Indicator Kriging#

class IndicatorKriging(datapoints, indicator_variograms, unknown_locations, kriging_type='ok', process_mean=None, neighbors_range=None, no_neighbors=4, use_all_neighbors_in_range=False, allow_approximate_solutions=False, get_expected_values=True)[source]

Class performs indicator kriging.

Parameters:

datapointsnumpy ndarray: The known locations [x, y, value].
indicator_variogramsIndicatorVariograms: Modeled variograms for each threshold.
unknown_locationsnumpy ndarray: Points where we want to estimate value (x, y) <-or-> (lon, lat).
kriging_typestr, default = ‘ok’: Type of kriging to perform. Possible values: ‘ok’ - ordinary kriging, ‘sk’ - simple kriging.
process_meanfloat: The mean value of a process over a study area. Should be know before processing. That’s why Simple Kriging has a limited number of applications. You must have multiple samples and well-known area to know this parameter.
neighbors_rangefloat, default=None: The maximum distance where we search for neighbors. If None is given then range is selected from the theoretical_model rang attribute.
no_neighborsint, default = 4: The number of the n-closest neighbors used for interpolation.
use_all_neighbors_in_rangebool, default = False: True: if the real number of neighbors within the neighbors_range is greater than the number_of_neighbors parameter then take all of them anyway.
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.
get_expected_valuesbool, default=True: If True then expected values and variances are calculated.

Attributes:

thresholdsnumpy ndarray: Thresholds used for indicator kriging.
coordinatesnumpy ndarray: Coordinates of unknown locations.
indicator_predictionsnumpy ndarray: Indicator kriging predictions for each threshold and each unknown location.
expected_valuesnumpy ndarray: Expected values derived from indicator_predictions for each unknown location.
variancesnumpy ndarray: Variances derived from indicator_predictions for each unknown location.

Methods

get_indicator_maps()	Returns dictionary with thresholds and indicator maps for each of them.
get_expected_values()	Returns two arrays: one array with coordinates and expected values, and the second with coordinates and variances.

get_expected_values_maps()[source]

Method returns expected values and variances for each threshold.

Returns:

expected_values, variancesnumpy ndarray, numpy ndarray: Expected values and variances.

get_indicator_maps()[source]

Method returns indicator map for each threshold.

Returns:

indicator_mapsDict: Indicator map for each threshold.