compute_local_spatial_autocorrelation

Format

compute_local_spatial_autocorrelation(table, column, result_table=None, key_column=None, 
    weights=None, weights_def=None, save_weights_as=None, spatial_col=None, crs=None)

Parameters

The parameters for this pre-defined function are described in the following table.

Parameter	Description
`table`	Name of the spatial table.
`column`	The column of interest whose values are used to compute the local autocorrelation index.
`result_table`	If specified, it calls the function `oml.create` to store a pandas `DataFrame` containing the results of the local autocorrelation in a table with the specified name.
`key_column`	If specified, the specified column is added to the resulting `DataFrame`. Otherwise, a column with the DataFrame’s index is attached to the result.
`weights`	Required when trying to use spatial weights already stored in the data store. Internally it calls the function `olm.ds.load`. The supported parameters are `ds_name` and `obj_name`, indicating the data store name and object name, respectively.
`weights_def`	Required if the parameter `weights` is not specified. Establishes the relationship between neighboring locations. This is passed as a json object specifying the type of the weights definition and its parameters. Each parameter is defined in detail in API Reference documentation. The following lists the supported types and parameters: KNN: `[k]` Kernel: `[bandwidth, fixed, k, function]` DistanceBand: `[threshold, p, alpha, binary]` Queen Rook
`save_weights_as`	Only used if `weights_def` is defined. Specifies how the spatial weights are stored in the data store. The value is a json file that determines the parameters of `oml.ds.save`. The supported parameters are: `[ds_name, obj_name, overwrite_ds, append, overwrite_obj, grantable, compression]`. Some parameter names slightly differ from those in the `oml.ds.save` function. The parameter `overwrite_obj` is used to indicate whether an already existing object should be replaced with the current object.
`spatial_col`	Specifies the column containing the geometries. The column can be specified in the table’s metadata. If not specified, the column name is retrieved from the table.
`crs`	Specifies the Coordinate Reference System. If not specified, it is inferred from the table.

Example

The following example calculates the local Moran’s I statistic for each row in a table from a specific column and uses the spatial weights already saved in a data store. It uses the median_income column and the spatial weights from the spatial data store with the object name la_bg_knn4, corresponding to the spatial weights calculated with the K-nearest neighbors method with K=4.

select *
    from table( 
        pyqEval(
            '{  
                "oml_connect": true, 
                "table": "oml_user.la_block_groups", 
                "key_column": "geoid",
                "column": "median_income",
                "weights": {"ds_name":"spatial", "obj_name": "la_bg_knn4"}
            }',
            '{ "geoid": "VARCHAR2(50)", "I": "NUMBER", "p_value": "NUMBER",  "z_value": "NUMBER", "quadrant": "NUMBER" }',
            'compute_local_spatial_autocorrelation'
        )
    );

For each row in the table, the result contains the following:

The local Moran’s I statistic.
The p-value.
The z-value.
The belonging quadrant.
1. A high value surrounded by high values.
2. A low value around high values.
3. A low value surrounded by low values.
4. A high value around high values.

Description of compute_local_spatial_autocorrelation.png follows

Description of the illustration compute_local_spatial_autocorrelation.png