Buyout Model Implementation

CHANGELOG.md

@@ -17,6 +17,7 @@ and this project adheres to [Semantic Versioning](http://semver.org/).
 ### Added
 - Gas Facility Damage Analysis [#568](https://github.com/IN-CORE/pyincore/issues/568)
 - Copyrights to transportation recovery analysis [#579](https://github.com/IN-CORE/pyincore/issues/579)
+- Buyout Model Analyses [#539](https://github.com/IN-CORE/pyincore/issues/539)
 
 ### Fixed
 - Permission error in clearing cache process [#563](https://github.com/IN-CORE/pyincore/issues/563)

docs/source/modules.rst

@@ -35,6 +35,11 @@ analyses/buildingportfolio
 ..  autoclass:: pyincore.analyses.buildingportfolio.recovery.BuildingData
     :members:
 
+analyses/buyoutdecision
+=======================
+..  autoclass:: buyoutdecision.buyoutdecision.BuyoutDecision
+    :members:
+
 analyses/capitalshocks
 ======================
 

pyincore/analyses/buyoutdecision/__init__.py

@@ -0,0 +1,7 @@
+# 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.buyoutdecision.buyoutdecision import BuyoutDecision

pyincore/analyses/buyoutdecision/buyoutdecision.py

@@ -0,0 +1,177 @@
+# 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
+from pyincore import BaseAnalysis
+from pyincore.utils.dataprocessutil import DataProcessUtil
+
+
+class BuyoutDecision(BaseAnalysis):
+    """A framework to select households for buyout based on past and future flood damaged.
+
+    Args:
+    incore_client(IncoreClient): Service authentication.
+
+    """
+
+    def __init__(self, incore_client):
+        super(BuyoutDecision, self).__init__(incore_client)
+
+    def run(self):
+        # Get input parameters
+        fema_buyout_cap = self.get_parameter('fema_buyout_cap')
+        residential_archetypes = self.get_parameter('residential_archetypes')
+
+        # Get input datasets
+        past_building_damage = self.get_input_dataset('past_building_damage').get_dataframe_from_csv(low_memory=False)
+        future_building_damage = self.get_input_dataset('future_building_damage').get_dataframe_from_csv(
+            low_memory=False)
+
+        building_inventory = self.get_input_dataset('buildings').get_dataframe_from_shapefile()
+
+        hua = (self.get_input_dataset('housing_unit_allocation').get_dataframe_from_csv(low_memory=False))
+        pop_dislocation = self.get_input_dataset('population_dislocation').get_dataframe_from_csv(low_memory=False)
+
+        buyout_decision_df = self.buyout_decision(past_building_damage, future_building_damage, building_inventory, hua,
+                                                  pop_dislocation, fema_buyout_cap, residential_archetypes)
+        # Create the result dataset
+        self.set_result_csv_data("result", buyout_decision_df, self.get_parameter("result_name") + "_loss",
+                                 "dataframe")
+
+    def buyout_decision(self, past_building_damage, future_building_damage, building_inventory, hua, pop_dislocation,
+                        fema_buyout_cap, residential_archetpyes):
+        """Select households for buyout based on past and future flood damaged.
+
+        Args:
+        past_building_damage (DataFrame): Past building damage.
+        future_building_damage (DataFrame):  Future event building damage.
+        building_inventory (DataFrame): Building inventory.
+        hua (DataFrame): Housing unit allocation.
+        pop_dislocation (DataFrame): Population dislocation from past hazard event.
+        fema_buyout_cap (float): FEMA buyout cap.
+        residential_archetpyes (list): Residential archetypes.
+
+        Returns:
+        buyout_decision_df (DataFrame): A dataframe with buyout decision for each household.
+        """
+
+        past_building_max_damage = DataProcessUtil.get_max_damage_state(past_building_damage)
+        future_building_max_damage = DataProcessUtil.get_max_damage_state(future_building_damage)
+
+        # Criterion 1: Filter only residential buildings with damage state DS3 from past building damage
+        buyout_inventory = pd.merge(building_inventory, past_building_max_damage, on='guid', how='outer')
+        buyout_inventory = buyout_inventory[buyout_inventory['arch_wind'].isin(residential_archetpyes)
+                                            & (buyout_inventory['max_state'] == 'DS_3')]
+        buyout_inventory.rename(columns={'max_state': 'max_state_past_damage'}, inplace=True)
+
+        # Criterion 2: Filter only residential buildings with damage state DS3 from predicted future building damage
+        buyout_inventory = pd.merge(buyout_inventory, future_building_max_damage, on='guid', how='inner')
+        buyout_inventory = buyout_inventory[buyout_inventory['max_state'] == 'DS_3']
+        buyout_inventory.rename(columns={'max_state': 'max_state_future_damage'}, inplace=True)
+
+        # Criterion 3: Fall within the FEMA buyout cap
+        buyout_inventory = buyout_inventory[buyout_inventory['appr_bldg'] <= fema_buyout_cap]
+        buyout_inventory = buyout_inventory[
+            ["guid", "appr_bldg", "max_state_future_damage", "max_state_past_damage", "geometry"]]
+
+        # Criterion 4: Use HUA to filter out buildings with 0 occupants
+        buyout_inventory = pd.merge(buyout_inventory, hua, on='guid', how='left')
+        buyout_inventory = buyout_inventory[(buyout_inventory['numprec'] != 0) & (~buyout_inventory['numprec'].isna())]
+
+        # Removing any rows with NAN values in column "Race"
+        buyout_inventory = buyout_inventory.dropna(subset=['race'])
+
+        # Merging with population dislocation
+        buyout_inventory = pd.merge(buyout_inventory, pop_dislocation[['huid', 'dislocated']], on='huid', how='left')
+
+        # Create a new column showing the appraisal value of each building ('appr_bldg' divided by the number of times
+        # a guid is repeated)
+        # For the instances that a structure has more than one housing units.
+        buyout_inventory['count'] = buyout_inventory.groupby('guid')['guid'].transform('count')
+        buyout_inventory['housing_unit_appraisal_value'] = buyout_inventory['appr_bldg'] / buyout_inventory['count']
+
+        # Cleaning the dataframe
+        buyout_inventory.drop(['blockid', 'bgid', 'tractid', 'FIPScounty',
+                               'gqtype', 'BLOCKID10_str', 'placeNAME10', 'geometry_y'], axis=1, inplace=True)
+        buyout_inventory.rename(columns={'appr_bldg': 'building_appraisal_value', 'ownershp': 'ownership',
+                                         'dislocated_combined_dmg': 'dislocated', 'count': 'number_of_housing_units',
+                                         'geometry_x': 'geometry'},
+                                inplace=True)
+        buyout_inventory = buyout_inventory[
+            ['guid', 'huid', 'building_appraisal_value', 'housing_unit_appraisal_value', 'geometry',
+             'number_of_housing_units', 'numprec', 'ownership', 'race', 'hispan', 'family', 'vacancy', 'incomegroup',
+             'hhinc', 'randincome', 'poverty', 'huestimate', 'dislocated', 'max_state_future_damage',
+             'max_state_past_damage', 'x', 'y', ]]
+
+        return buyout_inventory
+
+    def get_spec(self):
+        return {
+            "name": "buyout-decision",
+            "description": "Buyout decision framework",
+            "input_parameters": [
+                {
+                    'id': 'fema_buyout_cap',
+                    'required': True,
+                    'description': 'FEMA buyout cap',
+                    'type': float,
+                },
+                {
+                    'id': 'residential_archetypes',
+                    'required': True,
+                    'description': 'Residential archetypes',
+                    'type': list,
+                },
+                {
+                    'id': 'result_name',
+                    'required': True,
+                    'description': 'Result name',
+                    'type': str,
+                }
+            ],
+            "input_datasets": [
+                {
+                    'id': 'past_building_damage',
+                    'required': True,
+                    'description': 'Building Damage Results',
+                    'type': ['ergo:buildingDamageVer6'],
+                },
+                {
+                    'id': 'future_building_damage',
+                    'required': True,
+                    'description': 'Building Damage Results',
+                    'type': ['ergo:buildingDamageVer6'],
+                },
+                {
+                    'id': 'buildings',
+                    'required': True,
+                    'description': 'Building Inventory',
+                    'type': ['ergo:buildingInventoryVer4', 'ergo:buildingInventoryVer5',
+                             'ergo:buildingInventoryVer6', 'ergo:buildingInventoryVer7'],
+                },
+                {
+                    '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': 'population_dislocation',
+                    'required': True,
+                    'description': 'Population Dislocation from past hazard event',
+                    'type': ['incore:popDislocation']
+                }
+            ],
+            "output_datasets": [
+                {
+                    'id': 'result',
+                    'label': 'Buyout Decision Results',
+                    'description': 'Buyout Decision Results',
+                    'type': ['incore:buyoutDecision']
+                }
+            ]
+        }

tests/pyincore/analyses/buyoutdecision/test_buyoutdecision.py

@@ -0,0 +1,46 @@
+# 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.buyoutdecision import BuyoutDecision
+from pyincore import IncoreClient, Dataset
+import pyincore.globals as pyglobals
+import pandas as pd
+
+
+def BuyoutDecisionTest():
+    client = IncoreClient(pyglobals.INCORE_API_DEV_URL)
+
+    past_building_damage_id = "6632d2605da5fd22b268511f"
+    future_building_damage_id = "6632d45b5da5fd22b2685136"
+    past_pop_dislocation_id = "6632d5205da5fd22b26878bb"
+
+    hua_id = "64227016b18d026e7c80d2bc"
+
+    buildings_id = "63ff69a96d3b2a308baaca12"
+
+    fema_buyout_cap = 321291.600
+    residential_archetypes = [1, 2, 3, 4, 5, 17]
+
+    buyout_decision = BuyoutDecision(client)
+    buyout_decision.set_parameter("fema_buyout_cap", fema_buyout_cap)
+    buyout_decision.set_parameter("residential_archetypes", residential_archetypes)
+    buyout_decision.set_parameter("result_name", "galveston_buyout")
+
+    buyout_decision.load_remote_input_dataset("buildings", buildings_id)
+    buyout_decision.load_remote_input_dataset("housing_unit_allocation", hua_id)
+    buyout_decision.load_remote_input_dataset("past_building_damage", past_building_damage_id)
+    buyout_decision.load_remote_input_dataset("future_building_damage", future_building_damage_id)
+    buyout_decision.load_remote_input_dataset("population_dislocation", past_pop_dislocation_id)
+
+    buyout_decision.run_analysis()
+
+    result = buyout_decision.get_output_dataset("result")
+    result_df = result.get_dataframe_from_csv()
+    print(result_df.head())
+    print(len(result_df))
+
+
+if __name__ == "__main__":
+    BuyoutDecisionTest()