ST_Aggr_Intersection

ST_Aggr_Intersection
Two aggregated groups of input polygons (blue and orange) and the resulting intersections.

ST_Aggr_Intersection operates on a grouped DataFrame and returns the intersection of geometries in each group. You can group your DataFrame using DataFrame.groupBy() or with a GROUP BY clause in a SQL statement. An empty geometry is returned when no geometries intersect.

When there are coincident points, the first input geometry is used.

To find the intersection of geometries in each record, use ST_Intersection.

FunctionSyntax
Pythonaggr_intersection(geometry)
SQLST_Aggr_Intersection(geometry)
ScalaaggrIntersection(geometry)

For more details, go to the GeoAnalytics for Microsoft Fabric API reference for aggr_intersection.

Examples

PythonPythonSQLScala
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from geoanalytics_fabric.sql import functions as ST, Polygon

data = [
    (1, Polygon([[[5,5],[12,5],[12,10],[5,10],[5,5]]])),
    (1, Polygon([[[10,8],[14,8],[14,15],[10,15],[10,8]]])),
    (2, Polygon([[[6,8],[20,8],[20,20],[6,20],[6,8]]]))
]

df = spark.createDataFrame(data, ["id", "polygon"])

df.groupBy("id")\
  .agg(ST.aggr_intersection("polygon").alias("aggr_intersection"))\
  .show(truncate=False)
Result
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+---+--------------------------------------------------+
|id |aggr_intersection                                 |
+---+--------------------------------------------------+
|1  |{"rings":[[[10,8],[10,10],[12,10],[12,8],[10,8]]]}|
|2  |{"rings":[[[6,8],[20,8],[20,20],[6,20],[6,8]]]}   |
+---+--------------------------------------------------+

Version table

ReleaseNotes

1.0.0-beta

Python, SQL, and Scala functions introduced

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