sedona.spark.stats package
Subpackages
Submodules
sedona.spark.stats.weighting module
Weighting functions for spatial data.
- sedona.spark.stats.weighting.add_binary_distance_band_column(dataframe: DataFrame, threshold: float, include_zero_distance_neighbors: bool = True, include_self: bool = False, geometry: str | None = None, use_spheroid: bool = False, saved_attributes: List[str] = None, result_name: str = 'weights') DataFrame[source]
Annotates a dataframe with a weights column containing the other records within the threshold and their weight.
Weights will always be 1.0. The dataframe should contain at least one GeometryType column. Rows must be unique. If one geometry column is present it will be used automatically. If two are present, the one named ‘geometry’ will be used. If more than one are present and neither is named ‘geometry’, the column name must be provided. The new column is named by result_name, ‘weights’ by default.
- Parameters:
dataframe – DataFrame with geometry column
threshold – Distance threshold for considering neighbors
include_zero_distance_neighbors – whether to include neighbors that are 0 distance. Default is True
include_self – whether to include self in the list of neighbors
geometry – name of the geometry column
use_spheroid – whether to use a cartesian or spheroidal distance calculation. Default is false
saved_attributes – the attributes to save in the neighbor column. Default is all columns.
result_name – the name of the resulting column. Default is ‘weights’.
- Returns:
The input DataFrame with a weight column added containing neighbors and their weights (always 1) added to each row.
- sedona.spark.stats.weighting.add_distance_band_column(dataframe: DataFrame, threshold: float, binary: bool = True, alpha: float = -1.0, include_zero_distance_neighbors: bool = False, include_self: bool = False, self_weight: float = 1.0, geometry: str | None = None, use_spheroid: bool = False, saved_attributes: List[str] = None, result_name: str = 'weights') DataFrame[source]
Annotates a dataframe with a weights column containing the other records within the threshold and their weight.
The dataframe should contain at least one GeometryType column. Rows must be unique. If one geometry column is present it will be used automatically. If two are present, the one named ‘geometry’ will be used. If more than one are present and neither is named ‘geometry’, the column name must be provided. The new column is named by result_name, ‘weights’ by default.
- Parameters:
dataframe – DataFrame with geometry column
threshold – Distance threshold for considering neighbors
binary – whether to use binary weights or inverse distance weights for neighbors (dist^alpha)
alpha – alpha to use for inverse distance weights ignored when binary is true
include_zero_distance_neighbors – whether to include neighbors that are 0 distance. If 0 distance neighbors are included, binary is false, and alpha is negative, their weight is infinite (division by zero)
include_self – whether to include self in the list of neighbors
self_weight – the value to use for the self weight
geometry – name of the geometry column
use_spheroid – whether to use a cartesian or spheroidal distance calculation. Default is false
saved_attributes – the attributes to save in the neighbor column. Default is all columns.
result_name – the name of the resulting column. Default is ‘weights’.
- Returns:
The input DataFrame with a weight column added containing neighbors and their weights added to each row.
- sedona.spark.stats.weighting.add_weighted_distance_band_column(dataframe: DataFrame, threshold: float, alpha: float, include_zero_distance_neighbors: bool = True, include_self: bool = False, self_weight: float = 1.0, geometry: str | None = None, use_spheroid: bool = False, saved_attributes: List[str] = None, result_name: str = 'weights') DataFrame[source]
Annotates a dataframe with a weights column containing the other records within the threshold and their weight.
Weights will be distance^alpha. The dataframe should contain at least one GeometryType column. Rows must be unique. If one geometry column is present it will be used automatically. If two are present, the one named ‘geometry’ will be used. If more than one are present and neither is named ‘geometry’, the column name must be provided. The new column is named by result_name, ‘weights’ by default.
- Parameters:
dataframe – DataFrame with geometry column
threshold – Distance threshold for considering neighbors
alpha – exponent of the distance weights, computed as dist^alpha. Use a negative value, such as -1.0, for inverse distance weights
include_zero_distance_neighbors – whether to include neighbors that are 0 distance. Default is True. With a negative alpha, their weight is infinite (division by zero)
include_self – whether to include self in the list of neighbors
self_weight – the value to use for the self weight. Default is 1.0
geometry – name of the geometry column
use_spheroid – whether to use a cartesian or spheroidal distance calculation. Default is false
saved_attributes – the attributes to save in the neighbor column. Default is all columns.
result_name – the name of the resulting column. Default is ‘weights’.
- Returns:
The input DataFrame with a weight column added containing neighbors and their weights (dist^alpha) added to each row.