PostGIS - RelytONE docs

Relyt ONE integrates PostGIS 3.5.4 with GDAL 3.11, providing industry-leading spatial database capabilities for storing, querying, and analyzing geographic data directly in PostgreSQL. PostGIS extends PostgreSQL with support for geographic objects, spatial indexes, and hundreds of spatial functions for processing and analyzing vector and raster data.

Key Features

Spatial Data Types: Support for points, lines, polygons, and complex geometries
Coordinate Systems: Full projection support with thousands of spatial reference systems
Spatial Indexing: High-performance GiST and BRIN indexes for spatial queries
Geometry Operations: Distance calculations, intersections, unions, buffers, and more
Standards Compliant: OGC-compliant implementation of SQL/MM specifications
Raster Support: Store and analyze raster data with GDAL 3.11 integration

Quick Start

Step 1: Enable PostGIS Extension

First, enable the PostGIS extension in your database:

CREATE EXTENSION IF NOT EXISTS postgis;

Verify the installation and check the version:

SELECT PostGIS_Version();

Expected Output:

3.5 USE_GEOS=1 USE_PROJ=1 USE_STATS=1

Step 2: Create a Spatial Table

Create a table with geometry columns to store spatial data:

CREATE TABLE cities (
  id SERIAL PRIMARY KEY,
  name VARCHAR(100),
  population INTEGER,
  geom GEOMETRY(Point, 4326)
);

COMMENT ON COLUMN cities.id IS 'Primary key, auto-increment';
COMMENT ON COLUMN cities.name IS 'City name';
COMMENT ON COLUMN cities.population IS 'Population count';
COMMENT ON COLUMN cities.geom IS 'Geographic point (longitude, latitude) in WGS84';

The GEOMETRY(Point, 4326) type specifies:

Point: The geometry type (point, linestring, polygon, etc.)
4326: The SRID (Spatial Reference System Identifier) for WGS84 coordinates

Step 3: Insert Spatial Data

Insert cities with their geographic coordinates:

INSERT INTO cities (name, population, geom) VALUES
  ('San Francisco', 873965, ST_SetSRID(ST_MakePoint(-122.4194, 37.7749), 4326)),
  ('New York', 8336817, ST_SetSRID(ST_MakePoint(-74.0060, 40.7128), 4326)),
  ('Los Angeles', 3979576, ST_SetSRID(ST_MakePoint(-118.2437, 34.0522), 4326)),
  ('Chicago', 2693976, ST_SetSRID(ST_MakePoint(-87.6298, 41.8781), 4326)),
  ('Seattle', 753675, ST_SetSRID(ST_MakePoint(-122.3321, 47.6062), 4326));

You can also use the simpler ST_Point function:

INSERT INTO cities (name, population, geom) VALUES
  ('Boston', 692600, ST_GeomFromText('POINT(-71.0589 42.3601)', 4326));

Step 4: Create Spatial Index

Create a spatial index to accelerate spatial queries:

CREATE INDEX cities_geom_idx 
ON cities 
USING GIST (geom);

Benefits:

Dramatically improves performance for spatial queries
Essential for large datasets with millions of geometries
Uses R-tree-like indexing structure

Step 5: Basic Spatial Queries

Distance Calculations

Find cities within 500 km of San Francisco:

SELECT 
  name, 
  population,
  ST_Distance(
    geom::geography,
    ST_SetSRID(ST_MakePoint(-122.4194, 37.7749), 4326)::geography
  ) / 1000 AS distance_km
FROM cities
WHERE ST_DWithin(
  geom::geography,
  ST_SetSRID(ST_MakePoint(-122.4194, 37.7749), 4326)::geography,
  500000  -- 500 km in meters
)
ORDER BY distance_km;

Nearest Neighbor Search

Find the 3 closest cities to a given point:

SELECT 
  name,
  ST_Distance(
    geom::geography,
    ST_SetSRID(ST_MakePoint(-118.2437, 34.0522), 4326)::geography
  ) / 1000 AS distance_km
FROM cities
ORDER BY geom <-> ST_SetSRID(ST_MakePoint(-118.2437, 34.0522), 4326)
LIMIT 3;

The <-> operator uses the spatial index for efficient nearest neighbor queries.

Step 6: Working with Polygons

Create a table for geographic regions:

CREATE TABLE regions (
  id SERIAL PRIMARY KEY,
  name VARCHAR(100),
  geom GEOMETRY(Polygon, 4326)
);

Insert a polygon representing a geographic area:

INSERT INTO regions (name, geom) VALUES
  ('Bay Area', ST_GeomFromText(
    'POLYGON((
      -123.0 38.0,
      -122.0 38.0,
      -122.0 37.0,
      -123.0 37.0,
      -123.0 38.0
    ))', 4326
  ));

Find cities within a region:

SELECT c.name, c.population
FROM cities c
JOIN regions r ON ST_Contains(r.geom, c.geom)
WHERE r.name = 'Bay Area';

Step 7: Advanced Spatial Operations

Buffer Operations

Create a 100 km buffer around a city:

SELECT 
  name,
  ST_Buffer(geom::geography, 100000)::geometry AS buffer_geom
FROM cities
WHERE name = 'San Francisco';

Area and Distance Calculations

Calculate the area of a polygon in square kilometers:

SELECT 
  name,
  ST_Area(geom::geography) / 1000000 AS area_sq_km
FROM regions;

Intersection Detection

Check if two geometries intersect:

SELECT 
  c.name AS city,
  r.name AS region
FROM cities c
CROSS JOIN regions r
WHERE ST_Intersects(c.geom, r.geom);

Centroid Calculation

Find the geometric center of a polygon:

SELECT 
  name,
  ST_AsText(ST_Centroid(geom)) AS centroid
FROM regions;

Step 8: Converting Coordinate Systems

Transform coordinates between different spatial reference systems:

-- Convert from WGS84 (4326) to Web Mercator (3857)
SELECT 
  name,
  ST_AsText(ST_Transform(geom, 3857)) AS mercator_coords
FROM cities
LIMIT 3;

Step 9: Export Spatial Data

Export geometries in various formats:

-- GeoJSON format
SELECT 
  name,
  ST_AsGeoJSON(geom) AS geojson
FROM cities
LIMIT 1;

-- Well-Known Text (WKT)
SELECT 
  name,
  ST_AsText(geom) AS wkt
FROM cities
LIMIT 1;

-- KML format
SELECT 
  name,
  ST_AsKML(geom) AS kml
FROM cities
LIMIT 1;

Common Spatial Functions

Function	Description	Example
`ST_Distance`	Calculate distance between geometries	`ST_Distance(geom1, geom2)`
`ST_DWithin`	Check if geometries are within distance	`ST_DWithin(geom1, geom2, 1000)`
`ST_Contains`	Check if geometry contains another	`ST_Contains(polygon, point)`
`ST_Intersects`	Check if geometries intersect	`ST_Intersects(geom1, geom2)`
`ST_Buffer`	Create buffer around geometry	`ST_Buffer(geom, 1000)`
`ST_Area`	Calculate area of polygon	`ST_Area(geom::geography)`
`ST_Length`	Calculate length of linestring	`ST_Length(geom::geography)`
`ST_Union`	Combine multiple geometries	`ST_Union(geom1, geom2)`
`ST_Centroid`	Find center point	`ST_Centroid(geom)`
`ST_Transform`	Transform between coordinate systems	`ST_Transform(geom, 3857)`

Best Practices

Use Geography vs Geometry: Cast to geography type for accurate distance calculations on Earth’s surface
Always Create Spatial Indexes: Essential for performance on large datasets
Specify SRID: Always specify the spatial reference system (e.g., 4326 for WGS84)
Use Appropriate Functions: Use ST_DWithin instead of ST_Distance < x for better performance
Validate Geometries: Use ST_IsValid() to check geometry validity before operations

Next Steps

Explore PostGIS documentation for comprehensive function reference
Learn about raster data support with GDAL
Check out topology features for advanced spatial relationships
Integrate with QGIS, ArcGIS, or other GIS tools using standard PostgreSQL connections

For advanced features including 3D geometries, routing, geocoding, and raster analysis, refer to the official PostGIS documentation.

Documentation Index

​Key Features

​Quick Start

​Step 1: Enable PostGIS Extension

​Step 2: Create a Spatial Table

​Step 3: Insert Spatial Data

​Step 4: Create Spatial Index

​Step 5: Basic Spatial Queries

​Distance Calculations

​Nearest Neighbor Search

​Step 6: Working with Polygons

​Step 7: Advanced Spatial Operations

​Buffer Operations

​Area and Distance Calculations

​Intersection Detection

​Centroid Calculation

​Step 8: Converting Coordinate Systems

​Step 9: Export Spatial Data

​Common Spatial Functions

​Best Practices

​Next Steps