Make tribal overlap set score N (#2004)

* Add "Is a Tribal DAC" field (#1998)

* Add tribal DACs to score N final (#1998)

* Add new fields to downloads (#1998)

* Make a int a float (#1998)

* Update field names, apply feedback (#1998)

data/data-pipeline/data_pipeline/etl/score/constants.py

@@ -279,6 +279,8 @@ TILES_SCORE_COLUMNS = {
     field_names.SCORE_N_COMMUNITIES
     + field_names.ADJACENT_MEAN_SUFFIX: "SN_DON",
     field_names.SCORE_N_COMMUNITIES: "SN_NO_DON",
+    field_names.IS_TRIBAL_DAC: "SN_T",
+    field_names.PERCENT_OF_TRACT_IS_DAC: "SN_PERC",
     field_names.EXPECTED_POPULATION_LOSS_RATE_LOW_INCOME_FIELD: "EPLRLI",
     field_names.EXPECTED_AGRICULTURE_LOSS_RATE_LOW_INCOME_FIELD: "EALRLI",
     field_names.EXPECTED_BUILDING_LOSS_RATE_LOW_INCOME_FIELD: "EBLRLI",
@@ -472,4 +474,5 @@ TILES_SCORE_FLOAT_COLUMNS = [
     field_names.AML_BOOLEAN,
     field_names.HISTORIC_REDLINING_SCORE_EXCEEDED,
     field_names.PERCENT_OF_TRIBAL_AREA_IN_TRACT,
+    field_names.PERCENT_OF_TRACT_IS_DAC,
 ]

data/data-pipeline/data_pipeline/etl/score/etl_score.py

@@ -505,6 +505,7 @@ class ScoreETL(ExtractTransformLoad):
             field_names.IMPUTED_INCOME_FLAG_FIELD_NAME,
             field_names.ELIGIBLE_FUDS_BINARY_FIELD_NAME,
             field_names.HISTORIC_REDLINING_SCORE_EXCEEDED,
+            field_names.IS_TRIBAL_DAC,
         ]
 
         # For some columns, high values are "good", so we want to reverse the percentile