Group by proximity

Groups records that are within spatial or spatiotemporal proximity to each other.

Usage notes

• The output result is a copy of the input with a new field named group_id. The group_id field represents the grouping of records. Records with the same group_id value are in the same group. The group numbers represent membership in a particular group and do not imply value. The group numbers may not be sequential or the same number in repeated runs of the tool.

• The supported spatial relationships and input geometries are described in the following table:

  	Intersects	Touches	Geodesic Near	Planar Near
  Point
  Linestring
  Polygon

• The spatial relationship definitions are outlined below.

• When 'PlanarNear' is specified with setSpatialRelationship(), it is required that the input DataFrame's geometry column is projected or the tool will fail. You can transform your data to a projected coordinate system by using ST_Transform.

  Learn more about coordinate systems and transformations

• The supported temporal relationships and temporal types are described in the following table:

  	Intersect	Near
  None
  Instant
  Interval

• The temporal relationship definitions are outlined below.

• You can specify any of the following combinations of relationships:

  • A spatial relationship value
  • A spatial relationship and a temporal relationship
  • A spatial relationship and an attribute relationship
  • A spatial relationship, temporal relationship, and an attribute relationship

• Records are grouped when all specified relationships are met.

• The attribute expression is a symmetric operation. The tool takes a single DataFrame that's compared against itself to group. Because of this, the input dataset is denoted as both a and b, and all expressions should include both a and b.

• When specifying the attribute relationship you can create a Spark SQL expression or an Arcade expression. For example, to group all records where the column Amount has the same value do the following:

  • SQL: a.Amount = b.Amount
  • Arcade: $a.Amount == $b.Amount

Limitations

• Values will not be grouped across the anti-meridian.

Results

In addition to the original fields, the following additional fields are included:

Performance notes

Improve the performance of Group By Proximity 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.
• When using the planar or geodesic near, use a smaller distance.
• When using the spatial relationship parameter, the planar near option is faster than the geodesic near option.
• When using the temporal relationship parameter's near option, use a smaller temporal near distance.

Similar capabilities

Similar tools:

• Spatiotemporal join
• Overlay

The following functions complete spatial overlay operations:

• ST_Aggr_Intersection
• ST_Aggr_union
• ST_Contains
• ST_Crosses
• ST_Difference
• ST_Dwithin
• ST_Equals
• ST_Intersection
• ST_Intersects
• ST_Overlaps
• ST_SymDifference
• ST_Touches
• ST_Union
• ST_Within

Syntax

For more details, go to the GeoAnalytics for Microsoft Fabric API reference for group by proximity.

Examples

Run Group by Proximity

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# Imports
from geoanalytics_fabric.tools import GroupByProximity
from geoanalytics_fabric.sql import functions as ST

# Path to the USA rivers and streams data
usa_rivers_data_path = "https://services.arcgis.com/P3ePLMYs2RVChkJx/ArcGIS/rest" \
            "/services/USA_Rivers_and_Streams/FeatureServer/0"

# Create an Oregon rivers DataFrame from the USA rivers and streams data
oregon_rivers_df = spark.read.format("feature-service") \
                                .load(usa_rivers_data_path) \
                                .where("State = 'OR'")

# Run the Group by Proximity tool to find intersecting rivers and streams
result = GroupByProximity() \
           .setSpatialRelationship(spatial_relationship="Intersects") \
           .run(dataframe=oregon_rivers_df)

# View the first 5 rows of the result DataFrame
result.select("State", "Name", "Region", "Feature", "Miles", "GROUP_ID") \
    .sort("Miles", ascending=False).show(5)

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+-----+----------------+------+-------+------+--------+
|State|            Name|Region|Feature| Miles|GROUP_ID|
+-----+----------------+------+-------+------+--------+
|   OR|Willamette River|    17| Stream| 160.8|    2374|
|   OR|  John Day River|    17| Stream|122.73|     154|
|   OR|   Nehalem River|    17| Stream|107.92|    1819|
|   OR|  John Day River|    17| Stream|105.58|     154|
|   OR| Deschutes River|    17| Stream|105.26|    1264|
+-----+----------------+------+-------+------+--------+
only showing top 5 rows

Plot results

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# Plot the grouped results
# Create an Oregon boundary DataFrame and transform geometry to NAD 1983 StatePlane Oregon
usa_states_path = "https://services.arcgis.com/P3ePLMYs2RVChkJx/ArcGIS/rest" \
          "/services/USA_State_Boundaries/FeatureServer/0"
oregon_df = spark.read.format("feature-service").load(usa_states_path) \
                            .where("STATE_NAME == 'Oregon'") \
                            .withColumn("shape", ST.transform("shape", 6558))

# Transform the grouped rivers' geometry to NAD 1983 StatePlane Oregon spatial reference
result = result.withColumn("shape", ST.transform("shape", 6558))

# Plot the result DataFrame with the Oregon data
oregon_plot = oregon_df.st.plot(facecolor="none", linewidth = 2, edgecolors="black",
                             figsize=(16,10), basemap="light")
result_plot = result.st.plot(geometry="shape" ,cmap_values="GROUP_ID",
                             is_categorical=True, cmap="tab20c",
                             ax=oregon_plot )
result_plot.set_title("Oregon rivers grouped by proximity")
result_plot.set_xlabel("X (Meters)")
result_plot.set_ylabel("Y (Meters)");

Version table

Links to helpful information

• Visualizing results
• Spatial relationships
• Temporal relationships