ST_EuclideanDistance

ST_EuclideanDistance takes two linestring columns and returns a double column. The output column represents the Euclidean distance between the two input linestrings.

A distance is calculated from each vertex in the first input linestring to the corresponding vertices in the second input linestring. The Euclidean distance is the average of these distances.

ST_Euclidean_Distance
Two linestrings (orange and blue). A distance (red line) is calculated for each vertex pair. Euclidean distance is the average of these distances.

The result will be in the same units as the input geometry data. For example, if the input geometries are in a spatial reference that uses meters, the result values will be in meters.

If the two geometry columns are in different spatial references, the function will automatically transform the second geometry into the spatial reference of the first.

If one of the input geometries has an unknown spatial reference the function will use planar distance calculations.

ST_EuclideanDistance does not take m-values and z-values into account. If the input linestring has any of these values, they will be ignored in the Euclidean distance calculation.

If the number of vertices in the first input linestring is different than the number of vertices in the second input linestring, the result will be null.

This function can be used to measure the similarity between two linestrings. For more details, go to the core topic for similarity measures.

FunctionSyntax
Pythoneuclidean_distance(linestring1, linestring2)
SQLST_EuclideanDistance(linestring1, linestring2)
ScalaeuclideanDistance(linestring1, linestring2)

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

Examples

PythonPythonSQLScala
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from geoanalytics_fabric.sql import functions as ST, Linestring

data = [
    ("LINESTRING (10 10, 20 20)", "LINESTRING (10 30, 20 40)"),
    ("LINESTRING (0 20, 10 20)", "LINESTRING (0 20, -10 20)")
]

df = spark.createDataFrame(data, ["line1_wkt", "line2_wkt"]) \
          .withColumn("line1", ST.line_from_text("line1_wkt", 4326)) \
          .withColumn("line2", ST.line_from_text("line2_wkt", 4326))

df.select(ST.euclidean_distance("line1", "line2").alias("euclidean_distance")).show()
Result
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+------------------+
|euclidean_distance|
+------------------+
|              20.0|
|              10.0|
+------------------+

Version table

ReleaseNotes

1.0.0-beta

Python, SQL, and Scala functions introduced

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