Overlay

Overlay combines two DataFrames into a single DataFrame using one of five overlay types: Intersect, Erase, Union, Identity, or Symmetric Difference.

Usage notes

• Overlay requires that two inputs are specified using the input_dataframe and overlay_dataframe parameters in the run() method. The two supported overlay types and input geometries are described in the following tables:

  	Intersect	Erase	Union	Identity	Symmetrical Difference
  Point and point
  Point and linestring
  Point and polygon
  Linestring and point
  Linestring and linestring
  Linestring and polygon
  Polygon and point
  Polygon and linestring
  Polygon and polygon

• The following table summarizes the overlay operations:

  Overlay type	Description
  Intersect	The geometries or portions of geometries in the overlay DataFrame that overlap the input DataFrame are returned. This is the default.
  Erase	The geometries or portions of geometries in the input DataFrame that do not overlap the overlay DataFrame are returned.
  Union	The result will contain a geometric union of the input DataFrame and overlay DataFrame. All geometries and attributes will be returned.
  Identity	The result will contain geometries or portions of geometries that overlap in both the input DataFrame and overlay DataFrame.
  Symmetric Difference	The result will contain geometries or portions of geometries of the input DataFrame and the overlay DataFrame that do not overlap.

• When applying the indentity, intersect, symmetric difference, or union overlay types, fields from both DataFrames (input_dataframe and overlay_dataframe) are in the result. If the inputs have the same field names, the overlay fields will be renamed fieldname_overlay. For example, if your input_dataframe had the fields county and state, and the overlay_dataframe had the field county, your output DataFrame would have the fields county, state, and county_overlay.

Limitations

Sliver polygon results may be excluded based on the tolerance of the processing spatial reference.

Results

The tool outputs records that include the following fields in addition to the input_dataframe fields:

Performance notes

Improve the performance of Overlay 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.

Similar capabilities

Use the Spatiotemporal join tool if you want to join two DataFrames based on their spatial relationship.

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 Engine API reference for overlay.

Examples

Run Overlay

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

# Imports
from geoanalytics.tools import Overlay
from geoanalytics.sql import functions as ST
from pyspark.sql import functions as F

# Paths to development area and planned pathway datasets
development_path="https://services.arcgis.com/P3ePLMYs2RVChkJx/ArcGIS/rest/" \
            "/services/DevelopmentProjectArea/FeatureServer/0"
pathway_path="https://services.arcgis.com/P3ePLMYs2RVChkJx/ArcGIS/rest/" \
            "services/DevA_Pathways/FeatureServer/1"

# Create a DataFrame for the development areas and pathways
development_df=spark.read.format("feature-service").load(development_path)
pathway_df=spark.read.format("feature-service").load(pathway_path)

# Use Overlay to union the pathways with the development area.
# The result will be a DataFrame containing the development area enhanced with
#    planned pathway geometries and information
overlay_result=Overlay() \
            .setOverlayType(overlay_type="Union") \
            .run(input_dataframe=development_df, overlay_dataframe=pathway_df)
overlay_result.filter(overlay_result["shapeType"] == 'Street') \
              .select("OBJECTID", "shapeType", F.round("Z_Min", 13).alias("Z_Min"), F.round("Z_Max", 13).alias("Z_Max")) \
              .sort("Z_Max", "Z_Min", ascending=False) \
              .show(5)

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+--------+---------+----------------+----------------+
|OBJECTID|shapeType|           Z_Min|           Z_Max|
+--------+---------+----------------+----------------+
|       1|   Street|50.1885900000052|52.4717399999936|
|       1|   Street|42.6825300000055|52.4717399999936|
|       1|   Street|35.4103199999954|50.1885900000052|
|       1|   Street|47.8472699999984|48.1978600000002|
|       1|   Street|34.7740400000039|48.1978600000002|
+--------+---------+----------------+----------------+
only showing top 5 rows

Plot results

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# Plot the resulting development and pathway areas
result_plot=overlay_result.st.plot(facecolor="lightblue",
                                     edgecolors="navy",
                                     figsize=(16,10),
                                     basemap="light")
result_plot.set_title("Development area (Multnomah, Oregon) enhanced with" \
         "information for planned pathways")
result_plot.set_xlabel("X (Meters)")
result_plot.set_ylabel("Y (Meters)")

Version table

Links to helpful information

• Visualizing results
• Spatial relationships