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| Original file line number | Diff line number | Diff line change |
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| # Copyright (c) 2024 University of Illinois and others. All rights reserved. | ||
| # | ||
| # This program and the accompanying materials are made available under the | ||
| # terms of the Mozilla Public License v2.0 which accompanies this distribution, | ||
| # and is available at https://www.mozilla.org/en-US/MPL/2.0/ | ||
|
|
||
| from pyincore.analyses.equitymetric.equitymetric import EquityMetric | ||
| from pyincore.analyses.equitymetric.equitymetricutil import EquityMetricUtil |
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| @@ -0,0 +1,140 @@ | ||
| # Copyright (c) 2024 University of Illinois and others. All rights reserved. | ||
| # | ||
| # This program and the accompanying materials are made available under the | ||
| # terms of the Mozilla Public License v2.0 which accompanies this distribution, | ||
| # and is available at https://www.mozilla.org/en-US/MPL/2.0/ | ||
|
|
||
| import numpy as np | ||
| from pyincore import BaseAnalysis | ||
| from pyincore.analyses.equitymetric.equitymetricutil import EquityMetricUtil | ||
|
|
||
|
|
||
| class EquityMetric(BaseAnalysis): | ||
| """Computes equity metric. | ||
| Args: | ||
| incore_client: Service client with authentication info | ||
| """ | ||
|
|
||
| def __init__(self, incore_client): | ||
| super(EquityMetric, self).__init__(incore_client) | ||
|
|
||
| def run(self): | ||
| """Execute equity metric analysis""" | ||
|
|
||
| division_decision_column = self.get_parameter("division_decision_column") | ||
| scarce_resource_df = self.get_input_dataset( | ||
| "scarce_resource" | ||
| ).get_dataframe_from_csv() | ||
| hua_df = self.get_input_dataset( | ||
| "housing_unit_allocation" | ||
| ).get_dataframe_from_csv() | ||
| if division_decision_column == "SVI" and "SVI" not in hua_df.columns: | ||
| hua_df = EquityMetricUtil.prepare_svi_as_division_decision(hua_df) | ||
|
|
||
| merged_df = hua_df.merge( | ||
| scarce_resource_df, how="inner", left_on="guid", right_on="guid" | ||
| ) | ||
|
|
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| equity_metric = self.equity_metric(merged_df, division_decision_column) | ||
|
|
||
| self.set_result_csv_data( | ||
| "equity_metric", | ||
| equity_metric, | ||
| name=self.get_parameter("result_name") + "_equity_metric", | ||
| ) | ||
|
|
||
| return True | ||
|
|
||
| def equity_metric(self, merged_df, division_decision_column): | ||
| """ | ||
| Compute equity metric | ||
| Args: | ||
| merged_df: Merging housing unit allocation and scarce resource to create dataframes | ||
| division_decision_column: column name of the division decision variable e.g. SVI | ||
| Returns: | ||
| equity_metric: equity metric values that consist of Theil’s T Value, Between Zone Inequality, Within Zone Inequality | ||
| """ | ||
| # Calculation of households in each group | ||
| total_1 = merged_df[merged_df[division_decision_column] > 0].shape[ | ||
| 0 | ||
| ] # socially vulnerable populations | ||
| total_2 = merged_df[merged_df[division_decision_column] < 1].shape[ | ||
| 0 | ||
| ] # non socially vulnerable populations | ||
| total_households = ( | ||
| total_1 + total_2 | ||
| ) # for non-vacant households (i.e., non-vacant are not included) | ||
|
|
||
| # Metric Computation | ||
| scarce_resource = merged_df["scarce_resource"] | ||
| yi = scarce_resource / np.sum(scarce_resource) | ||
| Yg_1 = np.sum(yi[merged_df[division_decision_column] > 0]) | ||
| Yg_2 = np.sum(yi[merged_df[division_decision_column] < 1]) | ||
| TheilT = np.sum(yi * np.log(yi * total_households)) | ||
| bzi = np.sum(yi[merged_df[division_decision_column] > 0]) * np.log( | ||
| np.average(yi[merged_df[division_decision_column] > 0]) / np.average(yi) | ||
| ) + np.sum(yi[merged_df[division_decision_column] < 1]) * np.log( | ||
| np.average(yi[merged_df[division_decision_column] < 1]) / np.average(yi) | ||
| ) | ||
| wzi = Yg_1 * np.sum( | ||
| yi[merged_df[division_decision_column] > 0] | ||
| / Yg_1 | ||
| * np.log((yi[merged_df[division_decision_column] > 0] / Yg_1 * total_1)) | ||
| ) + Yg_2 * np.sum( | ||
| yi[merged_df[division_decision_column] < 1] | ||
| / Yg_2 | ||
| * np.log((yi[merged_df[division_decision_column] < 1] / Yg_2 * total_2)) | ||
| ) | ||
|
|
||
| return [{"Theils T": TheilT, "BZI": bzi, "WZI": wzi}] | ||
|
|
||
| def get_spec(self): | ||
| """Get specifications of the Equity Metric analysis. | ||
| Returns: | ||
| obj: A JSON object of specifications of the Equity Metric analysis. | ||
| """ | ||
| return { | ||
| "name": "equity-metric", | ||
| "description": "Equity metric analysis", | ||
| "input_parameters": [ | ||
| { | ||
| "id": "result_name", | ||
| "required": True, | ||
| "description": "result dataset name", | ||
| "type": str, | ||
| }, | ||
| { | ||
| "id": "division_decision_column", | ||
| "required": True, | ||
| "description": "Division decision. " | ||
| "Binary variable associated with each household used to group it into two groups " | ||
| "(e.g. low income vs non low income, minority vs non-minority, " | ||
| "social vulnerability)", | ||
| "type": str, | ||
| }, | ||
| ], | ||
| "input_datasets": [ | ||
| { | ||
| "id": "housing_unit_allocation", | ||
| "required": True, | ||
| "description": "A csv file with the merged dataset of the inputs, aka Probabilistic" | ||
| "House Unit Allocation", | ||
| "type": ["incore:housingUnitAllocation"], | ||
| }, | ||
| { | ||
| "id": "scarce_resource", | ||
| "required": True, | ||
| "description": "Scarce resource dataset e.g. probability of service, return time, etc", | ||
| "type": ["incore:scarceResource"], | ||
| }, | ||
| ], | ||
| "output_datasets": [ | ||
| { | ||
| "id": "equity_metric", | ||
| "description": "CSV file of equity metric, including Theil’s T Value, Between Zone Inequality, Within Zone Inequality", | ||
| "type": "incore:equityMetric", | ||
| } | ||
| ], | ||
| } |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,57 @@ | ||
| # Copyright (c) 2024 University of Illinois and others. All rights reserved. | ||
| # | ||
| # This program and the accompanying materials are made available under the | ||
| # terms of the Mozilla Public License v2.0 which accompanies this distribution, | ||
| # and is available at https://www.mozilla.org/en-US/MPL/2.0/ | ||
|
|
||
| import pandas as pd | ||
|
|
||
|
|
||
| class EquityMetricUtil: | ||
| @staticmethod | ||
| def prepare_svi_as_division_decision(hua_df): | ||
| """ | ||
| socially vulnerability as division decision variable which is a binary variable associated with each household | ||
| used to group it into two groups | ||
| Args: | ||
| hua_df: | ||
| Returns: | ||
| """ | ||
| # Add variable to indicate if high socially vulnerability for metric's computation | ||
| median_income = hua_df["randincome"].median() | ||
|
|
||
| condition1 = hua_df["randincome"] <= median_income | ||
| condition2 = hua_df["ownershp"] == 2 | ||
| condition3 = hua_df["race"] != 1 | ||
| condition4 = hua_df["hispan"] != 0 | ||
|
|
||
| hua_df["SVI"] = condition1 & condition2 & condition3 & condition4 | ||
| hua_df["SVI"] = (hua_df["SVI"]).astype(int) | ||
|
|
||
| return hua_df | ||
|
|
||
| @staticmethod | ||
| def prepare_return_time_as_scarce_resource(return_df): | ||
| return_sequence = return_df.iloc[:, 4:94] | ||
| # add return time to the scarce resource dataset | ||
| time_to_return = EquityMetricUtil._time_to_return(return_sequence) | ||
| return_df["Return Time"] = pd.to_numeric(time_to_return) | ||
| return_df["scarce_resource"] = 91 - return_df["Return Time"] | ||
|
|
||
| return return_df | ||
|
|
||
| @staticmethod | ||
| def _time_to_return(return_sequence): | ||
| # now create a for loop to determine the time for each row | ||
| time_to_return = [] | ||
| for i in range(0, return_sequence.shape[0]): | ||
| if max(return_sequence.iloc[i]) == 4: | ||
| column_index = (return_sequence == 4).idxmax(axis=1)[i] | ||
| else: | ||
| # assuming for 5 that it is never recovered, so we set it to max time interval of 90 | ||
| column_index = 90 | ||
| time_to_return.append(column_index) | ||
|
|
||
| return time_to_return |
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