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 = asBinary(geometry); 
text WKT = asText(geometry); 
geometry = GeomFromWKB(bytea WKB, SRID); 
geometry = 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 ( 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:

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

bytea EWKB = asEWKB(geometry); 
text EWKT = asEWKT(geometry); 
geometry = GeomFromEWKB(bytea EWKB); 
geometry = 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 ( 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:

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:

The GEOMETRY_COLUMNS table definition is as follows:

CREATE TABLE geometry_columns ( 
  f_table_catalog    VARRCHAR(256) NOT NULL, 
  f_table_schema     VARCHAR(256) NOT NULL,
  f_table_nam        VARCHAR(256) NOT NULL, 
  f_geometry_column  VARCHAR(256) NOT NULL, 
  coord_dimension    INTEGER NOT NULL, 
  srid               INTEGER NOT NULL, 
  type               VARCHAR(30) NOT NULL 
)

The columns are as follows:

Creating a table with spatial data is done in two stages:

Here is an example of SQL used to create a table and add a spatial column (assuming that an SRID of 128 exists already):

CREATE TABLE parks ( 
  park_id    INTEGER, 
  park_name  VARCHAR,
  park_date  DATE,
  park_type  VARCHAR
);
SELECT AddGeometryColumn('parks', 'park_geom', 128, 'MULTIPOLYGON', 2 );

Here is another example, using the generic "geometry" type and the undefined SRID value of -1:

CREATE TABLE roads ( 
  road_id INTEGER,
  road_name VARCHAR
);
SELECT AddGeometryColumn( 'roads', 'roads_geom', -1, 'GEOMETRY', 3 );

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 validity of geometries you can use the IsValid() function:

 gisdb=# select isvalid('LINESTRING(0 0, 1 1)'), 
        isvalid('LINESTRING(0 0,0 0)'); 

 isvalid | 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 (isvalid(the_geom));

If you encounter any strange error messages such as "GEOS Intersection() threw an error!" or "JTS 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.

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,GeomFromText('LINESTRING(191232 243118,191108 243242)',-1),'Jeff Rd'); 
INSERT INTO roads (road_id, roads_geom, road_name) 
  VALUES (2,GeomFromText('LINESTRING(189141 244158,189265 244817)',-1),'Geordie Rd'); 
INSERT INTO roads (road_id, roads_geom, road_name) 
  VALUES (3,GeomFromText('LINESTRING(192783 228138,192612 229814)',-1),'Paul St'); 
INSERT INTO roads (road_id, roads_geom, road_name) 
  VALUES (4,GeomFromText('LINESTRING(189412 252431,189631 259122)',-1),'Graeme Ave'); 
INSERT INTO roads (road_id, roads_geom, road_name) 
  VALUES (5,GeomFromText('LINESTRING(190131 224148,190871 228134)',-1),'Phil Tce'); 
INSERT INTO roads (road_id, roads_geom, road_name) 
  VALUES (6,GeomFromText('LINESTRING(198231 263418,198213 268322)',-1),'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. The loader has several operating modes distinguished by command line flags:

Note that -a, -c, -d and -p are mutually exclusive.

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

# shp2pgsql shaperoads 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 myschema.roadstable | psql -d roadsdb

The most straightforward means of pulling data out of the database is to use a SQL select query and dump the resulting columns into a parsable text file:

db=# SELECT road_id, 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 by using the "GeomFromText()" function. So, for example:

SELECT road_id, road_name 
  FROM roads 
  WHERE roads_geom ~= GeomFromText('LINESTRING(191232 243118,191108 243242)',-1);

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

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.

SELECT road_id, road_name 
FROM roads 
WHERE roads_geom && GeomFromText('POLYGON((...))',-1);

The above query will use the bounding box of the polygon for comparison purposes.

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

SELECT AsText(roads_geom) AS geom 
FROM roads 
WHERE 
  roads_geom && SetSRID('BOX3D(191232 243117,191232 243119)'::box3d,-1);

Note the use of the SRID, to specify the projection of the BOX3D. The value -1 is used to indicate no specified SRID.

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] ); 

Building a spatial index is a computationally intensive exercise: on tables of around 1 million rows, on a 300MHz Solaris machine, we have found building a GiST index takes about 1 hour. After building an index, it is important to force PostgreSQL to collect table statistics, which are used to optimize query plans:

VACUUM ANALYZE [table_name] [column_name]; 
-- This is only needed for PostgreSQL 7.4 installations and below 
SELECT UPDATE_GEOMETRY_STATS([table_name], [column_name]);

GiST indexes have two advantages over R-Tree indexes in PostgreSQL. Firstly, GiST indexes are "null safe", meaning they can index columns which include null values. Secondly, GiST indexes support the concept of "lossiness" which is important when dealing with GIS objects larger than the PostgreSQL 8K page size. Lossiness allows PostgreSQL to store only the "important" part of an object in an index -- in the case of GIS objects, just the bounding box. GIS objects larger than 8K will cause R-Tree indexes to fail in the process of being built.

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 does not optimize the use of GiST indexes well, so sometimes searches which should use a spatial index instead default to a sequence 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 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, GeomFromText('POINT(100000 200000)', -1)) < 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 && operator to reduce the number of distance calculations required:

SELECT the_geom 
FROM geom_table 
WHERE the_geom && 'BOX3D(90900 190900, 100100 200100)'::box3d 
  AND
ST_Distance(the_geom, GeomFromText('POINT(100000 200000)', -1)) < 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 distance() function. Notice that the BOX3D 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 ST_Intersects(r.the_geom, m.the_geom)
  AND m.name = 'PRINCE GEORGE';

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

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)

To use PostGIS with Mapserver, you will need to know about how to configure Mapserver, which is beyond the scope of this documentation. This section will cover specific PostGIS issues and configuration details.

To use PostGIS with Mapserver, you will need:

Mapserver accesses PostGIS/PostgreSQL data like any other PostgreSQL client -- using libpq. This means that Mapserver can be installed on any machine with network access to the PostGIS server, as long as the system has the libpq PostgreSQL client libraries.

The USING pseudo-SQL clause is used to add some information to help mapserver understand the results of more complex queries. More specifically, when either a view or a subselect is used as the source table (the thing to the right of "FROM" in a DATA definition) it is more difficult for mapserver to automatically determine a unique identifier for each row and also the SRID for the table. The USING clause can provide mapserver with these two pieces of information as follows:

DATA "the_geom FROM (
  SELECT 
    table1.the_geom AS the_geom, 
    table1.oid AS oid, 
    table2.data AS data 
  FROM table1 
  LEFT JOIN table2 
  ON table1.id = table2.id
) AS new_table USING UNIQUE oid USING SRID=-1"

Lets start with a simple example and work our way up. Consider the following Mapserver layer definition:

LAYER 
  CONNECTIONTYPE postgis 
  NAME "roads"
  CONNECTION "user=theuser password=thepass dbname=thedb host=theserver" 
  DATA "the_geom FROM roads" 
  STATUS ON 
  TYPE LINE 
  CLASS 
    COLOR 0 0 0 
  END 
END

This layer will display all the road geometries in the roads table as black lines.

Now lets say we want to show only the highways until we get zoomed in to at least a 1:100000 scale - the next two layers will achieve this effect:

LAYER 
  CONNECTION "user=theuser password=thepass dbname=thedb host=theserver" 
  DATA "the_geom FROM roads"
  MINSCALE 100000 
  STATUS ON 
  TYPE LINE 
  FILTER "road_type = 'highway'" 
  CLASS 
    COLOR 0 0 0 
  END 
END 
LAYER 
  CONNECTION "user=theuser password=thepass dbname=thedb host=theserver"
  DATA "the_geom FROM roads" 
  MAXSCALE 100000 
  STATUS ON 
  TYPE LINE
  CLASSITEM road_type 
  CLASS 
    EXPRESSION "highway" 
    STYLE
      WIDTH 2 
      COLOR 255 0 0  
    END
  END 
  CLASS  
    COLOR 0 0 0 
  END 
END

The first layer is used when the scale is greater than 1:100000, and displays only the roads of type "highway" as black lines. The FILTER option causes only roads of type "highway" to be displayed.

The second layer is used when the scale is less than 1:100000, and will display highways as double-thick red lines, and other roads as regular black lines.

So, we have done a couple of interesting things using only mapserver functionality, but our DATA SQL statement has remained simple. Suppose that the name of the road is stored in another table (for whatever reason) and we need to do a join to get it and label our roads.

LAYER 
  CONNECTION "user=theuser password=thepass dbname=thedb host=theserver" 
  DATA "the_geom FROM (SELECT roads.oid AS oid, roads.the_geom AS the_geom, 
        road_names.name as name FROM roads LEFT JOIN road_names ON 
        roads.road_name_id = road_names.road_name_id) 
        AS named_roads USING UNIQUE oid USING SRID=-1" 
  MAXSCALE 20000 
  STATUS ON 
  TYPE ANNOTATION 
  LABELITEM name
  CLASS 
    LABEL 
      ANGLE auto 
      SIZE 8 
      COLOR 0 192 0 
      TYPE truetype 
      FONT arial
    END
  END 
END

This annotation layer adds green labels to all the roads when the scale gets down to 1:20000 or less. It also demonstrates how to use an SQL join in a DATA definition.

...

...