Find Dwell Locations

Find Dwell Locations determines dwell locations from time-sequential points in a track. Dwell locations are defined as sequential observations with little or no movement over a certain period of time.

Depending on the field of application, this may be referred to as stay points or idle detection. Tracks are identified by one or more track fields. The result DataFrame contains the dwell locations as points, a convex hull of the dwell locations, or a mean center point of the dwell. The result also contains the count of points within a dwell location, the start and end time of the dwell, the duration of the dwell, and any additional statistics that have been calculated. Each track can have 0, 1, or more dwell locations.

Usage notes

• The input DataFrame must have time-enabled points that represent an instant in time.

• Dwell locations are defined as sequential observations with little or no movement over a certain period of time.

• Results are points representing instants in time, or polygons representing an interval in time. The start and end of the interval are determined by the time of the first and last points in a dwell.

• Dwell locations can only be detected in tracks with more than one point.

• Tracks are represented by the unique combination of one or more track fields. Specify one or more fields to identify tracks using setTrackFields().

• By default, dwell locations 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 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. The input DataFrame's geometry must have a projected coordinate system that supports wrapping around the international date line, for example, a global projection such as World Cylindrical Equal Area. You can transform your data to a projected coordinate system by using ST_Transform.

    Learn more about coordinate systems and transformations

  • The dataset is not in a local projection—If the input DataFrame is in a local projection, use the planar distance method. For example, use the planar method to examine dwell locations in a single state. The input DataFrame must be set to a spatial reference local to the dataset.

• Output dwell locations can be represented in five ways. The following table shows an example of each:

Output type	Description	Example
All points	Every point is returned even if it is not part of a dwell. The resulting points have time type instant. Only a count statistic is calculated for this output type. The count represents the number of rows that belong to a single dwell. Non-dwell points will have a count of 0.
Dwell points	Only points that are part of a dwell are returned. The resulting points have time type instant. Only a count statistic is calculated for this output type. The count represents the number of points that belong to a single dwell.
Mean centers	Each dwell has a single point returned representing the mean center of the dwell in distance and time. The resulting points have time type interval. The count of points in the dwell is always calculated. You can optionally calculate statistics on this type of dwell result. By default, no statistics are calculated.
Convex hulls	Each dwell is represented by a convex hull polygon of the dwell result. The resulting rows have time type interval. The count of rows in the dwell is always calculated. You can optionally calculate statistics on this type of dwell result. By default, no statistics are calculated.

• You can optionally split tracks using setTimeBoundarySplit(). This 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.

• When choosing parameters to calculate dwell locations, consider the type of observation and the scale of dwell that you want to find. The following are examples of how to specify dwell calculations:

  • Ship DataFrame that has vesselID and tripID fields.

    • Use the vesselID and tripID fields as the identifiers to calculate dwell locations along distinct routes.

    • Use a time tolerance of 1 hour and a distance tolerance of 1 nautical mile to discover where vessels stay within 1 nautical mile for at least 1 hour.

  • Animal tracker DataFrame that has an animalID field.

    • Use the animalID field as the identifier to compare dwell locations of specific animals.

    • To determine the range of an animal, use a time tolerance of 3 days, and a distance tolerance of 10 miles to discover animal habitats of interest.

    • For a smaller area of interest, use a time tolerance of 2 hours, and a distance tolerance of 100 meters.

Limitations

• Inputs must be point DataFrames with time-enabled records of time type instant.

• Any points that do not have time will not be included in the analysis.

• The DwellID assigned to the same dwell output geometry could change after rerunning the tool. Try persisting the output by calling .persist() on the output DataFrame before querying the DwellID field.

• When calculating the convex hull and a dwell location is completely stationary (one unique location) or composed of two unique points, a small value based on the tolerance of the spatial reference used in an analysis will be used as the width, height, or diameter to create output polygons instead of convex hulls. These polygons can be used for visualization and do not represent the spatial extent of the dwell. Examples of these cases are described in the following table:

Input case	Description	Example
Coincident (one spatially-unique location)	If the input rows are stacked (coincident), the resulting convex hull will be an invalid polygon. In this example, the coincident input rows are represented by the red dot in the center of the yellow polygon. The yellow polygon represents the output convex hull result for coincident points. The blue polygon represents what a true convex hull looks like when there are four noncoincident points in a single dwell location.
Colinear (two spatially-unique locations)	If the input rows are in a line (most common with two spatially-unique locations), the resulting convex hull will be an invalid polygon. In this example, colinear points are represented by red dots in the yellow polygon. The yellow polygon represents the output convex hull result for colinear points.

Results

When the output type is 'DwellMeanCenters' or 'DwellConvexHulls', the only input fields that will be included in the output records are the fields used in setTrackFields(), as well as these additional fields:

Performance notes

Improve the performance of Find Dwell Locations 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.
• Specify a value of DwellPoints or DwellMeanCenters using setOutputType().
• Subdivide tracks as much as possible by using setTrackField() inputs.
• Split the tracks using setTimeBoundarySplit().

Similar capabilities

The following tools perform similar capabilities:

• Reconstruct Tracks
• Calculate Field
• Calculate Motion Statistics
• Snap Tracks
• TRK_FindDwells

How Find Dwell Locations works

• Dwell locations are determined using both time (setDwellMinDuration()) and distance (setDwellMaxDistance()) values. First, the tool assigns points to a track using a unique identifier (setTrackFields()). Track order is determined by the recorded time for each point. Then the distance between the first observation in a track and the next is calculated. Points are considered to be part of a dwell if two temporally consecutive points stay within the given distance for at least the given duration. When two points are found to be part of a dwell, the first point in the dwell is used as a reference point, and the tool finds consecutive points that are within the specified distance of the reference point in the dwell. Once all points within the specified distance are found, the tool collects the dwell points and calculates their mean center. Points before and after the current dwell are added to the dwell if they are within the given distance of the dwell location's mean center. This process continues until the end of the track.

• Input DataFrames are summarized into dwell locations using a unique identifier. For all output types, count of points and time duration are calculated for each dwell location.

• If you selected a summarized output option (DwellMeanCenters or DwellConvexHulls), each track can optionally apply 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 using addSummaryField().

• The First and Last statistics return the first or last value in a track. For example, with a time-ordered track with the following values: [Toronto,Guelph,Montreal], the first value is Toronto, and the last value is Montreal.

• The count statistic (for strings and numeric fields) counts the number of non-null values. The count of the following values equals 5: [0, 1, 10, 5, null, 6] = 5. The count of this set of values equals 3: [Primary, Primary, Secondary, null] = 3.

Syntax

For more details, go to the GeoAnalytics for Microsoft Fabric API reference for find dwell locations.

Examples

Run Find Dwell Locations

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# Imports
from geoanalytics_fabric.tools import FindDwellLocations
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)

# Use Find Dwell Locations to find where users do not move more than 30 meters
# for at least 5 minutes
result = FindDwellLocations() \
            .setTrackFields("user_id") \
            .setDistanceMethod(distance_method="Planar") \
            .setDwellMaxDistance(max_distance=30, max_distance_unit="Meters") \
            .setDwellMinDuration(min_duration=5, min_duration_unit="Minutes") \
            .setOutputType(output_type="Dwellpoints") \
            .run(dataframe=df)

# Convert DwellDuration from milliseconds to minutes
result = result.withColumn("DwellDuration_minutes", F.col("DwellDuration") / 60000)

# Show the first 5 dwell result records from the result
result.filter(result["user_id"] == 'user3') \
      .select("user_id", "COUNT", "DwellDuration_minutes", "MeanDistance") \
      .sort("MeanDistance", ascending=False).show(5)

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+-------+-----+---------------------+-----------------+
|user_id|COUNT|DwellDuration_minutes|     MeanDistance|
+-------+-----+---------------------+-----------------+
|  user3|   35|                 34.0|39.45365389971781|
|  user3|   35|                 34.0|39.45365389971781|
|  user3|   35|                 34.0|39.45365389971781|
|  user3|   35|                 34.0|39.45365389971781|
|  user3|   35|                 34.0|39.45365389971781|
+-------+-----+---------------------+-----------------+
only showing top 5 rows

Plot results

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# Plot the dwell locations for user3 to visualize where dwells occur along tracks
seattle_example_tracks_plot = df.where("user_id = 'user3'").st.plot(color="lightgrey",
                                                                    figsize=(14,8),
                                                                    basemap="dark")
result_plot = result.where("user_id = 'user3'") \
    .st.plot(cmap_values="DwellDuration_minutes",
             legend=True,
             ax=seattle_example_tracks_plot)
result_plot.set_title("Input locations and dwell locations for user3")
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