Optimizing Spatial Queries with Distance-Bound kNN Join

Queries in k-Nearest Neighbors (kNN) Joins can at times be very inefficient. This inefficiency occurs when the join fetches neighbors without regard to the distance from the query point. The output is a lot of irrelevant data, needless calculations, and a longer processing duration.

The Distance-Bound kNN Join addresses this problem. It improves upon the kNN join by imposing a distance limit during the spatial partitioning stage. With this method, we can reduce the volume of data that is filtered to increase processing speed and make the entire process more efficient.

In a standard kNN join, additional filtering is done on the results after obtaining the nearest neighbors to exclude all returned points that are further than the distance threshold. Even though this approach works, it incurs a high computational cost and does a lot of work for distant neighbors, for example

SELECT
    incident_id,
    station_id,
    distance
FROM (
    SELECT
        i.incident_id,
        fs.station_id,
        ST_Distance(i.geometry, fs.geometry) AS distance
    FROM
        incidents i
    JOIN
        fire_stations fs
    ON
        ST_KNN(i.geometry, fs.geometry, 3, True)
) AS knn_results
WHERE
    distance < 5000  -- distance in meters
ORDER BY
    incident_id,
    distance;        

The Distance-Bound kNN Join puts a new spin on the issue with the processing of the spatial partitioning. Rather than filtering the whole dataset and processing it in parts and pieces, the distance threshold is utilized sooner—in this case, during partitioning of the dataset. This means that only relevant spatial partitions are processed, which makes the whole process more efficient.

SELECT
        i.incident_id,
        fs.station_id,
        ST_Distance(i.geometry, fs.geometry) AS distance
FROM
        incidents i
JOIN
        fire_stations fs
 ON
        ST_KNN(i.geometry, fs.geometry, 3, True, 5000) -- distance in meters
ORDER BY
    incident_id,
    distance;        

This method performs distance filtering after retrieving the nearest neighbors, which may include stations beyond the desired distance, leading to less efficient queries. The kNN join algorithm would not automatically consider optimizations that can be done by pushing down the distance filtering to the varies stages in the kNN join process. In addition, it is complex SQL queries because user has to use the kNN join statement within a subquery, which adds complexity to the SQL.

This new approach is ideal for use cases where only nearby results matter, such as:

• Emergency Response: Quickly locating facilities, like fire stations, within a critical distance of incidents.
• Urban Planning: Identifying amenities or services (e.g., bus stops, parks) within walking distance of residential areas.
• Retail and Logistics: Identifying potential customers or optimizing delivery routes within a defined proximity.

By integrating the distance filter directly into the spatial partitioning process, the Distance-Bound kNN Join in WherobotsDB significantly reduces the data processed, making spatial queries faster and more relevant. This optimization enhances performance, scalability, and the quality of your spatial data analysis.