Summary: PostGIS extends Postgres with GEOMETRY and GEOGRAPHY data types, spatial functions (ST_DWithin, ST_Intersects, ST_Buffer, ST_Distance), and GiST indexing for querying coordinates, polygons, and location-based data. Use this page to enable PostGIS on Neon with CREATE EXTENSION, store and query spatial data, and tune performance for large geospatial datasets. For related extensions such as pgRouting, PostGIS SFCGAL, and PostGIS Tiger Geocoder, see the PostGIS-related extensions page.

Work with geospatial data in Postgres using PostGIS

The postgis extension provides support for spatial data - coordinates, maps and polygons, encompassing geographical and location-based information. It introduces new data types, functions, and operators to manage and analyze spatial data effectively.

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This guide introduces you to the postgis extension - how to enable it, store and query spatial data, and perform geospatial analysis with real-world examples. Geospatial data is crucial in fields like urban planning, environmental science, and logistics.

Note: PostGIS is an open-source extension for Postgres that can be installed on any Neon Project using the instructions below. Detailed installation instructions and compatibility information can be found at PostGIS Documentation.

For information about PostGIS-related extensions, including pgrouting, H3_PostGIS, PostGIS SFCGAL, and PostGIS Tiger Geocoder, see PostGIS-related extensions.

Version availability:

Please refer to the list of all extensions available in Neon for up-to-date information.

You can enable the extension by running the following CREATE EXTENSION statement in the Neon SQL Editor or from a client such as psql that is connected to Neon.

CREATE EXTENSION IF NOT EXISTS postgis;

For information about using the Neon SQL Editor, see Query with Neon's SQL Editor. For information about using the psql client with Neon, see Connect with psql.

Create a table with spatial data

Suppose you're managing a city's public transportation system. You can create a table to store the locations of bus stops.

CREATE TABLE bus_stops (
    id SERIAL PRIMARY KEY,
    name VARCHAR(255),
    location GEOGRAPHY(Point)
);

Here, the location column is of type GEOGRAPHY(Point), which is a spatial data type provided by the postgis extension and used to store points on the Earth's surface.

Inserting data

Data can be inserted into the table using regular INSERT statements.

INSERT INTO bus_stops (name, location)
VALUES
    ('Main St & 3rd Ave', ST_Point(-73.935242, 40.730610)),
    ('Elm St & 5th Ave', ST_Point(-73.991070, 40.730824));

The ST_Point function is used to create a point from the specified longitude and latitude.

Querying spatial data

Now, we can perform spatial queries using the built-in functions provided by PostGIS. For example, below we try to find points within a certain distance from a reference.

Query:

SELECT name FROM bus_stops
WHERE ST_DWithin(location, ST_Point(-73.95, 40.7305)::GEOGRAPHY, 2000);

This query returns the following:

| name               |
|--------------------|
| Main St & 3rd Ave  |

The ST_DWithin function returns true if the distance between two points is less than or equal to the specified distance (when used with the GEOGRAPHY type, the unit is meters).

PostGIS extends Postgres data types to handle spatial data. The primary spatial types are:

• GEOMETRY: A flexible type for spatial data, supporting various shapes. It models shapes in the cartesian coordinate plane. Each GEOMETRY value is also associated with a spatial reference system (SRS), which defines the coordinate system and units of measurement.
• GEOGRAPHY: Specifically designed for large-scale spatial operations on the Earth's surface, factoring in the Earth's curvature. The coordinates for a GEOGRAPHY shape are specified in degrees of longitude and latitude.

The actual shapes are stored as a set of coordinates. For example, a point is stored as a pair of coordinates, a line as a set of points, and a polygon as a set of lines.

PostGIS provides a number of other functions for spatial analysis - area, distance, intersection, and more. To illustrate, we'll create dataset representing a small set of landmarks and roads in a fictional city and run spatial queries on it.

Creating the test dataset

CREATE TABLE landmarks (
    id SERIAL PRIMARY KEY,
    name VARCHAR(255),
    location GEOMETRY(Point)
);

CREATE TABLE roads (
    id SERIAL PRIMARY KEY,
    name VARCHAR(255),
    path GEOMETRY(LineString)
);

INSERT INTO landmarks (name, location)
VALUES
    ('Park', ST_Point(100, 200)),
    ('Museum', ST_Point(200, 300)),
    ('Library', ST_Point(300, 200));

INSERT INTO roads (name, path)
VALUES
    ('Main Street', ST_MakeLine(ST_Point(100, 200), ST_Point(200, 300))),
    ('Second Street', ST_MakeLine(ST_Point(200, 300), ST_Point(300, 200)));

Nearest landmark to a given point

Finding the nearest places to a given point is a common spatial query. We can use the ST_Distance function to find the distance between two points and order the results by distance.

SELECT name, ST_Distance(location, ST_GeomFromText('POINT(150 250)')) AS distance
FROM landmarks
ORDER BY distance
LIMIT 1;

This query returns the following:

| name   | distance |
|--------|----------|
| Park   | 70.7107  |

Intersection of Roads

We can use the ST_Intersects function to find if two roads intersect. To ensure we don't get duplicate pairs of roads, we filter out pairs where the first road has a higher id than the second road.

SELECT a.name, b.name
FROM roads a AS name_A, roads b AS name_B
WHERE a.id < b.id AND ST_Intersects(a.path, b.path);

This query returns the following:

| name_A         | name_B         |
|----------------|----------------|
| Main Street    | Second Street  |

Buffer zone around a landmark

Say, the municipal council wants to create a buffer zone of 50 units around landmarks and check which roads intersect these zones. ST_Buffer computes an area around the given point with the specified radius.

SELECT l.name AS landmark, r.name AS road
FROM landmarks l, roads r
WHERE ST_Intersects(r.path, ST_Buffer(l.location, 50));

This query returns the following:

| landmark | road          |
|----------|---------------|
| Park     | Main Street   |
| Museum   | Main Street   |
| Museum   | Second Street |
| Library  | Second Street |

Line of Sight Between Landmarks

To check if there's a direct line of sight (no roads intersecting) between two landmarks, we can combine two postgis functions.

SELECT
    'No direct line of sight' AS info
FROM
    landmarks l1, landmarks l2, roads r
WHERE
    l1.name = 'Park' AND l2.name = 'Library' AND
    ST_Intersects(ST_MakeLine(l1.location, l2.location), r.path)
LIMIT 1;

This query returns the following:

| info                     |
|--------------------------|
| No direct line of sight  |

This tells us there's no direct line of sight between the Park and the Library.

When working with PostGIS, thinking about performance is crucial, especially when dealing with large datasets or complex spatial queries.

GIST (Generalized Search Tree) is the default spatial index in PostGIS. GiST indexes are well-suited for multidimensional data, like points, lines, and polygons. It can significantly improve query performance, especially for spatial search operations and joins.

CREATE INDEX spatial_index_name ON landmarks USING GIST(location);

• Unnecessary Casting: GEOMETRY and GEOGRAPHY are the two primary data types in postgis, and a lot of functions are overloaded to work with both. However, casting between the two types can be expensive, so it's best to store data in the more frequently used type.
• Use Appropriate Precision: Reducing the precision of coordinates can often improve performance without significantly impacting the results.

These examples provide a quick introduction to handling and analyzing spatial data in PostgresQL. We saw how to create tables with spatial data, insert data, and perform spatial queries using the postgis extension. It offers a powerful set of tools, with functions for calculating distances, identifying spatial relationships, and aggregating spatial data.

• PostGIS Documentation
• PostGIS Intro Workshop

• Extension explorer
• anon
• btree_gin
• btree_gist
• citext
• cube
• dblink
• dict_int
• earthdistance
• fuzzystrmatch
• hstore
• intarray
• lakebase_text
• lakebase_tokenizer
• lakebase_vector
• ltree
• neon
• neon_utils
• online_advisor
• pgcrypto
• pgvector
• pgrag
• pg_cron
• pg_graphql
• pg_mooncake
• pg_partman
• pg_prewarm
• pg_session_jwt
• pg_stat_statements
• pg_repack
• pg_search
• pg_tiktoken
• pg_trgm
• pg_uuidv7
• pgrowlocks
• pgstattuple
• plv8
• postgis-related
• postgres_fdw
• tablefunc
• timescaledb
• unaccent
• uuid-ossp
• wal2json
• xml2

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