compute_spatial_weights
Format
compute_spatial_weights(table, weights_def, save_weights_as,
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. |
weights_def |
Defines 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 the API Reference
documentation.
The following lists the supported types and parameters:
|
save_weights_as |
Specifies how the object is 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 code calculates the spatial weights of the dataset from the
table specified in the table
parameter, using the strategy defined
in the weights_def
parameter. This example uses the K-nearest
neighbor approach with K=4
. The result is saved into the spatial
data store with the object name la_bg_knn4
.
select *
from table(
pyqEval(
par_lst => '{
"oml_connect": true,
"table": "oml_user.la_block_groups",
"weights_def": {"type": "KNN", "k": 4},
"save_weights_as": {"ds_name": "spatial", "obj_name": "la_bg_knn4", "append": true, "overwrite_obj": true}
}',
out_fmt => 'XML',
scr_name => 'compute_spatial_weights'
)
);