Agent skill
design-postgis-tables
Comprehensive PostGIS spatial table design reference covering geometry types, coordinate systems, spatial indexing, and performance patterns for location-based applications
Install this agent skill to your Project
npx add-skill https://github.com/timescale/pg-aiguide/tree/main/skills/design-postgis-tables
SKILL.md
PostGIS Spatial Table Design
Before You Start (5 Questions)
- What is the geographic scope (single city/region vs global)?
- What are your primary query patterns (within-radius, bbox, intersects, nearest-neighbor)?
- What units do you need for distance/area (meters vs CRS units), and how accurate must they be?
- What is the expected scale (rows, write rate), and is the data mostly append-only?
- Do you need 3D (Z) or measures (M), or is 2D enough?
SQL injection note: When turning these patterns into application code, use parameterized queries for user-provided values (WKT/WKB, coordinates, IDs, radii). Avoid string-concatenating untrusted input into SQL; for dynamic identifiers, use safe identifier quoting/whitelisting.
Core Rules
- Always use PostGIS geometry/geography types instead of PostgreSQL's built-in geometric types (
POINT,LINE,POLYGON,CIRCLE). PostGIS types provide true spatial capabilities. - Choose between GEOMETRY and GEOGRAPHY based on your use case: GEOMETRY for projected/local data with Cartesian math; GEOGRAPHY for global data requiring accurate spherical calculations.
- Always specify SRID (Spatial Reference Identifier) when creating geometry columns. Use
4326(WGS84) for GPS/global data, appropriate local projections for regional data. - Create spatial indexes on all geometry/geography columns using GiST (default). Consider BRIN only for very large GEOMETRY tables where rows are naturally ordered on disk and you can tolerate coarser filtering.
- Use constraint-based type enforcement with
GEOMETRY(type, SRID)syntax to ensure data integrity.
Geometry vs Geography
When to Use GEOMETRY
- Local/regional data within a single coordinate system
- Projected coordinates (meters, feet) for accurate area/distance calculations
- Complex spatial operations (buffering, unions, intersections)
- Performance-critical queries (Cartesian math is faster)
- Data already in a projected CRS (UTM, State Plane, etc.)
-- Regional data with projected coordinates (UTM Zone 10N for California)
CREATE TABLE local_parcels (
id BIGINT GENERATED ALWAYS AS IDENTITY PRIMARY KEY,
parcel_number TEXT NOT NULL,
boundary GEOMETRY(POLYGON, 26910), -- UTM Zone 10N (meters)
area_sqm DOUBLE PRECISION GENERATED ALWAYS AS (ST_Area(boundary)) STORED
);
When to Use GEOGRAPHY
- Global data spanning multiple continents/hemispheres
- GPS coordinates (latitude/longitude in decimal degrees)
- Accurate distance calculations on Earth's surface (great circle)
- Simple spatial operations (distance, containment)
- Data from GPS devices, geocoding services, or web maps
-- Global data with geodetic calculations
CREATE TABLE global_offices (
id BIGINT GENERATED ALWAYS AS IDENTITY PRIMARY KEY,
name TEXT NOT NULL,
city TEXT NOT NULL,
location GEOGRAPHY(POINT, 4326) -- WGS84 (lat/lon)
);
-- Distance in meters (accurate spherical calculation)
SELECT
a.name AS office_a,
b.name AS office_b,
ST_Distance(a.location, b.location) / 1000 AS distance_km
FROM global_offices a
CROSS JOIN global_offices b
WHERE a.id < b.id;
Comparison Table
| Aspect | GEOMETRY | GEOGRAPHY |
|---|---|---|
| Coordinate system | Any SRID (projected or geodetic) | WGS84 (SRID 4326) only |
| Distance units | CRS units (degrees, meters, feet) | Meters (always) |
| Distance accuracy | Depends on projection | True spheroidal distance |
| Area accuracy | Accurate in projected CRS | Accurate on sphere |
| Function support | Full (300+ functions) | Limited (~40 functions) |
| Performance | Faster (Cartesian math) | Slower (spherical math) |
| Index type | GiST, BRIN, SP-GiST | GiST only |
| Best for | Regional/local data, complex analysis | Global data, GPS tracking |
Geometry Types
Point Types
-- Single location (stores, sensors, events)
location GEOMETRY(POINT, 4326)
-- Multiple discrete locations (multi-branch business)
locations GEOMETRY(MULTIPOINT, 4326)
-- 3D point with elevation
location_3d GEOMETRY(POINTZ, 4326)
-- Point with measure value (linear referencing)
location_m GEOMETRY(POINTM, 4326)
Use POINT for: Store locations, sensor positions, event coordinates, addresses, POIs Use MULTIPOINT for: Multiple related locations stored as single feature
Line Types
-- Single path (road segment, river, route)
path GEOMETRY(LINESTRING, 4326)
-- Multiple paths (road network, transit lines)
network GEOMETRY(MULTILINESTRING, 4326)
-- 3D line with elevation profile
trail_3d GEOMETRY(LINESTRINGZ, 4326)
Use LINESTRING for: Roads, rivers, pipelines, GPS tracks, routes Use MULTILINESTRING for: Disconnected road segments, river systems
Polygon Types
-- Single area (parcel, building footprint, zone)
boundary GEOMETRY(POLYGON, 4326)
-- Multiple areas (archipelago, fragmented habitat)
territories GEOMETRY(MULTIPOLYGON, 4326)
-- 3D polygon (building with height)
footprint_3d GEOMETRY(POLYGONZ, 4326)
Use POLYGON for: Property boundaries, administrative areas, service zones Use MULTIPOLYGON for: Countries with islands, fragmented regions
Generic Types
-- Any geometry type (flexible schema)
geom GEOMETRY(GEOMETRY, 4326)
-- Collection of mixed types
features GEOMETRY(GEOMETRYCOLLECTION, 4326)
Use GEOMETRY for: Flexible schemas accepting multiple types Avoid GEOMETRYCOLLECTION: Prefer homogeneous types for better indexing
Coordinate Systems (SRID)
Common SRIDs
| SRID | Name | Use Case | Units |
|---|---|---|---|
| 4326 | WGS84 | GPS, global data, web maps | Degrees |
| 3857 | Web Mercator | Web map tiles (display only) | Meters |
| 26910-26919 | UTM Zones (US) | Regional analysis | Meters |
| 32601-32660 | UTM Zones (North) | Regional analysis | Meters |
| 32701-32760 | UTM Zones (South) | Regional analysis | Meters |
SRID Best Practices
- Store in WGS84 (4326) for interoperability and GPS data
- Transform to projected CRS for accurate measurements
- Never mix SRIDs in spatial operations without explicit transformation
- Use appropriate local CRS for area/distance calculations requiring high precision
-- Store in WGS84, calculate in UTM
CREATE TABLE survey_points (
id BIGINT GENERATED ALWAYS AS IDENTITY PRIMARY KEY,
location GEOMETRY(POINT, 4326), -- Storage: WGS84
CONSTRAINT valid_location CHECK (ST_IsValid(location))
);
-- Calculate distance in meters using UTM projection
SELECT
a.id AS point_a,
b.id AS point_b,
ST_Distance(
ST_Transform(a.location, 26910), -- Transform to UTM
ST_Transform(b.location, 26910)
) AS distance_meters
FROM survey_points a
CROSS JOIN survey_points b
WHERE a.id < b.id;
Spatial Indexing
GiST Index (Default)
Most versatile spatial index. Use for all geometry/geography columns.
-- Geometry (most common)
CREATE INDEX idx_your_table_geom_gist ON your_table_name USING GIST (geom);
-- Geography (GiST is the supported option)
CREATE INDEX idx_your_table_geog_gist ON your_table_name USING GIST (geog);
-- Analyze after index creation
VACUUM ANALYZE your_table_name;
Supports: All spatial operators (&&, @>, <@, ~=, <->)
Best for: General-purpose spatial queries, mixed query patterns
BRIN Index
Block Range Index for very large, naturally ordered datasets.
-- BRIN for very large, append-only GEOMETRY tables (geography uses GiST)
CREATE INDEX idx_your_table_geom_brin
ON your_table_name
USING BRIN (geom)
WITH (pages_per_range = 128);
Supports: Bounding box operators (&&, @>, <@)
Best for: Append-only tables, time-series spatial data, very large datasets (>100M rows)
Trade-off: Much smaller than GiST, but less precise filtering
SP-GiST Index
Space-partitioned GiST for point data with specific distributions.
-- SP-GiST for GEOMETRY(POINT, ...) only
CREATE INDEX idx_sensors_location_spgist
ON sensors
USING SPGIST (location);
Best for: Point-only data, quadtree-friendly distributions Not for: Complex geometries, mixed types
Index Selection Guide
| Scenario | Index Type | Reasoning |
|---|---|---|
| General spatial queries | GiST | Most versatile, supports all operators |
| Very large, append-only | BRIN | Tiny footprint, good for time-ordered data |
| Point-only, uniform distribution | SP-GiST | Efficient for point lookups |
| Geography columns | GiST | Only supported option |
| Composite spatial + attribute | GiST + B-tree | Separate indexes or expression index |
Table Design Examples
Points of Interest (POI)
CREATE TABLE pois (
id BIGINT GENERATED ALWAYS AS IDENTITY PRIMARY KEY,
name TEXT NOT NULL,
category TEXT NOT NULL,
location GEOGRAPHY(POINT, 4326) NOT NULL,
address TEXT,
metadata JSONB DEFAULT '{}',
created_at TIMESTAMPTZ NOT NULL DEFAULT now(),
CONSTRAINT valid_category CHECK (category IN (
'restaurant', 'hotel', 'gas_station', 'hospital', 'school'
))
);
-- Spatial index
CREATE INDEX idx_pois_location ON pois USING GIST (location);
-- Category + location for filtered spatial queries
CREATE INDEX idx_pois_category ON pois (category);
-- Find restaurants within 1km
SELECT name, address,
ST_Distance(
location,
ST_SetSRID(ST_MakePoint(-122.4194, 37.7749), 4326)::GEOGRAPHY
) AS distance_m
FROM pois
WHERE category = 'restaurant'
AND ST_DWithin(
location,
ST_SetSRID(ST_MakePoint(-122.4194, 37.7749), 4326)::GEOGRAPHY,
1000
)
ORDER BY distance_m;
Property Parcels
CREATE TABLE parcels (
id BIGINT GENERATED ALWAYS AS IDENTITY PRIMARY KEY,
parcel_id TEXT NOT NULL UNIQUE,
owner_name TEXT,
boundary GEOMETRY(MULTIPOLYGON, 4326) NOT NULL,
centroid GEOMETRY(POINT, 4326) GENERATED ALWAYS AS (ST_Centroid(boundary)) STORED,
area_sqm DOUBLE PRECISION GENERATED ALWAYS AS (
ST_Area(boundary::GEOGRAPHY)
) STORED,
perimeter_m DOUBLE PRECISION GENERATED ALWAYS AS (
ST_Perimeter(boundary::GEOGRAPHY)
) STORED,
CONSTRAINT valid_boundary CHECK (ST_IsValid(boundary)),
CONSTRAINT closed_boundary CHECK (ST_IsClosed(ST_ExteriorRing(ST_GeometryN(boundary, 1))))
);
CREATE INDEX idx_parcels_boundary ON parcels USING GIST (boundary);
CREATE INDEX idx_parcels_centroid ON parcels USING GIST (centroid);
-- Find parcels intersecting a search area
SELECT parcel_id, owner_name, area_sqm
FROM parcels
WHERE ST_Intersects(boundary, ST_MakeEnvelope(-122.5, 37.7, -122.4, 37.8, 4326));
GPS Tracking
CREATE TABLE gps_tracks (
id BIGINT GENERATED ALWAYS AS IDENTITY PRIMARY KEY,
device_id TEXT NOT NULL,
recorded_at TIMESTAMPTZ NOT NULL,
location GEOGRAPHY(POINT, 4326) NOT NULL,
speed_kmh DOUBLE PRECISION,
heading DOUBLE PRECISION,
accuracy_m DOUBLE PRECISION
);
-- Composite index for device + time queries
CREATE INDEX idx_gps_device_time ON gps_tracks (device_id, recorded_at DESC);
-- Spatial index for location queries
CREATE INDEX idx_gps_location ON gps_tracks USING GIST (location);
-- Note: GEOGRAPHY supports GiST; BRIN is for GEOMETRY (when appropriate).
-- Create linestring from track points
SELECT
device_id,
ST_MakeLine(location::GEOMETRY ORDER BY recorded_at) AS track_line,
MIN(recorded_at) AS start_time,
MAX(recorded_at) AS end_time
FROM gps_tracks
WHERE device_id = 'device_001'
AND recorded_at >= '2024-01-01'
GROUP BY device_id;
Service Areas / Coverage Zones
CREATE TABLE service_zones (
id BIGINT GENERATED ALWAYS AS IDENTITY PRIMARY KEY,
zone_name TEXT NOT NULL,
zone_type TEXT NOT NULL,
boundary GEOMETRY(POLYGON, 4326) NOT NULL,
population INTEGER,
active BOOLEAN NOT NULL DEFAULT true,
CONSTRAINT valid_zone_type CHECK (zone_type IN ('delivery', 'service', 'coverage')),
CONSTRAINT valid_boundary CHECK (ST_IsValid(boundary))
);
CREATE INDEX idx_zones_boundary ON service_zones USING GIST (boundary);
CREATE INDEX idx_zones_active ON service_zones (active) WHERE active = true;
-- Check if location is within any active service zone
SELECT zone_name, zone_type
FROM service_zones
WHERE active = true
AND ST_Contains(boundary, ST_SetSRID(ST_MakePoint(-122.4194, 37.7749), 4326));
Performance Patterns
Use ST_DWithin Instead of ST_Distance
-- SLOW: calculates distance for all rows
SELECT * FROM pois
WHERE ST_Distance(location, ref_point) < 1000;
-- FAST: uses spatial index
SELECT * FROM pois
WHERE ST_DWithin(location, ref_point, 1000);
Use && for Bounding Box Pre-filtering
-- Bounding box operator leverages spatial index
SELECT * FROM parcels
WHERE boundary && ST_MakeEnvelope(-122.5, 37.7, -122.4, 37.8, 4326)
AND ST_Intersects(boundary, search_polygon);
Avoid Functions on Indexed Columns
-- SLOW: function prevents index usage
SELECT * FROM parcels WHERE ST_Area(boundary) > 10000;
-- FAST: use generated column with regular index
ALTER TABLE parcels ADD COLUMN area_sqm DOUBLE PRECISION
GENERATED ALWAYS AS (ST_Area(boundary::GEOGRAPHY)) STORED;
CREATE INDEX idx_parcels_area ON parcels (area_sqm);
SELECT * FROM parcels WHERE area_sqm > 10000;
Simplify Geometries for Display
-- Reduce complexity for web display (tolerance in CRS units)
SELECT
id,
name,
ST_AsGeoJSON(ST_Simplify(boundary, 0.0001)) AS geojson
FROM parcels;
Use Appropriate Precision
-- Reduce coordinate precision for storage efficiency
UPDATE locations SET geom = ST_ReducePrecision(geom, 0.000001);
-- GeoJSON with limited decimal places
SELECT ST_AsGeoJSON(location, 6) AS geojson FROM pois;
Data Validation
Geometry Validity Checks
-- Add validity constraint
ALTER TABLE parcels ADD CONSTRAINT valid_geom CHECK (ST_IsValid(boundary));
-- Find and fix invalid geometries
SELECT id, ST_IsValidReason(boundary) AS reason
FROM parcels
WHERE NOT ST_IsValid(boundary);
-- Attempt to fix invalid geometries
UPDATE parcels
SET boundary = ST_MakeValid(boundary)
WHERE NOT ST_IsValid(boundary);
SRID Consistency
-- Verify SRID consistency
SELECT DISTINCT ST_SRID(geom) FROM spatial_table;
-- Enforce SRID with constraint
ALTER TABLE locations ADD CONSTRAINT enforce_srid
CHECK (ST_SRID(location) = 4326);
Coordinate Range Validation
-- Ensure coordinates are within valid WGS84 bounds
ALTER TABLE global_locations ADD CONSTRAINT valid_coords CHECK (
ST_X(location::GEOMETRY) BETWEEN -180 AND 180 AND
ST_Y(location::GEOMETRY) BETWEEN -90 AND 90
);
Do Not Use
- PostgreSQL built-in types (
POINT,LINE,POLYGON,CIRCLE) - use PostGIS types instead - SRID 0 (undefined) - always specify the correct SRID
- ST_Distance for filtering - use ST_DWithin for index-supported distance queries
- Mixed SRIDs in operations - always transform to common SRID first
- GEOGRAPHY for complex analysis - use GEOMETRY with appropriate projection
- Over-precise coordinates - GPS accuracy is ~3-5m, 6 decimal places (0.1m) is sufficient
Common Pitfalls
- Longitude/Latitude order: PostGIS uses
(longitude, latitude)=(X, Y), not(lat, lon) - GEOGRAPHY distance units: Always in meters, regardless of display
- Index not used: Run
EXPLAIN ANALYZEto verify spatial index usage - Transform performance: Cache transformed geometries for repeated queries
- Large geometries: Consider ST_Subdivide for very complex polygons
- SQL injection / unsafe dynamic SQL: Don't concatenate untrusted input into SQL. Parameterize values; for dynamic identifiers use safe quoting (
quote_ident,format('%I', ...)) or strict allowlists.
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