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In [1]:

from pyspark.sql import SparkSession
from pyspark import StorageLevel
import geopandas as gpd
import pandas as pd
from pyspark.sql.types import StructType
from pyspark.sql.types import StructField
from pyspark.sql.types import StringType
from pyspark.sql.types import LongType
from shapely.geometry import Point
from shapely.geometry import Polygon

from sedona.spark import *
from sedona.core.geom.envelope import Envelope

In [2]:

config = SedonaContext.builder() .\
    config('spark.jars.packages',
           'org.apache.sedona:sedona-spark-3.4_2.12:1.5.1,'
           'org.datasyslab:geotools-wrapper:1.5.1-28.2,'
           'uk.co.gresearch.spark:spark-extension_2.12:2.11.0-3.4'). \
    config('spark.jars.repositories', 'https://artifacts.unidata.ucar.edu/repository/unidata-all'). \
    getOrCreate()

sedona = SedonaContext.create(config)

https://artifacts.unidata.ucar.edu/repository/unidata-all added as a remote repository with the name: repo-1
Ivy Default Cache set to: /root/.ivy2/cache
The jars for the packages stored in: /root/.ivy2/jars
org.apache.sedona#sedona-spark-shaded-3.4_2.12 added as a dependency
org.datasyslab#geotools-wrapper added as a dependency
uk.co.gresearch.spark#spark-extension_2.12 added as a dependency
:: resolving dependencies :: org.apache.spark#spark-submit-parent-7eb0c6dc-19b8-477f-b252-512428246ef7;1.0
	confs: [default]
	found org.apache.sedona#sedona-spark-shaded-3.4_2.12;1.5.1 in central
	found edu.ucar#cdm-core;5.4.2 in repo-1
	found edu.ucar#udunits;5.4.2 in repo-1
	found edu.ucar#httpservices;5.4.2 in repo-1
	found com.google.guava#guava;30.1-jre in central
	found com.google.guava#failureaccess;1.0.1 in central
	found com.google.guava#listenablefuture;9999.0-empty-to-avoid-conflict-with-guava in central

:: loading settings :: url = jar:file:/opt/spark/jars/ivy-2.5.1.jar!/org/apache/ivy/core/settings/ivysettings.xml

	found com.google.code.findbugs#jsr305;3.0.2 in central
	found org.checkerframework#checker-qual;3.5.0 in central
	found com.google.errorprone#error_prone_annotations;2.3.4 in central
	found com.google.j2objc#j2objc-annotations;1.3 in central
	found org.apache.httpcomponents#httpclient;4.5.13 in central
	found org.apache.httpcomponents#httpcore;4.4.13 in central
	found commons-logging#commons-logging;1.2 in central
	found commons-codec#commons-codec;1.11 in central
	found com.beust#jcommander;1.78 in central
	found com.google.protobuf#protobuf-java;3.12.4 in central
	found com.google.re2j#re2j;1.3 in central
	found joda-time#joda-time;2.10.3 in central
	found org.jdom#jdom2;2.0.6 in central
	found org.slf4j#slf4j-api;1.7.36 in central
	found org.apache.httpcomponents#httpmime;4.5.13 in central
	found org.datasyslab#geotools-wrapper;1.5.1-28.2 in central
	found uk.co.gresearch.spark#spark-extension_2.12;2.11.0-3.4 in central
	found com.github.scopt#scopt_2.12;4.1.0 in central
:: resolution report :: resolve 292ms :: artifacts dl 10ms
	:: modules in use:
	com.beust#jcommander;1.78 from central in [default]
	com.github.scopt#scopt_2.12;4.1.0 from central in [default]
	com.google.code.findbugs#jsr305;3.0.2 from central in [default]
	com.google.errorprone#error_prone_annotations;2.3.4 from central in [default]
	com.google.guava#failureaccess;1.0.1 from central in [default]
	com.google.guava#guava;30.1-jre from central in [default]
	com.google.guava#listenablefuture;9999.0-empty-to-avoid-conflict-with-guava from central in [default]
	com.google.j2objc#j2objc-annotations;1.3 from central in [default]
	com.google.protobuf#protobuf-java;3.12.4 from central in [default]
	com.google.re2j#re2j;1.3 from central in [default]
	commons-codec#commons-codec;1.11 from central in [default]
	commons-logging#commons-logging;1.2 from central in [default]
	edu.ucar#cdm-core;5.4.2 from repo-1 in [default]
	edu.ucar#httpservices;5.4.2 from repo-1 in [default]
	edu.ucar#udunits;5.4.2 from repo-1 in [default]
	joda-time#joda-time;2.10.3 from central in [default]
	org.apache.httpcomponents#httpclient;4.5.13 from central in [default]
	org.apache.httpcomponents#httpcore;4.4.13 from central in [default]
	org.apache.httpcomponents#httpmime;4.5.13 from central in [default]
	org.apache.sedona#sedona-spark-shaded-3.4_2.12;1.5.1 from central in [default]
	org.checkerframework#checker-qual;3.5.0 from central in [default]
	org.datasyslab#geotools-wrapper;1.5.1-28.2 from central in [default]
	org.jdom#jdom2;2.0.6 from central in [default]
	org.slf4j#slf4j-api;1.7.36 from central in [default]
	uk.co.gresearch.spark#spark-extension_2.12;2.11.0-3.4 from central in [default]
	---------------------------------------------------------------------
	|                  |            modules            ||   artifacts   |
	|       conf       | number| search|dwnlded|evicted|| number|dwnlded|
	---------------------------------------------------------------------
	|      default     |   25  |   0   |   0   |   0   ||   25  |   0   |
	---------------------------------------------------------------------
:: retrieving :: org.apache.spark#spark-submit-parent-7eb0c6dc-19b8-477f-b252-512428246ef7
	confs: [default]
	0 artifacts copied, 25 already retrieved (0kB/9ms)
24/01/20 22:54:19 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
Setting default log level to "WARN".
To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).

In [3]:

sc = sedona.sparkContext

Create SpatialRDD¶

Reading to PointRDD from CSV file¶

Suppose we want load the CSV file into Apache Sedona PointRDD

testattribute0,-88.331492,32.324142,testattribute1,testattribute2
testattribute0,-88.175933,32.360763,testattribute1,testattribute2
testattribute0,-88.388954,32.357073,testattribute1,testattribute2
testattribute0,-88.221102,32.35078,testattribute1,testattribute2
testattribute0,-88.323995,32.950671,testattribute1,testattribute2
testattribute0,-88.231077,32.700812,testattribute1,testattribute2

In [4]:

point_rdd = PointRDD(sc, "data/arealm-small.csv", 1, FileDataSplitter.CSV, True, 10)

In [5]:

## Getting approximate total count
point_rdd.approximateTotalCount

Out[5]:

In [6]:

# getting boundary for PointRDD or any other SpatialRDD, it returns Envelope object which inherits from
# shapely.geometry.Polygon
point_rdd.boundary()

/usr/local/lib/python3.10/dist-packages/sedona/core/geom/envelope.py:27: ShapelyDeprecationWarning: Setting custom attributes on geometry objects is deprecated, and will raise an AttributeError in Shapely 2.0
  self.minx = minx
/usr/local/lib/python3.10/dist-packages/sedona/core/geom/envelope.py:28: ShapelyDeprecationWarning: Setting custom attributes on geometry objects is deprecated, and will raise an AttributeError in Shapely 2.0
  self.maxx = maxx
/usr/local/lib/python3.10/dist-packages/sedona/core/geom/envelope.py:29: ShapelyDeprecationWarning: Setting custom attributes on geometry objects is deprecated, and will raise an AttributeError in Shapely 2.0
  self.miny = miny
/usr/local/lib/python3.10/dist-packages/sedona/core/geom/envelope.py:30: ShapelyDeprecationWarning: Setting custom attributes on geometry objects is deprecated, and will raise an AttributeError in Shapely 2.0
  self.maxy = maxy

Out[6]:

In [7]:

# To run analyze please use function analyze
point_rdd.analyze()

Out[7]:

True

In [8]:

# Finding boundary envelope for PointRDD or any other SpatialRDD, it returns Envelope object which inherits from
# shapely.geometry.Polygon
point_rdd.boundaryEnvelope

Out[8]:

In [9]:

# Calculate number of records without duplicates
point_rdd.countWithoutDuplicates()

Out[9]:

In [10]:

# Getting source epsg code
point_rdd.getSourceEpsgCode()

Out[10]:

''

In [11]:

# Getting target epsg code
point_rdd.getTargetEpsgCode()

Out[11]:

''

In [12]:

# Spatial partitioning data
point_rdd.spatialPartitioning(GridType.KDBTREE)

Out[12]:

True

Operations on RawSpatialRDD¶

rawSpatialRDD method returns RDD which consists of GeoData objects which has 2 attributes

geom: shapely.geometry.BaseGeometry

userData: str

You can use any operations on those objects and spread across machines

In [13]:

# take firs element
point_rdd.rawSpatialRDD.take(1)

Out[13]:

[Geometry: Point userData: testattribute0	testattribute1	testattribute2]

In [14]:

# collect to Python list
point_rdd.rawSpatialRDD.collect()[:5]

Out[14]:

[Geometry: Point userData: testattribute0	testattribute1	testattribute2,
 Geometry: Point userData: testattribute0	testattribute1	testattribute2,
 Geometry: Point userData: testattribute0	testattribute1	testattribute2,
 Geometry: Point userData: testattribute0	testattribute1	testattribute2,
 Geometry: Point userData: testattribute0	testattribute1	testattribute2]

In [15]:

# apply map functions, for example distance to Point(52 21)
point_rdd.rawSpatialRDD.map(lambda x: x.geom.distance(Point(21, 52))).take(5)

Out[15]:

[111.08786851399313,
 110.92828303170774,
 111.1385974283527,
 110.97450594034112,
 110.97122518072091]

Transforming to GeoPandas¶

Loaded data can be transformed to GeoPandas DataFrame in a few ways¶

Directly from RDD¶

In [16]:

point_rdd_to_geo = point_rdd.rawSpatialRDD.map(lambda x: [x.geom, *x.getUserData().split("\t")])

In [17]:

point_gdf = gpd.GeoDataFrame(
    point_rdd_to_geo.collect(), columns=["geom", "attr1", "attr2", "attr3"], geometry="geom"
)

In [18]:

point_gdf[:5]

Out[18]:

	geom	attr1	attr2	attr3
0	POINT (-88.33149 32.32414)	testattribute0	testattribute1	testattribute2
1	POINT (-88.17593 32.36076)	testattribute0	testattribute1	testattribute2
2	POINT (-88.38895 32.35707)	testattribute0	testattribute1	testattribute2
3	POINT (-88.22110 32.35078)	testattribute0	testattribute1	testattribute2
4	POINT (-88.32399 32.95067)	testattribute0	testattribute1	testattribute2

Using Adapter¶

In [19]:

# Adapter allows you to convert geospatial data types introduced with sedona to other ones

In [20]:

spatial_df = Adapter.\
    toDf(point_rdd, ["attr1", "attr2", "attr3"], sedona).\
    createOrReplaceTempView("spatial_df")

spatial_gdf = sedona.sql("Select attr1, attr2, attr3, geometry as geom from spatial_df")

In [21]:

spatial_gdf.show(5, False)

+--------------+--------------+--------------+----------------------------+
|attr1         |attr2         |attr3         |geom                        |
+--------------+--------------+--------------+----------------------------+
|testattribute0|testattribute1|testattribute2|POINT (-88.331492 32.324142)|
|testattribute0|testattribute1|testattribute2|POINT (-88.175933 32.360763)|
|testattribute0|testattribute1|testattribute2|POINT (-88.388954 32.357073)|
|testattribute0|testattribute1|testattribute2|POINT (-88.221102 32.35078) |
|testattribute0|testattribute1|testattribute2|POINT (-88.323995 32.950671)|
+--------------+--------------+--------------+----------------------------+
only showing top 5 rows

In [22]:

gpd.GeoDataFrame(spatial_gdf.toPandas(), geometry="geom")[:5]

Out[22]:

	attr1	attr2	attr3	geom
0	testattribute0	testattribute1	testattribute2	POINT (-88.33149 32.32414)
1	testattribute0	testattribute1	testattribute2	POINT (-88.17593 32.36076)
2	testattribute0	testattribute1	testattribute2	POINT (-88.38895 32.35707)
3	testattribute0	testattribute1	testattribute2	POINT (-88.22110 32.35078)
4	testattribute0	testattribute1	testattribute2	POINT (-88.32399 32.95067)

With DataFrame creation¶

In [23]:

schema = StructType(
    [
        StructField("geometry", GeometryType(), False),
        StructField("attr1", StringType(), False),
        StructField("attr2", StringType(), False),
        StructField("attr3", StringType(), False),
    ]
)

In [24]:

geo_df = sedona.createDataFrame(point_rdd_to_geo, schema, verifySchema=False)

In [25]:

gpd.GeoDataFrame(geo_df.toPandas(), geometry="geometry")[:5]

Out[25]:

	geometry	attr1	attr2	attr3
0	POINT (-88.33149 32.32414)	testattribute0	testattribute1	testattribute2
1	POINT (-88.17593 32.36076)	testattribute0	testattribute1	testattribute2
2	POINT (-88.38895 32.35707)	testattribute0	testattribute1	testattribute2
3	POINT (-88.22110 32.35078)	testattribute0	testattribute1	testattribute2
4	POINT (-88.32399 32.95067)	testattribute0	testattribute1	testattribute2

Load Typed SpatialRDDs¶

Currently The library supports 5 typed SpatialRDDs:

RectangleRDD

PointRDD

PolygonRDD

LineStringRDD

CircleRDD

In [26]:

rectangle_rdd = RectangleRDD(sc, "data/zcta510-small.csv", FileDataSplitter.CSV, True, 11)
point_rdd = PointRDD(sc, "data/arealm-small.csv", 1, FileDataSplitter.CSV, False, 11)
polygon_rdd = PolygonRDD(sc, "data/primaryroads-polygon.csv", FileDataSplitter.CSV, True, 11)
linestring_rdd = LineStringRDD(sc, "data/primaryroads-linestring.csv", FileDataSplitter.CSV, True)

In [27]:

rectangle_rdd.analyze()
point_rdd.analyze()
polygon_rdd.analyze()
linestring_rdd.analyze()

Out[27]:

True

Spatial Partitioning¶

Apache Sedona spatial partitioning method can significantly speed up the join query. Three spatial partitioning methods are available: KDB-Tree, Quad-Tree and R-Tree. Two SpatialRDD must be partitioned by the same way.

In [28]:

point_rdd.spatialPartitioning(GridType.KDBTREE)

Out[28]:

True

Create Index¶

Apache Sedona provides two types of spatial indexes, Quad-Tree and R-Tree. Once you specify an index type, Apache Sedona will build a local tree index on each of the SpatialRDD partition.

In [29]:

point_rdd.buildIndex(IndexType.RTREE, True)

SpatialJoin¶

Spatial join is operation which combines data based on spatial relations like:

intersects

touches

within

etc

To Use Spatial Join in GeoPyspark library please use JoinQuery object, which has implemented below methods:

SpatialJoinQuery(spatialRDD: SpatialRDD, queryRDD: SpatialRDD, useIndex: bool, considerBoundaryIntersection: bool) -> RDD

DistanceJoinQuery(spatialRDD: SpatialRDD, queryRDD: SpatialRDD, useIndex: bool, considerBoundaryIntersection: bool) -> RDD

spatialJoin(queryWindowRDD: SpatialRDD, objectRDD: SpatialRDD, joinParams: JoinParams) -> RDD

DistanceJoinQueryFlat(spatialRDD: SpatialRDD, queryRDD: SpatialRDD, useIndex: bool, considerBoundaryIntersection: bool) -> RDD

SpatialJoinQueryFlat(spatialRDD: SpatialRDD, queryRDD: SpatialRDD, useIndex: bool, considerBoundaryIntersection: bool) -> RDD

Example SpatialJoinQueryFlat PointRDD with RectangleRDD¶

In [30]:

# partitioning the data
point_rdd.spatialPartitioning(GridType.KDBTREE)
rectangle_rdd.spatialPartitioning(point_rdd.getPartitioner())
# building an index
point_rdd.buildIndex(IndexType.RTREE, True)
# Perform Spatial Join Query
result = JoinQuery.SpatialJoinQueryFlat(point_rdd, rectangle_rdd, False, True)

As result we will get RDD[GeoData, GeoData] It can be used like any other Python RDD. You can use map, take, collect and other functions

In [31]:

result

Out[31]:

MapPartitionsRDD[63] at map at FlatPairRddConverter.scala:30

In [32]:

result.take(2)

Out[32]:

[[Geometry: Polygon userData: , Geometry: Point userData: ],
 [Geometry: Polygon userData: , Geometry: Point userData: ]]

In [33]:

result.collect()[:3]

Out[33]:

[[Geometry: Polygon userData: , Geometry: Point userData: ],
 [Geometry: Polygon userData: , Geometry: Point userData: ],
 [Geometry: Polygon userData: , Geometry: Point userData: ]]

In [34]:

# getting distance using SpatialObjects
result.map(lambda x: x[0].geom.distance(x[1].geom)).take(5)

Out[34]:

[0.0, 0.0, 0.0, 0.0, 0.0]

In [35]:

# getting area of polygon data
result.map(lambda x: x[0].geom.area).take(5)

Out[35]:

[0.051572544132000575,
 0.051572544132000575,
 0.05189354027999942,
 0.057069904940998895,
 0.057069904940998895]

In [36]:

# Base on result you can create DataFrame object, using map function and build DataFrame from RDD

In [37]:

schema = StructType(
    [
        StructField("geom_left", GeometryType(), False),
        StructField("geom_right", GeometryType(), False)
    ]
)

In [38]:

# Set verifySchema to False
spatial_join_result = result.map(lambda x: [x[0].geom, x[1].geom])
sedona.createDataFrame(spatial_join_result, schema, verifySchema=False).show(5, True)

+--------------------+--------------------+
|           geom_left|          geom_right|
+--------------------+--------------------+
|POLYGON ((-87.082...|POINT (-87.075409...|
|POLYGON ((-87.082...|POINT (-87.08084 ...|
|POLYGON ((-87.092...|POINT (-87.08084 ...|
|POLYGON ((-87.285...|POINT (-87.095533...|
|POLYGON ((-87.285...|POINT (-87.124441...|
+--------------------+--------------------+
only showing top 5 rows

In [39]:

# Above code produces DataFrame with geometry Data type

In [40]:

sedona.createDataFrame(spatial_join_result, schema, verifySchema=False).printSchema()

root
 |-- geom_left: geometry (nullable = false)
 |-- geom_right: geometry (nullable = false)

We can create DataFrame object from Spatial Pair RDD using Adapter object as follows

In [41]:

Adapter.toDf(result, ["attr1"], ["attr2"], sedona).show(5, True)

+--------------------+-----+--------------------+-----+
|              geom_1|attr1|              geom_2|attr2|
+--------------------+-----+--------------------+-----+
|POLYGON ((-87.082...|     |POINT (-87.075409...|     |
|POLYGON ((-87.082...|     |POINT (-87.08084 ...|     |
|POLYGON ((-87.092...|     |POINT (-87.08084 ...|     |
|POLYGON ((-87.285...|     |POINT (-87.095533...|     |
|POLYGON ((-87.285...|     |POINT (-87.124441...|     |
+--------------------+-----+--------------------+-----+
only showing top 5 rows

This also produce DataFrame with geometry DataType

In [42]:

Adapter.toDf(result, ["attr1"], ["attr2"], sedona).printSchema()

root
 |-- geom_1: geometry (nullable = true)
 |-- attr1: string (nullable = true)
 |-- geom_2: geometry (nullable = true)
 |-- attr2: string (nullable = true)

We can create RDD which will be of type RDD[GeoData, List[GeoData]] We can for example calculate number of Points within some polygon data

To do that we can use code specified below

In [43]:

point_rdd.spatialPartitioning(GridType.KDBTREE)
rectangle_rdd.spatialPartitioning(point_rdd.getPartitioner())

In [44]:

spatial_join_result_non_flat = JoinQuery.SpatialJoinQuery(point_rdd, rectangle_rdd, False, True)

In [45]:

# number of point for each polygon
number_of_points = spatial_join_result_non_flat.map(lambda x: [x[0].geom, x[1].__len__()])

In [46]:

schema = StructType([
    StructField("geometry", GeometryType(), False),
    StructField("number_of_points", LongType(), False)
])

In [47]:

sedona.createDataFrame(number_of_points, schema, verifySchema=False).show()

+--------------------+----------------+
|            geometry|number_of_points|
+--------------------+----------------+
|POLYGON ((-87.114...|              15|
|POLYGON ((-87.082...|              12|
|POLYGON ((-86.697...|               1|
|POLYGON ((-87.285...|              26|
|POLYGON ((-87.105...|              15|
|POLYGON ((-86.816...|               6|
|POLYGON ((-87.229...|               7|
|POLYGON ((-87.092...|               5|
|POLYGON ((-86.749...|               4|
|POLYGON ((-86.860...|              12|
+--------------------+----------------+

KNNQuery¶

Spatial KNNQuery is operation which help us find answer which k number of geometries lays closest to other geometry.

For Example: 5 closest Shops to your home. To use Spatial KNNQuery please use object KNNQuery which has one method:

SpatialKnnQuery(spatialRDD: SpatialRDD, originalQueryPoint: BaseGeometry, k: int,  useIndex: bool)-> List[GeoData]

Finds 5 closest points from PointRDD to given Point¶

In [48]:

result = KNNQuery.SpatialKnnQuery(point_rdd, Point(-84.01, 34.01), 5, False)

In [49]:

result

Out[49]:

[Geometry: Point userData: ,
 Geometry: Point userData: ,
 Geometry: Point userData: ,
 Geometry: Point userData: ,
 Geometry: Point userData: ]

As Reference geometry you can also use Polygon or LineString object

In [50]:

polygon = Polygon(
    [(-84.237756, 33.904859), (-84.237756, 34.090426),
     (-83.833011, 34.090426), (-83.833011, 33.904859),
     (-84.237756, 33.904859)
    ])
polygons_nearby = KNNQuery.SpatialKnnQuery(polygon_rdd, polygon, 5, False)

In [51]:

polygons_nearby

Out[51]:

[Geometry: Polygon userData: ,
 Geometry: Polygon userData: ,
 Geometry: Polygon userData: ,
 Geometry: Polygon userData: ,
 Geometry: Polygon userData: ]

In [52]:

polygons_nearby[0].geom.wkt

Out[52]:

'POLYGON ((-83.993559 34.087259, -83.993559 34.131247, -83.959903 34.131247, -83.959903 34.087259, -83.993559 34.087259))'

RangeQuery¶

A spatial range query takes as input a range query window and an SpatialRDD and returns all geometries that intersect / are fully covered by the query window. RangeQuery has one method:

SpatialRangeQuery(self, spatialRDD: SpatialRDD, rangeQueryWindow: BaseGeometry, considerBoundaryIntersection: bool, usingIndex: bool) -> RDD

In [53]:

from sedona.core.geom.envelope import Envelope

In [54]:

query_envelope = Envelope(-85.01, -60.01, 34.01, 50.01)

result_range_query = RangeQuery.SpatialRangeQuery(linestring_rdd, query_envelope, False, False)

/usr/local/lib/python3.10/dist-packages/sedona/core/geom/envelope.py:27: ShapelyDeprecationWarning: Setting custom attributes on geometry objects is deprecated, and will raise an AttributeError in Shapely 2.0
  self.minx = minx
/usr/local/lib/python3.10/dist-packages/sedona/core/geom/envelope.py:28: ShapelyDeprecationWarning: Setting custom attributes on geometry objects is deprecated, and will raise an AttributeError in Shapely 2.0
  self.maxx = maxx
/usr/local/lib/python3.10/dist-packages/sedona/core/geom/envelope.py:29: ShapelyDeprecationWarning: Setting custom attributes on geometry objects is deprecated, and will raise an AttributeError in Shapely 2.0
  self.miny = miny
/usr/local/lib/python3.10/dist-packages/sedona/core/geom/envelope.py:30: ShapelyDeprecationWarning: Setting custom attributes on geometry objects is deprecated, and will raise an AttributeError in Shapely 2.0
  self.maxy = maxy

In [55]:

result_range_query

Out[55]:

MapPartitionsRDD[127] at map at GeometryRddConverter.scala:30

In [56]:

result_range_query.take(6)

Out[56]:

[Geometry: LineString userData: ,
 Geometry: LineString userData: ,
 Geometry: LineString userData: ,
 Geometry: LineString userData: ,
 Geometry: LineString userData: ,
 Geometry: LineString userData: ]

In [57]:

# Creating DataFrame from result

In [58]:

schema = StructType([StructField("geometry", GeometryType(), False)])

In [59]:

sedona.createDataFrame(
    result_range_query.map(lambda x: [x.geom]),
    schema,
    verifySchema=False
).show(5, True)

+--------------------+
|            geometry|
+--------------------+
|LINESTRING (-72.1...|
|LINESTRING (-72.4...|
|LINESTRING (-72.4...|
|LINESTRING (-73.4...|
|LINESTRING (-73.6...|
+--------------------+
only showing top 5 rows

Load From other Formats¶

GeoPyspark allows to load the data from other Data formats like:

GeoJSON

Shapefile

WKB

WKT

In [60]:

## ShapeFile - load to SpatialRDD

In [61]:

shape_rdd = ShapefileReader.readToGeometryRDD(sc, "data/polygon")

In [62]:

shape_rdd

Out[62]:

<sedona.core.SpatialRDD.spatial_rdd.SpatialRDD at 0xffff725e64a0>

In [63]:

Adapter.toDf(shape_rdd, sedona).show(5, True)

+--------------------+
|            geometry|
+--------------------+
|MULTIPOLYGON (((1...|
|MULTIPOLYGON (((-...|
|MULTIPOLYGON (((1...|
|POLYGON ((118.362...|
|MULTIPOLYGON (((-...|
+--------------------+
only showing top 5 rows

In [64]:

## GeoJSON - load to SpatialRDD

{ "type": "Feature", "properties": { "STATEFP": "01", "COUNTYFP": "077", "TRACTCE": "011501", "BLKGRPCE": "5", "AFFGEOID": "1500000US010770115015", "GEOID": "010770115015", "NAME": "5", "LSAD": "BG", "ALAND": 6844991, "AWATER": 32636 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -87.621765, 34.873444 ], [ -87.617535, 34.873369 ], [ -87.6123, 34.873337 ], [ -87.604049, 34.873303 ], [ -87.604033, 34.872316 ], [ -87.60415, 34.867502 ], [ -87.604218, 34.865687 ], [ -87.604409, 34.858537 ], [ -87.604018, 34.851336 ], [ -87.603716, 34.844829 ], [ -87.603696, 34.844307 ], [ -87.603673, 34.841884 ], [ -87.60372, 34.841003 ], [ -87.603879, 34.838423 ], [ -87.603888, 34.837682 ], [ -87.603889, 34.83763 ], [ -87.613127, 34.833938 ], [ -87.616451, 34.832699 ], [ -87.621041, 34.831431 ], [ -87.621056, 34.831526 ], [ -87.62112, 34.831925 ], [ -87.621603, 34.8352 ], [ -87.62158, 34.836087 ], [ -87.621383, 34.84329 ], [ -87.621359, 34.844438 ], [ -87.62129, 34.846387 ], [ -87.62119, 34.85053 ], [ -87.62144, 34.865379 ], [ -87.621765, 34.873444 ] ] ] } },

In [65]:

geo_json_rdd = GeoJsonReader.readToGeometryRDD(sc, "data/testPolygon.json")

In [66]:

geo_json_rdd

Out[66]:

<sedona.core.SpatialRDD.spatial_rdd.SpatialRDD at 0xffff7260cdc0>

In [67]:

Adapter.toDf(geo_json_rdd, sedona).drop("AWATER").show(5, True)

+--------------------+-------+--------+-------+--------+--------------------+------------+----+----+--------+
|            geometry|STATEFP|COUNTYFP|TRACTCE|BLKGRPCE|            AFFGEOID|       GEOID|NAME|LSAD|   ALAND|
+--------------------+-------+--------+-------+--------+--------------------+------------+----+----+--------+
|POLYGON ((-87.621...|     01|     077| 011501|       5|1500000US01077011...|010770115015|   5|  BG| 6844991|
|POLYGON ((-85.719...|     01|     045| 021102|       4|1500000US01045021...|010450211024|   4|  BG|11360854|
|POLYGON ((-86.000...|     01|     055| 001300|       3|1500000US01055001...|010550013003|   3|  BG| 1378742|
|POLYGON ((-86.574...|     01|     089| 001700|       2|1500000US01089001...|010890017002|   2|  BG| 1040641|
|POLYGON ((-85.382...|     01|     069| 041400|       1|1500000US01069041...|010690414001|   1|  BG| 8243574|
+--------------------+-------+--------+-------+--------+--------------------+------------+----+----+--------+
only showing top 5 rows

In [68]:

## WKT - loading to SpatialRDD

In [69]:

wkt_rdd = WktReader.readToGeometryRDD(sc, "data/county_small.tsv", 0, True, False)

In [70]:

wkt_rdd

Out[70]:

<sedona.core.SpatialRDD.spatial_rdd.SpatialRDD at 0xffff7260efe0>

In [71]:

Adapter.toDf(wkt_rdd, sedona).printSchema()

root
 |-- geometry: geometry (nullable = true)

In [72]:

Adapter.toDf(wkt_rdd, sedona).show(5, True)

+--------------------+
|            geometry|
+--------------------+
|POLYGON ((-97.019...|
|POLYGON ((-123.43...|
|POLYGON ((-104.56...|
|POLYGON ((-96.910...|
|POLYGON ((-98.273...|
+--------------------+
only showing top 5 rows

In [73]:

## WKB - load to SpatialRDD

In [74]:

wkb_rdd = WkbReader.readToGeometryRDD(sc, "data/county_small_wkb.tsv", 0, True, False)

In [75]:

Adapter.toDf(wkb_rdd, sedona).show(5, True)

+--------------------+
|            geometry|
+--------------------+
|POLYGON ((-97.019...|
|POLYGON ((-123.43...|
|POLYGON ((-104.56...|
|POLYGON ((-96.910...|
|POLYGON ((-98.273...|
+--------------------+
only showing top 5 rows

Converting RDD Spatial join result to DF directly, avoiding jvm python serde¶

In [76]:

point_rdd.spatialPartitioning(GridType.KDBTREE)
rectangle_rdd.spatialPartitioning(point_rdd.getPartitioner())
# building an index
point_rdd.buildIndex(IndexType.RTREE, True)
# Perform Spatial Join Query
result = JoinQueryRaw.SpatialJoinQueryFlat(point_rdd, rectangle_rdd, False, True)

In [77]:

# without passing column names, the result will contain only two geometries columns
geometry_df = Adapter.toDf(result, sedona)

In [78]:

geometry_df.printSchema()

root
 |-- leftgeometry: geometry (nullable = true)
 |-- rightgeometry: geometry (nullable = true)

In [79]:

geometry_df.show(5)

+--------------------+--------------------+
|        leftgeometry|       rightgeometry|
+--------------------+--------------------+
|POLYGON ((-87.285...|POINT (-87.28468 ...|
|POLYGON ((-87.285...|POINT (-87.278485...|
|POLYGON ((-87.285...|POINT (-87.280556...|
|POLYGON ((-87.285...|POINT (-87.270187...|
|POLYGON ((-87.285...|POINT (-87.268766...|
+--------------------+--------------------+
only showing top 5 rows

In [80]:

geometry_df.collect()[0]

Out[80]:

Row(leftgeometry=<shapely.geometry.polygon.Polygon object at 0xffff72618520>, rightgeometry=<shapely.geometry.point.Point object at 0xffff7261a0e0>)

Passing column names¶

In [81]:

geometry_df = Adapter.toDf(result, ["left_user_data"], ["right_user_data"], sedona)

In [82]:

geometry_df.show(5)

+--------------------+--------------+--------------------+---------------+
|        leftgeometry|left_user_data|       rightgeometry|right_user_data|
+--------------------+--------------+--------------------+---------------+
|POLYGON ((-87.285...|              |POINT (-87.28468 ...|           null|
|POLYGON ((-87.285...|              |POINT (-87.278485...|           null|
|POLYGON ((-87.285...|              |POINT (-87.280556...|           null|
|POLYGON ((-87.285...|              |POINT (-87.270187...|           null|
|POLYGON ((-87.285...|              |POINT (-87.268766...|           null|
+--------------------+--------------+--------------------+---------------+
only showing top 5 rows

Converting RDD Spatial join result to DF directly, avoiding jvm python serde¶

In [83]:

query_envelope = Envelope(-85.01, -60.01, 34.01, 50.01)

result_range_query = RangeQueryRaw.SpatialRangeQuery(linestring_rdd, query_envelope, False, False)

In [84]:

# converting to df
gdf = Adapter.toDf(result_range_query, sedona)

In [85]:

gdf.show(5)

+--------------------+
|            geometry|
+--------------------+
|LINESTRING (-72.1...|
|LINESTRING (-72.4...|
|LINESTRING (-72.4...|
|LINESTRING (-73.4...|
|LINESTRING (-73.6...|
+--------------------+
only showing top 5 rows

In [86]:

gdf.printSchema()

root
 |-- geometry: geometry (nullable = true)

In [87]:

# Passing column names
# converting to df
gdf_with_columns = Adapter.toDf(result_range_query, sedona, ["_c1"])

In [88]:

gdf_with_columns.show(5)

+--------------------+---+
|            geometry|_c1|
+--------------------+---+
|LINESTRING (-72.1...|   |
|LINESTRING (-72.4...|   |
|LINESTRING (-72.4...|   |
|LINESTRING (-73.4...|   |
|LINESTRING (-73.6...|   |
+--------------------+---+
only showing top 5 rows

In [89]:

gdf_with_columns.printSchema()

root
 |-- geometry: geometry (nullable = true)
 |-- _c1: string (nullable = true)

In [ ]: