The OpenGIS specification defines two standard ways of expressing spatial objects: the Well-Known Text (WKT) form and the Well-Known Binary (WKB) form. Both WKT and WKB include information about the type of the object and the coordinates which form the object.

Examples of the text representations (WKT) of the spatial objects of the features are as follows:

The OpenGIS specification also requires that the internal storage format of spatial objects include a spatial referencing system identifier (SRID). The SRID is required when creating spatial objects for insertion into the database.

Input/Output of these formats are available using the following interfaces:

bytea WKB = ST_AsBinary(geometry);
text WKT = ST_AsText(geometry);
geometry = ST_GeomFromWKB(bytea WKB, SRID);
geometry = ST_GeometryFromText(text WKT, SRID);

For example, a valid insert statement to create and insert an OGC spatial object would be:

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

OGC formats only support 2D geometries, and the associated SRID is *never* embedded in the input/output representations.

PostGIS extended formats are currently superset of OGC one (every valid WKB/WKT is a valid EWKB/EWKT) but this might vary in the future, specifically if OGC comes out with a new format conflicting with our extensions. Thus you SHOULD NOT rely on this feature!

PostGIS EWKB/EWKT add 3DM, 3DZ, 4D coordinates support and embedded SRID information.

Examples of the text representations (EWKT) of the extended spatial objects of the features are as follows.

Conversion between these formats is available using the following interfaces:

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

For example, a valid insert statement to create and insert a PostGIS spatial object would be:

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

The "canonical forms" of a PostgreSQL type are the representations you get with a simple query (without any function call) and the one which is guaranteed to be accepted with a simple insert, update or copy. For the PostGIS 'geometry' type these are:

- Output
  - binary: EWKB
	ascii: HEXEWKB (EWKB in hex form)
- Input
  - binary: EWKB
	ascii: HEXEWKB|EWKT 

For example this statement reads EWKT and returns HEXEWKB in the process of canonical ascii input/output:

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

geometry
----------------------------------------------------
01010000200400000000000000000000000000000000000000
(1 row)

The SQL Multimedia Applications Spatial specification extends the simple features for SQL spec by defining a number of circularly interpolated curves.

The SQL-MM definitions include 3DM, 3DZ and 4D coordinates, but do not allow the embedding of SRID information.

The Well-Known Text extensions are not yet fully supported. Examples of some simple curved geometries are shown below:

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

The geography type does not support curves, TINS, or POLYHEDRALSURFACEs, but other geometry types are supported. Standard geometry type data will autocast to geography if it is of SRID 4326. You can also use the EWKT and EWKB conventions to insert data.

The geography fields get registered in the geography_columns system view.

Now, check the "geography_columns" view and see that your table is listed.

You can create a new table with a GEOGRAPHY column using the CREATE TABLE syntax.

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

Note that the location column has type GEOGRAPHY and that geography type supports two optional modifiers: a type modifier that restricts the kind of shapes and dimensions allowed in the column; an SRID modifier that restricts the coordinate reference identifier to a particular number.

Allowable values for the type modifier are: POINT, LINESTRING, POLYGON, MULTIPOINT, MULTILINESTRING, MULTIPOLYGON. The modifier also supports dimensionality restrictions through suffixes: Z, M and ZM. So, for example a modifier of 'LINESTRINGM' would only allow line strings with three dimensions in, and would treat the third dimension as a measure. Similarly, 'POINTZM' would expect four dimensional data.

If you do not specify an SRID, the SRID will default to 4326 WGS 84 long/lat will be used, and all calculations will proceed using WGS84.

Once you have created your table, you can see it in the GEOGRAPHY_COLUMNS table:

-- See the contents of the metadata view
SELECT * FROM geography_columns;

You can insert data into the table the same as you would if it was using a GEOMETRY column:

-- 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)');

Creating an index works the same as GEOMETRY. 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 );

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).

-- Show a distance query and note, London is outside the 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 from Seattle to London (LINESTRING(-122.33 47.606, 0.0 51.5)) comes to Reykjavik (POINT(-21.96 64.15)).

-- Distance calculation using GEOGRAPHY (122.2km)
  SELECT ST_Distance('LINESTRING(-122.33 47.606, 0.0 51.5)'::geography, 'POINT(-21.96 64.15)'::geography);

-- Distance calculation using GEOMETRY (13.3 "degrees")
  SELECT ST_Distance('LINESTRING(-122.33 47.606, 0.0 51.5)'::geometry, 'POINT(-21.96 64.15)'::geometry);

Testing different lon/lat projects. Any long lat spatial reference system listed in spatial_ref_sys table is allowed.

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

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

                    geography
----------------------------------------------------
 0101000020AB1000000000000000C05EC00000000000004140
(1 row)

-- NAD83 UTM zone meters, yields error since its a meter based projection
SELECT 'SRID=26910;POINT(-123 34)'::geography;

ERROR:  Only lon/lat coordinate systems are supported in geography.
LINE 1: SELECT 'SRID=26910;POINT(-123 34)'::geography;

The GEOGRAPHY type calculates the true shortest distance over the sphere between Reykjavik and the great circle flight path between Seattle and London.

Great Circle mapper 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 might be called "degrees", but the result doesn't correspond to any true angular difference between the points, so even calling them "degrees" is inaccurate.

The geography 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 type you choose should be conditioned on 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 14.11, “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 14.4, “PostGIS Geography Support Functions”

The spatial_ref_sys table is a PostGIS included and OGC compliant database table that lists over 3000 known spatial reference systems and details needed to transform/reproject between them.

Although the PostGIS spatial_ref_sys table contains over 3000 of the more commonly used spatial reference system definitions that can be handled by the proj library, it does not contain all known to man and you can define your own custom projection if you are familiar with proj4 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.

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

Some of the more 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 , Spatial reference systems for each NAD 83, WGS 84 UTM zone - UTM zones are one of the most ideal for measurement, but only cover 6-degree regions.

Various US state plane spatial reference systems (meter or feet based) - usually one or 2 exists per US state. Most of the meter ones are in the core set, but many of the feet based ones or ESRI created ones you will need to pull from spatialreference.org .

For details on determining which UTM zone to use for your area of interest, check out the utmzone PostGIS plpgsql helper function .

The SPATIAL_REF_SYS table definition is as follows:

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 SPATIAL_REF_SYS columns are as follows:

GEOMETRY_COLUMNS is a view reading from database system catalogs. Its structure is as follows:

\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 column meanings are:

Creating a table with spatial data, can be done in one step. As shown in the following example which creates a roads table with a 2D linestring geometry column in WGS84 long lat

CREATE TABLE ROADS (ID serial, ROAD_NAME text, geom geometry(LINESTRING,4326) );

We can add additional columns using standard ALTER TABLE command as we do in this next example where we add a 3-D linestring.

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

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, we 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

PostGIS is compliant with the Open Geospatial Consortium’s (OGC) OpenGIS Specifications. As such, many PostGIS methods require, or more accurately, assume that geometries that are operated on are both simple and 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.

According to the OGC Specifications, a simple geometry is one that has no anomalous geometric points, such as self intersection or self tangency and primarily refers to 0 or 1-dimensional geometries (i.e. [MULTI]POINT, [MULTI]LINESTRING ). Geometry validity, on the other hand, primarily refers to 2-dimensional geometries (i.e. [MULTI]POLYGON) and defines the set of assertions that characterizes a valid polygon. The description of each geometric class includes specific conditions that further detail geometric simplicity and validity.

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

MULTIPOINT s are simple if no two coordinates ( POINT s) are equal (have identical coordinate values).

A LINESTRING is simple if it does not pass through the same POINT twice (except for the endpoints, in which case it is referred to as a linear ring and additionally considered closed).

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

(a) and (c) are simple LINESTRING s, (b) and (d) are not.

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

(e)

(f)

(g)

(e) and (f) are simple MULTILINESTRING s, (g) is not.

By definition, a POLYGON is always simple . It is valid if no two rings in the boundary (made up of an exterior ring and interior rings) cross. The boundary of a POLYGON may intersect at a POINT but only as a tangent (i.e. not on a line). A POLYGON may not have cut lines or spikes and the interior rings must be contained entirely within the exterior ring.

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

(h) and (i) are valid POLYGON s, (j-m) cannot be represented as single POLYGON s, but (j) and (m) could be represented as a valid MULTIPOLYGON .

A MULTIPOLYGON is valid if and only if all of its elements are valid and the interiors of no two elements intersect. The boundaries of any two elements may touch, but only at a finite number of POINT s.

(n)

(o)

(p)

(n) and (o) are not valid MULTIPOLYGON s. (p) , however, is valid.

Most of the functions implemented by the GEOS library rely on the assumption that your geometries are valid as specified by the OpenGIS Simple Feature Specification. To check simplicity or validity of geometries you can use the ST_IsSimple() and ST_IsValid()

-- Typically, it doesn't make sense to check
-- for validity on linear features since it will always return TRUE.
-- But in this example, PostGIS extends the definition of the OGC IsValid
-- by returning false if a LineString has less than 2 *distinct* vertices.
gisdb=# SELECT
   ST_IsValid('LINESTRING(0 0, 1 1)'),
   ST_IsValid('LINESTRING(0 0, 0 0, 0 0)');

 st_isvalid | st_isvalid
------------+-----------
      t     |     f

By default, PostGIS does not apply this validity check on geometry input, because testing for validity needs lots of CPU time for complex geometries, especially polygons. If you do not trust your data sources, you can manually enforce such a check to your tables by adding a check constraint:

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

If you encounter any strange error messages such as "GEOS Intersection() threw an error!" when calling PostGIS functions with valid input geometries, you likely found an error in either PostGIS or one of the libraries it uses, and you should contact the PostGIS developers. The same is true if a PostGIS function returns an invalid geometry for valid input.

	Strictly compliant OGC geometries cannot have Z or M values. The ST_IsValid() function won't consider higher dimensioned geometries invalid! Invocations of AddGeometryColumn() will add a constraint checking geometry dimensions, so it is enough to specify 2 there.

It is sometimes the case that the typical spatial predicates ( ST_Intersects , ST_Contains , ST_Crosses , ST_Touches , ...) are insufficient in and of themselves to adequately provide that desired spatial filter.

For example, consider a linear dataset representing a road network. It may be the task of a GIS analyst to identify all road segments that cross each other, not at a point, but on a line, perhaps invalidating some business rule. In this case, ST_Crosses does not adequately provide the necessary spatial filter since, for linear features, it returns true only where they cross at a point. One two-step solution might be to first perform the actual intersection ( ST_Intersection ) of pairs of road segments that spatially intersect ( ST_Intersects ), and then compare the intersection's ST_GeometryType with ' LINESTRING ' (properly dealing with cases that return GEOMETRYCOLLECTION s of [MULTI]POINT s, [MULTI]LINESTRING s, etc.). A more elegant / faster solution may indeed be desirable.

A second [theoretical] example may be that of a GIS analyst trying to locate all wharfs or docks that intersect a lake's boundary on a line and where only one end of the wharf is up on shore. In other words, where a wharf is within, but not completely within a lake, intersecting the boundary of a lake on a line, and where the wharf's endpoints are both completely within and on the boundary of the lake. The analyst may need to use a combination of spatial predicates to isolate the sought after 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 ... (needless to say, this could get quite complicated)

So enters the Dimensionally Extended 9 Intersection Model, or DE-9IM for short.

4.3.6.1. Theory

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."

Boundary

The boundary of a geometry is the set of geometries of the next lower dimension. For POINT s, which have a dimension of 0, the boundary is the empty set. The boundary of a LINESTRING are the two endpoints. For POLYGON s, the boundary is the linework that make up the exterior and interior rings.

Interior

The interior of a geometry are those points of a geometry that are left when the boundary is removed. For POINT s, the interior is the POINT itself. The interior of a LINESTRING are the set of real points between the endpoints. For POLYGON s, the interior is the areal surface inside the polygon.

Exterior

The exterior of a geometry is the universe, an areal surface, not on the interior or boundary of the geometry.

Given geometry a , where the I(a) , B(a) , and E(a) are the Interior , Boundary , and Exterior of a, the mathematical representation of the matrix is:

	Interior	Boundary	Exterior
Interior	dim( I(a) ∩ I(b) )	dim( I(a) ∩ B(b) )	dim( I(a) ∩ E(b) )
Boundary	dim( B(a) ∩ I(b) )	dim( B(a) ∩ B(b) )	dim( B(a) ∩ E(b) )
Exterior	dim( E(a) ∩ I(b) )	dim( E(a) ∩ B(b) )	dim( E(a) ∩ E(b) )

Where dim(a) is the dimension of a as specified by ST_Dimension but has the domain of {0,1,2,T,F,*}

• 0 => point

• 1 => line

• 2 => area

• T => {0,1,2}

• F => empty set

• * => don't care

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

	Interior Boundary Exterior Interior dim(...) = 2 dim(...) = 1 dim(...) = 2 Boundary dim(...) = 1 dim(...) = 0 dim(...) = 1 Exterior dim(...) = 2 dim(...) = 1 dim(...) = 2

Read from left to right and from top to bottom, the dimensional matrix is represented, ' 212101212 '.

A relate matrix that would therefore represent our first example of two lines that intersect on a line would be: ' 1*1***1** '

-- Identify road segments that cross on 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**');

A relate matrix that represents the second example of wharfs partly on the lake's shoreline would be ' 102101FF2 '

-- Identify wharfs 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');

For more information or reading, see:

• OpenGIS Simple Features Implementation Specification for SQL (version 1.1, section 2.1.13.2)

• Dimensionally Extended Nine-Intersection Model (DE-9IM)

• GeoTools: Point Set Theory and the DE-9IM Matrix

• Encyclopedia of GIS By Hui Xiong

If you can convert your data to a text representation, then using formatted SQL might be the easiest way to get your data into PostGIS. As with Oracle and other SQL databases, data can be bulk loaded by piping a large text file full of SQL "INSERT" statements into the SQL terminal monitor.

A data upload 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 data file can be piped into PostgreSQL very easily using the "psql" SQL terminal monitor:

psql -d [database] -f roads.sql

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

In addition to the shp2pgsql command-line loader, there is an shp2pgsql-gui graphical interface with most of the options as the command-line loader, but 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 uploading 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 upload can be done all in one step using UNIX pipes:

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

The most straightforward means of pulling data out of the database is to use a SQL select query to reduce the number of RECORDS and COLUMNS returned 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)

However, there will be times when some kind of restriction is necessary to cut down the number of fields returned. In the case of attribute-based restrictions, just use the same SQL syntax as normal with a non-spatial table. In the case of spatial restrictions, the following operators are available/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 directly 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. In addition to GIS indexing, GiST is 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.

Once a GIS 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" and is a generic form of indexing that has been introduced in PostgreSQL 9.5. BRIN is a lossy kind of index, and its main usage is to provide a compromise for both read and write performance. Its primary goal is to handle very large tables for which some of the columns have some natural correlation with their physical location within the table. In addition to GIS indexing, BRIN is used to speed up searches on various kinds of regular or irregular data structures (integer, arrays etc).

Once a GIS 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). GiST indexes are really 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 storage size, and the penalty in write workload. Otherwise, BRIN index can be considered as an alternative.

The idea of a BRIN index is to store only the bouding box englobing all the geometries contained in all the rows in a set of table blocks, called a range. Obviously, this indexing method will only be efficient if the data is physically ordered in a way where the resulting bouding boxes for block ranges will be mutually exclusive. The resulting index will be really small, but will be less efficient than a GiST index in many cases.

Building a BRIN index is way less intensive than building a GiST index. It's quite common to build a BRIN index in more than ten time less than a GiST index would have required. As a BRIN index only store one bouding box for one to many table blocks, it's pretty common to consume up to a thousand time less disk space for this kind of indexes.

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 help to get better performance.

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

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

The above syntax will always build a 2D-index. To get a 3D-dimensional index, you can create one using this syntax

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

These above syntaxes will use the default number or block in a range, which is 128. To specify the number of blocks you want to summarise in a range, you can create one using this syntax

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

Also, keep in mind that a BRIN index will only store one index value 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 drop of performance by choosing the operator class whith the least number of dimensions of the stored geometries

Also the "geography" datatype is supported for BRIN indexing. The syntax for building a BRIN index on a "geography" column is as follows:

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

The above syntax will always build a 2D-index for geospatial objects on the spheroid.

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

SP-GiST stands for "Space-Partitioned Generalized Search Tree" and is a generic form of indexing 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 GIS indexing, SP-GiST is used to speed up searches on many kinds of data, such as phone routing, ip routing, substring search, etc.

As it is the case for GiST indexes, SP-GiST indexes are lossy, in the sense that they store the bounding box englobing the spatial objects. SP-GiST indexes can be considered as an alternative to GiST indexes. The performance tests reveal that SP-GiST indexes are especially beneficial when there are many overlapping objects, that is, with so-called “spaghetti data”.

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 the index is built, the query planner transparently decides when to use index information to speed up a query plan. Unfortunately, the PostgreSQL query planner sometimes does not optimize the use of GiST indexes well, so sometimes searches which should use a spatial index instead may perform a sequential scan of the whole table.

If you find your spatial indexes are not being used (or your attribute indexes, for that matter) there are a couple things you can do:

When constructing a query it is important to remember that only the bounding-box-based operators such as && can take advantage of the GiST spatial index. Functions such as ST_Distance() cannot use the index to optimize their operation. For example, the following query would be quite slow on a large table:

SELECT the_geom
FROM geom_table
WHERE ST_Distance(the_geom, 'SRID=312;POINT(100000 200000)') < 100

This query is selecting all the geometries in geom_table which are within 100 units of the point (100000, 200000). It will be slow because it is calculating the distance between each point in the table and our specified point, ie. one ST_Distance() calculation for each row in the table. We can avoid this by using the single step index accelerated function ST_DWithin to reduce the number of distance calculations required:

SELECT the_geom
FROM geom_table
WHERE ST_DWithin(the_geom, 'SRID=312;POINT(100000 200000)', 100)

This query selects the same geometries, but it does it in a more efficient way. Assuming there is a GiST index on the_geom, the query planner will recognize that it can use the index to reduce the number of rows before calculating the result of the ST_Distance() function. Notice that the ST_MakeEnvelope geometry which is used in the && operation is a 200 unit square box centered on the original point - this is our "query box". The && operator uses the index to quickly reduce the result set down to only those geometries which have bounding boxes that overlap the "query box". Assuming that our query box is much smaller than the extents of the entire geometry table, this will drastically reduce the number of distance calculations that need to be done.

The examples in this section will make use of two tables, a table of linear roads, and a table of polygonal municipality boundaries. The table definitions for the bc_roads table is:

Column      | Type              | Description
------------+-------------------+-------------------
gid         | integer           | Unique ID
name        | character varying | Road Name
the_geom    | geometry          | Location Geometry (Linestring)

The table definition for the bc_municipality table is:

Column     | Type              | Description
-----------+-------------------+-------------------
gid        | integer           | Unique ID
code       | integer           | Unique ID
name       | character varying | City / Town Name
the_geom   | geometry          | Location Geometry (Polygon)

SELECT sum(ST_Length(the_geom))/1000 AS km_roads FROM bc_roads;

km_roads
------------------
70842.1243039643
(1 row)

SELECT
  ST_Area(the_geom)/10000 AS hectares
FROM bc_municipality
WHERE name = 'PRINCE GEORGE';

hectares
------------------
32657.9103824927
(1 row)

SELECT
  name,
  ST_Area(the_geom)/10000 AS hectares
FROM
  bc_municipality
ORDER BY hectares DESC
LIMIT 1;

name           | hectares
---------------+-----------------
TUMBLER RIDGE  | 155020.02556131
(1 row)

SELECT
  m.name,
  sum(ST_Length(r.the_geom))/1000 as roads_km
FROM
  bc_roads AS r,
  bc_municipality AS m
WHERE
  ST_Contains(m.the_geom, r.the_geom)
GROUP BY m.name
ORDER BY roads_km;

name                        | roads_km
----------------------------+------------------
SURREY                      | 1539.47553551242
VANCOUVER                   | 1450.33093486576
LANGLEY DISTRICT            | 833.793392535662
BURNABY                     | 773.769091404338
PRINCE GEORGE               | 694.37554369147
...

CREATE TABLE pg_roads as
SELECT
  ST_Intersection(r.the_geom, m.the_geom) AS intersection_geom,
  ST_Length(r.the_geom) AS rd_orig_length,
  r.*
FROM
  bc_roads AS r,
  bc_municipality AS m
WHERE
  m.name = 'PRINCE GEORGE'
	AND ST_Intersects(r.the_geom, m.the_geom);

SELECT
  sum(ST_Length(r.the_geom))/1000 AS kilometers
FROM
  bc_roads r,
  bc_municipality m
WHERE
	r.name = 'Douglas St'
	AND m.name = 'VICTORIA'
	AND ST_Intersects(m.the_geom, r.the_geom);

kilometers
------------------
4.89151904172838
(1 row)

SELECT gid, name, ST_Area(the_geom) AS area
FROM bc_municipality
WHERE ST_NRings(the_geom) > 1
ORDER BY area DESC LIMIT 1;

gid  | name         | area
-----+--------------+------------------
12   | SPALLUMCHEEN | 257374619.430216
(1 row)