H3 Index for Spatial Data in Oracle Database 23

With the release of Oracle Database 23c, H3 is now natively supported, enabling developers and analysts to perform advanced spatial data indexing and aggregation more efficiently.👌

H3 provides a hierarchical hexagonal grid system that enables flexible spatial data representation. H3 indexes locations using hexagonal cells at different resolution levels from large hexagons at low resolutions to finer granularity at higher resolutions. This hierarchical structure enables flexible spatial analysis, where data can be aggregated or zoomed in based on different levels of granularity. Unlike traditional rectangular grids, hexagons offer more uniform adjacency relationships, making them ideal for spatial aggregation, clustering, and heatmaps.

 

📌Why H3 indexing in Oracle Database 23 is an important feature?

•  Suitable for handling large geospatial datasets efficiently.now oracle by this new feature allows for efficient querying of large datasets, improving spatial analytics and visualization.

•  More uniform adjacency relations compared to square grids. This means that each hexagon has six neighbors of equal distance, making it better suited for proximity-based queries like nearest neighbor search and geospatial clustering. 

•  Oracle 23c includes built-in functions for working with H3 indexes, allowing users to generate, manipulate, and analyze H3-based spatial data.These H3 functions include:

SDO_UTIL.TO_H3(geometry, resolution): Converts a spatial geometry into an H3 index.
SDO_UTIL.FROM_H3(h3_index): Converts an H3 index back into a spatial geometry.
SDO_UTIL.POLYGON_TO_H3(geometry, resolution): Returns all H3 indexes within a given polygon.
SDO_UTIL.H3_TO_POLYGON(h3_index): Converts an H3 index into a polygon geometry.

SDO_UTIL.H3_NEIGHBORS(h3_index, k): Retrieves neighboring H3 cells within a specified distance.

📌When should we Use H3 Indexing?

H3 Indexing is Ideal for spatial analytics, clustering, and efficient data aggregation. and Traditional Spatial Indexes are Suited for precise spatial queries and complex geometries.in below you can see some cases it is better to use the H3 Index :

• Summarizing spatial data into regions or zones for analysis. Hexagons provide uniform adjacency and reduce distortion compared to rectangular grids.

• Representing spatial data as a visual heatmap or density map. Hexagons create visually appealing and uniform heatmaps.H3 cells are consistent in size and shape, avoiding distortions seen in rectangular grids.

• Geospatial Clustering ,Identifying patterns or groupings in spatial datasets.Hexagons naturally group nearby points, making clustering more intuitive.H3’s hierarchical structure allows clustering at multiple scales.

• Geofencing. Geofencing Is Defining virtual boundaries for real-time monitoring or alerts . Hexagons provide uniform coverage, making geofencing more accurate.H3 cells can dynamically adjust to changing boundaries. For example, you want to Send alerts when assets enter or exit a geofenced area.

• Finding Nearest Neighbor points of interest within a specific area.Hexagons simplify proximity analysis due to their uniform adjacency.H3 indexing speeds up nearest-neighbor queries by reducing the search space.

• Optimizing delivery routes, resource allocation, or fleet management. Hexagons provide a consistent framework for analyzing spatial patterns.H3 indexing helps identify optimal routes and resource distribution. for example ambulances project and delivery trucks based on hexagonal regions.

• Analyzing real-time spatial data for decision-making. Hexagons enable fast aggregation and analysis of streaming data.H3 indexing improves query performance for real-time applications.for example Monitoring real-time traffic conditions using hexagonal grids.

📌Implementing H3 Indexing in Oracle 23

Assume you have a database table containing GPS coordinates of delivery points, and you aim to group them into hexagonal zones using H3 indexing.

Step 1: Prepare Data 

CREATE TABLE delivery_points (

id NUMBER PRIMARY KEY,

name VARCHAR2(50),

longitude NUMBER,

latitude NUMBER

);

 INSERT INTO delivery_points (id, name, longitude,
latitude) VALUES (1, 'Point A', -122.4194, 37.7749);  
INSERT INTO delivery_points (id, name, longitude, latitude) VALUES (2,
'Point B', -121.8863, 37.3382);  
INSERT INTO delivery_points (id, name, longitude, latitude) VALUES (3,
'Point C', -123.0438, 38.5791);  

Step 2: Add an H3 Index Column:

 ALTER TABLE delivery_points ADD h3_index VARCHAR2(20);

Step 3: Populate the H3 Index Column

UPDATE delivery_points SET h3_index = SDO_H3.TO_H3(latitude, longitude, 7);
In this function, the parameters are (latitude, longitude, resolution). 
The resolution level 7 determines the size of the hexagonal cells; higher numbers correspond to finer granularity.
 Step 4: Querying Data Using H3 Indexing
To count the number of delivery points within each hexagonal zone.Aggregate delivery points into hexagonal zones
SELECT h3_index, COUNT(*) AS point_count
FROM delivery_points
GROUP BY h3_index
ORDER BY point_count DESC; 
Now here You have a dataset of delivery points across a city, and you want to analyze the distribution of deliveries by region.

Without H3 Index:Use rectangular grids or polygons for aggregation.
Rectangular grids can lead to uneven regions and distorted results, especially near the edges of the grid.

With H3:Use hexagonal grids for aggregation.
Hexagons provide uniform regions and reduce distortion, leading to more accurate and visually appealing results.
This is the Benefits of Using H3 Indexing

📌Comparing the H3 Index and Traditional Spatial Indexes

Feature H3 Index Spatial Index (R-tree) Indexing Structure Hierarchical hexagonal grid Tree-based (R-tree) Best For Spatial analytics, aggregations, geofencing, clustering Precise spatial queries, geometry operations Resolution Levels Multiple hierarchical levels (zoom in/out easily) Fixed resolution, based on geometry Query Performance Fast for proximity search, heatmaps, clustering Optimized for point-in-polygon, intersection, nearest neighbor Storage & Indexing Overhead Requires storing precomputed H3 indexes in a column Uses SDO_INDEX with spatial geometries Flexibility Easier to scale across resolutions Better for exact shape-based queries
👉TIP: Combining  H3 Spatial Indexing with vector tiles can lead to more interactive and insightful maps.  👀 I suggest you read this article about Vector Tiles in the below link : https://lidagholizadeh.blogspot.com/2025/02/vector-tile-in-oracle-spatial-23.html