Rename Social Vulnerability to Social Vulnerability Score

CHANGELOG.md

@@ -6,6 +6,11 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/)
 and this project adheres to [Semantic Versioning](http://semver.org/).
 
 
+## [Unreleased]
+
+### Changed
+- Rename Social Vulnerability Analysis to Social Vulnerability Index Analysis [#556](https://github.com/IN-CORE/pyincore/issues/556)
+
 ## [1.18.1] - 2024-04-30
 
 ### Changed

docs/source/modules.rst

@@ -306,6 +306,13 @@ analyses/socialvulnerability
 ============================
 ..  autoclass:: socialvulnerability.socialvulnerability.SocialVulnerability
     :members:
+..  deprecated:: 1.19.0
+    This class will be deprecated soon. Use :class:`socialvulnerabilityscore.SocialVulnerabilityScore` instead.
+
+analyses/socialvulnerabilityscore
+============================
+..  autoclass:: socialvulnerabilityscore.socialvulnerabilityscore.SocialVulnerabilityScore
+    :members:
 
 analyses/tornadoepndamage
 =========================

environment.yml

@@ -20,3 +20,4 @@ dependencies:
   - rtree>=1.1.0
   - scipy>=1.11.3
   - shapely>=2.0.2
+  - deprecated>=1.2.14

pyincore/analyses/socialvulnerability/socialvulnerability.py

@@ -1,163 +1,19 @@
-# Copyright (c) 2021 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 deprecated.sphinx import deprecated
 
+from pyincore.analyses.socialvulnerabilityscore import SocialVulnerabilityScore
 
-class SocialVulnerability(BaseAnalysis):
-    """This analysis computes a social vulnerability score for per associated zone in census data.
-
-    The computation extracts zoning and a social vulnerability score obtained by computing demographic features of
-    interest against national average values.
-
-    The output of the computation is a dataset CSV format.
-
-    Contributors
-        | Science: Elaina Sutley, Amin Enderami
-        | Implementation:  Amin Enderami, Santiago Núñez-Corrales, and NCSA IN-CORE Dev Team
-
-    Related publications
-
-    Args:
-        incore_client (IncoreClient): Service authentication.
-
-    """
 
+@deprecated(version='1.19.0', reason="This class will be deprecated soon. Use SocialVulnerabilityScore instead.")
+class SocialVulnerability():
     def __init__(self, incore_client):
-        super(SocialVulnerability, self).__init__(incore_client)
-
-    def run(self):
-        """Execute the social vulnerability analysis using known parameters."""
-        df_navs = pd.DataFrame(self.get_input_dataset('national_vulnerability_feature_averages').get_csv_reader())
-
-        df_dem = pd.DataFrame(self.get_input_dataset('social_vulnerability_demographic_factors').get_csv_reader())
-
-        # Make sure data types match
-        df_dem["factor_white_nonHispanic"] = df_dem["factor_white_nonHispanic"].astype(float)
-        df_dem["factor_owner_occupied"] = df_dem["factor_owner_occupied"].astype(float)
-        df_dem["factor_earning_higher_than_national_poverty_rate"] =\
-            df_dem["factor_earning_higher_than_national_poverty_rate"].astype(float)
-        df_dem["factor_over_25_with_high_school_diploma_or_higher"] =\
-            df_dem["factor_over_25_with_high_school_diploma_or_higher"].astype(float)
-        df_dem["factor_without_disability_age_18_to_65"] =\
-            df_dem["factor_without_disability_age_18_to_65"].astype(float)
-
-        self.social_vulnerability_model(df_navs, df_dem)
-
-    def social_vulnerability_model(self, df_navs, df_dem):
-        """
-
-        Args:
-            df_navs (pd.DataFrame): dataframe containing the national average values for vulnerability factors
-            df_dem (pd.DataFrame): dataframe containing demographic factors required for the vulnerability score
-
-        Returns:
-
-        """
-
-        # Compute the social vulnerability index
-        df_sv = self.compute_svs(df_dem, df_navs)
-
-        # Save into a CSV file
-        result_name = self.get_parameter("result_name")
-        self.set_result_csv_data("sv_result", df_sv,
-                                 name=result_name,
-                                 source="dataframe")
-
-    @staticmethod
-    def compute_svs(df, df_navs):
-        """ Computation of the social vulnerability score and corresponding zoning
+        self._delegate = SocialVulnerabilityScore(incore_client)
 
-        Args:
-            df (pd.DataFrame): dataframe for the census geographic unit of interest
-            df_navs (pd.DataFrame): dataframe containing national average values
-
-        Returns:
-            pd.DataFrame: Social vulnerability score and corresponding zoning data
+    def __getattr__(self, name):
         """
-        navs = df_navs['average'].astype(float).array
-
-        df['R1'] = df['factor_white_nonHispanic'] / navs[0]
-        df['R2'] = df['factor_owner_occupied'] / navs[1]
-        df['R3'] = df['factor_earning_higher_than_national_poverty_rate'] / navs[2]
-        df['R4'] = df['factor_over_25_with_high_school_diploma_or_higher'] / navs[3]
-        df['R5'] = df['factor_without_disability_age_18_to_65'] / navs[4]
-        df['SVS'] = df.apply(lambda row: (row['R1'] + row['R2'] + row['R3'] + row['R4'] + row['R5']) / 5, axis=1)
-
-        maximum_nav = 1/navs
-        std = abs(1 - (sum(maximum_nav) / len(maximum_nav))) / 3
-
-        lb_2 = 1 - 1.5*std
-        lb_1 = 1 - 0.5*std
-        ub_1 = 1 + 0.5*std
-        ub_2 = 1 + 1.5*std
-
-        zones = []
-
-        for svs in df['SVS'].tolist():
-            if svs < lb_2:
-                new_zone = 'High Vulnerable (zone5)'
-            elif svs < lb_1:
-                new_zone = 'Medium to High Vulnerable (zone4)'
-            elif svs < ub_1:
-                new_zone = 'Medium Vulnerable (zone3)'
-            elif svs < ub_2:
-                new_zone = 'Medium to Low Vulnerable (zone2)'
-            elif svs > ub_2:
-                new_zone = 'Low Vulnerable (zone1)'
-            else:
-                new_zone = 'No Data'
-            zones.append(new_zone)
-
-        df['zone'] = zones
-        df = df.sort_values(by="GEO_ID")
-
-        return df
-
-    def get_spec(self):
-        """Get specifications of the housing serial recovery model.
-
-        Returns:
-            obj: A JSON object of specifications of the social vulnerability model.
-
+        Delegate attribute access to the SocialVulnerabilityScore instance.
         """
-        return {
-            'name': 'social-vulnerability',
-            'description': 'Social vulnerability score model',
-            'input_parameters': [
-                {
-                    'id': 'result_name',
-                    'required': True,
-                    'description': 'Result CSV dataset name',
-                    'type': str
-                },
-            ],
-            'input_datasets': [
-                {
-                    'id': 'national_vulnerability_feature_averages',
-                    'required': True,
-                    'description': 'A csv file with national vulnerability feature averages',
-                    'type': ['incore:socialVulnerabilityFeatureAverages']
-                },
-                {
-                    'id': 'social_vulnerability_demographic_factors',
-                    'required': True,
-                    'description': 'A csv file with social vulnerability demographic factors for a given geographic '
-                                   'type',
-                    'type': ['incore:socialVulnerabilityDemFactors']
-                }
-            ],
-            'output_datasets': [
-                {
-                    'id': 'sv_result',
-                    'parent_type': 'social_vulnerability_score',
-                    'description': 'A csv file with zones containing demographic factors'
-                                   'qualified by a social vulnerability score',
-                    'type': 'incore:socialVulnerabilityScore'
-                }
-            ]
-        }
+        return getattr(self._delegate, name)

pyincore/analyses/socialvulnerabilityscore/__init__.py

@@ -0,0 +1,8 @@
+# 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.socialvulnerabilityscore.socialvulnerabilityscore import SocialVulnerabilityScore

pyincore/analyses/socialvulnerabilityscore/socialvulnerabilityscore.py

@@ -0,0 +1,163 @@
+# 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
+
+
+class SocialVulnerabilityScore(BaseAnalysis):
+    """This analysis computes a social vulnerability score for per associated zone in census data.
+
+    The computation extracts zoning and a social vulnerability score obtained by computing demographic features of
+    interest against national average values.
+
+    The output of the computation is a dataset CSV format.
+
+    Contributors
+        | Science: Elaina Sutley, Amin Enderami
+        | Implementation:  Amin Enderami, Santiago Núñez-Corrales, and NCSA IN-CORE Dev Team
+
+    Related publications
+
+    Args:
+        incore_client (IncoreClient): Service authentication.
+
+    """
+
+    def __init__(self, incore_client):
+        super(SocialVulnerabilityScore, self).__init__(incore_client)
+
+    def run(self):
+        """Execute the social vulnerability score analysis using known parameters."""
+        df_navs = pd.DataFrame(self.get_input_dataset('national_vulnerability_feature_averages').get_csv_reader())
+
+        df_dem = pd.DataFrame(self.get_input_dataset('social_vulnerability_demographic_factors').get_csv_reader())
+
+        # Make sure data types match
+        df_dem["factor_white_nonHispanic"] = df_dem["factor_white_nonHispanic"].astype(float)
+        df_dem["factor_owner_occupied"] = df_dem["factor_owner_occupied"].astype(float)
+        df_dem["factor_earning_higher_than_national_poverty_rate"] =\
+            df_dem["factor_earning_higher_than_national_poverty_rate"].astype(float)
+        df_dem["factor_over_25_with_high_school_diploma_or_higher"] =\
+            df_dem["factor_over_25_with_high_school_diploma_or_higher"].astype(float)
+        df_dem["factor_without_disability_age_18_to_65"] =\
+            df_dem["factor_without_disability_age_18_to_65"].astype(float)
+
+        self.social_vulnerability_score_model(df_navs, df_dem)
+
+    def social_vulnerability_score_model(self, df_navs, df_dem):
+        """
+
+        Args:
+            df_navs (pd.DataFrame): dataframe containing the national average values for vulnerability factors
+            df_dem (pd.DataFrame): dataframe containing demographic factors required for the vulnerability score
+
+        Returns:
+
+        """
+
+        # Compute the social vulnerability score index
+        df_sv = self.compute_svs(df_dem, df_navs)
+
+        # Save into a CSV file
+        result_name = self.get_parameter("result_name")
+        self.set_result_csv_data("sv_result", df_sv,
+                                 name=result_name,
+                                 source="dataframe")
+
+    @staticmethod
+    def compute_svs(df, df_navs):
+        """ Computation of the social vulnerability score and corresponding zoning
+
+        Args:
+            df (pd.DataFrame): dataframe for the census geographic unit of interest
+            df_navs (pd.DataFrame): dataframe containing national average values
+
+        Returns:
+            pd.DataFrame: Social vulnerability score and corresponding zoning data
+        """
+        navs = df_navs['average'].astype(float).array
+
+        df['R1'] = df['factor_white_nonHispanic'] / navs[0]
+        df['R2'] = df['factor_owner_occupied'] / navs[1]
+        df['R3'] = df['factor_earning_higher_than_national_poverty_rate'] / navs[2]
+        df['R4'] = df['factor_over_25_with_high_school_diploma_or_higher'] / navs[3]
+        df['R5'] = df['factor_without_disability_age_18_to_65'] / navs[4]
+        df['SVS'] = df.apply(lambda row: (row['R1'] + row['R2'] + row['R3'] + row['R4'] + row['R5']) / 5, axis=1)
+
+        maximum_nav = 1/navs
+        std = abs(1 - (sum(maximum_nav) / len(maximum_nav))) / 3
+
+        lb_2 = 1 - 1.5*std
+        lb_1 = 1 - 0.5*std
+        ub_1 = 1 + 0.5*std
+        ub_2 = 1 + 1.5*std
+
+        zones = []
+
+        for svs in df['SVS'].tolist():
+            if svs < lb_2:
+                new_zone = 'High Vulnerable (zone5)'
+            elif svs < lb_1:
+                new_zone = 'Medium to High Vulnerable (zone4)'
+            elif svs < ub_1:
+                new_zone = 'Medium Vulnerable (zone3)'
+            elif svs < ub_2:
+                new_zone = 'Medium to Low Vulnerable (zone2)'
+            elif svs > ub_2:
+                new_zone = 'Low Vulnerable (zone1)'
+            else:
+                new_zone = 'No Data'
+            zones.append(new_zone)
+
+        df['zone'] = zones
+        df = df.sort_values(by="GEO_ID")
+
+        return df
+
+    def get_spec(self):
+        """Get specifications of the housing serial recovery model.
+
+        Returns:
+            obj: A JSON object of specifications of the social vulnerability score model.
+
+        """
+        return {
+            'name': 'social-vulnerability-score',
+            'description': 'Social vulnerability score model',
+            'input_parameters': [
+                {
+                    'id': 'result_name',
+                    'required': True,
+                    'description': 'Result CSV dataset name',
+                    'type': str
+                },
+            ],
+            'input_datasets': [
+                {
+                    'id': 'national_vulnerability_feature_averages',
+                    'required': True,
+                    'description': 'A csv file with national vulnerability feature averages',
+                    'type': ['incore:socialVulnerabilityFeatureAverages']
+                },
+                {
+                    'id': 'social_vulnerability_demographic_factors',
+                    'required': True,
+                    'description': 'A csv file with social vulnerability score demographic factors for a given geographic '
+                                   'type',
+                    'type': ['incore:socialVulnerabilityDemFactors']
+                }
+            ],
+            'output_datasets': [
+                {
+                    'id': 'sv_result',
+                    'parent_type': 'social_vulnerability_score',
+                    'description': 'A csv file with zones containing demographic factors'
+                                   'qualified by a social vulnerability score',
+                    'type': 'incore:socialVulnerabilityScore'
+                }
+            ]
+        }

requirements.imports

@@ -15,3 +15,4 @@ shapely
 pycodestyle
 pytest
 python-jose
+Deprecated

requirements.min

@@ -16,3 +16,4 @@ requests>=2.31.0
 rtree>=1.1.0
 scipy>=1.11.3
 shapely>=2.0.2
+Deprecated>=1.2.14

requirements.txt

@@ -15,3 +15,4 @@ requests>=2.31.0
 rtree>=1.1.0
 scipy>=1.11.3
 shapely>=2.0.2
+Deprecated>=1.2.14

setup.py

@@ -67,6 +67,7 @@
         'rtree>=1.1.0',
         'scipy>=1.11.3',
         'shapely>=2.0.2',
+        'Deprecated>=1.2.14'
     ],
 
     extras_require={