oraclesai.analysis

class LocalMoranITest(data, column_name, weights=None, weights_definition=None, **kwargs)

An implementation of the Local Moran’s I spatial autocorrelation test.

COL_I (‘I’)

Name of the column containing the Moran I value in the resulting output data returned by get_output_data().

COL_P_VALUE (‘P_VALUE’)

Name of the column containing the p values in the resulting output data returned by get_output_data()

COL_Z_VALUE (‘Z_VALUE’)

Name of the column containing the z values in the resulting output data returned by get_output_data()

COL_Q (‘QUADRANT’)

Name of the column containing the quadrant information in the resulting output data returned by get_output_data()

classmethod create(data, weights_or_def, column_name=None)

Create an instance of LocalMoranITest. The test is applied on a column from the given data using the specified spatial weights.

Parameters:

  • data – Either, an instance of SpatialDataFrame, geopandas.GeoDataFrame or an Iterable of float values.

  • weights_or_def – A SpatialWeightsDefinition instance or SpatialWeights object.

  • column_name – Name of the column from the data parameter. If None, data must be an Iterable of float values or a dataframe containing only one data column (it may contain a geometry and index columns.)

Returns:

A LocalMoranITest object.

get_coldspot(max_p_value=0.05)

Returns a subset of the input data containing samples which lay into the cold spot.

Parameters:

max_p_value – Maximum p value for a sample to be considered to be in the cold spot.

Returns:

A subset of the data used to run the analysis. The type is the same than the input data.

get_hotspot(max_p_value=0.05)

Returns a subset of the input data containing samples which lay into the hot spot.

Parameters:

max_p_value – Maximum p value for a sample to be considered to be in the hot spot.

Returns:

A subset of the data used to run the analysis. The type is the same than the input data.

get_outliers(max_p_value=0.05)

Returns a subset of the input data containing samples which are considered outliers.

Parameters:

max_p_value – Maximum p value for a sample to be considered to be in the outliers.

Returns:

A subset of the data used to run the analysis. The type is the same than the input data.

get_output_data()

Returns the input data plus local spatial autocorrelation test columns COL_I, COL_P_VALUE, COL_Z_VALUE and COL_Q.

Returns:

A data set of the same type as the data used to run the analysis.

property i_list

List of Moran’s I values

property p_values

List of p values

property quadrants

List of int values representing quadrants. Different quadrant values are defined at Quadrant

property z_values

List of z values

class MoranITest(data, weights=None, weights_definition=None, column_name=None, **kwargs)

An implementation of the Moran I spatial autocorrelation test.

classmethod create(data, weights_or_def, column_name=None)

Create an instance of MoranITest. The returned instance contains the results of the global Moran’s I spatial autocorrelation test applied on the specified column from the given data.

Parameters:

  • data – Either, an instance of SpatialDataFrame, geopandas.GeoDataFrame or an Iterable of float values.

  • weightsor_or_def – Either, a spatial weights definition or computed spatial weights as instances of SpatialWeights or pysal Weights.

  • column_name – Name of the column from the data parameter. If None, data must be an Iterable of float values or a dataframe containing only one data column (it may contain a geometry and index columns.)

Returns:

A MoranITest object.

property expected_i: float

Expected Moran I value if no spatial autocorrelation.

property i: float

Moran I value.

property p_value: float

P Value.

property spatial_weights

Spatial weights object used to compute Moran I.

property z_value

Z Value.

class Quadrant(**keyargs)

Defines values for representing quadrants in a plane which are associated with different types of spatial autocorrelations.

Attributes:

HOTSPOT (1)

Quadrant for high-high values.

DOUGHNUT (2)

Quadrant for high-low values.

COLDSPOT (3)

Quadrant for low-low values.

DIAMOND (4)

Quadrant for low-high values.

spatial_colocation_analysis(feature_data, spatial_weights_definition, interest_category=None, neighbor_category=None, neighbor_feature_data=None, is_time_window_analysis=False, interest_time_window=None, neighbor_time_widow=None, n_permutations=None)

Spatial colocation measures and analyze relationships between point features of different classes from the same spatial layer and, or from different spatial layers. Colocation Analysis is a tool that measures proximity patterns between two categories of point features, A and B, using the Local Colocation Quotient (LCLQ) statistic. For each feature of the Category of Interest (category A), it calculates its LCLQ score. If the proportion of points of category B within its neighborhood is more than the global proportion of B points, the LCLQ value will be high. • If there are more A points or more points from categories other than B within its neighborhood, the LCLQ score will be small.

Parameters:

  • feature_data – GeoDataFrame of SpatialDataFrame. The data that will be used for the colocation analysis

  • spatial_weights_definition – SpatialWeightsDefinition. Specifies how the distance between neighbors is calculated

  • interest_category – tuple. default=None. Two values indicating the field and value of the category of interest

  • neighbor_category – tuple. default=None. Two values indicating the field and value of the neighboring category

  • neighbor_feature_data – GeoDataFrame of SpatialDataFrame. default=None. If present, the neighboring observations come from here

  • is_time_window_analysis – boolean, default=False. Indicates if time window_analysis will be used

  • interest_time_window – tuple, default=None. Requires is_time_window_analysis to be true. It contains three values indicating the field, start time, end time for interest category

  • neighbor_time_widow – tuple, default=None. Requires is_time_window_analysis to be true. It contains three values indicating the field, start time, end time for neighboring category

  • n_permutations – int, default=None. The number of permutations used to calculate the significance level of the colocation quotient scores. If None, the significance level is None