ST_Buffer

ST_Buffer takes a geometry column and a numeric distance value and returns a polygon column. The resulting buffer polygons represent the area that is less than or equal to the specified planar distance from each input geometry. The distance can be specified as a single value or a numeric column. The distance value should be in the same units as the input geometry. For example, if your input geometry is in a spatial reference that uses meters, you should specify the distance in meters. To create a buffer polygon using geodesic distance calculations use ST_GeodesicBuffer.

FunctionSyntax
Pythonbuffer(geometry, distance)
SQLST_Buffer(geometry, distance)
Scalabuffer(geometry, distance)

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

This function implements the OpenGIS Simple Features Implementation Specification for SQL 1.2.1.

Python and SQL Examples

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from geoanalytics.sql import functions as ST

data = [
    ("POINT (10 30)",),
    ("MULTIPOINT (0 0, 5 5, 0 5)", ),
    ("LINESTRING (15 15, 10 15, 12 2)", ),
    ("POLYGON ((20 30, 18 28, 22 35, 40 20))", )
]

df = spark.createDataFrame(data, ["wkt"])\
          .select(ST.geom_from_text("wkt").alias("geometry"))

df = df.withColumn("buffer", ST.buffer("geometry", 2))

ax = df.st.plot("geometry", facecolor="none", edgecolor="red")
df.st.plot("buffer", ax=ax, facecolor="none", edgecolor="blue")
Plotting example for ST_Buffer
Plotted result for ST_Buffer.

Scala Example

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import com.esri.geoanalytics.sql.{functions => ST}
import org.apache.spark.sql.{functions => F}

case class GeometryRow(wkt: String)
val data = Seq(GeometryRow("POINT (10 30)"),
               GeometryRow("MULTIPOINT (0 0, 5 5, 0 5)"),
               GeometryRow("LINESTRING (15 15, 10 15, 12 2)"),
               GeometryRow("POLYGON ((20 30, 18 28, 22 35, 40 20))"))

val df = spark.createDataFrame(data)
              .select(ST.geomFromText($"wkt").alias("geometry"))
              .withColumn("buffer", ST.buffer($"geometry", 2))
              .withColumn("buffer_area", F.round(ST.area($"buffer"), 3))


df.select("buffer", "buffer_area").show()
Result
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+--------------------+-----------+
|              buffer|buffer_area|
+--------------------+-----------+
|{"rings":[[[12,30...|     12.557|
|{"rings":[[[2,0],...|     37.672|
|{"rings":[[[12,0]...|     83.951|
|{"rings":[[[40,18...|    188.535|
+--------------------+-----------+

Version table

ReleaseNotes

1.0.0

Python and SQL functions introduced

1.5.0

Scala function introduced

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