Nearest K Spatial Join Node
The Nearest K Spatial Join node is used to find the K nearest neighbors between two spatial datasets based on their geometric locations. The node takes two sets of geometries: origin and destination, and assigns each origin geometry its nearest K destination geometries.
Configuration Options:
Origin Geometry: Select the column that contains the geometries of the origin dataset.
Origin ID Column: Choose the unique identifier column for the origin geometries.
Destination Geometry: Select the column that contains the geometries of the destination dataset.
Destination ID Column: Choose the unique identifier column for the destination geometries.
Top K: Define the number of nearest destination geometries to be retrieved for each origin geometry.
Use Case:
This node is useful in spatial analysis applications such as:
Finding the nearest points of interest (e.g., nearest hospitals, stores, or bus stops).
Analyzing proximity relationships between different geographical datasets.
Assigning spatial dependencies in geospatial machine learning tasks.
Output:
The node outputs a table where each row represents a spatial relationship between an origin geometry and its closest K destination geometries, along with their corresponding identifiers.
The Nearest K Spatial Join node is used to find the K nearest neighbors between two spatial datasets based on their geometric locations. The node takes two sets of geometries: origin and destination, and assigns each origin geometry its nearest K destination geometries.
Configuration Options:
Origin Geometry: Select the column that contains the geometries of the origin dataset.
Origin ID Column: Choose the unique identifier column for the origin geometries.
Destination Geometry: Select the column that contains the geometries of the destination dataset.
Destination ID Column: Choose the unique identifier column for the destination geometries.
Top K: Define the number of nearest destination geometries to be retrieved for each origin geometry.
Use Case:
This node is useful in spatial analysis applications such as:
Finding the nearest points of interest (e.g., nearest hospitals, stores, or bus stops).
Analyzing proximity relationships between different geographical datasets.
Assigning spatial dependencies in geospatial machine learning tasks.
Output:
The node outputs a table where each row represents a spatial relationship between an origin geometry and its closest K destination geometries, along with their corresponding identifiers.