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Block - Poisson Kriging#

centroid_poisson_kriging(semivariogram_model, blocks, point_support, unknown_block, unknown_block_point_support, number_of_neighbors, is_weighted_by_point_support=True, raise_when_negative_prediction=True, raise_when_negative_error=True, allow_approximate_solutions=False)[source]

Function performs centroid-based Poisson Kriging of blocks (areal) data.

Parameters:
semivariogram_modelTheoreticalVariogram

A fitted variogram.

blocksUnion[Blocks, gpd.GeoDataFrame, pd.DataFrame, np.ndarray]
Blocks with aggregated data.

  • Blocks: Blocks() class object.

  • GeoDataFrame and DataFrame must have columns: centroid_x, centroid_y, ds, index. Geometry column with polygons is not used.

  • numpy array: [[block index, centroid x, centroid y, value]].

point_supportUnion[Dict, np.ndarray, gpd.GeoDataFrame, pd.DataFrame, PointSupport]
The point support of polygons.

  • Dict: {block id: [[point x, point y, value]]},

  • numpy array: [[block id, x, y, value]],

  • DataFrame and GeoDataFrame: columns={x_col, y_col, ds, index},

  • PointSupport.

unknown_blocknumpy array

[index, centroid x, centroid y]

unknown_block_point_supportnumpy array

Points within block [[x, y, point support value]]

number_of_neighborsint

The minimum number of neighbours that can potentially affect block.

is_weighted_by_point_supportbool, default = True

Are distances between blocks weighted by the point support?

raise_when_negative_predictionbool, default=True

Raise error when prediction is negative.

raise_when_negative_errorbool, default=True

Raise error when prediction error is negative.

allow_approximate_solutionsbool, default=False

Allows the approximation of kriging weights based on the OLS algorithm. We don’t recommend set it to True if you don’t know what are you doing. This parameter can be useful when you have clusters in your dataset, that can lead to singular or near-singular matrix creation.

Returns:
resultsList

[unknown block index, prediction, error]

Raises:
ValueError

Prediction or prediction error are negative.

Warns:
ExperimentalFeatureWarning

Directional Kriging is in early-phase and may contain bugs.

area_to_area_pk(semivariogram_model, blocks, point_support, unknown_block, unknown_block_point_support, number_of_neighbors, raise_when_negative_prediction=True, raise_when_negative_error=True, log_process=True)[source]

Function predicts areal value in an unknown location based on the area-to-area Poisson Kriging

Parameters:
semivariogram_modelTheoreticalVariogram

A fitted variogram.

blocksUnion[Blocks, gpd.GeoDataFrame, pd.DataFrame, np.ndarray]
Blocks with aggregated data.

  • Blocks: Blocks() class object.

  • GeoDataFrame and DataFrame must have columns: centroid_x, centroid_y, ds, index. Geometry column with polygons is not used.

  • numpy array: [[block index, centroid x, centroid y, value]].

point_supportUnion[Dict, np.ndarray, gpd.GeoDataFrame, pd.DataFrame, PointSupport]
The point support of polygons.

  • Dict: {block id: [[point x, point y, value]]},

  • numpy array: [[block id, x, y, value]],

  • DataFrame and GeoDataFrame: columns={x_col, y_col, ds, index},

  • PointSupport.

unknown_blocknumpy array

[index, centroid x, centroid y]

unknown_block_point_supportnumpy array

Points within block [[x, y, point support value]]

number_of_neighborsint

The minimum number of neighbours that can potentially affect block.

raise_when_negative_predictionbool, default=True

Raise error when prediction is negative.

raise_when_negative_errorbool, default=True

Raise error when prediction error is negative.

log_processbool, default=True

Log process info and debug info.

Returns:
resultsList

[unknown block index, prediction, error]

Raises:
ValueError

Prediction or prediction error are negative.

Warns:
ExperimentalFeatureWarning

Directional Kriging is in early-phase and may contain bugs.

area_to_point_pk(semivariogram_model, blocks, point_support, unknown_block, unknown_block_point_support, number_of_neighbors, max_range=None, raise_when_negative_prediction=True, raise_when_negative_error=True, err_to_nan=True)[source]

Function predicts areal value in the unknown location based on the area-to-area Poisson Kriging

Parameters:
semivariogram_modelTheoreticalVariogram

A fitted variogram.

blocksUnion[Blocks, gpd.GeoDataFrame, pd.DataFrame, np.ndarray]
Blocks with aggregated data.

  • Blocks: Blocks() class object.

  • GeoDataFrame and DataFrame must have columns: centroid_x, centroid_y, ds, index. Geometry column with polygons is not used.

  • numpy array: [[block index, centroid x, centroid y, value]].

point_supportUnion[Dict, np.ndarray, gpd.GeoDataFrame, pd.DataFrame, PointSupport]
The point support of polygons.

  • Dict: {block id: [[point x, point y, value]]},

  • numpy array: [[block id, x, y, value]],

  • DataFrame and GeoDataFrame: columns={x_col, y_col, ds, index},

  • PointSupport.

unknown_blocknumpy array

[index, centroid x, centroid y]

unknown_block_point_supportnumpy array

Points within block [[x, y, point support value]]

number_of_neighborsint

The minimum number of neighbours that can potentially affect block.

max_rangefloat , default=None

The maximum distance to search for a neighbors, if None given then algorithm uses the theoretical variogram’s range.

raise_when_negative_predictionbool, default=True

Raise error when prediction is negative.

raise_when_negative_errorbool, default=True

Raise error when prediction error is negative.

err_to_nanbool, default=True

ValueError to NaN.

Returns:
resultsList

[(unknown point coordinates), prediction, error]

Raises:
ValueError

Prediction or prediction error are negative.

Warns:
ExperimentalFeatureWarning

Directional Kriging is in early-phase and may contain bugs.

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