ST_Aggr_MeanCenter

ST_Aggr_MeanCenter operates on a grouped DataFrame and returns the weighted aggregated centroid (mean center) of geometries in each group. You can optionally specify a numeric weight column which is used to weight locations according to their relative importance. The default is unweighted. You can group your DataFrame using DataFrame.groupBy() or with a GROUP BY clause in a SQL statement.

Function	Syntax
Python	`aggr_mean_center(geometry, weight=None)`
SQL	`ST_Aggr_MeanCenter(geometry, weight)`

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

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.groupBy("id")\
  .agg(ST.aggr_mean_center("polygon").alias("aggr_meancenter"))\
  .show(truncate=False)

Result
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+---+-------------------+
|id |aggr_meancenter    |
+---+-------------------+
|1  |{"x":10.25,"y":9.5}|
|2  |{"x":13,"y":14}    |
+---+-------------------+

Version table

Release	Notes
1.0.0	Function introduced

ST_Aggr_MeanCenter

Examples

Version table

Related topics