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Spider:Spatial Data Generator

Sedona offers a spatial data generator called Spider. It is a data source that generates random spatial data based on the user-specified parameters.

Quick Start

Once you have your SedonaContext object created, you can create a DataFrame with the spider data source.

df_random_points = sedona.read.format("spider").load(n=1000, distribution='uniform')
df_random_boxes = sedona.read.format("spider").load(n=1000, distribution='gaussian', geometryType='box', maxWidth=0.05, maxHeight=0.05)
df_random_polygons = sedona.read.format("spider").load(n=1000, distribution='bit', geometryType='polygon', minSegment=3, maxSegment=5, maxSize=0.1)

Now we have three DataFrames with random spatial data. We can show the first three rows of the df_random_points DataFrame to verify the data is generated correctly.

df_random_points.show(3, False)

Output:

+---+---------------------------------------------+
|id |geometry                                     |
+---+---------------------------------------------+
|1  |POINT (0.8781393502074886 0.5925787985028703)|
|2  |POINT (0.3159498147172185 0.1907316577342276)|
|3  |POINT (0.2618294441170143 0.3623164670133922)|
+---+---------------------------------------------+
only showing top 3 rows

The generated DataFrame has two columns: id and geometry. The id column is the unique identifier of each record, and the geometry column is the randomly generated spatial data.

We can plot all 3 DataFrames using the following code.

import matplotlib.pyplot as plt
import geopandas as gpd

# Convert DataFrames to GeoDataFrames
gdf_random_points = gpd.GeoDataFrame(df_random_points.toPandas(), geometry='geometry')
gdf_random_boxes = gpd.GeoDataFrame(df_random_boxes.toPandas(), geometry='geometry')
gdf_random_polygons = gpd.GeoDataFrame(df_random_polygons.toPandas(), geometry='geometry')

# Create a figure and a set of subplots
fig, axes = plt.subplots(1, 3, figsize=(15, 5))

# Plot each GeoDataFrame on a different subplot
gdf_random_points.plot(ax=axes[0], color='blue', markersize=5)
axes[0].set_title('Random Points')

gdf_random_boxes.boundary.plot(ax=axes[1], color='red')
axes[1].set_title('Random Boxes')

gdf_random_polygons.boundary.plot(ax=axes[2], color='green')
axes[2].set_title('Random Polygons')

# Adjust the layout
plt.tight_layout()

# Show the plot
plt.show()

Output:

Random Spatial Data

You can browse the SpiderWeb website to play with the parameters and see how they affect the generated data. Once you are satisfied with the parameters, you can use them in your Spider DataFrame creation code. The following sections will explain the parameters in detail.

Common Parameters

The following parameters are common to all distributions.

Parameter Description Default Value
n Number of records to generate 100
distribution Distribution type. See Distributions for details. uniform
numPartitions Number of partitions to generate The default parallelism of your Spark Context
seed Random seed Current timestamp in milliseconds

Warning

The same seed parameter may produce different results with different Java versions or Sedona versions.

Distributions

Spider supports generating random points, boxes and polygons under various distributions. You can explore the capabilities of Spider by visiting the SpiderWeb website. You can specify the distribution type using the distribution parameter. The parameters for each distribution are listed below.

Uniform Distribution

The uniform distribution generates random geometries in the unit square [0, 1] x [0, 1]. This distribution can be selected by setting the distribution parameter to uniform.

Parameter Description Default Value
geometryType Geometry type, either point, box or polygon point
maxWidth Maximum width of the generated boxes 0.01
maxHeight Maximum height of the generated boxes 0.01
minSegment Minimum number of segments of the generated polygons 3
maxSegment Maximum number of segments of the generated polygons 3
maxSize Maximum size of the generated polygons 0.01

Example:

import geopandas as gpd
df = sedona.read.format("spider").load(n=300, distribution='uniform', geometryType='box', maxWidth=0.05, maxHeight=0.05)
gpd.GeoDataFrame(df.toPandas(), geometry='geometry').boundary.plot()

Uniform Distribution

Gaussian Distribution

The Gaussian distribution generates random geometries in a Gaussian distribution with mean [0.5, 0.5] and standard deviation [0.1, 0.1]. This distribution can be selected by setting the distribution parameter to gaussian.

Parameter Description Default Value
geometryType Geometry type, either point, box or polygon point
maxWidth Maximum width of the generated boxes 0.01
maxHeight Maximum height of the generated boxes 0.01
minSegment Minimum number of segments of the generated polygons 3
maxSegment Maximum number of segments of the generated polygons 3
maxSize Maximum size of the generated polygons 0.01

Example:

import geopandas as gpd
df = sedona.read.format("spider").load(n=300, distribution='gaussian', geometryType='polygon', maxSize=0.05)
gpd.GeoDataFrame(df.toPandas(), geometry='geometry').boundary.plot()

Gaussian Distribution

Bit Distribution

The bit distribution generates random geometries in a bit distribution. This distribution can be selected by setting the distribution parameter to bit.

Parameter Description Default Value
geometryType Geometry type, either point, box or polygon point
probability Probability of setting a bit 0.2
digits Number of digits in the generated data 10
maxWidth Maximum width of the generated boxes 0.01
maxHeight Maximum height of the generated boxes 0.01
minSegment Minimum number of segments of the generated polygons 3
maxSegment Maximum number of segments of the generated polygons 3
maxSize Maximum size of the generated polygons 0.01

Example:

import geopandas as gpd
df = sedona.read.format("spider").load(n=300, distribution='bit', geometryType='point', probability=0.2, digits=10)
gpd.GeoDataFrame(df.toPandas(), geometry='geometry').plot(markersize=1)

Bit Distribution

Diagonal Distribution

The diagonal distribution generates random geometries on the diagonal line y = x with some dispersion for geometries that are not exactly on the diagonal. This distribution can be selected by setting the distribution parameter to diagonal.

Parameter Description Default Value
geometryType Geometry type, either point, box or polygon point
percentage The percentage of records that are perfectly on the diagonal 0.5
buffer For points not exactly on the diagonal, the buffer in which they are dispersed 0.5
maxWidth Maximum width of the generated boxes 0.01
maxHeight Maximum height of the generated boxes 0.01
minSegment Minimum number of segments of the generated polygons 3
maxSegment Maximum number of segments of the generated polygons 3
maxSize Maximum size of the generated polygons 0.01

Example:

import geopandas as gpd
df = sedona.read.format("spider").load(n=300, distribution='diagonal', geometryType='point', percentage=0.5, buffer=0.5)
gpd.GeoDataFrame(df.toPandas(), geometry='geometry').plot(markersize=1)

Diagonal Distribution

Sierpinski Distribution

The Sierpinski distribution generates random geometries distributed on a Sierpinski triangle. This distribution can be selected by setting the distribution parameter to sierpinski.

Parameter Description Default Value
geometryType Geometry type, either point, box or polygon point
maxWidth Maximum width of the generated boxes 0.01
maxHeight Maximum height of the generated boxes 0.01
minSegment Minimum number of segments of the generated polygons 3
maxSegment Maximum number of segments of the generated polygons 3
maxSize Maximum size of the generated polygons 0.01

Example:

import geopandas as gpd
df = sedona.read.format("spider").load(n=2000, distribution='sierpinski', geometryType='point')
gpd.GeoDataFrame(df.toPandas(), geometry='geometry').plot(markersize=1)

Sierpinski Distribution

Parcel Distribution

This generator produces boxes that resemble parcel areas. It works by recursively splitting the input domain (unit square) along the longest dimension and then randomly dithering each generated box to add some randomness. This generator can only generate boxes. This distribution can be selected by setting the distribution parameter to parcel.

Parameter Description Default Value
dither The amount of dithering as a ratio of the side length. Allowed range [0, 1] 0.5
splitRange The allowed range for splitting boxes. Allowed range [0.0, 0.5] 0.0 means all values are allowed. 0.5 means always split in half. 0.5

Example:

import geopandas as gpd
df = sedona.read.format("spider").load(n=300, distribution='parcel', dither=0.5, splitRange=0.5)
gpd.GeoDataFrame(df.toPandas(), geometry='geometry').boundary.plot()

Parcel Distribution

Note

The number of partitions generated by the parcel distribution is always power of 4. This is for guaranteeing the quality of the generated data. If the specified numPartitions is not a power of 4, it will be automatically adjusted to the nearest power of 4 smaller or equal to the specified value.

Affine Transformation

The random spatial data generated by Spider are mostly in the unit square [0, 1] x [0, 1]. If you need to generate random spatial data in a different region, you can specify affine transformation parameters to scale and translate the data to the target region.

The following code demonstrates how to generate random spatial data in a different region using affine transformation.

The affine transformation parameters are:

Parameter Description Default Value
translateX Translate the data horizontally 0
translateY Translate the data vertically 0
scaleX Scale the data horizontally 1
scaleY Scale the data vertically 1
skewX Skew the data horizontally 0
skewY Skew the data vertically 0

The affine transformation is applied to the generated data as follows:

x' = translateX + scaleX * x + skewX * y
y' = translateY + skewY * x + scaleY * y

Example:

import geopandas as gpd
df_random_points = sedona.read.format("spider").load(n=1000, distribution='uniform', translateX=0.5, translateY=0.5, scaleX=2, scaleY=2)
gpd.GeoDataFrame(df_random_points.toPandas(), geometry='geometry').plot(markersize=1)

The data is now in the region [0.5, 2.5] x [0.5, 2.5].

Affine Transformation

References