Retrieve the PostGIS source archive from the downloads website https://postgis.net/stuff/postgis-3.4.4dev.tar.gz

wget https://postgis.net/stuff/postgis-3.4.4dev.tar.gz
tar -xvzf postgis-3.4.4dev.tar.gz
cd postgis-3.4.4dev

This will create a directory called postgis-3.4.4dev in the current working directory.

Alternatively, checkout the source from the git repository https://git.osgeo.org/gitea/postgis/postgis/ .

git clone https://git.osgeo.org/gitea/postgis/postgis.git postgis
cd postgis
sh autogen.sh
    

Change into the newly created postgis directory to continue the installation.

./configure

PostGIS has the following requirements for building and usage:

Required

Optional

As with most linux installations, the first step is to generate the Makefile that will be used to build the source code. This is done by running the shell script

./configure

With no additional parameters, this command will attempt to automatically locate the required components and libraries needed to build the PostGIS source code on your system. Although this is the most common usage of ./configure, the script accepts several parameters for those who have the required libraries and programs in non-standard locations.

The following list shows only the most commonly used parameters. For a complete list, use the --help or --help=short parameters.

	If you obtained PostGIS from the code repository , the first step is really to run the script ./autogen.sh This script will generate the configure script that in turn is used to customize the installation of PostGIS. If you instead obtained PostGIS as a tarball, running ./autogen.sh is not necessary as configure has already been generated.

Once the Makefile has been generated, building PostGIS is as simple as running

make

The last line of the output should be "PostGIS was built successfully. Ready to install."

As of PostGIS v1.4.0, all the functions have comments generated from the documentation. If you wish to install these comments into your spatial databases later, run the command which requires docbook. The postgis_comments.sql and other package comments files raster_comments.sql, topology_comments.sql are also packaged in the tar.gz distribution in the doc folder so no need to make comments if installing from the tar ball. Comments are also included as part of the CREATE EXTENSION install.

make comments

Introduced in PostGIS 2.0. This generates html cheat sheets suitable for quick reference or for student handouts. This requires xsltproc to build and will generate 4 files in doc folder topology_cheatsheet.html, tiger_geocoder_cheatsheet.html, raster_cheatsheet.html, postgis_cheatsheet.html

You can download some pre-built ones available in html and pdf from PostGIS / PostgreSQL Study Guides

make cheatsheets

The PostGIS extensions are built and installed automatically if you are using PostgreSQL 9.1+.

If you are building from source repository, you need to build the function descriptions first. These get built if you have docbook installed. You can also manually build with the statement:

make comments

Building the comments is not necessary if you are building from a release tar ball since these are packaged pre-built with the tar ball already.

The extensions should automatically build as part of the make install process. You can if needed build from the extensions folders or copy files if you need them on a different server.

cd extensions
cd postgis
make clean
make
export PGUSER=postgres #overwrite psql variables
make check #to test before install
make install
# to test extensions
make check RUNTESTFLAGS=--extension

	make check uses psql to run tests and as such can use psql environment variables. Common ones useful to override are PGUSER,PGPORT, and PGHOST. Refer to psql environment variables

The extension files will always be the same for the same version of PostGIS and PostgreSQL regardless of OS, so it is fine to copy over the extension files from one OS to another as long as you have the PostGIS binaries already installed on your servers.

If you want to install the extensions manually on a separate server different from your development, You need to copy the following files from the extensions folder into the PostgreSQL / share / extension folder of your PostgreSQL install as well as the needed binaries for regular PostGIS if you don't have them already on the server.

Once you do that, you should see postgis, postgis_topology as available extensions in PgAdmin -> extensions.

If you are using psql, you can verify that the extensions are installed by running this query:

SELECT name, default_version,installed_version
FROM pg_available_extensions WHERE name LIKE 'postgis%' or name LIKE 'address%';

             name             | default_version | installed_version
------------------------------+-----------------+-------------------
 address_standardizer         | 3.4.4dev         | 3.4.4dev
 address_standardizer_data_us | 3.4.4dev         | 3.4.4dev
 postgis                      | 3.4.4dev         | 3.4.4dev
 postgis_raster               | 3.4.4dev         | 3.4.4dev
 postgis_sfcgal               | 3.4.4dev         |
 postgis_tiger_geocoder       | 3.4.4dev         | 3.4.4dev
 postgis_topology             | 3.4.4dev         |
(6 rows)

If you have the extension installed in the database you are querying, you'll see mention in the installed_version column. If you get no records back, it means you don't have postgis extensions installed on the server at all. PgAdmin III 1.14+ will also provide this information in the extensions section of the database browser tree and will even allow upgrade or uninstall by right-clicking.

If you have the extensions available, you can install postgis extension in your database of choice by either using pgAdmin extension interface or running these sql commands:

CREATE EXTENSION postgis;
CREATE EXTENSION postgis_raster;
CREATE EXTENSION postgis_sfcgal;
CREATE EXTENSION fuzzystrmatch; --needed for postgis_tiger_geocoder
--optional used by postgis_tiger_geocoder, or can be used standalone
CREATE EXTENSION address_standardizer;
CREATE EXTENSION address_standardizer_data_us;
CREATE EXTENSION postgis_tiger_geocoder;
CREATE EXTENSION postgis_topology;

In psql you can use to see what versions you have installed and also what schema they are installed.

\connect mygisdb
\x
\dx postgis*

List of installed extensions
-[ RECORD 1 ]-------------------------------------------------
Name        | postgis
Version     | 3.4.4dev
Schema      | public
Description | PostGIS geometry, geography, and raster spat..
-[ RECORD 2 ]-------------------------------------------------
Name        | postgis_raster
Version     | 3.0.0dev
Schema      | public
Description | PostGIS raster types and functions
-[ RECORD 3 ]-------------------------------------------------
Name        | postgis_tiger_geocoder
Version     | 3.4.4dev
Schema      | tiger
Description | PostGIS tiger geocoder and reverse geocoder
-[ RECORD 4 ]-------------------------------------------------
Name        | postgis_topology
Version     | 3.4.4dev
Schema      | topology
Description | PostGIS topology spatial types and functions

Extension tables spatial_ref_sys, layer, topology can not be explicitly backed up. They can only be backed up when the respective postgis or postgis_topology extension is backed up, which only seems to happen when you backup the whole database. As of PostGIS 2.0.1, only srid records not packaged with PostGIS are backed up when the database is backed up so don't go around changing srids we package and expect your changes to be there. Put in a ticket if you find an issue. The structures of extension tables are never backed up since they are created with CREATE EXTENSION and assumed to be the same for a given version of an extension. These behaviors are built into the current PostgreSQL extension model, so nothing we can do about it.

If you installed 3.4.4dev, without using our wonderful extension system, you can change it to be extension based by running the below commands to package the functions in their respective extension. Installing using `unpackaged` was removed in PostgreSQL 13, so you are advised to switch to an extension build before upgrading to PostgreSQL 13.

CREATE EXTENSION postgis FROM unpackaged;
CREATE EXTENSION postgis_raster FROM unpackaged;
CREATE EXTENSION postgis_topology FROM unpackaged;
CREATE EXTENSION postgis_tiger_geocoder FROM unpackaged;

If you wish to test the PostGIS build, run

make check

The above command will run through various checks and regression tests using the generated library against an actual PostgreSQL database.

	If you configured PostGIS using non-standard PostgreSQL, GEOS, or Proj locations, you may need to add their library locations to the LD_LIBRARY_PATH environment variable.

	Currently, the make check relies on the PATH and PGPORT environment variables when performing the checks - it does not use the PostgreSQL version that may have been specified using the configuration parameter --with-pgconfig. So make sure to modify your PATH to match the detected PostgreSQL installation during configuration or be prepared to deal with the impending headaches.

If successful, make check will produce the output of almost 500 tests. The results will look similar to the following (numerous lines omitted below):

     CUnit - A unit testing framework for C - Version 2.1-3
     http://cunit.sourceforge.net/

	.
	.
	.

Run Summary:    Type  Total    Ran Passed Failed Inactive
              suites     44     44    n/a      0        0
               tests    300    300    300      0        0
             asserts   4215   4215   4215      0      n/a
Elapsed time =    0.229 seconds

	.
	.
	.

Running tests

	.
	.
	.

Run tests: 134
Failed: 0


-- if you build with SFCGAL

	.
	.
	.

Running tests

	.
	.
	.

Run tests: 13
Failed: 0

-- if you built with raster support

	.
	.
	.

Run Summary:    Type  Total    Ran Passed Failed Inactive
              suites     12     12    n/a      0        0
               tests     65     65     65      0        0
             asserts  45896  45896  45896      0      n/a


	.
	.
	.

Running tests

	.
	.
	.

Run tests: 101
Failed: 0

-- topology regress

.
.
.

Running tests

	.
	.
	.

Run tests: 51
Failed: 0

-- if you built --with-gui, you should see this too

     CUnit - A unit testing framework for C - Version 2.1-2
     http://cunit.sourceforge.net/

	.
	.
	.

Run Summary:    Type  Total    Ran Passed Failed Inactive
              suites      2      2    n/a      0        0
               tests      4      4      4      0        0
             asserts      4      4      4      0      n/a

The postgis_tiger_geocoder and address_standardizer extensions, currently only support the standard PostgreSQL installcheck. To test these use the below. Note: the make install is not necessary if you already did make install at root of PostGIS code folder.

For address_standardizer:

cd extensions/address_standardizer
make install
make installcheck
	  

Output should look like:

============== dropping database "contrib_regression" ==============
DROP DATABASE
============== creating database "contrib_regression" ==============
CREATE DATABASE
ALTER DATABASE
============== running regression test queries        ==============
test test-init-extensions     ... ok
test test-parseaddress        ... ok
test test-standardize_address_1 ... ok
test test-standardize_address_2 ... ok

=====================
 All 4 tests passed.
=====================

For tiger geocoder, make sure you have postgis and fuzzystrmatch extensions available in your PostgreSQL instance. The address_standardizer tests will also kick in if you built postgis with address_standardizer support:

cd extensions/postgis_tiger_geocoder
make install
make installcheck
	  

output should look like:

============== dropping database "contrib_regression" ==============
DROP DATABASE
============== creating database "contrib_regression" ==============
CREATE DATABASE
ALTER DATABASE
============== installing fuzzystrmatch               ==============
CREATE EXTENSION
============== installing postgis                     ==============
CREATE EXTENSION
============== installing postgis_tiger_geocoder      ==============
CREATE EXTENSION
============== installing address_standardizer        ==============
CREATE EXTENSION
============== running regression test queries        ==============
test test-normalize_address   ... ok
test test-pagc_normalize_address ... ok

=====================
All 2 tests passed.
=====================

To install PostGIS, type

make install

This will copy the PostGIS installation files into their appropriate subdirectory specified by the --prefix configuration parameter. In particular:

If you previously ran the make comments command to generate the postgis_comments.sql, raster_comments.sql file, install the sql file by running

make comments-install

	postgis_comments.sql, raster_comments.sql, topology_comments.sql was separated from the typical build and installation targets since with it comes the extra dependency of xsltproc.

One of the many complaints of folks is the address normalizer function Normalize_Address function that normalizes an address for prepping before geocoding. The normalizer is far from perfect and trying to patch its imperfectness takes a vast amount of resources. As such we have integrated with another project that has a much better address standardizer engine. To use this new address_standardizer, you compile the extension as described in Section 2.3, “Installing and Using the address standardizer” and install as an extension in your database.

Once you install this extension in the same database as you have installed postgis_tiger_geocoder, then the Pagc_Normalize_Address can be used instead of Normalize_Address. This extension is tiger agnostic, so can be used with other data sources such as international addresses. The tiger geocoder extension does come packaged with its own custom versions of rules table ( tiger.pagc_rules) , gaz table (tiger.pagc_gaz), and lex table (tiger.pagc_lex). These you can add and update to improve your standardizing experience for your own needs.

The load process downloads data from the census website for the respective nation files, states requested, extracts the files, and then loads each state into its own separate set of state tables. Each state table inherits from the tables defined in tiger schema so that its sufficient to just query those tables to access all the data and drop a set of state tables at any time using the Drop_State_Tables_Generate_Script if you need to reload a state or just don't need a state anymore.

In order to be able to load data you'll need the following tools:

If you are upgrading from tiger_2010, you'll need to first generate and run Drop_Nation_Tables_Generate_Script. Before you load any state data, you need to load the nation wide data which you do with Loader_Generate_Nation_Script. Which will generate a loader script for you. Loader_Generate_Nation_Script is a one-time step that should be done for upgrading (from a prior year tiger census data) and for new installs.

To load state data refer to Loader_Generate_Script to generate a data load script for your platform for the states you desire. Note that you can install these piecemeal. You don't have to load all the states you want all at once. You can load them as you need them.

After the states you desire have been loaded, make sure to run the:

SELECT install_missing_indexes();

as described in Install_Missing_Indexes.

To test that things are working as they should, try to run a geocode on an address in your state using Geocode

First upgrade your postgis_tiger_geocoder extension as follows:

ALTER EXTENSION postgis_tiger_geocoder UPDATE;

Next drop all nation tables and load up the new ones. Generate a drop script with this SQL statement as detailed in Drop_Nation_Tables_Generate_Script

SELECT drop_nation_tables_generate_script();

Run the generated drop SQL statements.

Generate a nation load script with this SELECT statement as detailed in Loader_Generate_Nation_Script

For windows

SELECT loader_generate_nation_script('windows'); 

For unix/linux

SELECT loader_generate_nation_script('sh');

Refer to Section 2.4.1, “Tiger Geocoder Enabling your PostGIS database” for instructions on how to run the generate script. This only needs to be done once.

	You can have a mix of different year state tables and can upgrade each state separately. Before you upgrade a state you first need to drop the prior year state tables for that state using Drop_State_Tables_Generate_Script.

These settings are configured in postgresql.conf:

constraint_exclusion

shared_buffers

max_worker_processes This setting is only available for PostgreSQL 9.4+. For PostgreSQL 9.6+ this setting has additional importance in that it controls the max number of processes you can have for parallel queries.

work_mem - sets the size of memory used for sort operations and complex queries

maintenance_work_mem - the memory size used for VACUUM, CREATE INDEX, etc.

max_parallel_workers_per_gather

This setting is only available for PostgreSQL 9.6+ and will only affect PostGIS 2.3+, since only PostGIS 2.3+ supports parallel queries. If set to higher than 0, then some queries such as those involving relation functions like ST_Intersects can use multiple processes and can run more than twice as fast when doing so. If you have a lot of processors to spare, you should change the value of this to as many processors as you have. Also make sure to bump up max_worker_processes to at least as high as this number.

If you are using PostgreSQL 9.1+ and have compiled and installed the extensions/postgis modules, you can turn a database into a spatial one using the EXTENSION mechanism.

Core postgis extension includes geometry, geography, spatial_ref_sys and all the functions and comments. Raster and topology are packaged as a separate extension.

Run the following SQL snippet in the database you want to enable spatially:

      CREATE EXTENSION IF NOT EXISTS plpgsql;
      CREATE EXTENSION postgis;
      CREATE EXTENSION postgis_raster; -- OPTIONAL
      CREATE EXTENSION postgis_topology; -- OPTIONAL

	This is generally only needed if you cannot or don't want to get PostGIS installed in the PostgreSQL extension directory (for example during testing, development or in a restricted environment).

Adding PostGIS objects and function definitions into your database is done by loading the various sql files located in [prefix]/share/contrib as specified during the build phase.

The core PostGIS objects (geometry and geography types, and their support functions) are in the postgis.sql script. Raster objects are in the rtpostgis.sql script. Topology objects are in the topology.sql script.

For a complete set of EPSG coordinate system definition identifiers, you can also load the spatial_ref_sys.sql definitions file and populate the spatial_ref_sys table. This will permit you to perform ST_Transform() operations on geometries.

If you wish to add comments to the PostGIS functions, you can find them in the postgis_comments.sql script. Comments can be viewed by simply typing \dd [function_name] from a psql terminal window.

Run the following Shell commands in your terminal:

    DB=[yourdatabase]
    SCRIPTSDIR=`pg_config --sharedir`/contrib/postgis-3.3/

    # Core objects
    psql -d ${DB} -f ${SCRIPTSDIR}/postgis.sql
    psql -d ${DB} -f ${SCRIPTSDIR}/spatial_ref_sys.sql
    psql -d ${DB} -f ${SCRIPTSDIR}/postgis_comments.sql # OPTIONAL

    # Raster support (OPTIONAL)
    psql -d ${DB} -f ${SCRIPTSDIR}/rtpostgis.sql
    psql -d ${DB} -f ${SCRIPTSDIR}/raster_comments.sql # OPTIONAL

    # Topology support (OPTIONAL)
    psql -d ${DB} -f ${SCRIPTSDIR}/topology.sql
    psql -d ${DB} -f ${SCRIPTSDIR}/topology_comments.sql # OPTIONAL

If you installed your database using extensions, you'll need to upgrade using the extension model as well. If you installed using the old sql script way, you are advised to switch your install to extensions because the script way is no longer supported.

3.4.1.1. Soft Upgrade 9.1+ using extensions

If you originally installed PostGIS with extensions, then you need to upgrade using extensions as well. Doing a minor upgrade with extensions, is fairly painless.

If you are running PostGIS 3 or above, then you should use the PostGIS_Extensions_Upgrade function to upgrade to the latest version you have installed.

SELECT postgis_extensions_upgrade();

If you are running PostGIS 2.5 or lower, then do the following:

ALTER EXTENSION postgis UPDATE;
SELECT postgis_extensions_upgrade();
-- This second call is needed to rebundle postgis_raster extension
SELECT postgis_extensions_upgrade();

If you have multiple versions of PostGIS installed, and you don't want to upgrade to the latest, you can explicitly specify the version as follows:

ALTER EXTENSION postgis UPDATE TO "3.4.4dev";
ALTER EXTENSION postgis_topology UPDATE TO "3.4.4dev";

If you get an error notice something like:

No migration path defined for … to 3.4.4dev

Then you'll need to backup your database, create a fresh one as described in Section 3.3.1, “Spatially enable database using EXTENSION” and then restore your backup on top of this new database.

If you get a notice message like:

Version "3.4.4dev" of extension "postgis" is already installed

Then everything is already up to date and you can safely ignore it. UNLESS you're attempting to upgrade from an development version to the next (which doesn't get a new version number); in that case you can append "next" to the version string, and next time you'll need to drop the "next" suffix again:

ALTER EXTENSION postgis UPDATE TO "3.4.4devnext";
ALTER EXTENSION postgis_topology UPDATE TO "3.4.4devnext";

	If you installed PostGIS originally without a version specified, you can often skip the reinstallation of postgis extension before restoring since the backup just has CREATE EXTENSION postgis and thus picks up the newest latest version during restore.

	If you are upgrading PostGIS extension from a version prior to 3.0.0, you will have a new extension postgis_raster which you can safely drop, if you don't need raster support. You can drop as follows: DROP EXTENSION postgis_raster;

3.4.1.2. Soft Upgrade Pre 9.1+ or without extensions

This section applies only to those who installed PostGIS not using extensions. If you have extensions and try to upgrade with this approach you'll get messages like:

can't drop … because postgis extension depends on it

NOTE: if you are moving from PostGIS 1.* to PostGIS 2.* or from PostGIS 2.* prior to r7409, you cannot use this procedure but would rather need to do a HARD UPGRADE.

After compiling and installing (make install) you should find a set of *_upgrade.sql files in the installation folders. You can list them all with:

ls `pg_config --sharedir`/contrib/postgis-3.4.4dev/*_upgrade.sql

Load them all in turn, starting from postgis_upgrade.sql.

psql -f postgis_upgrade.sql -d your_spatial_database

The same procedure applies to raster, topology and sfcgal extensions, with upgrade files named rtpostgis_upgrade.sql, topology_upgrade.sql and sfcgal_upgrade.sql respectively. If you need them:

psql -f rtpostgis_upgrade.sql -d your_spatial_database

psql -f topology_upgrade.sql -d your_spatial_database

psql -f sfcgal_upgrade.sql -d your_spatial_database

You are advised to switch to an extension based install by running

psql -c "SELECT postgis_extensions_upgrade();"

	If you can't find the postgis_upgrade.sql specific for upgrading your version you are using a version too early for a soft upgrade and need to do a HARD UPGRADE.

The PostGIS_Full_Version function should inform you about the need to run this kind of upgrade using a "procs need upgrade" message.

By HARD UPGRADE we mean full dump/reload of postgis-enabled databases. You need a HARD UPGRADE when PostGIS objects' internal storage changes or when SOFT UPGRADE is not possible. The Release Notes appendix reports for each version whether you need a dump/reload (HARD UPGRADE) to upgrade.

The dump/reload process is assisted by the postgis_restore script which takes care of skipping from the dump all definitions which belong to PostGIS (including old ones), allowing you to restore your schemas and data into a database with PostGIS installed without getting duplicate symbol errors or bringing forward deprecated objects.

Supplementary instructions for windows users are available at Windows Hard upgrade.

The Procedure is as follows:

Errors may arise in the following cases:

The Open Geospatial Consortium (OGC) developed the Simple Features Access standard (SFA) to provide a model for geospatial data. It defines the fundamental spatial type of Geometry, along with operations which manipulate and transform geometry values to perform spatial analysis tasks. PostGIS implements the OGC Geometry model as the PostgreSQL data types geometry and geography.

Geometry is an abstract type. Geometry values belong to one of its concrete subtypes which represent various kinds and dimensions of geometric shapes. These include the atomic types Point, LineString, LinearRing and Polygon, and the collection types MultiPoint, MultiLineString, MultiPolygon and GeometryCollection. The Simple Features Access - Part 1: Common architecture v1.2.1 adds subtypes for the structures PolyhedralSurface, Triangle and TIN.

Geometry models shapes in the 2-dimensional Cartesian plane. The PolyhedralSurface, Triangle, and TIN types can also represent shapes in 3-dimensional space. The size and location of shapes are specified by their coordinates. Each coordinate has a X and Y ordinate value determining its location in the plane. Shapes are constructed from points or line segments, with points specified by a single coordinate, and line segments by two coordinates.

Coordinates may contain optional Z and M ordinate values. The Z ordinate is often used to represent elevation. The M ordinate contains a measure value, which may represent time or distance. If Z or M values are present in a geometry value, they must be defined for each point in the geometry. If a geometry has Z or M ordinates the coordinate dimension is 3D; if it has both Z and M the coordinate dimension is 4D.

Geometry values are associated with a spatial reference system indicating the coordinate system in which it is embedded. The spatial reference system is identified by the geometry SRID number. The units of the X and Y axes are determined by the spatial reference system. In planar reference systems the X and Y coordinates typically represent easting and northing, while in geodetic systems they represent longitude and latitude. SRID 0 represents an infinite Cartesian plane with no units assigned to its axes. See Section 4.5, “Spatial Reference Systems”.

The geometry dimension is a property of geometry types. Point types have dimension 0, linear types have dimension 1, and polygonal types have dimension 2. Collections have the dimension of the maximum element dimension.

A geometry value may be empty. Empty values contain no vertices (for atomic geometry types) or no elements (for collections).

An important property of geometry values is their spatial extent or bounding box, which the OGC model calls envelope. This is the 2 or 3-dimensional box which encloses the coordinates of a geometry. It is an efficient way to represent a geometry's extent in coordinate space and to check whether two geometries interact.

The geometry model allows evaluating topological spatial relationships as described in Section 5.1.1, “Dimensionally Extended 9-Intersection Model”. To support this the concepts of interior, boundary and exterior are defined for each geometry type. Geometries are topologically closed, so they always contain their boundary. The boundary is a geometry of dimension one less than that of the geometry itself.

The OGC geometry model defines validity rules for each geometry type. These rules ensure that geometry values represents realistic situations (e.g. it is possible to specify a polygon with a hole lying outside the shell, but this makes no sense geometrically and is thus invalid). PostGIS also allows storing and manipulating invalid geometry values. This allows detecting and fixing them if needed. See Section 4.4, “Geometry Validation”

POINT (1 2)
POINT Z (1 2 3)
POINT ZM (1 2 3 4)

LINESTRING (1 2, 3 4, 5 6)

LINEARRING (0 0 0, 4 0 0, 4 4 0, 0 4 0, 0 0 0)

POLYGON ((0 0 0,4 0 0,4 4 0,0 4 0,0 0 0),(1 1 0,2 1 0,2 2 0,1 2 0,1 1 0))

MULTIPOINT ( (0 0), (1 2) )

MULTILINESTRING ( (0 0,1 1,1 2), (2 3,3 2,5 4) )

MULTIPOLYGON (((1 5, 5 5, 5 1, 1 1, 1 5)), ((6 5, 9 1, 6 1, 6 5)))

GEOMETRYCOLLECTION ( POINT(2 3), LINESTRING(2 3, 3 4))

POLYHEDRALSURFACE Z (
  ((0 0 0, 0 0 1, 0 1 1, 0 1 0, 0 0 0)),
  ((0 0 0, 0 1 0, 1 1 0, 1 0 0, 0 0 0)),
  ((0 0 0, 1 0 0, 1 0 1, 0 0 1, 0 0 0)),
  ((1 1 0, 1 1 1, 1 0 1, 1 0 0, 1 1 0)),
  ((0 1 0, 0 1 1, 1 1 1, 1 1 0, 0 1 0)),
  ((0 0 1, 1 0 1, 1 1 1, 0 1 1, 0 0 1)) )

TRIANGLE ((0 0, 0 9, 9 0, 0 0))

TIN Z ( ((0 0 0, 0 0 1, 0 1 0, 0 0 0)), ((0 0 0, 0 1 0, 1 1 0, 0 0 0)) )

The ISO/IEC 13249-3 SQL Multimedia - Spatial standard (SQL/MM) extends the OGC SFA to define Geometry subtypes containing curves with circular arcs. The SQL/MM types support 3DM, 3DZ and 4D coordinates.

	All floating point comparisons within the SQL-MM implementation are performed to a specified tolerance, currently 1E-8.

CIRCULARSTRING(0 0, 1 1, 1 0)

CIRCULARSTRING(0 0, 4 0, 4 4, 0 4, 0 0)

COMPOUNDCURVE( CIRCULARSTRING(0 0, 1 1, 1 0),(1 0, 0 1))

CURVEPOLYGON(
  CIRCULARSTRING(0 0, 4 0, 4 4, 0 4, 0 0),
  (1 1, 3 3, 3 1, 1 1) )

CURVEPOLYGON(
  COMPOUNDCURVE( CIRCULARSTRING(0 0,2 0, 2 1, 2 3, 4 3),
                 (4 3, 4 5, 1 4, 0 0)),
  CIRCULARSTRING(1.7 1, 1.4 0.4, 1.6 0.4, 1.6 0.5, 1.7 1) )

MULTICURVE( (0 0, 5 5), CIRCULARSTRING(4 0, 4 4, 8 4))

MULTISURFACE(
  CURVEPOLYGON(
    CIRCULARSTRING( 0 0, 4 0, 4 4, 0 4, 0 0),
    (1 1, 3 3, 3 1, 1 1)),
  ((10 10, 14 12, 11 10, 10 10), (11 11, 11.5 11, 11 11.5, 11 11)))

The OGC SFA specification defines two formats for representing geometry values for external use: Well-Known Text (WKT) and Well-Known Binary (WKB). Both WKT and WKB include information about the type of the object and the coordinates which define it.

Well-Known Text (WKT) provides a standard textual representation of spatial data. Examples of WKT representations of spatial objects are:

Input and output of WKT is provided by the functions ST_AsText and ST_GeomFromText:

text WKT = ST_AsText(geometry);
geometry = ST_GeomFromText(text WKT, SRID);

For example, a statement to create and insert a spatial object from WKT and a SRID is:

INSERT INTO geotable ( geom, name )
  VALUES ( ST_GeomFromText('POINT(-126.4 45.32)', 312), 'A Place');

Well-Known Binary (WKB) provides a portable, full-precision representation of spatial data as binary data (arrays of bytes). Examples of the WKB representations of spatial objects are:

Input and output of WKB is provided by the functions ST_AsBinary and ST_GeomFromWKB:

bytea WKB = ST_AsBinary(geometry);
geometry = ST_GeomFromWKB(bytea WKB, SRID);

For example, a statement to create and insert a spatial object from WKB is:

INSERT INTO geotable ( geom, name )
  VALUES ( ST_GeomFromWKB('\x0101000000000000000000f03f000000000000f03f', 312), 'A Place');

OGC SFA specifications initially supported only 2D geometries, and the geometry SRID is not included in the input/output representations. The OGC SFA specification 1.2.1 (which aligns with the ISO 19125 standard) adds support for 3D (ZYZ) and measured (XYM and XYZM) coordinates, but still does not include the SRID value.

Because of these limitations PostGIS defined extended EWKB and EWKT formats. They provide 3D (XYZ and XYM) and 4D (XYZM) coordinate support and include SRID information. Including all geometry information allows PostGIS to use EWKB as the format of record (e.g. in DUMP files).

EWKB and EWKT are used for the "canonical forms" of PostGIS data objects. For input, the canonical form for binary data is EWKB, and for text data either EWKB or EWKT is accepted. This allows geometry values to be created by casting a text value in either HEXEWKB or EWKT to a geometry value using ::geometry. For output, the canonical form for binary is EWKB, and for text it is HEXEWKB (hex-encoded EWKB).

For example this statement creates a geometry by casting from an EWKT text value, and outputs it using the canonical form of HEXEWKB:

SELECT 'SRID=4;POINT(0 0)'::geometry;
  geometry
  ----------------------------------------------------
  01010000200400000000000000000000000000000000000000

PostGIS EWKT output has a few differences to OGC WKT:

EWKT avoids over-specifying dimensionality and the inconsistencies that can occur with the OGC/ISO format, such as:

	PostGIS extended formats are currently a superset of the OGC ones, so that every valid OGC WKB/WKT is also valid EWKB/EWKT. However, this might vary in the future, if the OGC extends a format in a way that conflicts with the PosGIS definition. Thus you SHOULD NOT rely on this compatibility!

Examples of the EWKT text representation of spatial objects are:

Input and output using these formats is available using the following functions:

bytea EWKB = ST_AsEWKB(geometry);
text EWKT = ST_AsEWKT(geometry);
geometry = ST_GeomFromEWKB(bytea EWKB);
geometry = ST_GeomFromEWKT(text EWKT);

For example, a statement to create and insert a PostGIS spatial object using EWKT is:

INSERT INTO geotable ( geom, name )
  VALUES ( ST_GeomFromEWKT('SRID=312;POINTM(-126.4 45.32 15)'), 'A Place' )

You can create a table to store geography data using the CREATE TABLE SQL statement with a column of type geography. The following example creates a table with a geography column storing 2D LineStrings in the WGS84 geodetic coordinate system (SRID 4326):

CREATE TABLE global_points (
    id SERIAL PRIMARY KEY,
    name VARCHAR(64),
    location geography(POINT,4326)
  );

The geography type supports two optional type modifiers:

Examples of creating tables with geography columns:

Geography fields are registered in the geography_columns system view. You can query the geography_columns view and see that the table is listed:

SELECT * FROM geography_columns;

Creating a spatial index works the same as for geometry columns. PostGIS will note that the column type is GEOGRAPHY and create an appropriate sphere-based index instead of the usual planar index used for GEOMETRY.

-- Index the test table with a spherical index
CREATE INDEX global_points_gix ON global_points USING GIST ( location );

You can insert data into geography tables in the same way as geometry. Geometry data will autocast to the geography type if it has SRID 4326. The EWKT and EWKB formats can also be used to specify geography values.

-- Add some data into the test table
INSERT INTO global_points (name, location) VALUES ('Town', 'SRID=4326;POINT(-110 30)');
INSERT INTO global_points (name, location) VALUES ('Forest', 'SRID=4326;POINT(-109 29)');
INSERT INTO global_points (name, location) VALUES ('London', 'SRID=4326;POINT(0 49)');

Any geodetic (long/lat) spatial reference system listed in spatial_ref_sys table may be specified as a geography SRID. Non-geodetic coordinate systems raise an error if used.

-- NAD 83 lon/lat
SELECT 'SRID=4269;POINT(-123 34)'::geography;
                    geography
----------------------------------------------------
 0101000020AD1000000000000000C05EC00000000000004140

-- NAD27 lon/lat
SELECT 'SRID=4267;POINT(-123 34)'::geography;
                    geography
----------------------------------------------------
 0101000020AB1000000000000000C05EC00000000000004140

-- NAD83 UTM zone meters - gives an error since it is a meter-based planar projection
SELECT 'SRID=26910;POINT(-123 34)'::geography;

ERROR:  Only lon/lat coordinate systems are supported in geography.

Query and measurement functions use units of meters. So distance parameters should be expressed in meters, and return values should be expected in meters (or square meters for areas).

-- A distance query using a 1000km tolerance
SELECT name FROM global_points WHERE ST_DWithin(location, 'SRID=4326;POINT(-110 29)'::geography, 1000000);

You can see the power of geography in action by calculating how close a plane flying a great circle route from Seattle to London (LINESTRING(-122.33 47.606, 0.0 51.5)) comes to Reykjavik (POINT(-21.96 64.15)) (map the route).

The geography type calculates the true shortest distance of 122.235 km over the sphere between Reykjavik and the great circle flight path between Seattle and London.

-- Distance calculation using GEOGRAPHY
SELECT ST_Distance('LINESTRING(-122.33 47.606, 0.0 51.5)'::geography, 'POINT(-21.96 64.15)'::geography);
   st_distance
-----------------
 122235.23815667

The geometry type calculates a meaningless cartesian distance between Reykjavik and the straight line path from Seattle to London plotted on a flat map of the world. The nominal units of the result is "degrees", but the result doesn't correspond to any true angular difference between the points, so even calling them "degrees" is inaccurate.

-- Distance calculation using GEOMETRY
SELECT ST_Distance('LINESTRING(-122.33 47.606, 0.0 51.5)'::geometry, 'POINT(-21.96 64.15)'::geometry);
      st_distance
--------------------
 13.342271221453624

The geography data type allows you to store data in longitude/latitude coordinates, but at a cost: there are fewer functions defined on GEOGRAPHY than there are on GEOMETRY; those functions that are defined take more CPU time to execute.

The data type you choose should be determined by the expected working area of the application you are building. Will your data span the globe or a large continental area, or is it local to a state, county or municipality?

Refer to Section 12.11, “PostGIS Function Support Matrix” for compare between what is supported for Geography vs. Geometry. For a brief listing and description of Geography functions, refer to Section 12.4, “PostGIS Geography Support Functions”

A simple geometry is one that has no anomalous geometric points, such as self intersection or self tangency.

A POINT is inherently simple as a 0-dimensional geometry object.

MULTIPOINTs are simple if no two coordinates (POINTs) are equal (have identical coordinate values).

A LINESTRING is simple if it does not pass through the same point twice, except for the endpoints. If the endpoints of a simple LineString are identical it is called closed and referred to as a Linear Ring.

(a) and (c) are simple LINESTRINGs. (b) and (d) are not simple. (c) is a closed Linear Ring.

(a)	(b)
(c)	(d)

A MULTILINESTRING is simple only if all of its elements are simple and the only intersection between any two elements occurs at points that are on the boundaries of both elements.

(e) and (f) are simple MULTILINESTRINGs. (g) is not simple.

(e)

(f)

(g)

POLYGONs are formed from linear rings, so valid polygonal geometry is always simple.

To test if a geometry is simple use the ST_IsSimple function:

SELECT
   ST_IsSimple('LINESTRING(0 0, 100 100)') AS straight,
   ST_IsSimple('LINESTRING(0 0, 100 100, 100 0, 0 100)') AS crossing;

 straight | crossing
----------+----------
 t        | f

Generally, PostGIS functions do not require geometric arguments to be simple. Simplicity is primarily used as a basis for defining geometric validity. It is also a requirement for some kinds of spatial data models (for example, linear networks often disallow lines that cross). Multipoint and linear geometry can be made simple using ST_UnaryUnion.

Geometry validity primarily applies to 2-dimensional geometries (POLYGONs and MULTIPOLYGONs) . Validity is defined by rules that allow polygonal geometry to model planar areas unambiguously.

A POLYGON is valid if:

(h) and (i) are valid POLYGONs. (j-m) are invalid. (j) can be represented as a valid MULTIPOLYGON.

(h)	(i)	(j)
(k)	(l)	(m)

A MULTIPOLYGON is valid if:

(n) is a valid MULTIPOLYGON. (o) and (p) are invalid.

(n)

(o)

(p)

These rules mean that valid polygonal geometry is also simple.

For linear geometry the only validity rule is that LINESTRINGs must have at least two points and have non-zero length (or equivalently, have at least two distinct points.) Note that non-simple (self-intersecting) lines are valid.

SELECT
   ST_IsValid('LINESTRING(0 0, 1 1)') AS len_nonzero,
   ST_IsValid('LINESTRING(0 0, 0 0, 0 0)') AS len_zero,
   ST_IsValid('LINESTRING(10 10, 150 150, 180 50, 20 130)') AS self_int;

 len_nonzero | len_zero | self_int
-------------+----------+----------
 t           | f        | t

POINT and MULTIPOINT geometries have no validity rules.

PostGIS allows creating and storing both valid and invalid Geometry. This allows invalid geometry to be detected and flagged or fixed. There are also situations where the OGC validity rules are stricter than desired (examples of this are zero-length linestrings and polygons with inverted holes.)

Many of the functions provided by PostGIS rely on the assumption that geometry arguments are valid. For example, it does not make sense to calculate the area of a polygon that has a hole defined outside of the polygon, or to construct a polygon from a non-simple boundary line. Assuming valid geometric inputs allows functions to operate more efficiently, since they do not need to check for topological correctness. (Notable exceptions are that zero-length lines and polygons with inversions are generally handled correctly.) Also, most PostGIS functions produce valid geometry output if the inputs are valid. This allows PostGIS functions to be chained together safely.

If you encounter unexpected error messages when calling PostGIS functions (such as "GEOS Intersection() threw an error!"), you should first confirm that the function arguments are valid. If they are not, then consider using one of the techniques below to ensure the data you are processing is valid.

	If a function reports an error with valid inputs, then you may have found an error in either PostGIS or one of the libraries it uses, and you should report this to the PostGIS project. The same is true if a PostGIS function returns an invalid geometry for valid input.

To test if a geometry is valid use the ST_IsValid function:

SELECT ST_IsValid('POLYGON ((20 180, 180 180, 180 20, 20 20, 20 180))');
-----------------
 t

Information about the nature and location of an geometry invalidity are provided by the ST_IsValidDetail function:

SELECT valid, reason, ST_AsText(location) AS location
    FROM ST_IsValidDetail('POLYGON ((20 20, 120 190, 50 190, 170 50, 20 20))') AS t;

 valid |      reason       |                  location
-------+-------------------+---------------------------------------------
 f     | Self-intersection | POINT(91.51162790697674 141.56976744186045)

In some situations it is desirable to correct invalid geometry automatically. Use the ST_MakeValid function to do this. (ST_MakeValid is a case of a spatial function that does allow invalid input!)

By default, PostGIS does not check for validity when loading geometry, because validity testing can take a lot of CPU time for complex geometries. If you do not trust your data sources, you can enforce a validity check on your tables by adding a check constraint:

ALTER TABLE mytable
  ADD CONSTRAINT geometry_valid_check
	CHECK (ST_IsValid(geom));

The SPATIAL_REF_SYS table used by PostGIS is an OGC-compliant database table that defines the available spatial reference systems. It holds the numeric SRIDs and textual descriptions of the coordinate systems.

The spatial_ref_sys table definition is:

CREATE TABLE spatial_ref_sys (
  srid       INTEGER NOT NULL PRIMARY KEY,
  auth_name  VARCHAR(256),
  auth_srid  INTEGER,
  srtext     VARCHAR(2048),
  proj4text  VARCHAR(2048)
)

The columns are:

When retrieving spatial reference system definitions for use in transformations, PostGIS uses fhe following strategy:

The PostGIS spatial_ref_sys table contains over 3000 of the most common spatial reference system definitions that are handled by the PROJ projection library. But there are many coordinate systems that it does not contain. You can add SRS definitions to the table if you have the required information about the spatial reference system. Or, you can define your own custom spatial reference system if you are familiar with PROJ constructs. Keep in mind that most spatial reference systems are regional and have no meaning when used outside of the bounds they were intended for.

A resource for finding spatial reference systems not defined in the core set is http://spatialreference.org/

Some commonly used spatial reference systems are: 4326 - WGS 84 Long Lat, 4269 - NAD 83 Long Lat, 3395 - WGS 84 World Mercator, 2163 - US National Atlas Equal Area, and the 60 WGS84 UTM zones. UTM zones are one of the most ideal for measurement, but only cover 6-degree regions. (To determine which UTM zone to use for your area of interest, see the utmzone PostGIS plpgsql helper function.)

US states use State Plane spatial reference systems (meter or feet based) - usually one or 2 exists per state. Most of the meter-based ones are in the core set, but many of the feet-based ones or ESRI-created ones will need to be copied from spatialreference.org.

You can even define non-Earth-based coordinate systems, such as Mars 2000 This Mars coordinate system is non-planar (it's in degrees spheroidal), but you can use it with the geography type to obtain length and proximity measurements in meters instead of degrees.

Here is an example of loading a custom coordinate system using an unassigned SRID and the PROJ definition for a US-centric Lambert Conformal projection:

INSERT INTO spatial_ref_sys (srid, proj4text)
VALUES ( 990000,
  '+proj=lcc  +lon_0=-95 +lat_0=25 +lat_1=25 +lat_2=25 +x_0=0 +y_0=0 +datum=WGS84 +units=m +no_defs'
);

You can create a table to store geometry data using the CREATE TABLE SQL statement with a column of type geometry. The following example creates a table with a geometry column storing 2D (XY) LineStrings in the BC-Albers coordinate system (SRID 3005):

CREATE TABLE roads (
    id SERIAL PRIMARY KEY,
    name VARCHAR(64),
    geom geometry(LINESTRING,3005)
  );

The geometry type supports two optional type modifiers:

Examples of creating tables with geometry columns:

It is possible to have more than one geometry column in a table. This can be specified when the table is created, or a column can be added using the ALTER TABLE SQL statement. This example adds a column that can hold 3D LineStrings:

ALTER TABLE roads ADD COLUMN geom2 geometry(LINESTRINGZ,4326);

The OGC Simple Features Specification for SQL defines the GEOMETRY_COLUMNS metadata table to describe geometry table structure. In PostGIS geometry_columns is a view reading from database system catalog tables. This ensures that the spatial metadata information is always consistent with the currently defined tables and views. The view structure is:

\d geometry_columns

             View "public.geometry_columns"
      Column       |          Type          | Modifiers
-------------------+------------------------+-----------
 f_table_catalog   | character varying(256) |
 f_table_schema    | character varying(256) |
 f_table_name      | character varying(256) |
 f_geometry_column | character varying(256) |
 coord_dimension   | integer                |
 srid              | integer                |
 type              | character varying(30)  |

The columns are:

Two of the cases where you may need this are the case of SQL Views and bulk inserts. For bulk insert case, you can correct the registration in the geometry_columns table by constraining the column or doing an alter table. For views, you could expose using a CAST operation. Note, if your column is typmod based, the creation process would register it correctly, so no need to do anything. Also views that have no spatial function applied to the geometry will register the same as the underlying table geometry column.

-- Lets say you have a view created like this
CREATE VIEW public.vwmytablemercator AS
	SELECT gid, ST_Transform(geom, 3395) As geom, f_name
	FROM public.mytable;

-- For it to register correctly
-- You need to cast the geometry
--
DROP VIEW public.vwmytablemercator;
CREATE VIEW  public.vwmytablemercator AS
	SELECT gid, ST_Transform(geom, 3395)::geometry(Geometry, 3395) As geom, f_name
	FROM public.mytable;

-- If you know the geometry type for sure is a 2D POLYGON then you could do
DROP VIEW public.vwmytablemercator;
CREATE VIEW  public.vwmytablemercator AS
	SELECT gid, ST_Transform(geom,3395)::geometry(Polygon, 3395) As geom, f_name
	FROM public.mytable;

--Lets say you created a derivative table by doing a bulk insert
SELECT poi.gid, poi.geom, citybounds.city_name
INTO myschema.my_special_pois
FROM poi INNER JOIN citybounds ON ST_Intersects(citybounds.geom, poi.geom);

-- Create 2D index on new table
CREATE INDEX idx_myschema_myspecialpois_geom_gist
  ON myschema.my_special_pois USING gist(geom);

-- If your points are 3D points or 3M points,
-- then you might want to create an nd index instead of a 2D index
CREATE INDEX my_special_pois_geom_gist_nd
	ON my_special_pois USING gist(geom gist_geometry_ops_nd);

-- To manually register this new table's geometry column in geometry_columns.
-- Note it will also change the underlying structure of the table to
-- to make the column typmod based.
SELECT populate_geometry_columns('myschema.my_special_pois'::regclass);

-- If you are using PostGIS 2.0 and for whatever reason, you
-- you need the constraint based definition behavior
-- (such as case of inherited tables where all children do not have the same type and srid)
-- set optional use_typmod argument to false
SELECT populate_geometry_columns('myschema.my_special_pois'::regclass, false); 

Although the old-constraint based method is still supported, a constraint-based geometry column used directly in a view, will not register correctly in geometry_columns, as will a typmod one. In this example we define a column using typmod and another using constraints.

CREATE TABLE pois_ny(gid SERIAL PRIMARY KEY, poi_name text, cat text, geom geometry(POINT,4326));
SELECT AddGeometryColumn('pois_ny', 'geom_2160', 2160, 'POINT', 2, false);

If we run in psql

\d pois_ny;

We observe they are defined differently -- one is typmod, one is constraint

                                  Table "public.pois_ny"
  Column   |         Type          |                       Modifiers

-----------+-----------------------+------------------------------------------------------
 gid       | integer               | not null default nextval('pois_ny_gid_seq'::regclass)
 poi_name  | text                  |
 cat       | character varying(20) |
 geom      | geometry(Point,4326)  |
 geom_2160 | geometry              |
Indexes:
    "pois_ny_pkey" PRIMARY KEY, btree (gid)
Check constraints:
    "enforce_dims_geom_2160" CHECK (st_ndims(geom_2160) = 2)
    "enforce_geotype_geom_2160" CHECK (geometrytype(geom_2160) = 'POINT'::text
        OR geom_2160 IS NULL)
    "enforce_srid_geom_2160" CHECK (st_srid(geom_2160) = 2160)

In geometry_columns, they both register correctly

SELECT f_table_name, f_geometry_column, srid, type
	FROM geometry_columns
	WHERE f_table_name = 'pois_ny';

f_table_name | f_geometry_column | srid | type
-------------+-------------------+------+-------
pois_ny      | geom              | 4326 | POINT
pois_ny      | geom_2160         | 2160 | POINT

However -- if we were to create a view like this

CREATE VIEW vw_pois_ny_parks AS
SELECT *
  FROM pois_ny
  WHERE cat='park';

SELECT f_table_name, f_geometry_column, srid, type
	FROM geometry_columns
	WHERE f_table_name = 'vw_pois_ny_parks';

The typmod based geom view column registers correctly, but the constraint based one does not.

   f_table_name   | f_geometry_column | srid |   type
------------------+-------------------+------+----------
 vw_pois_ny_parks | geom              | 4326 | POINT
 vw_pois_ny_parks | geom_2160         |    0 | GEOMETRY

This may change in future versions of PostGIS, but for now to force the constraint-based view column to register correctly, you need to do this:

DROP VIEW vw_pois_ny_parks;
CREATE VIEW vw_pois_ny_parks AS
SELECT gid, poi_name, cat,
  geom,
  geom_2160::geometry(POINT,2160) As geom_2160
  FROM pois_ny
  WHERE cat = 'park';
SELECT f_table_name, f_geometry_column, srid, type
	FROM geometry_columns
	WHERE f_table_name = 'vw_pois_ny_parks';

   f_table_name   | f_geometry_column | srid | type
------------------+-------------------+------+-------
 vw_pois_ny_parks | geom              | 4326 | POINT
 vw_pois_ny_parks | geom_2160         | 2160 | POINT

If spatial data can be converted to a text representation (as either WKT or WKB), then using SQL might be the easiest way to get data into PostGIS. Data can be bulk-loaded into PostGIS/PostgreSQL by loading a text file of SQL INSERT statements using the psql SQL utility.

A SQL load file (roads.sql for example) might look like this:

BEGIN;
INSERT INTO roads (road_id, roads_geom, road_name)
  VALUES (1,'LINESTRING(191232 243118,191108 243242)','Jeff Rd');
INSERT INTO roads (road_id, roads_geom, road_name)
  VALUES (2,'LINESTRING(189141 244158,189265 244817)','Geordie Rd');
INSERT INTO roads (road_id, roads_geom, road_name)
  VALUES (3,'LINESTRING(192783 228138,192612 229814)','Paul St');
INSERT INTO roads (road_id, roads_geom, road_name)
  VALUES (4,'LINESTRING(189412 252431,189631 259122)','Graeme Ave');
INSERT INTO roads (road_id, roads_geom, road_name)
  VALUES (5,'LINESTRING(190131 224148,190871 228134)','Phil Tce');
INSERT INTO roads (road_id, roads_geom, road_name)
  VALUES (6,'LINESTRING(198231 263418,198213 268322)','Dave Cres');
COMMIT;

The SQL file can be loaded into PostgreSQL using psql:

psql -d [database] -f roads.sql

The shp2pgsql data loader converts Shapefiles into SQL suitable for insertion into a PostGIS/PostgreSQL database either in geometry or geography format. The loader has several operating modes selected by command line flags.

There is also a shp2pgsql-gui graphical interface with most of the options as the command-line loader. This may be easier to use for one-off non-scripted loading or if you are new to PostGIS. It can also be configured as a plugin to PgAdminIII.

An example session using the loader to create an input file and loading it might look like this:

# shp2pgsql -c -D -s 4269 -i -I shaperoads.shp myschema.roadstable > roads.sql
# psql -d roadsdb -f roads.sql

A conversion and load can be done in one step using UNIX pipes:

# shp2pgsql shaperoads.shp myschema.roadstable | psql -d roadsdb

The most straightforward way of extracting spatial data out of the database is to use a SQL SELECT query to define the data set to be extracted and dump the resulting columns into a parsable text file:

db=# SELECT road_id, ST_AsText(road_geom) AS geom, road_name FROM roads;

road_id | geom                                    | road_name
--------+-----------------------------------------+-----------
	  1 | LINESTRING(191232 243118,191108 243242) | Jeff Rd
	  2 | LINESTRING(189141 244158,189265 244817) | Geordie Rd
	  3 | LINESTRING(192783 228138,192612 229814) | Paul St
	  4 | LINESTRING(189412 252431,189631 259122) | Graeme Ave
	  5 | LINESTRING(190131 224148,190871 228134) | Phil Tce
	  6 | LINESTRING(198231 263418,198213 268322) | Dave Cres
	  7 | LINESTRING(218421 284121,224123 241231) | Chris Way
(6 rows)

There will be times when some kind of restriction is necessary to cut down the number of records returned. In the case of attribute-based restrictions, use the same SQL syntax as used with a non-spatial table. In the case of spatial restrictions, the following functions are useful:

Next, you can use these operators in queries. Note that when specifying geometries and boxes on the SQL command line, you must explicitly turn the string representations into geometries function. The 312 is a fictitious spatial reference system that matches our data. So, for example:

SELECT road_id, road_name
  FROM roads
  WHERE roads_geom='SRID=312;LINESTRING(191232 243118,191108 243242)'::geometry;

The above query would return the single record from the "ROADS_GEOM" table in which the geometry was equal to that value.

To check whether some of the roads passes in the area defined by a polygon:

SELECT road_id, road_name
FROM roads
WHERE ST_Intersects(roads_geom, 'SRID=312;POLYGON((...))');

The most common spatial query will probably be a "frame-based" query, used by client software, like data browsers and web mappers, to grab a "map frame" worth of data for display.

When using the "&&" operator, you can specify either a BOX3D as the comparison feature or a GEOMETRY. When you specify a GEOMETRY, however, its bounding box will be used for the comparison.

Using a "BOX3D" object for the frame, such a query looks like this:

SELECT ST_AsText(roads_geom) AS geom
FROM roads
WHERE
  roads_geom && ST_MakeEnvelope(191232, 243117,191232, 243119,312);

Note the use of the SRID 312, to specify the projection of the envelope.

The pgsql2shp table dumper connects to the database and converts a table (possibly defined by a query) into a shape file. The basic syntax is:

pgsql2shp [<options>] <database> [<schema>.]<table>

pgsql2shp [<options>] <database> <query>

The commandline options are:

GiST stands for "Generalized Search Tree" and is a generic form of indexing for multi-dimensional data. PostGIS uses an R-Tree index implemented on top of GiST to index spatial data. GiST is the most commonly-used and versatile spatial index method, and offers very good query performance. Other implementations of GiST are used to speed up searches on all kinds of irregular data structures (integer arrays, spectral data, etc) which are not amenable to normal B-Tree indexing. For more information see the PostgreSQL manual.

Once a spatial data table exceeds a few thousand rows, you will want to build an index to speed up spatial searches of the data (unless all your searches are based on attributes, in which case you'll want to build a normal index on the attribute fields).

The syntax for building a GiST index on a "geometry" column is as follows:

CREATE INDEX [indexname] ON [tablename] USING GIST ( [geometryfield] ); 

The above syntax will always build a 2D-index. To get the an n-dimensional index for the geometry type, you can create one using this syntax:

CREATE INDEX [indexname] ON [tablename] USING GIST ([geometryfield] gist_geometry_ops_nd);

Building a spatial index is a computationally intensive exercise. It also blocks write access to your table for the time it creates, so on a production system you may want to do in in a slower CONCURRENTLY-aware way:

CREATE INDEX CONCURRENTLY [indexname] ON [tablename] USING GIST ( [geometryfield] ); 

After building an index, it is sometimes helpful to force PostgreSQL to collect table statistics, which are used to optimize query plans:

VACUUM ANALYZE [table_name] [(column_name)];

BRIN stands for "Block Range Index". It is a general-purpose index method introduced in PostgreSQL 9.5. BRIN is a lossy index method, meaning that a secondary check is required to confirm that a record matches a given search condition (which is the case for all provided spatial indexes). It provides much faster index creation and much smaller index size, with reasonable read performance. Its primary purpose is to support indexing very large tables on columns which have a correlation with their physical location within the table. In addition to spatial indexing, BRIN can speed up searches on various kinds of attribute data structures (integer, arrays etc). For more information see the PostgreSQL manual.

Once a spatial table exceeds a few thousand rows, you will want to build an index to speed up spatial searches of the data. GiST indexes are very performant as long as their size doesn't exceed the amount of RAM available for the database, and as long as you can afford the index storage size, and the cost of index update on write. Otherwise, for very large tables BRIN index can be considered as an alternative.

A BRIN index stores the bounding box enclosing all the geometries contained in the rows in a contiguous set of table blocks, called a block range. When executing a query using the index the block ranges are scanned to find the ones that intersect the query extent. This is efficient only if the data is physically ordered so that the bounding boxes for block ranges have minimal overlap (and ideally are mutually exclusive). The resulting index is very small in size, but is typically less performant for read than a GiST index over the same data.

Building a BRIN index is much less CPU-intensive than building a GiST index. It's common to find that a BRIN index is ten times faster to build than a GiST index over the same data. And because a BRIN index stores only one bounding box for each range of table blocks, it's common to use up to a thousand times less disk space than a GiST index.

You can choose the number of blocks to summarize in a range. If you decrease this number, the index will be bigger but will probably provide better performance.

For BRIN to be effective, the table data should be stored in a physical order which minimizes the amount of block extent overlap. It may be that the data is already sorted appropriately (for instance, if it is loaded from another dataset that is already sorted in spatial order). Otherwise, this can be accomplished by sorting the data by a one-dimensional spatial key. One way to do this is to create a new table sorted by the geometry values (which in recent PostGIS versions uses an efficient Hilbert curve ordering):

CREATE TABLE table_sorted AS
   SELECT * FROM table  ORDER BY geom;

Alternatively, data can be sorted in-place by using a GeoHash as a (temporary) index, and clustering on that index:

CREATE INDEX idx_temp_geohash ON table
    USING btree (ST_GeoHash( ST_Transform( geom, 4326 ), 20));
CLUSTER table USING idx_temp_geohash;

The syntax for building a BRIN index on a geometry column is:

CREATE INDEX [indexname] ON [tablename] USING BRIN ( [geome_col] ); 

The above syntax builds a 2D index. To build a 3D-dimensional index, use this syntax:

CREATE INDEX [indexname] ON [tablename]
    USING BRIN ([geome_col] brin_geometry_inclusion_ops_3d);

You can also get a 4D-dimensional index using the 4D operator class:

CREATE INDEX [indexname] ON [tablename]
    USING BRIN ([geome_col] brin_geometry_inclusion_ops_4d);

The above commands use the default number of blocks in a range, which is 128. To specify the number of blocks to summarise in a range, use this syntax

CREATE INDEX [indexname] ON [tablename]
    USING BRIN ( [geome_col] ) WITH (pages_per_range = [number]); 

Keep in mind that a BRIN index only stores one index entry for a large number of rows. If your table stores geometries with a mixed number of dimensions, it's likely that the resulting index will have poor performance. You can avoid this performance penalty by choosing the operator class with the least number of dimensions of the stored geometries

The geography datatype is supported for BRIN indexing. The syntax for building a BRIN index on a geography column is:

CREATE INDEX [indexname] ON [tablename] USING BRIN ( [geog_col] ); 

The above syntax builds a 2D-index for geospatial objects on the spheroid.

Currently, only "inclusion support" is provided, meaning that just the &&, ~ and @ operators can be used for the 2D cases (for both geometry and geography), and just the &&& operator for 3D geometries. There is currently no support for kNN searches.

An important difference between BRIN and other index types is that the database does not maintain the index dynamically. Changes to spatial data in the table are simply appended to the end of the index. This will cause index search performance to degrade over time. The index can be updated by performing a VACUUM, or by using a special function brin_summarize_new_values(regclass). For this reason BRIN may be most appropriate for use with data that is read-only, or only rarely changing. For more information refer to the manual.

To summarize using BRIN for spatial data:

SP-GiST stands for "Space-Partitioned Generalized Search Tree" and is a generic form of indexing for multi-dimensional data types that supports partitioned search trees, such as quad-trees, k-d trees, and radix trees (tries). The common feature of these data structures is that they repeatedly divide the search space into partitions that need not be of equal size. In addition to spatial indexing, SP-GiST is used to speed up searches on many kinds of data, such as phone routing, ip routing, substring search, etc. For more information see the PostgreSQL manual.

As it is the case for GiST indexes, SP-GiST indexes are lossy, in the sense that they store the bounding box enclosing spatial objects. SP-GiST indexes can be considered as an alternative to GiST indexes.

Once a GIS data table exceeds a few thousand rows, an SP-GiST index may be used to speed up spatial searches of the data. The syntax for building an SP-GiST index on a "geometry" column is as follows:

CREATE INDEX [indexname] ON [tablename] USING SPGIST ( [geometryfield] ); 

The above syntax will build a 2-dimensional index. A 3-dimensional index for the geometry type can be created using the 3D operator class:

CREATE INDEX [indexname] ON [tablename] USING SPGIST ([geometryfield] spgist_geometry_ops_3d);

Building a spatial index is a computationally intensive operation. It also blocks write access to your table for the time it creates, so on a production system you may want to do in in a slower CONCURRENTLY-aware way:

CREATE INDEX CONCURRENTLY [indexname] ON [tablename] USING SPGIST ( [geometryfield] ); 

After building an index, it is sometimes helpful to force PostgreSQL to collect table statistics, which are used to optimize query plans:

VACUUM ANALYZE [table_name] [(column_name)];

An SP-GiST index can accelerate queries involving the following operators:

There is no support for kNN searches at the moment.

Ordinarily, indexes invisibly speed up data access: once an index is built, the PostgreSQL query planner automatically decides when to use it to improve query performance. But there are some situations where the planner does not choose to use existing indexes, so queries end up using slow sequential scans instead of a spatial index.

If you find your spatial indexes are not being used, there are a few things you can do:

For more information see the Postgres manual section on Query Planning.

According to the OpenGIS Simple Features Implementation Specification for SQL, "the basic approach to comparing two geometries is to make pair-wise tests of the intersections between the Interiors, Boundaries and Exteriors of the two geometries and to classify the relationship between the two geometries based on the entries in the resulting 'intersection' matrix."

In the theory of point-set topology, the points in a geometry embedded in 2-dimensional space are categorized into three sets:

The Dimensionally Extended 9-Intersection Model (DE-9IM) describes the spatial relationship between two geometries by specifying the dimensions of the 9 intersections between the above sets for each geometry. The intersection dimensions can be formally represented in a 3x3 intersection matrix.

For a geometry g the Interior, Boundary, and Exterior are denoted using the notation I(g), B(g), and E(g). Also, dim(s) denotes the dimension of a set s with the domain of {0,1,2,F}:

Using this notation, the intersection matrix for two geometries a and b is:

Visually, for two overlapping polygonal geometries, this looks like:

	Interior Boundary Exterior Interior dim( I(a) ∩ I(b) ) = 2 dim( I(a) ∩ B(b) = 1 dim( I(a) ∩ E(b) ) = 2 Boundary dim( B(a) ∩ I(b) ) = 1 dim( B(a) ∩ B(b) ) = 0 dim( B(a) ∩ E(b) ) = 1 Exterior dim( E(a) ∩ I(b) ) = 2 dim( E(a) ∩ B(b) ) = 1 dim( E(a) ∩ E(b) = 2

Reading from left to right and top to bottom, the intersection matrix is represented as the text string '212101212'.

For more information, refer to:

To make it easy to determine common spatial relationships, the OGC SFS defines a set of named spatial relationship predicates. PostGIS provides these as the functions ST_Contains, ST_Crosses, ST_Disjoint, ST_Equals, ST_Intersects, ST_Overlaps, ST_Touches, ST_Within. It also defines the non-standard relationship predicates ST_Covers, ST_CoveredBy, and ST_ContainsProperly.

Spatial predicates are usually used as conditions in SQL WHERE or JOIN clauses. The named spatial predicates automatically use a spatial index if one is available, so there is no need to use the bounding box operator && as well. For example:

SELECT city.name, state.name, city.geom
FROM city JOIN state ON ST_Intersects(city.geom, state.geom);

For more details and illustrations, see the PostGIS Workshop.

In some cases the named spatial relationships are insufficient to provide a desired spatial filter condition.

For example, consider a linear dataset representing a road network. It may be required to identify all road segments that cross each other, not at a point, but in a line (perhaps to validate some business rule). In this case ST_Crosses does not provide the necessary spatial filter, since for linear features it returns true only where they cross at a point. A two-step solution would be to first compute the actual intersection (ST_Intersection) of pairs of road lines that spatially intersect (ST_Intersects), and then check if the intersection's ST_GeometryType is 'LINESTRING' (properly dealing with cases that return GEOMETRYCOLLECTIONs of [MULTI]POINTs, [MULTI]LINESTRINGs, etc.). Clearly, a simpler and faster solution is desirable.

A second example is locating wharves that intersect a lake's boundary on a line and where one end of the wharf is up on shore. In other words, where a wharf is within but not completely contained by a lake, intersects the boundary of a lake on a line, and where exactly one of the wharf's endpoints is within or on the boundary of the lake. It is possible to use a combination of spatial predicates to find the required features: ST_Contains(lake, wharf) = TRUE ST_ContainsProperly(lake, wharf) = FALSE ST_GeometryType(ST_Intersection(wharf, lake)) = 'LINESTRING' ST_NumGeometries(ST_Multi(ST_Intersection(ST_Boundary(wharf), ST_Boundary(lake)))) = 1 ... but needless to say, this is quite complicated.

These requirements can be met by computing the full DE-9IM intersection matrix. PostGIS provides the ST_Relate function to do this:

SELECT ST_Relate( 'LINESTRING (1 1, 5 5)',
                  'POLYGON ((3 3, 3 7, 7 7, 7 3, 3 3))' );
st_relate
-----------
1010F0212

To test a particular spatial relationship, an intersection matrix pattern is used. This is the matrix representation augmented with the additional symbols {T,*}:

Using intersection matrix patterns, specific spatial relationships can be evaluated in a more succinct way. The ST_Relate and the ST_RelateMatch functions can be used to test intersection matrix patterns. For the first example above, the intersection matrix pattern specifying two lines intersecting in a line is '1*1***1**':

-- Find road segments that intersect in a line
SELECT a.id
FROM roads a, roads b
WHERE a.id != b.id
      AND a.geom && b.geom
      AND ST_Relate(a.geom, b.geom, '1*1***1**');

For the second example, the intersection matrix pattern specifying a line partly inside and partly outside a polygon is '102101FF2':

-- Find wharves partly on a lake's shoreline
SELECT a.lake_id, b.wharf_id
FROM lakes a, wharfs b
WHERE a.geom && b.geom
      AND ST_Relate(a.geom, b.geom, '102101FF2');

Current PostgreSQL versions (including 9.6) suffer from a query optimizer weakness regarding TOAST tables. TOAST tables are a kind of "extension room" used to store large (in the sense of data size) values that do not fit into normal data pages (like long texts, images or complex geometries with lots of vertices), see the PostgreSQL Documentation for TOAST for more information).

The problem appears if you happen to have a table with rather large geometries, but not too many rows of them (like a table containing the boundaries of all European countries in high resolution). Then the table itself is small, but it uses lots of TOAST space. In our example case, the table itself had about 80 rows and used only 3 data pages, but the TOAST table used 8225 pages.

Now issue a query where you use the geometry operator && to search for a bounding box that matches only very few of those rows. Now the query optimizer sees that the table has only 3 pages and 80 rows. It estimates that a sequential scan on such a small table is much faster than using an index. And so it decides to ignore the GIST index. Usually, this estimation is correct. But in our case, the && operator has to fetch every geometry from disk to compare the bounding boxes, thus reading all TOAST pages, too.

To see whether your suffer from this issue, use the "EXPLAIN ANALYZE" postgresql command. For more information and the technical details, you can read the thread on the PostgreSQL performance mailing list: http://archives.postgresql.org/pgsql-performance/2005-02/msg00030.php

and newer thread on PostGIS https://lists.osgeo.org/pipermail/postgis-devel/2017-June/026209.html

The PostgreSQL people are trying to solve this issue by making the query estimation TOAST-aware. For now, here are two workarounds:

The first workaround is to force the query planner to use the index. Send "SET enable_seqscan TO off;" to the server before issuing the query. This basically forces the query planner to avoid sequential scans whenever possible. So it uses the GIST index as usual. But this flag has to be set on every connection, and it causes the query planner to make misestimations in other cases, so you should "SET enable_seqscan TO on;" after the query.

The second workaround is to make the sequential scan as fast as the query planner thinks. This can be achieved by creating an additional column that "caches" the bbox, and matching against this. In our example, the commands are like:

SELECT AddGeometryColumn('myschema','mytable','bbox','4326','GEOMETRY','2');
UPDATE mytable SET bbox = ST_Envelope(ST_Force2D(geom));

Now change your query to use the && operator against bbox instead of geom_column, like:

SELECT geom_column
FROM mytable
WHERE bbox && ST_SetSRID('BOX3D(0 0,1 1)'::box3d,4326);

Of course, if you change or add rows to mytable, you have to keep the bbox "in sync". The most transparent way to do this would be triggers, but you also can modify your application to keep the bbox column current or run the UPDATE query above after every modification.