Calculate motion statistics

Calculate Motion Statistics calculates movement statistics and descriptors for time-enabled points that represent one or more moving entities. The input DataFrame must be point geometries with an instant timestamp.

Usage notes

• Specify one or more fields to identify unique entities, also known as tracks. 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.

• By default, all supported statistics will be calculated for each input point if possible. Alternatively, you can choose one or more groups of statistics to calculate using setMotionStatistics().

• By default, distances between track observations are created using the geodesic distance method. To override this default behavior, use setDistanceMethod(). It is recommended that you use geodesic distance in the following circumstances:

  • Tracks cross the international date line—When using the geodesic method, input DataFrames that cross the international date line will have 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.

  • Your DataFrame is not in a local projection—If your input DataFrame is in a local projection, use the planar distance method. For example, use the planar method to examine trace events within a single state. Your input DataFrame or processing spatial reference must be set to a spatial reference local to your dataset.

• Minimum, maximum, average and total statistics are calculated using the current observation and a number of previous observations defined by setTrackHistoryWindow(). Other statistics are always calculated using only the current and previous observation and do not consider the track history window value. For example, if you set a track history window value of 5 and specify Speed as the value for setMotionStatistics(), the current observation and the previous four observations will be used to calculate MinSpeed, MaxSpeed, and AvgSpeed fields at each observation, while the Speed field will be calculated using only the current and previous observations at each point. The value specified for setTrackHistoryWindow() must be greater than 1. The default is 3.

• If there are fewer observations in a track's history than the value specified using setTrackHistoryWindow(), statistics starting with Min, Max, Avg, or Tot will be calculated using all observations in the track history.

• Statistics will not be calculated for the first record in each track. Statistics in the acceleration group will not be calculated for the first two records in each track.

• The result values will be in the units specified using setStatisticUnits().

• When calculating statistics on large tracks, you can use setTimeBoundarySplit() to split the large tracks into smaller tracks to improve performance.

• See the Results section below for a list of the calculated statistic fields.

Limitations

• The input DataFrame must be points that have a timestamp representing an instant in time. Any records that do not have time or geometry are not included in the result.

Results

The result of Calculate Motion Statistics is a copy of the input DataFrame with a new field for each calculated statistic.

The following statistic fields will be calculated for the distance group:

The following statistic fields will be calculated for the speed group:

The following statistics fields will be calculated for the acceleration group:

The following statistic fields will be calculated for the duration group:

The following statistics fields will be calculated for the elevation group:

The following statistics fields will be calculated for the slope group:

The following statistics fields will be calculated for the idle group:

The following statistics field will be calculated for the bearing group:

Performance notes

Improve the performance of Calculate Motion Statistics 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.
• Decrease the track history window size using setTrackHistoryWindow().
• Specify a value for setTimeBoundarySplit() to break up large tracks at a defined time interval.

Similar capabilities

The following tools perform similar capabilities:

• Calculate Field
• Reconstruct Tracks
• Find Dwell Locations
• Detect Incidents
• Snap Tracks

How Calculate Motion Statistics works

The diagram below shows a track with six sequential points. The statistics are calculated based on each row's elevation, distance, and time.

The following table summarizes the statistics calculations for the diagram above. All calculations are evaluated at point 5 and the track history window is 3. An observation is considered idling if it has moved less than 32 meters in 1 minute.

Syntax

For more details, go to the GeoAnalytics Engine API reference for calculate motion statistics.

Examples

Run Calculate Motion Statistics

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# Log in
import geoanalytics
geoanalytics.auth(username="myusername", password="mypassword")

# Imports
from geoanalytics.tools import CalculateMotionStatistics
from geoanalytics.sql import functions as ST

# 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)

# Calculate Motion Statistics to enhance your data with speed and idling statistics
result = CalculateMotionStatistics() \
             .setTrackFields("user_id") \
             .setTrackHistoryWindow(track_history_window=5) \
             .setMotionStatistics("Speed", "idle") \
             .setIdleTolerance(distance_tolerance=100,
                               distance_tolerance_unit="Feet",
                               time_tolerance=5,
                               time_tolerance_unit="Minutes") \
             .setDistanceMethod(distance_method="Planar") \
             .setStatisticUnits(speed_unit="FeetPerSecond") \
             .run(dataframe=df)

# Show a selection of columns for the first 5 records in the result DataFrame
result.filter(result["user_id"] == 'user1') \
      .select("user_id", "Speed", "MinSpeed", "MaxSpeed","AvgSpeed", "Idling", "TotIdleTime") \
      .sort("AvgSpeed", ascending=False) \
      .show(5)

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+-------+------------------+------------------+-----------------+------------------+------+-----------+
|user_id|             Speed|          MinSpeed|         MaxSpeed|          AvgSpeed|Idling|TotIdleTime|
+-------+------------------+------------------+-----------------+------------------+------+-----------+
|  user1|5.2204847202976685|5.2204847202976685|6.892290697309563|5.7618999711114105| false|        0.0|
|  user1| 5.526445079215372| 5.164426033535724|6.892290697309563|5.7478852994209255| false|        0.0|
|  user1| 6.278522175413303| 4.358639317727936|6.452378996093401| 5.644197059655965| false|        0.0|
|  user1| 5.408379387623038| 4.638848823449657|6.892290697309563| 5.525986235479496| false|        0.0|
|  user1| 4.575716510069646| 4.451734493505993|6.881149550040065|  5.47720959430053| false|        0.0|
+-------+------------------+------------------+-----------------+------------------+------+-----------+
only showing top 5 rows

Plotting results

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# Plot the enhanced results and show the average speed of user6
result_plot = result.where("user_id = 'user6'").st.plot(cmap_values="AvgSpeed",
                                                        cmap="plasma",
                                                        legend=True,
                                                        figsize=(14,8),
                                                        aspect="auto",
                                                        basemap="light")
result_plot.set_title("Average speed in feet per second for user6")
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