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:
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()
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()
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)
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)
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)
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()
!!!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].
References
- Puloma Katiyar, Tin Vu, Sara Migliorini, Alberto Belussi, Ahmed Eldawy. “SpiderWeb: A Spatial Data Generator on the Web”, ACM SIGSPATIAL 2020, Seattle, WA
- Beast Spatial Data Generator: https://bitbucket.org/bdlabucr/beast/src/master/doc/spatial-data-generator.md
- SpiderWeb: A Spatial Data Generator on the Web: https://spider.cs.ucr.edu/
- SpiderWeb YouTube Video: https://www.youtube.com/watch?v=h0xCG6Swdqw