Dissolve Boundaries Workflow

This covers the Spark SQL workflow that replicates the Dissolve Boundaries tool. Dissolve Boundaries merges geometries that intersect or have the same field value into a single geometry. This workflow will dissolve the USA States data by region, calculate the summary statistics for each dissolved region, and convert the dissolved multipart geometries into singlepart geometries.

Steps

Import

1. In your notebook, import geoanalytics_fabric.

   PythonPythonScala
   Use dark colors for code blocksCopy

   1
   2
   3
   4
   5
   import geoanalytics_fabric
   from geoanalytics_fabric.sql import functions as ST
   from pyspark_fabric.sql import functions as F

Read the sample data and plot

1. Create a DataFrame from a feature service of the state boundaries in the United States and display columns of interest.

   PythonPythonScala
   Use dark colors for code blocksCopy

   1
   2
   3
   4
   5
   6
   7
   8
   # Create a DataFrame from the USA States Boundaries feature service
   url = "https://services.arcgis.com/P3ePLMYs2RVChkJx/ArcGIS/rest/services/USA_State_Boundaries/FeatureServer/0"
   df = spark.read.format("feature-service").load(url)
   
   # Display the first 5 rows of the DataFrame
   df.select('STATE_NAME', "SUB_REGION", "POP2010").show(5)
   Result

   Use dark colors for code blocksCopy

   1
   2
   3
   4
   5
   6
   7
   8
   9
   10
   +----------+----------+--------+
   |STATE_NAME|SUB_REGION| POP2010|
   +----------+----------+--------+
   |    Alaska|   Pacific|  710231|
   |California|   Pacific|37253956|
   |    Hawaii|   Pacific| 1360301|
   |     Idaho|  Mountain| 1567582|
   |    Nevada|  Mountain| 2700551|
   +----------+----------+--------+
   only showing top 5 rows

2. Plot the USA States data.

   Python

   Use dark colors for code blocksCopy

   1
   2
   3
   4
   5
   6
   7
   8
   # Plot the USA States data
   df_plot = df.st.plot(figsize=(14, 14), basemap='light')
   
   df_plot.set_title("USA States")
   df_plot.set_xlabel("Longitude")
   df_plot.set_ylabel("Latitude")
   

Dissolve States by region

1. Use the ST_Aggr_Union Python function to dissolve the States by the SUB_REGION field to create multipart geometries.

   PythonPythonScala
   Use dark colors for code blocksCopy

   1
   2
   3
   4
   5
   6
   # Dissolve by SUB_REGION and create multipart geometries
   df_dissolved_multipart = df.groupBy("SUB_REGION").agg(ST.aggr_union("shape").alias("dissolved_geom_multipart")) \
                                                           .withColumn("wkt", ST.as_text("dissolved_geom_multipart"))
   df_dissolved_multipart.show(10)
   Result

   Use dark colors for code blocksCopy

   1
   2
   3
   4
   5
   6
   7
   8
   9
   10
   11
   12
   13
   +------------------+------------------------+--------------------+
   |        SUB_REGION|dissolved_geom_multipart|                 wkt|
   +------------------+------------------------+--------------------+
   |           Pacific|    {"rings":[[[-1.78...|MULTIPOLYGON (((-...|
   |          Mountain|    {"rings":[[[-1.32...|POLYGON ((-1.3263...|
   |West South Central|    {"rings":[[[-1.17...|MULTIPOLYGON (((-...|
   |West North Central|    {"rings":[[[-1.05...|POLYGON ((-1.0583...|
   |East South Central|    {"rings":[[[-9469...|POLYGON ((-946995...|
   |       New England|    {"rings":[[[-8185...|MULTIPOLYGON (((-...|
   |    South Atlantic|    {"rings":[[[-8993...|MULTIPOLYGON (((-...|
   |East North Central|    {"rings":[[[-9804...|MULTIPOLYGON (((-...|
   |   Middle Atlantic|    {"rings":[[[-8403...|MULTIPOLYGON (((-...|
   +------------------+------------------------+--------------------+

2. Plot the dissolved multipart geometries.

   Python

   Use dark colors for code blocksCopy

   1
   2
   3
   4
   5
   6
   7
   8
   9
   10
   # Plot the dissolved multipart geometries
   df_dissolved_multipart_plot = df_dissolved_multipart.st.plot(cmap_values="SUB_REGION", is_categorical=True, cmap="Paired",
                                                                legend=True, legend_kwds={'title':"USA Region"},
                                                                figsize=(14, 14), edgecolor="black", basemap="light")
   
   df_dissolved_multipart_plot.set_title("USA States dissolved multipart by region")
   df_dissolved_multipart_plot.set_xlabel("Longitude")
   df_dissolved_multipart_plot.set_ylabel("Latitude")
   

Calculate summary statistics for the dissolved regions

A full list of summary statistics can be found in summary statistics.

1. Calculate the total population for each region.

   PythonPythonScala
   Use dark colors for code blocksCopy

   1
   2
   3
   4
   # Get the sum of the population for each "SUB_REGION"
   df.groupBy("SUB_REGION").sum().select("SUB_REGION", "sum(POP2010)").show(10)
   Result

   Use dark colors for code blocksCopy

   1
   2
   3
   4
   5
   6
   7
   8
   9
   10
   11
   12
   13
   +------------------+------------+
   |        SUB_REGION|sum(POP2010)|
   +------------------+------------+
   |           Pacific|    49880102|
   |West South Central|    36346202|
   |   Middle Atlantic|    40872375|
   |    South Atlantic|    59777037|
   |East North Central|    46421564|
   |       New England|    14444865|
   |          Mountain|    22065451|
   |East South Central|    18432505|
   |West North Central|    20505437|
   +------------------+------------+

2. Calculate the number of States within each region.

   PythonPythonScala
   Use dark colors for code blocksCopy

   1
   2
   3
   4
   # Get the count of States within each "SUB_REGION"
   df.groupBy("SUB_REGION").count().select("SUB_REGION", "count").show(10)
   Result

   Use dark colors for code blocksCopy

   1
   2
   3
   4
   5
   6
   7
   8
   9
   10
   11
   12
   13
   +------------------+-----+
   |        SUB_REGION|count|
   +------------------+-----+
   |West South Central|    4|
   |West North Central|    7|
   |    South Atlantic|    9|
   |           Pacific|    5|
   |       New England|    6|
   |          Mountain|    8|
   |   Middle Atlantic|    3|
   |East South Central|    4|
   |East North Central|    5|
   +------------------+-----+

Create dissolved singlepart geometries

1. Convert the dissolved multipart geometries into dissolved singlepart geometries using the ST_Geometries function and the Spark Explode function.

   PythonPythonScala
   Use dark colors for code blocksCopy

   1
   2
   3
   4
   5
   6
   7
   8
   9
   10
   # Create dissolved singlepart geometries from the dissolved multipart geometries
   df_dissolved_singlepart = df_dissolved_multipart.select("SUB_REGION",
       F.explode(ST.geometries("dissolved_geom_multipart")) \
       .alias("dissolved_geom_singlepart")) \
       .withColumn("wkt", ST.as_text("dissolved_geom_singlepart")) \
       .withColumn("index", F.monotonically_increasing_id())
   
   df_dissolved_singlepart.orderBy("SUB_REGION", desc=False).show(20)
   Result

   Use dark colors for code blocksCopy

   1
   2
   3
   4
   5
   6
   7
   8
   9
   10
   11
   12
   13
   14
   15
   16
   17
   18
   19
   20
   21
   22
   23
   24
   25
   +------------------+----------------------------+--------------------+-----+
   |        SUB_REGION|dissolved_geom_non_multipart|                 wkt|index|
   +------------------+----------------------------+--------------------+-----+
   |East North Central|        {"rings":[[[-9851...|POLYGON ((-985149...|   79|
   |East North Central|        {"rings":[[[-9804...|POLYGON ((-980408...|   77|
   |East North Central|        {"rings":[[[-9851...|POLYGON ((-985185...|   80|
   |East North Central|        {"rings":[[[-9688...|POLYGON ((-968863...|   78|
   |East North Central|        {"rings":[[[-9334...|POLYGON ((-933466...|   81|
   |East South Central|        {"rings":[[[-9469...|POLYGON ((-946995...|   57|
   |   Middle Atlantic|        {"rings":[[[-8403...|POLYGON ((-840342...|   82|
   |   Middle Atlantic|        {"rings":[[[-8264...|POLYGON ((-826401...|   85|
   |   Middle Atlantic|        {"rings":[[[-8158...|POLYGON ((-815894...|   84|
   |   Middle Atlantic|        {"rings":[[[-8210...|POLYGON ((-821005...|   83|
   |          Mountain|        {"rings":[[[-1.32...|POLYGON ((-1.3263...|   46|
   |       New England|        {"rings":[[[-7933...|POLYGON ((-793364...|   59|
   |       New England|        {"rings":[[[-7859...|POLYGON ((-785963...|   60|
   |       New England|        {"rings":[[[-8185...|POLYGON ((-818536...|   58|
   |       New England|        {"rings":[[[-7795...|POLYGON ((-779589...|   61|
   |       New England|        {"rings":[[[-7612...|POLYGON ((-761290...|   62|
   |           Pacific|        {"rings":[[[-1.78...|POLYGON ((-1.7819...|    0|
   |           Pacific|        {"rings":[[[-1.77...|POLYGON ((-1.7737...|    1|
   |           Pacific|        {"rings":[[[-1.75...|POLYGON ((-1.7552...|    2|
   |           Pacific|        {"rings":[[[-1.74...|POLYGON ((-1.7445...|    3|
   +------------------+----------------------------+--------------------+-----+
   only showing top 20 rows

2. Plot the dissolved singlepart geometries.

   Python

   Use dark colors for code blocksCopy

   1
   2
   3
   4
   5
   6
   7
   8
   9
   10
   11
   12
   13
   # Plot the dissolved singlepart geometries
   df_dissolved_singlepart_plot = df_dissolved_singlepart.st.plot(cmap_values="index",
                                                                        is_categorical=True,
                                                                        cmap="prism",
                                                                        figsize=(14, 14),
                                                                        edgecolor="black",
                                                                        basemap="light")
   
   df_dissolved_singlepart_plot.set_title("USA States dissolved singlepart by region")
   df_dissolved_singlepart_plot.set_xlabel("Longitude")
   df_dissolved_singlepart_plot.set_ylabel("Latitude")
   

What's next?

See below for some related topics:

• ST_Aggr_Intersection—Calculate the intersection of the geometries in each group.
• ST_Aggr_MeanCenter—Calculate the mean center of the geometries in each group.
• ST_Aggr_StdevEllipse—Calculate the standard-deviational ellipse of the geometries in each group.
• ST_Aggr_Union—Calculate the union of the geometries in each group.