TRK_SplitByDuration

TRK_SplitByDuration takes a track column and a duration and returns an array of tracks. The result array contains the input track split into segments with each segment no longer than the specified duration.

The duration can be defined using ST_CreateDuration or with a tuple containing a number and a unit string (e.g., (5, "minutes")).

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 Engine, see the core concept topic on tracks.

FunctionSyntax
Pythonsplit_by_duration(track, duration)
SQLTRK_SplitByDuration(track, duration)
ScalasplitByDuration(track, duration)

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

Python and SQL Examples

PythonPythonSQL
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from geoanalytics.sql import functions as ST
from geoanalytics.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)",),
    ("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_duration", TRK.split_by_duration("track", (10, "minutes")))

result.select(F.explode("split_by_duration"), F.monotonically_increasing_id().alias("id")) \
      .st.plot(is_categorical=True, cmap_values="id", cmap="prism", linewidths=10, figsize=(15, 8))
Plotted example for TRK_SplitByDuration
Plotted result for TRK_SplitByDuration.

Scala Example

Scala
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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($"lineWkt", F.lit(4326)))
              .withColumn("split_by_duration", TRK.splitByDuration($"track",  F.lit(struct(F.lit(10), F.lit("minutes")))))
              .withColumn("result_tracks", F.explode($"split_by_duration"))

df.select("result_tracks").show(5, truncate = false)
Result
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+------------------------------------------------------------------------------------------------------------------------------------------------------------------+
|result_tracks                                                                                                                                                     |
+------------------------------------------------------------------------------------------------------------------------------------------------------------------+
|{"hasM":true,"paths":[[[-117.27,34.05,1.63345501e9],[-117.24148288973383,33.97015209125475,1.63345561e9]]]}                                                       |
|{"hasM":true,"paths":[[[-117.24148288973383,33.97015209125475,1.63345561e9],[-117.22,33.91,1.633456062e9],[-117.18403738317757,33.872654205607475,1.63345621e9]]]}|
|{"hasM":true,"paths":[[[-117.18403738317757,33.872654205607475,1.63345621e9],[-117.0382429906542,33.7212523364486,1.63345681e9]]]}                                |
|{"hasM":true,"paths":[[[-117.0382429906542,33.7212523364486,1.63345681e9],[-116.96,33.64,1.633457132e9]]]}                                                        |
|{"hasM":true,"paths":[[[-116.89,33.96,1.633575895e9],[-116.79064397424102,33.987598896044155,1.633576495e9]]]}                                                    |
+------------------------------------------------------------------------------------------------------------------------------------------------------------------+
only showing top 5 rows

Version table

ReleaseNotes

1.4.0

Python and SQL functions introduced

1.5.0

Scala function introduced

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