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 will be named ‘cluster’.

Parameters:

  • dataframe – DataFrame with geometry column

  • threshold – Distance threshold for considering neighbors

  • include_zero_distance_neighbors – whether to include neighbors that are 0 distance. If 0 distance neighbors are included and binary is false, values are infinity as per the floating point spec (divide by 0)

  • 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 will be named ‘cluster’.

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 and binary is false, values are infinity as per the floating point spec (divide by 0)

  • 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 will be named ‘cluster’.

Parameters:

  • dataframe – DataFrame with geometry column

  • threshold – Distance threshold for considering neighbors

  • alpha – alpha to use for inverse distance weights. Computation is dist^alpha. Default is -1.0

  • include_zero_distance_neighbors – whether to include neighbors that are 0 distance. If 0 distance neighbors are included and binary is false, values are infinity as per the floating point spec (divide by 0)

  • 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 (always 1) added to each row.

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