Add FUDS ETL (#1817)

* Add spatial join method (#1871)

Since we'll need to figure out the tracts for a large number of points
in future tickets, add a utility to handle grabbing the tract geometries
and adding tract data to a point dataset.

* Add FUDS, also jupyter lab (#1871)

* Add YAML configs for FUDS (#1871)

* Allow input geoid to be optional (#1871)

* Add FUDS ETL, tests, test-datae noteobook (#1871)

This adds the ETL class for Formerly Used Defense Sites (FUDS). This is
different from most other ETLs since these FUDS are not provided by
tract, but instead by geographic point, so we need to assign FUDS to
tracts and then do calculations from there.

* Floats -> Ints, as I intended (#1871)

* Floats -> Ints, as I intended (#1871)

* Formatting fixes (#1871)

* Add test false positive GEOIDs (#1871)

* Add gdal binaries (#1871)

* Refactor pandas code to be more idiomatic (#1871)

Per Emma, the more pandas-y way of doing my counts is using np.where to
add the values i need, then groupby and size. It is definitely more
compact, and also I think more correct!

* Update configs per Emma suggestions (#1871)

* Type fixed! (#1871)

* Remove spurious import from vscode (#1871)

* Snapshot update after changing col name (#1871)

* Move up GDAL (#1871)

* Adjust geojson strategy (#1871)

* Try running census separately first (#1871)

* Fix import order (#1871)

* Cleanup cache strategy (#1871)

* Download census data from S3 instead of re-calculating (#1871)

* Clarify pandas code per Emma (#1871)

data/data-pipeline/data_pipeline/etl/score/config/datasets.yml

@@ -117,6 +117,34 @@ datasets:
         field_type: float
         include_in_downloadable_files: true
         include_in_tiles: true
+  - long_name: "Formerly Used Defense Sites"
+    short_name: "FUDS"
+    module_name: "us_army_fuds"
+    load_fields:
+      - short_name: "fuds_count"
+        df_field_name: "ELIGIBLE_FUDS_COUNT_FIELD_NAME"
+        long_name: "Count of eligible Formerly Used Defense Site (FUDS) properties centroids"
+        description_short: 
+          "The number of FUDS marked as Eligible and Has Project in the tract."
+        field_type: int64
+        include_in_tiles: false
+        include_in_downloadable_files: false
+      - short_name: "not_fuds_ct"
+        df_field_name: "INELIGIBLE_FUDS_COUNT_FIELD_NAME"
+        long_name: "Count of ineligible Formerly Used Defense Site (FUDS) properties centroids"
+        description_short: 
+          "The number of FUDS marked as Ineligible or Project in the tract."
+        field_type: int64
+        include_in_tiles: false
+        include_in_downloadable_files: false
+      - short_name: "has_fuds"
+        df_field_name: "ELIGIBLE_FUDS_BINARY_FIELD_NAME"
+        long_name: "Is there at least one Formerly Used Defense Site (FUDS) in the tract?"
+        description_short: 
+          "Whether the tract has a FUDS"
+        field_type: bool
+        include_in_tiles: false
+        include_in_downloadable_files: false
   - long_name: "Example ETL"
     short_name: "Example"
     module_name: "example_dataset"
@@ -128,4 +156,3 @@ datasets:
         field_type: float
         include_in_tiles: true
         include_in_downloadable_files: true
-

data/data-pipeline/data_pipeline/etl/score/schemas/datasets.py

@@ -77,7 +77,7 @@ class DatasetsConfig:
         long_name: str
         short_name: str
         module_name: str
-        input_geoid_tract_field_name: str
         load_fields: List[LoadField]
+        input_geoid_tract_field_name: Optional[str] = None
 
     datasets: List[Dataset]