Reconstruct tracks

Reconstruct Tracks connects time-sequential points into tracks and summarizes records within the track. Tracks are identified by one or more track fields. The resulting DataFrame contains the track as a linestring or a polygon, the count of records within a track that have been summarized, and any additional statistics that have been specified.

Usage notes

• Reconstruct Tracks requires point or polygon geometries. The input DataFrame must be time enabled with records that represent an instant in time.

• You can specify one or more fields to identify tracks using setTrackFields(). Tracks are represented by the unique combination of one or more track fields. For example, if the flightID and Destination fields are used as track identifiers, the records ID007, Solden and ID007, Tokyo would be in different tracks since they have different values for the Destination field.

• Records that have a buffer applied will result in polygonal tracks. Input points that do not have a buffer applied will result in linestring tracks.

• Input DataFrames must be time-enabled and represent an instant in time. Results are linestrings or polygons that represent an interval in time. The start and end of the interval are determined by the time at the first and last records in a track.

  

• An input DataFrame with two distinct tracks (green and red) that have time type instant (left) and resulting tracks with time type interval (right) are shown.

• You can optionally apply a buffer to your input points. When you apply a buffer, the resulting tracks will be polygons.

  

• When buffering input points, each point is buffered. Then a convex hull is generated to create a polygonal track.

  

• For linestring results, only tracks that contain more than one point will be returned. If you apply a buffer, all inputs will be returned.

• Fields used in the buffer expression must be numeric and will be applied using the units of the input's spatial reference. See Arcade expressions for more information. You can use track-aware equations.

• By default, only the count of points or polygons in a track will be calculated. Optionally, use addSummaryField() to calculate additional numeric statistics (count, sum, minimum, maximum, range, mean, standard deviation, variance, first, and last) or string statistics (count, any, first, and last) for the rows summarized within a track.

• By default, tracks are created using the planar distance method when the input DataFrame is in a projected coordinate system and the geodesic distance method when the input DataFrame is in a geographic coordinate system. To override this default behavior, use setDistanceMethod(). It is recommended that you use geodesic distance in the following circumstances:

  • Tracks crossing the international date line—When using the geodesic method, input DataFrames that cross the international date line will result in tracks that correctly cross the international date line. Your input DataFrame or processing spatial reference must be set to a spatial reference that supports wrapping around the international date line, for example, a global projection such as World Cylindrical Equal Area.
  • Buffers—Input records can optionally be buffered.

• You can split tracks in the following ways:

  • setTimeSplit() - Splits tracks based on a time between input records. Applying a time split breaks up any track when input data is farther apart than the specified time. For example, if you have five records with the same track identifier and the times of [01:00, 02:00, 03:30, 06:00, 06:30] and set a time split of 2 hours, any records that are measured more than 2 hours apart will be split. In this example, the result would be a track with [01:00, 02:00, 03:30] and [06:00, 06:30], because the difference between 03:30 and 6:00 is greater than 2 hours.

  • setTimeBoundarySplit() - Splits tracks based on defined time intervals. Applying a time boundary split segments tracks at a defined interval. For example, if you set the time boundary to 1 day, starting at 9:00 a.m. on January 1, 1990, each track will be truncated at 9:00 a.m. every day. This split accelerates computing time, as it creates smaller tracks for analysis. If splitting by a recurring time boundary makes sense for your analysis, it is recommended for big data processing.

  • setDistanceSplit() - Splits tracks based on a distance between input records. Applying a distance split breaks up any track when input data is farther apart than the specified distance. For example, if you set a distance split of 5 kilometers, sequential records greater than 5 kilometers apart will be part of a different track.

  • setArcadeSplit() - Splits tracks based on an Arcade expression. Applying a split expression splits tracks based on values, geometry or time values. For example, you can split tracks when a field value is more than double the previous value in a track. To do this, using an example field named WindSpeed, you can use the following expression: var speed = TrackFieldWindow('WindSpeed', -1, 1); 2\* speed[0] \< speed[1]. Tracks will split when the previous value (speed[0]) is less than two times the current value.

• You can apply none, one, two, three, or four split options at the same time. All of the examples below use a gap split. The results, assuming you apply a time split of six hours, a time boundary of one day, and distance split of 16 kilometers, are as follows:

  

  Split option	Description
  Six input points with a time and location.	Input points with the same identifier. The distance between the points is marked on the top of the dotted line, and the time of each point measurement is marked below the points. There are four splits on the timeline. The red splits represent the time boundary split of 1 day, starting at 12:00 AM. The blue split represents the distance split, when the distance between two points is greater than 16 km. The purple split represents the time split, when the temporal distance between two sequential points is greater than 6 hours.
  1.	Example with no time split and no distance split.
  2.	Example with a time split of two hours. Any records greater than two hours apart are split into separate tracks.
  3.	Example with a time boundary of 1 day, starting at midnight. At each 1-day interval starting from the specified time (here 12:00 AM), a new track is created.
  4.	Example with a distance split of 16 kilometers. Any records greater than 16 kilometers apart (the records at 05:00 AM and 06:00 AM) are split into separate tracks.
  5.	Example with a time split of 6 hours and a time boundary of 1 day starting at 12:00 AM. Any records greater than six hours apart or that intersect with the time duration split at 12:00 AM are split into separate tracks.
  6.	Example with a time split of 6 hours and a distance split of 16 km. Any records greater than six hours apart (the records at 06:00 AM and 7:00 PM) or farther than 16 km apart are split into separate tracks.
  7.	Example with a distance split of 16 km and a time boundary of one starting at 12:00 AM. Any records greater than 16 km apart or that intersect with the time duration split at 12:00 AM are split into separate tracks.
  8.	Example with a distance split of 16 km, a time split of 6 hours, and a time boundary of one day starting at 12:00 AM. Any records greater than 16 km apart, or farther apart than 6 hours, or that intersect with the time duration split at 12:00 AM are split into separate tracks.

• When you split a track using a time split, distance split, or split expression, you can decide how segments between the split will be created. You have the following options:

  • Gap—Create a gap between the two records that were split.
  • FinishAfter—Create a segment that ends after the split.
  • StartBefore—Create a segment that ends and starts before the split.

• The following diagram shows an example of the split types:

  Three examples of time splits on the same input points (yellow) are shown.

  Time split option	Description
  Five input points with a time and location	Five input points with the same identifier. The time of each point is marked below the dotted line. There is one split between 2:00 a.m. and 5:00 a.m. for all examples. Each track is split into two segments between the third and fourth points on the track. The first track is green and the second is purple. How the tracks are split is defined by setSplitType().
  Gap (1)	Example with a gap between the two points that are split. This is the default.
  Finish After (2)	Example in which the track finishes after the split, at the fourth point. The second track starts at the fourth point.
  Start Before (3)	Example in which the track splits before the split, at the third point. The second track starts at the third point.

• The following are examples of why it may make sense to define tracks using the split parameters and the field identifier parameter using an airline flight as an example: An aircraft record has aircraftID, flightID, pilot_name, start_time, and flight_maneuver fields. The flight_maneuver field represents whether the aircraft is on land, ascending, descending, or at a constant altitude.

  • Use the aircraft ID field as the identifier to see where each plane has traveled.

  • Use the aircraft ID and the flight ID fields as the identifiers to compare distinct routes.

  • Use the aircraft ID field and the time boundary of one year to examine the flights for each aircraft for a year at a time.

  • Use the pilot name, aircraft ID, and start time fields to view each pilot's flight.

  • Use the aircraft ID field as the identifier, and split distances farther than 1,000 kilometers, to determine new tracks, given that a 1,000-kilometer jump in measurements should not belong to the same track.

  • Use the aircraft ID field as the identifier and split using an expression when the value in the flight_maneuver field changes. For example, var flight_manuever = TrackFieldWindow('maneuver', -1, 1); flight_maneuver[0] != flight_maneuver[1] checks whether the current value in a track and the previous value match. If not, the track is split.

Results

The tool outputs lines or polygons and includes the following fields:

Performance notes

Improve the performance of Reconstruct Tracks by doing one or more of the following:

• Only analyze the records in your area of interest. You can pick the records of interest by using one of the following SQL functions:

  • ST_Intersection—Clip to an area of interest represented by a polygon. This will modify your input records.
  • ST_BboxIntersects—Select records that intersect an envelope.
  • ST_EnvIntersects—Select records having an evelope that intersects the envelope of another geometry.
  • ST_Intersects—Select records that intersect another dataset or area of intersect represented by a polygon.
• Don't apply a buffer.
• Split your tracks using one of the splitting options. setTimeBoundarysplit() will have the biggest performance gains.

Similar functions

• Detect Incidents
• Find Dwell Locations
• Calculate Motion Statistics
• Calculate Field

Syntax

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

Examples

Run Reconstruct Tracks

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# Imports
from geoanalytics_fabric.tools import ReconstructTracks
from geoanalytics_fabric.sql import functions as ST
from pyspark.sql import functions as F

# Path to the Seattle example tracks data
data_path = r"https://services1.arcgis.com/36PP9fe9l4BSnArw/arcgis/rest/" \
			  "services/seattle_example_tracks/FeatureServer/0"

# Create a DataFrame from the Seattle example tracks data
df = spark.read.format("feature-service").load(data_path)

# Reconstruct tracks by user ID and return statistics for the horizontal accuracy column
result = ReconstructTracks() \
            .setTrackFields("user_id") \
            .setDistanceMethod(distance_method="Planar") \
            .addSummaryField(summary_field="horizontal_accuracy", statistic="Min") \
            .addSummaryField(summary_field="horizontal_accuracy", statistic="Max") \
            .addSummaryField(summary_field="horizontal_accuracy", statistic="Mean",
                             alias="avg_horizontal_accuracy") \
            .run(dataframe=df)

# Show a selection of columns and the length of the track for the first 5 results
result.select(ST.length(geometry="track_geometry").alias("length"), "user_id", "COUNT",
              "TRACK_DURATION", F.round("avg_horizontal_accuracy", 13).alias("avg_horizontal_accuracy")) \
      .sort("user_id", ascending=True).show(5)

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+------------------+-------+-----+--------------+-----------------------+
|            length|user_id|COUNT|TRACK_DURATION|avg_horizontal_accuracy|
+------------------+-------+-----+--------------+-----------------------+
|32953.688189848006|  user1|160.0|     160860000|         5.222359767449|
|18329.825427215314|  user2| 82.0|       4860000|        4.8091253320749|
|30329.946152668355|  user3|293.0|     106440000|        5.1580968157529|
| 39125.16386084201|  user4|339.0|      96900000|        4.9619108295966|
|17847.608263107366|  user5| 95.0|       5640000|        5.2804088837775|
+------------------+-------+-----+--------------+-----------------------+
only showing top 5 rows

Plot results

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# Plot the Reconstruct Tracks result
result_plot = result.st.plot(cmap_values="user_id", is_categorical=True, cmap="Paired",
                             legend=True,
                             legend_kwds={"title": "User ID", "loc": "lower left", "bbox_to_anchor": (0, 0.03)},
                             figsize=(10,12),
                             basemap="light")
result_plot.set_title("Reconstructed user tracks")
result_plot.set_xlabel("X (US Survey Feet)")
result_plot.set_ylabel("Y (US Survey Feet)");

Version table

Links to helpful information

• Visualizing results
• Enabling time on a DataFrame
• Using Arcade expressions