Add nation and HHS to Safegraph

safegraph/DETAILS.md

@@ -2,13 +2,15 @@
 
 We import census block group-level raw mobility indicators from Safegraph,
 calculate functions of the raw data, and then aggregate the data to the
-county and state levels.  MSA and HRR not yet implemented.
+county, state, HHS, and nation levels.
 
 ## Geographical Levels
 * `county`: reported using zero-padded FIPS codes.  The FIPS codes are
   obtained by zero-padding the census block group codes and taking the first
   five digits, which are by construction the corresponding county FIPS code.
+* `hhs`: reported using HHS region number 
 * `state`: reported using two-letter postal code
+* `nation`: reported using two-letter nation code. Just 'us' for now
 
 ## Metrics
 * `completely_home_prop`, defined as:

safegraph/delphi_safegraph/constants.py

@@ -16,5 +16,7 @@
     'county',
     'state',
     'msa',
-    'hrr'
+    'hrr',
+    'hhs',
+    'nation'
 ]

safegraph/delphi_safegraph/process.py

@@ -134,20 +134,14 @@ def aggregate(df, signal_names, geo_resolution='county'):
                                               new_code='state_id',
                                               new_col='geo_id',
                                               dropna=False)
-    elif geo_resolution == 'msa':
+    elif geo_resolution in ['msa', 'nation', 'hrr', 'hhs']:
         geo_transformed_df = gmpr.add_geocode(df,
                                               from_col='county_fips',
                                               from_code='fips',
-                                              new_code='msa',
-                                              new_col='geo_id',
-                                              dropna=False)
-    elif geo_resolution == 'hrr':
-        geo_transformed_df = gmpr.add_geocode(df,
-                                              from_col='county_fips',
-                                              from_code='fips',
-                                              new_code='hrr',
+                                              new_code=geo_resolution,
                                               new_col='geo_id',
                                               dropna=False)
+
     else:
         raise ValueError(
             f'`geo_resolution` must be one of {GEO_RESOLUTIONS}.')

safegraph/tests/test_process.py

@@ -57,8 +57,7 @@ def test_aggregate_state(self):
         assert df.shape == (52, 17)
 
     def test_aggregate_msa(self):
-        """Tests that aggregation at the state level creates non-zero-valued
-        signals."""
+        """Tests that aggregation at the state level creates non-zero-valued signals."""
         cbg_df = construct_signals(pd.read_csv('raw_data/sample_raw_data.csv'),
                                    SIGNALS)
         df = aggregate(cbg_df, SIGNALS, 'msa')
@@ -69,8 +68,7 @@ def test_aggregate_msa(self):
         assert df.shape == (372, 17)
 
     def test_aggregate_hrr(self):
-        """Tests that aggregation at the state level creates non-zero-valued
-        signals."""
+        """Tests that aggregation at the state level creates non-zero-valued signals."""
         cbg_df = construct_signals(pd.read_csv('raw_data/sample_raw_data.csv'),
                                    SIGNALS)
         df = aggregate(cbg_df, SIGNALS, 'hrr')
@@ -80,6 +78,28 @@ def test_aggregate_hrr(self):
         assert np.all(x[~np.isnan(x)] >= 0)
         assert df.shape == (306, 17)
 
+    def test_aggregate_nation(self):
+        """Tests that aggregation at the state level creates non-zero-valued signals."""
+        cbg_df = construct_signals(pd.read_csv('raw_data/sample_raw_data.csv'),
+                                   SIGNALS)
+        df = aggregate(cbg_df, SIGNALS, 'nation')
+
+        assert np.all(df[f'{SIGNALS[0]}_n'].values > 0)
+        x = df[f'{SIGNALS[0]}_se'].values
+        assert np.all(x[~np.isnan(x)] >= 0)
+        assert df.shape == (1, 17)
+
+    def test_aggregate_hhs(self):
+        """Tests that aggregation at the state level creates non-zero-valued signals."""
+        cbg_df = construct_signals(pd.read_csv('raw_data/sample_raw_data.csv'),
+                                   SIGNALS)
+        df = aggregate(cbg_df, SIGNALS, 'hhs')
+
+        assert np.all(df[f'{SIGNALS[0]}_n'].values > 0)
+        x = df[f'{SIGNALS[0]}_se'].values
+        assert np.all(x[~np.isnan(x)] >= 0)
+        assert df.shape == (10, 17)
+
     def test_files_in_past_week(self):
         """Tests that `files_in_past_week()` finds the file names corresponding
         to the previous 6 days."""