ST_Aggr_Union

ST_Aggr_Union operates on a grouped DataFrame and returns the union of geometries in each group. All geometries are required to have the same type. For example having point, linestring, and polygon geometry types in the same column is not supported. You can group your DataFrame using DataFrame.groupBy() or with a GROUP BY clause in a SQL statement.

To find the union of geometries in each record, use ST_Union.

Function	Syntax
Python	`aggr_union(geometry)`
SQL	`ST_Aggr_Union(geometry)`

For more details, go to the GeoAnalytics Engine API reference for aggr_union.

Examples

Python

SQL

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from geoanalytics.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.createOrReplaceTempView("df_view")

df.groupBy("id")\
  .agg(ST.aggr_union("polygon").alias("aggr_union"))\
  .show(truncate=False)

Result
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+---+-----------------------------------------------------------------------------+
|id |aggr_union                                                                   |
+---+-----------------------------------------------------------------------------+
|1  |{"rings":[[[5,5],[5,10],[10,10],[10,15],[14,15],[14,8],[12,8],[12,5],[5,5]]]}|
|2  |{"rings":[[[6,8],[20,8],[20,20],[6,20],[6,8]]]}                              |
+---+-----------------------------------------------------------------------------+

Version table

Release	Notes
1.0.0	Function introduced

ST_Aggr_Union

Examples

Version table

Related topics