The following code calculates the spatial lag of a specific column according to given spatial weights. For each row, it calculates the average value of a particular column from neighboring locations. It uses the median_income column and spatial weights from a datastore.

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)", "MEDIAN_INCOME_SLAG": "NUMBER" }',
            'create_spatial_lag'
        )
    );

The result contains the average income from neighboring locations for each row. Note that the index comes from the key_column parameter, which is the geoid column in this case.

Description of the illustration create_spatial_lag.png