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96 lines
3.1 KiB
Python
96 lines
3.1 KiB
Python
"""Utilities to help generate the score."""
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import data_pipeline.score.field_names as field_names
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import geopandas as gpd
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import pandas as pd
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from data_pipeline.etl.sources.geo_utils import get_tract_geojson
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from data_pipeline.utils import get_module_logger
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# XXX: @jorge I am torn about the coupling that importing from
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# etl.sources vs keeping the code DRY. Thoughts?
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logger = get_module_logger(__name__)
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def in_water_range(x: int):
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"""Check if a tract ID is within the water area ID range
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Input must be integer representation of the 6-char census tract code
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(equivalent to last 6 characters of full geo ID)
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Returns TRUE if ID is in the water area range.
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Returns FALSE if ID is NOT in the water area range.
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NB: Not currently in use; artifact of attempt to filter
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water areas using Census tract ID rangers.
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"""
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if x >= 990000 and x <= 990099:
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return True
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return False
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def full_geo_id_to_water_range_bool(x: str):
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"""Check if a geo ID is within the water area ID range.
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First, convert full ID string to int of last six digits,
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Then check if this int is in the correct range.
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Input should be the 11 character Census tract identifier,
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the last 6 characters of which are the census tract code.
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Returns TRUE if ID is in the water area range.
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Returns FALSE if ID is NOT in the water area range.
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NB: Not currently in use; artifact of attempt to filter
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water areas using Census tract ID rangers.
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"""
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num_x = int(x[-6:])
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return in_water_range(num_x)
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def calculate_tract_adjacency_scores(
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df: pd.DataFrame, score_column: str
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) -> pd.DataFrame:
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"""Calculate the mean score of each tract in df based on its neighbors
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Args:
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df (pandas.DataFrame): A dataframe with at least the following columns:
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* field_names.GEOID_TRACT_FIELD
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* score_column
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score_column (str): The name of the column that contains the scores
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to average
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Returns:
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df (pandas.DataFrame): A dataframe with two columns:
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* field_names.GEOID_TRACT_FIELD
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* {score_column}_ADJACENT_MEAN, which is the average of score_column for
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each tract that touches the tract identified
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in field_names.GEOID_TRACT_FIELD
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"""
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ORIGINAL_TRACT = "ORIGINAL_TRACT"
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logger.debug("Calculating tract adjacency scores")
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tract_data = get_tract_geojson()
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df: gpd.GeoDataFrame = tract_data.merge(
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df, on=field_names.GEOID_TRACT_FIELD
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)
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df = df.rename(columns={field_names.GEOID_TRACT_FIELD: ORIGINAL_TRACT})
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# remove water areas from input frame
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df = df[df[field_names.LAND_AREA_FIELD] > 0]
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logger.debug("Perfoming spatial join to find all adjacent tracts")
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adjacent_tracts: gpd.GeoDataFrame = df.sjoin(
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tract_data, predicate="touches"
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)
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logger.debug("Calculating means based on adjacency")
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return (
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adjacent_tracts.groupby(field_names.GEOID_TRACT_FIELD)[[score_column]]
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.mean()
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.reset_index()
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.rename(
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columns={
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score_column: f"{score_column}{field_names.ADJACENCY_INDEX_SUFFIX}",
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}
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)
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)
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