TRK_SplitByDistanceGap takes a track column and a distance and returns an array of tracks. The result array contains the input track split into segments wherever two vertices are farther apart than the specified distance. The track is split by removing the segment between the two vertices.
The distance can be defined using ST_CreateDistance or with a tuple
containing a number and a unit string (e.g., (10, "kilometers")).
Tracks are linestrings that represent the change in an entity's location over time. Each vertex in the linestring has a timestamp (stored as the M-value) and the vertices are ordered sequentially.
For more information on using tracks in GeoAnalytics for Microsoft Fabric, see the core concept topic on tracks.
| Function | Syntax |
|---|---|
| Python | split |
| SQL | TRK |
| Scala | split |
For more details, go to the GeoAnalytics for Microsoft Fabric API reference for split_by_distance_gap.
Python and SQL Examples
from geoanalytics_fabric.sql import functions as ST
from geoanalytics_fabric.tracks import functions as TRK
from pyspark.sql import functions as F
data = [
("LINESTRING M (-117.27 34.05 1633455010, -117.22 33.91 1633456062," +\
" -116.96 33.64 1633457132, -116.85 33.81 1633457224)",),
("LINESTRING M (-116.89 33.96 1633575895, -116.71 34.01 1633576982, -116.66 34.08 1633577061)",),
("LINESTRING M (-116.24 33.88 1633575234, -116.33 34.02 1633576336)",)
]
df = spark.createDataFrame(data, ["wkt"]).withColumn("track", ST.line_from_text("wkt", srid=4326))
result = df.withColumn("split_by_distance_gap", TRK.split_by_distance_gap("track", (15, "miles")))
ax = result.st.plot("track", edgecolor="lightgrey", linewidths=10, figsize=(15, 8))
result.select(F.explode("split_by_distance_gap")).st.plot(ax=ax, edgecolor="greenyellow", linewidths=3)
ax.legend(['Input track','Result track'], loc='lower right', fontsize='x-large')
Scala Example
import com.esri.geoanalytics.sql.{functions => ST}
import com.esri.geoanalytics.sql.{trackFunctions => TRK}
import org.apache.spark.sql.{functions => F}
case class lineRow(lineWkt: String)
val data = Seq(lineRow("LINESTRING M (-117.27 34.05 1633455010, -117.22 33.91 1633456062, -116.96 33.64 1633457132)"),
lineRow("LINESTRING M (-116.89 33.96 1633575895, -116.71 34.01 1633576982, -116.66 34.08 1633577061)"),
lineRow("LINESTRING M (-116.24 33.88 1633575234, -116.33 34.02 1633576336)"))
val df = spark.createDataFrame(data)
.withColumn("track", ST.lineFromText($"lineWkt", F.lit(4326)))
.withColumn("split_by_distance_gap", TRK.splitByDistanceGap($"track", F.lit(struct(F.lit(15), F.lit("miles")))))
.withColumn("result_tracks", F.explode($"split_by_distance_gap"))
df.select("result_tracks").show(truncate = false)+-------------------------------------------------------------------------------------------------------------------+
|result_tracks |
+-------------------------------------------------------------------------------------------------------------------+
|{"hasM":true,"paths":[[[-117.27,34.05,1.63345501e9],[-117.22,33.91,1.633456062e9]]]} |
|{"hasM":true,"paths":[[[-116.96,33.64,1.633457132e9]]]} |
|{"hasM":true,"paths":[[[-116.89,33.96,1.633575895e9],[-116.71,34.01,1.633576982e9],[-116.66,34.08,1.633577061e9]]]}|
|{"hasM":true,"paths":[[[-116.24,33.88,1.633575234e9],[-116.33,34.02,1.633576336e9]]]} |
+-------------------------------------------------------------------------------------------------------------------+Version table
| Release | Notes |
|---|---|
1.0.0-beta | Python, SQL, and Scala functions introduced |