILE_THRESHOLD = f"Greater than or equal to the {PERCENTILE}th percentile for DOT travel barriers" LEAD_PAINT_PROXY_PCTILE_THRESHOLD = ( f"Greater than or equal to the {PERCENTILE}th percentile for lead paint and" f" the median house value is less than {MEDIAN_HOUSE_VALUE_PERCENTILE}th " diff --git a/data/data-pipeline/data_pipeline/score/score_narwhal.py b/data/data-pipeline/data_pipeline/score/score_narwhal.py index e729134e..3c554fc5 100644 --- a/data/data-pipeline/data_pipeline/score/score_narwhal.py +++ b/data/data-pipeline/data_pipeline/score/score_narwhal.py @@ -246,6 +246,8 @@ class ScoreNarwhal(Score): # In Xth percentile or above for PM 2.5 (Source: EPA, Office of Air and Radiation (OAR) fusion of model and monitor data)] # or # In Xth percentile or above traffic proximity and volume (Source: 2017 U.S. Department of Transportation (DOT) traffic data + # or + # In Xth percentile or above for DOT Travel Disadvantage # AND # Low income: In Nth percentile or above for percent of block group population # of households where household income is less than or equal to twice the federal @@ -255,6 +257,7 @@ class ScoreNarwhal(Score): transportion_eligibility_columns = [ field_names.DIESEL_PARTICULATE_MATTER_LOW_INCOME_FIELD, field_names.TRAFFIC_PROXIMITY_LOW_INCOME_FIELD, + field_names.DOT_TRAVEL_BURDEN_LOW_INCOME_FIELD, ] self.df[field_names.DIESEL_EXCEEDS_PCTILE_THRESHOLD] = ( @@ -264,6 +267,14 @@ class ScoreNarwhal(Score): >= self.ENVIRONMENTAL_BURDEN_THRESHOLD ) + self.df[field_names.DOT_BURDEN_PCTILE_THRESHOLD] = ( + self.df[ + field_names.DOT_TRAVEL_BURDEN_FIELD + + field_names.PERCENTILE_FIELD_SUFFIX + ] + >= self.ENVIRONMENTAL_BURDEN_THRESHOLD + ) + self.df[field_names.TRAFFIC_PROXIMITY_PCTILE_THRESHOLD] = ( self.df[ field_names.TRAFFIC_FIELD + field_names.PERCENTILE_FIELD_SUFFIX @@ -274,6 +285,7 @@ class ScoreNarwhal(Score): self.df[field_names.TRAFFIC_THRESHOLD_EXCEEDED] = ( self.df[field_names.TRAFFIC_PROXIMITY_PCTILE_THRESHOLD] | self.df[field_names.DIESEL_EXCEEDS_PCTILE_THRESHOLD] + | self.df[field_names.DOT_BURDEN_PCTILE_THRESHOLD] ) self.df[field_names.DIESEL_PARTICULATE_MATTER_LOW_INCOME_FIELD] = ( @@ -286,6 +298,11 @@ class ScoreNarwhal(Score): & self.df[field_names.FPL_200_SERIES_IMPUTED_AND_ADJUSTED] ) + self.df[field_names.DOT_TRAVEL_BURDEN_LOW_INCOME_FIELD] = ( + self.df[field_names.DOT_BURDEN_PCTILE_THRESHOLD] + & self.df[field_names.FPL_200_SERIES_IMPUTED_AND_ADJUSTED] + ) + self._increment_total_eligibility_exceeded( transportion_eligibility_columns, skip_fips=constants.DROP_FIPS_FROM_NON_WTD_THRESHOLDS, diff --git a/data/data-pipeline/poetry.lock b/data/data-pipeline/poetry.lock index 7d134765..b7e685fb 100644 --- a/data/data-pipeline/poetry.lock +++ b/data/data-pipeline/poetry.lock @@ -95,10 +95,10 @@ optional = false python-versions = ">=3.5" [package.extras] -dev = ["coverage[toml] (>=5.0.2)", "hypothesis", "pympler", "pytest (>=4.3.0)", "mypy (>=0.900,!=0.940)", "pytest-mypy-plugins", "zope.interface", "furo", "sphinx", "sphinx-notfound-page", "pre-commit", "cloudpickle"] -docs = ["furo", "sphinx", "zope.interface", "sphinx-notfound-page"] -tests = ["coverage[toml] (>=5.0.2)", "hypothesis", "pympler", "pytest (>=4.3.0)", "mypy (>=0.900,!=0.940)", "pytest-mypy-plugins", "zope.interface", "cloudpickle"] -tests_no_zope = ["coverage[toml] (>=5.0.2)", "hypothesis", "pympler", "pytest (>=4.3.0)", "mypy (>=0.900,!=0.940)", "pytest-mypy-plugins", "cloudpickle"] +tests_no_zope = ["cloudpickle", "pytest-mypy-plugins", "mypy (>=0.900,!=0.940)", "pytest (>=4.3.0)", "pympler", "hypothesis", "coverage[toml] (>=5.0.2)"] +tests = ["cloudpickle", "zope.interface", "pytest-mypy-plugins", "mypy (>=0.900,!=0.940)", "pytest (>=4.3.0)", "pympler", "hypothesis", "coverage[toml] (>=5.0.2)"] +docs = ["sphinx-notfound-page", "zope.interface", "sphinx", "furo"] +dev = ["cloudpickle", "pre-commit", "sphinx-notfound-page", "sphinx", "furo", "zope.interface", "pytest-mypy-plugins", "mypy (>=0.900,!=0.940)", "pytest (>=4.3.0)", "pympler", "hypothesis", "coverage[toml] (>=5.0.2)"] [[package]] name = "babel" @@ -287,7 +287,7 @@ python-versions = ">=3.6" [[package]] name = "debugpy" -version = "1.6.2" +version = "1.6.3" description = "An implementation of the Debug Adapter Protocol for Python" category = "main" optional = false @@ -353,14 +353,14 @@ optional = false python-versions = ">=3.7" [package.extras] -all = ["redis", "ruamel.yaml", "configobj", "hvac"] -configobj = ["configobj"] -ini = ["configobj"] -redis = ["redis"] -test = ["pytest", "pytest-cov", "pytest-xdist", "pytest-mock", "flake8", "pep8-naming", "flake8-debugger", "flake8-print", "flake8-todo", "radon", "flask (>=0.12)", "django", "python-dotenv", "toml", "codecov", "redis", "hvac", "configobj"] -toml = ["toml"] -vault = ["hvac"] yaml = ["ruamel.yaml"] +vault = ["hvac"] +toml = ["toml"] +test = ["configobj", "hvac", "redis", "codecov", "toml", "python-dotenv", "django", "flask (>=0.12)", "radon", "flake8-todo", "flake8-print", "flake8-debugger", "pep8-naming", "flake8", "pytest-mock", "pytest-xdist", "pytest-cov", "pytest"] +redis = ["redis"] +ini = ["configobj"] +configobj = ["configobj"] +all = ["hvac", "configobj", "ruamel.yaml", "redis"] [[package]] name = "entrypoints" @@ -439,7 +439,7 @@ pyflakes = ">=2.3.0,<2.4.0" [[package]] name = "fonttools" -version = "4.34.4" +version = "4.35.0" description = "Tools to manipulate font files" category = "main" optional = false @@ -556,7 +556,7 @@ tornado = ">=6.1" traitlets = ">=5.1.0" [package.extras] -test = ["flaky", "ipyparallel", "pre-commit", "pytest-cov", "pytest-timeout", "pytest (>=6.0)"] +test = ["pytest (>=6.0)", "pytest-timeout", "pytest-cov", "pre-commit", "ipyparallel", "flaky"] [[package]] name = "ipython" @@ -580,15 +580,15 @@ pygments = "*" traitlets = ">=4.2" [package.extras] -all = ["Sphinx (>=1.3)", "ipykernel", "ipyparallel", "ipywidgets", "nbconvert", "nbformat", "nose (>=0.10.1)", "notebook", "numpy (>=1.17)", "pygments", "qtconsole", "requests", "testpath"] -doc = ["Sphinx (>=1.3)"] -kernel = ["ipykernel"] -nbconvert = ["nbconvert"] -nbformat = ["nbformat"] -notebook = ["notebook", "ipywidgets"] -parallel = ["ipyparallel"] +test = ["numpy (>=1.17)", "ipykernel", "nbformat", "pygments", "testpath", "requests", "nose (>=0.10.1)"] qtconsole = ["qtconsole"] -test = ["nose (>=0.10.1)", "requests", "testpath", "pygments", "nbformat", "ipykernel", "numpy (>=1.17)"] +parallel = ["ipyparallel"] +notebook = ["ipywidgets", "notebook"] +nbformat = ["nbformat"] +nbconvert = ["nbconvert"] +kernel = ["ipykernel"] +doc = ["Sphinx (>=1.3)"] +all = ["testpath", "requests", "qtconsole", "pygments", "numpy (>=1.17)", "notebook", "nose (>=0.10.1)", "nbformat", "nbconvert", "ipywidgets", "ipyparallel", "ipykernel", "Sphinx (>=1.3)"] [[package]] name = "ipython-genutils" @@ -615,7 +615,7 @@ traitlets = ">=4.3.1" widgetsnbextension = ">=3.6.0,<3.7.0" [package.extras] -test = ["pytest (>=3.6.0)", "pytest-cov", "mock"] +test = ["mock", "pytest-cov", "pytest (>=3.6.0)"] [[package]] name = "isort" @@ -681,7 +681,7 @@ dev = ["hypothesis"] [[package]] name = "jsonschema" -version = "4.9.1" +version = "4.10.0" description = "An implementation of JSON Schema validation for Python" category = "main" optional = false @@ -731,8 +731,8 @@ tornado = ">=6.0" traitlets = "*" [package.extras] -doc = ["ipykernel", "myst-parser", "sphinx-rtd-theme", "sphinx (>=1.3.6)", "sphinxcontrib-github-alt"] -test = ["codecov", "coverage", "ipykernel (>=6.5)", "ipython", "mypy", "pre-commit", "pytest", "pytest-asyncio (>=0.18)", "pytest-cov", "pytest-timeout"] +test = ["pytest-timeout", "pytest-cov", "pytest-asyncio (>=0.18)", "pytest", "pre-commit", "mypy", "ipython", "ipykernel (>=6.5)", "coverage", "codecov"] +doc = ["sphinxcontrib-github-alt", "sphinx (>=1.3.6)", "sphinx-rtd-theme", "myst-parser", "ipykernel"] [[package]] name = "jupyter-console" @@ -792,7 +792,7 @@ tornado = "*" traitlets = ">=4.1" [package.extras] -test = ["mock", "requests", "pip", "nose", "nbformat"] +test = ["nbformat", "nose", "pip", "requests", "mock"] [[package]] name = "jupyter-core" @@ -807,7 +807,7 @@ pywin32 = {version = ">=1.0", markers = "sys_platform == \"win32\" and platform_ traitlets = "*" [package.extras] -test = ["ipykernel", "pre-commit", "pytest", "pytest-cov", "pytest-timeout"] +test = ["pytest-timeout", "pytest-cov", "pytest", "pre-commit", "ipykernel"] [[package]] name = "jupyter-highlight-selected-word" @@ -979,10 +979,10 @@ optional = false python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, != 3.4.*" [package.extras] -cssselect = ["cssselect (>=0.7)"] -html5 = ["html5lib"] -htmlsoup = ["beautifulsoup4"] source = ["Cython (>=0.29.7)"] +htmlsoup = ["beautifulsoup4"] +html5 = ["html5lib"] +cssselect = ["cssselect (>=0.7)"] [[package]] name = "markupsafe" @@ -1022,12 +1022,12 @@ marshmallow = ">=3.13.0,<4.0" typing-inspect = ">=0.7.1" [package.extras] -dev = ["marshmallow-enum", "typeguard", "pre-commit (>=2.17,<3.0)", "sphinx", "pytest (>=5.4)", "pytest-mypy-plugins (>=1.2.0)", "typing-extensions (>=3.7.2)"] -docs = ["sphinx"] -enum = ["marshmallow-enum"] -lint = ["pre-commit (>=2.17,<3.0)"] -tests = ["pytest (>=5.4)", "pytest-mypy-plugins (>=1.2.0)", "typing-extensions (>=3.7.2)"] union = ["typeguard"] +tests = ["typing-extensions (>=3.7.2)", "pytest-mypy-plugins (>=1.2.0)", "pytest (>=5.4)"] +lint = ["pre-commit (>=2.17,<3.0)"] +enum = ["marshmallow-enum"] +docs = ["sphinx"] +dev = ["typing-extensions (>=3.7.2)", "pytest-mypy-plugins (>=1.2.0)", "pytest (>=5.4)", "sphinx", "pre-commit (>=2.17,<3.0)", "typeguard", "marshmallow-enum"] [[package]] name = "marshmallow-enum" @@ -1042,7 +1042,7 @@ marshmallow = ">=2.0.0" [[package]] name = "matplotlib" -version = "3.5.2" +version = "3.5.3" description = "Python plotting package" category = "main" optional = false @@ -1057,7 +1057,7 @@ packaging = ">=20.0" pillow = ">=6.2.0" pyparsing = ">=2.2.1" python-dateutil = ">=2.7" -setuptools_scm = ">=4" +setuptools_scm = ">=4,<7" [[package]] name = "matplotlib-inline" @@ -1165,9 +1165,9 @@ tornado = ">=6.1" traitlets = ">=4.2.1" [package.extras] -docs = ["sphinx", "nbsphinx", "sphinxcontrib-github-alt", "sphinx-rtd-theme", "myst-parser"] +test = ["requests-unixsocket", "pytest-tornasync", "pytest-cov", "selenium (==4.1.5)", "nbval", "testpath", "requests", "coverage", "pytest"] json-logging = ["json-logging"] -test = ["pytest", "coverage", "requests", "testpath", "nbval", "selenium (==4.1.5)", "pytest-cov", "pytest-tornasync", "requests-unixsocket"] +docs = ["myst-parser", "sphinx-rtd-theme", "sphinxcontrib-github-alt", "nbsphinx", "sphinx"] [[package]] name = "nbclient" @@ -1189,7 +1189,7 @@ test = ["black", "check-manifest", "flake8", "ipykernel", "ipython (<8.0.0)", "i [[package]] name = "nbconvert" -version = "6.5.2" +version = "6.5.3" description = "Converting Jupyter Notebooks" category = "main" optional = false @@ -1272,9 +1272,9 @@ tornado = ">=6.1" traitlets = ">=4.2.1" [package.extras] -docs = ["sphinx", "nbsphinx", "sphinxcontrib-github-alt", "sphinx-rtd-theme", "myst-parser"] +test = ["requests-unixsocket", "pytest-cov", "selenium", "nbval", "testpath", "requests", "coverage", "pytest"] json-logging = ["json-logging"] -test = ["pytest", "coverage", "requests", "testpath", "nbval", "selenium", "pytest-cov", "requests-unixsocket"] +docs = ["myst-parser", "sphinx-rtd-theme", "sphinxcontrib-github-alt", "nbsphinx", "sphinx"] [[package]] name = "notebook-shim" @@ -1288,11 +1288,11 @@ python-versions = ">=3.7" jupyter-server = ">=1.8,<2.0" [package.extras] -test = ["pytest", "pytest-tornasync", "pytest-console-scripts"] +test = ["pytest-console-scripts", "pytest-tornasync", "pytest"] [[package]] name = "numpy" -version = "1.23.1" +version = "1.23.2" description = "NumPy is the fundamental package for array computing with Python." category = "main" optional = false @@ -1330,16 +1330,16 @@ python-versions = ">=3.8" [package.dependencies] numpy = [ - {version = ">=1.18.5", markers = "platform_machine != \"aarch64\" and platform_machine != \"arm64\" and python_version < \"3.10\""}, - {version = ">=1.19.2", markers = "platform_machine == \"aarch64\" and python_version < \"3.10\""}, - {version = ">=1.20.0", markers = "platform_machine == \"arm64\" and python_version < \"3.10\""}, {version = ">=1.21.0", markers = "python_version >= \"3.10\""}, + {version = ">=1.20.0", markers = "platform_machine == \"arm64\" and python_version < \"3.10\""}, + {version = ">=1.19.2", markers = "platform_machine == \"aarch64\" and python_version < \"3.10\""}, + {version = ">=1.18.5", markers = "platform_machine != \"aarch64\" and platform_machine != \"arm64\" and python_version < \"3.10\""}, ] python-dateutil = ">=2.8.1" pytz = ">=2020.1" [package.extras] -test = ["hypothesis (>=5.5.3)", "pytest (>=6.0)", "pytest-xdist (>=1.31)"] +test = ["pytest-xdist (>=1.31)", "pytest (>=6.0)", "hypothesis (>=5.5.3)"] [[package]] name = "pandas-vet" @@ -1363,11 +1363,11 @@ python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*" [[package]] name = "papermill" -version = "2.3.4" +version = "2.4.0" description = "Parametrize and run Jupyter and nteract Notebooks" category = "dev" optional = false -python-versions = ">=3.6" +python-versions = ">=3.7" [package.dependencies] ansiwrap = "*" @@ -1381,15 +1381,15 @@ tenacity = "*" tqdm = ">=4.32.2" [package.extras] -all = ["boto3", "azure-datalake-store (>=0.0.30)", "azure-storage-blob (>=12.1.0)", "requests (>=2.21.0)", "gcsfs (>=0.2.0)", "pyarrow", "black (>=19.3b0)"] +all = ["boto3", "azure-datalake-store (>=0.0.30)", "azure-storage-blob (>=12.1.0)", "requests (>=2.21.0)", "gcsfs (>=0.2.0)", "pyarrow (>=2.0)", "black (>=19.3b0)"] azure = ["azure-datalake-store (>=0.0.30)", "azure-storage-blob (>=12.1.0)", "requests (>=2.21.0)"] black = ["black (>=19.3b0)"] -dev = ["boto3", "botocore", "codecov", "coverage", "google-compute-engine", "ipython (>=5.0)", "ipywidgets", "notebook", "mock", "moto", "pytest (>=4.1)", "pytest-cov (>=2.6.1)", "pytest-mock (>=1.10)", "pytest-env (>=0.6.2)", "requests (>=2.21.0)", "check-manifest", "attrs (>=17.4.0)", "pre-commit", "flake8", "tox", "bumpversion", "recommonmark", "pip (>=18.1)", "wheel (>=0.31.0)", "setuptools (>=38.6.0)", "twine (>=1.11.0)", "azure-datalake-store (>=0.0.30)", "azure-storage-blob (>=12.1.0)", "gcsfs (>=0.2.0)", "pyarrow", "black (>=19.3b0)"] +dev = ["boto3", "botocore", "codecov", "coverage", "google-compute-engine", "ipython (>=5.0)", "ipywidgets", "notebook", "moto", "pytest (>=4.1)", "pytest-cov (>=2.6.1)", "pytest-mock (>=1.10)", "pytest-env (>=0.6.2)", "requests (>=2.21.0)", "check-manifest", "attrs (>=17.4.0)", "pre-commit", "flake8", "tox", "bumpversion", "recommonmark", "pip (>=18.1)", "wheel (>=0.31.0)", "setuptools (>=38.6.0)", "twine (>=1.11.0)", "azure-datalake-store (>=0.0.30)", "azure-storage-blob (>=12.1.0)", "gcsfs (>=0.2.0)", "pyarrow (>=2.0)", "black (>=19.3b0)"] gcs = ["gcsfs (>=0.2.0)"] github = ["PyGithub (>=1.55)"] -hdfs = ["pyarrow"] +hdfs = ["pyarrow (>=2.0)"] s3 = ["boto3"] -test = ["boto3", "botocore", "codecov", "coverage", "google-compute-engine", "ipython (>=5.0)", "ipywidgets", "notebook", "mock", "moto", "pytest (>=4.1)", "pytest-cov (>=2.6.1)", "pytest-mock (>=1.10)", "pytest-env (>=0.6.2)", "requests (>=2.21.0)", "check-manifest", "attrs (>=17.4.0)", "pre-commit", "flake8", "tox", "bumpversion", "recommonmark", "pip (>=18.1)", "wheel (>=0.31.0)", "setuptools (>=38.6.0)", "twine (>=1.11.0)", "azure-datalake-store (>=0.0.30)", "azure-storage-blob (>=12.1.0)", "gcsfs (>=0.2.0)", "pyarrow", "black (>=19.3b0)"] +test = ["boto3", "botocore", "codecov", "coverage", "google-compute-engine", "ipython (>=5.0)", "ipywidgets", "notebook", "moto", "pytest (>=4.1)", "pytest-cov (>=2.6.1)", "pytest-mock (>=1.10)", "pytest-env (>=0.6.2)", "requests (>=2.21.0)", "check-manifest", "attrs (>=17.4.0)", "pre-commit", "flake8", "tox", "bumpversion", "recommonmark", "pip (>=18.1)", "wheel (>=0.31.0)", "setuptools (>=38.6.0)", "twine (>=1.11.0)", "azure-datalake-store (>=0.0.30)", "azure-storage-blob (>=12.1.0)", "gcsfs (>=0.2.0)", "pyarrow (>=2.0)", "black (>=19.3b0)"] [[package]] name = "parso" @@ -1467,8 +1467,8 @@ optional = false python-versions = ">=3.6" [package.extras] -testing = ["pytest-benchmark", "pytest"] -dev = ["tox", "pre-commit"] +dev = ["pre-commit", "tox"] +testing = ["pytest", "pytest-benchmark"] [[package]] name = "prometheus-client" @@ -1501,7 +1501,7 @@ optional = false python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*" [package.extras] -test = ["ipaddress", "mock", "enum34", "pywin32", "wmi"] +test = ["wmi", "pywin32", "enum34", "mock", "ipaddress"] [[package]] name = "ptyprocess" @@ -1537,7 +1537,7 @@ python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*" [[package]] name = "pydantic" -version = "1.9.1" +version = "1.9.2" description = "Data validation and settings management using python type hints" category = "main" optional = false @@ -1560,12 +1560,15 @@ python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*" [[package]] name = "pygments" -version = "2.12.0" +version = "2.13.0" description = "Pygments is a syntax highlighting package written in Python." category = "main" optional = false python-versions = ">=3.6" +[package.extras] +plugins = ["importlib-metadata"] + [[package]] name = "pylint" version = "2.14.5" @@ -1606,7 +1609,7 @@ optional = false python-versions = ">=3.6.8" [package.extras] -diagrams = ["railroad-diagrams", "jinja2"] +diagrams = ["jinja2", "railroad-diagrams"] [[package]] name = "pyproj" @@ -1660,7 +1663,7 @@ python-versions = ">=3.7" pytest = ">=5.0" [package.extras] -dev = ["pre-commit", "tox", "pytest-asyncio"] +dev = ["pytest-asyncio", "tox", "pre-commit"] [[package]] name = "pytest-snapshot" @@ -1686,7 +1689,7 @@ six = ">=1.5" [[package]] name = "pytz" -version = "2022.1" +version = "2022.2.1" description = "World timezone definitions, modern and historical" category = "main" optional = false @@ -1718,7 +1721,7 @@ python-versions = ">=3.6" [[package]] name = "pyzmq" -version = "23.2.0" +version = "23.2.1" description = "Python bindings for 0MQ" category = "main" optional = false @@ -1747,8 +1750,8 @@ qtpy = ">=2.0.1" traitlets = "<5.2.1 || >5.2.1,<5.2.2 || >5.2.2" [package.extras] +test = ["pytest-qt", "pytest", "flaky"] doc = ["Sphinx (>=1.3)"] -test = ["flaky", "pytest", "pytest-qt"] [[package]] name = "qtpy" @@ -1779,8 +1782,8 @@ idna = ">=2.5,<4" urllib3 = ">=1.21.1,<1.27" [package.extras] -socks = ["PySocks (>=1.5.6,!=1.5.7)"] use_chardet_on_py3 = ["chardet (>=3.0.2,<6)"] +socks = ["PySocks (>=1.5.6,!=1.5.7)"] [[package]] name = "rtree" @@ -1838,8 +1841,8 @@ optional = false python-versions = ">=2.7" [package.extras] -dev = ["Django (>=1.11)", "nose2", "tox", "check-manifest", "coverage", "flake8", "wheel", "zest.releaser", "readme-renderer (<25.0)", "colorama (<=0.4.1)"] -doc = ["sphinx", "sphinx-rtd-theme"] +doc = ["sphinx-rtd-theme", "sphinx"] +dev = ["colorama (<=0.4.1)", "readme-renderer (<25.0)", "zest.releaser", "wheel", "flake8", "coverage", "check-manifest", "tox", "nose2", "Django (>=1.11)"] [[package]] name = "send2trash" @@ -1856,16 +1859,15 @@ win32 = ["pywin32"] [[package]] name = "setuptools-scm" -version = "7.0.5" +version = "6.4.2" description = "the blessed package to manage your versions by scm tags" category = "main" optional = false -python-versions = ">=3.7" +python-versions = ">=3.6" [package.dependencies] packaging = ">=20.0" tomli = ">=1.0.0" -typing-extensions = "*" [package.extras] test = ["pytest (>=6.2)", "virtualenv (>20)"] @@ -1933,7 +1935,7 @@ pywinpty = {version = ">=1.1.0", markers = "os_name == \"nt\""} tornado = ">=6.1.0" [package.extras] -test = ["pre-commit", "pytest-timeout", "pytest (>=6.0)"] +test = ["pytest (>=6.0)", "pytest-timeout", "pre-commit"] [[package]] name = "textwrap3" @@ -1976,7 +1978,7 @@ python-versions = ">=3.6" [[package]] name = "tomlkit" -version = "0.11.3" +version = "0.11.4" description = "Style preserving TOML library" category = "main" optional = false @@ -2009,8 +2011,8 @@ toml = ">=0.9.4" virtualenv = ">=16.0.0,<20.0.0 || >20.0.0,<20.0.1 || >20.0.1,<20.0.2 || >20.0.2,<20.0.3 || >20.0.3,<20.0.4 || >20.0.4,<20.0.5 || >20.0.5,<20.0.6 || >20.0.6,<20.0.7 || >20.0.7" [package.extras] -docs = ["pygments-github-lexers (>=0.0.5)", "sphinx (>=2.0.0)", "sphinxcontrib-autoprogram (>=0.1.5)", "towncrier (>=18.5.0)"] -testing = ["flaky (>=3.4.0)", "freezegun (>=0.3.11)", "pytest (>=4.0.0)", "pytest-cov (>=2.5.1)", "pytest-mock (>=1.10.0)", "pytest-randomly (>=1.0.0)", "psutil (>=5.6.1)", "pathlib2 (>=2.3.3)"] +testing = ["pathlib2 (>=2.3.3)", "psutil (>=5.6.1)", "pytest-randomly (>=1.0.0)", "pytest-mock (>=1.10.0)", "pytest-cov (>=2.5.1)", "pytest (>=4.0.0)", "freezegun (>=0.3.11)", "flaky (>=3.4.0)"] +docs = ["towncrier (>=18.5.0)", "sphinxcontrib-autoprogram (>=0.1.5)", "sphinx (>=2.0.0)", "pygments-github-lexers (>=0.0.5)"] [[package]] name = "tox-poetry" @@ -2026,7 +2028,7 @@ toml = "*" tox = {version = ">=3.7.0", markers = "python_version >= \"3\""} [package.extras] -test = ["pylint", "pycodestyle", "pytest", "coverage"] +test = ["coverage", "pytest", "pycodestyle", "pylint"] [[package]] name = "tqdm" @@ -2053,7 +2055,7 @@ optional = false python-versions = ">=3.7" [package.extras] -test = ["pre-commit", "pytest"] +test = ["pytest", "pre-commit"] [[package]] name = "types-requests" @@ -2103,9 +2105,9 @@ optional = false python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*, <4" [package.extras] -brotli = ["brotlicffi (>=0.8.0)", "brotli (>=1.0.9)", "brotlipy (>=0.6.0)"] -secure = ["pyOpenSSL (>=0.14)", "cryptography (>=1.3.4)", "idna (>=2.0.0)", "certifi", "ipaddress"] socks = ["PySocks (>=1.5.6,!=1.5.7,<2.0)"] +secure = ["ipaddress", "certifi", "idna (>=2.0.0)", "cryptography (>=1.3.4)", "pyOpenSSL (>=0.14)"] +brotli = ["brotlipy (>=0.6.0)", "brotli (>=1.0.9)", "brotlicffi (>=0.8.0)"] [[package]] name = "us" @@ -2209,11 +2211,11 @@ python-versions = "^3.8" content-hash = "f3d61a8c4ca54c580ba8d459aa76a9d956e046c2b8339602497ce53393ba9983" [metadata.files] -ansiwrap = [] -anyio = [ - {file = "anyio-3.6.1-py3-none-any.whl", hash = "sha256:cb29b9c70620506a9a8f87a309591713446953302d7d995344d0d7c6c0c9a7be"}, - {file = "anyio-3.6.1.tar.gz", hash = "sha256:413adf95f93886e442aea925f3ee43baa5a765a64a0f52c6081894f9992fdd0b"}, +ansiwrap = [ + {file = "ansiwrap-0.8.4-py2.py3-none-any.whl", hash = "sha256:7b053567c88e1ad9eed030d3ac41b722125e4c1271c8a99ade797faff1f49fb1"}, + {file = "ansiwrap-0.8.4.zip", hash = "sha256:ca0c740734cde59bf919f8ff2c386f74f9a369818cdc60efe94893d01ea8d9b7"}, ] +anyio = [] appnope = [ {file = "appnope-0.1.3-py2.py3-none-any.whl", hash = "sha256:265a455292d0bd8a72453494fa24df5a11eb18373a60c7c0430889f22548605e"}, {file = "appnope-0.1.3.tar.gz", hash = "sha256:02bd91c4de869fbb1e1c50aafc4098827a7a54ab2f39d9dcba6c9547ed920e24"}, @@ -2248,10 +2250,7 @@ argon2-cffi-bindings = [ astroid = [] atomicwrites = [] attrs = [] -babel = [ - {file = "Babel-2.10.3-py3-none-any.whl", hash = "sha256:ff56f4892c1c4bf0d814575ea23471c230d544203c7748e8c68f0089478d48eb"}, - {file = "Babel-2.10.3.tar.gz", hash = "sha256:7614553711ee97490f732126dc077f8d0ae084ebc6a96e23db1482afabdb2c51"}, -] +babel = [] backcall = [ {file = "backcall-0.2.0-py2.py3-none-any.whl", hash = "sha256:fbbce6a29f263178a1f7915c1940bde0ec2b2a967566fe1c65c1dfb7422bd255"}, {file = "backcall-0.2.0.tar.gz", hash = "sha256:5cbdbf27be5e7cfadb448baf0aa95508f91f2bbc6c6437cd9cd06e2a4c215e1e"}, @@ -2260,30 +2259,38 @@ beautifulsoup4 = [ {file = "beautifulsoup4-4.11.1-py3-none-any.whl", hash = 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