PostGIS

Table of Contents

4.1. Spatial Data Model

4.1.1. OGC Geometry

4.1.2. SQL/MM Part 3 - Curves

4.1.3. WKT and WKB

4.2. Geometry Data Type

4.2.1. PostGIS EWKB and EWKT

4.3. Geography Data Type

4.3.1. Creating Geography Tables

4.3.2. Using Geography Tables

4.3.3. When to use the Geography data type

4.3.4. Geography Advanced FAQ

4.4. Geometry Validation

4.4.1. Simple Geometry

4.4.2. Valid Geometry

4.4.3. Managing Validity

4.5. Spatial Reference Systems

4.5.1. SPATIAL_REF_SYS Table

4.5.2. User-Defined Spatial Reference Systems

4.6. Spatial Tables

4.6.1. Creating a Spatial Table

4.6.2. GEOMETRY_COLUMNS View

4.6.3. Manually Registering Geometry Columns

4.7. Loading Spatial Data

4.7.1. Using SQL to Load Data

4.7.2. Using the Shapefile Loader

4.8. Extracting Spatial Data

4.8.1. Using SQL to Extract Data

4.8.2. Using the Shapefile Dumper

4.9. Spatial Indexes

4.9.1. GiST Indexes

4.9.2. BRIN Indexes

4.9.3. SP-GiST Indexes

4.9.4. Tuning Index Usage

4.1.1. OGC Geometry

4.1.2. SQL/MM Part 3 - Curves

4.1.3. WKT and WKB

4.1.1.1. Point

4.1.1.2. LineString

4.1.1.3. LinearRing

4.1.1.4. Polygon

4.1.1.5. MultiPoint

4.1.1.6. MultiLineString

4.1.1.7. MultiPolygon

4.1.1.8. GeometryCollection

4.1.1.9. PolyhedralSurface

4.1.1.10. Triangle

4.1.1.11. TIN

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”

A Point is a 0-dimensional geometry that represents a single location in coordinate space.

A LineString is a 1-dimensional line formed by a contiguous sequence of line segments. Each line segment is defined by two points, with the end point of one segment forming the start point of the next segment. An OGC-valid LineString has either zero or two or more points, but PostGIS also allows single-point LineStrings. LineStrings may cross themselves (self-intersect). A LineString is closed if the start and end points are the same. A LineString is simple if it does not self-intersect.

A LinearRing is a LineString which is both closed and simple. The first and last points must be equal, and the line must not self-intersect.

A Polygon is a 2-dimensional planar region, delimited by an exterior boundary (the shell) and zero or more interior boundaries (holes). Each boundary is a LinearRing.

A MultiPoint is a collection of Points.

A MultiLineString is a collection of LineStrings. A MultiLineString is closed if each of its elements is closed.

A MultiPolygon is a collection of non-overlapping, non-adjacent Polygons. Polygons in the collection may touch only at a finite number of points.

A GeometryCollection is a heterogeneous (mixed) collection of geometries.

A PolyhedralSurface is a contiguous collection of patches or facets which share some edges. Each patch is a planar Polygon. If the Polygon coordinates have Z ordinates then the surface is 3-dimensional.

A Triangle is a polygon defined by three distinct non-collinear vertices. Because a Triangle is a polygon it is specified by four coordinates, with the first and fourth being equal.

A TIN is a collection of non-overlapping Triangles representing a Triangulated Irregular Network.

4.1.2.1. CircularString

4.1.2.2. CompoundCurve

4.1.2.3. CurvePolygon

4.1.2.4. MultiCurve

4.1.2.5. MultiSurface

The [.emphasisISO/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.

CircularString is the basic curve type, similar to a LineString in the linear world. A single arc segment is specified by three points: the start and end points (first and third) and some other point on the arc. To specify a closed circle the start and end points are the same and the middle point is the opposite point on the circle diameter (which is the center of the arc). In a sequence of arcs the end point of the previous arc is the start point of the next arc, just like the segments of a LineString. This means that a CircularString must have an odd number of points greater than 1.

A CompoundCurve is a single continuous curve that may contain both circular arc segments and linear segments. That means that in addition to having well-formed components, the end point of every component (except the last) must be coincident with the start point of the following component.

A CurvePolygon is like a polygon, with an outer ring and zero or more inner rings. The difference is that a ring can be a CircularString or CompoundCurve as well as a LineString.

As of PostGIS 1.4 PostGIS supports compound curves in a curve polygon.

Example: A CurvePolygon with the shell defined by a CompoundCurve containing a CircularString and a LineString, and a hole defined by a CircularString

A MultiCurve is a collection of curves which can include LineStrings, CircularStrings or CompoundCurves.

A MultiSurface is a collection of surfaces, which can be (linear) Polygons or CurvePolygons.

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:

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

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:

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

4.2.1. PostGIS EWKB and EWKT

PostGIS implements the OGC Simple Features model by defining a PostgreSQL data type called geometry. It represents all of the geometry subtypes by using an internal type code (see GeometryType and ST_GeometryType). This allows modelling spatial features as rows of tables defined with a column of type geometry.

The geometry data type is opaque, which means that all access is done via invoking functions on geometry values. Functions allow creating geometry objects, accessing or updating all internal fields, and compute new geometry values. PostGIS supports all the functions specified in the OGC Simple feature access - Part 2: SQL option (SFS) specification, as well many others. See Chapter 8, PostGIS Reference for the full list of functions.

The SFA standard specifies that spatial objects include a Spatial Reference System identifier (SRID). The SRID is required when creating spatial objects for insertion into the database (it may be defaulted to 0). See ST_SRID and Section 4.5, “Spatial Reference Systems”

To make querying geometry efficient PostGIS defines various kinds of spatial indexes, and spatial operators to use them. See Section 4.9, “Spatial Indexes” and Section 5.2, “Using Spatial Indexes” for details.

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:

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:

Examples of the EWKT text representation of spatial objects are:

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

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

4.3.1. Creating Geography Tables

4.3.2. Using Geography Tables

4.3.3. When to use the Geography data type

4.3.4. Geography Advanced FAQ

The PostGIS geography data type provides native support for spatial features represented on "geographic" coordinates (sometimes called "geodetic" coordinates, or "lat/lon", or "lon/lat"). Geographic coordinates are spherical coordinates expressed in angular units (degrees).

The basis for the PostGIS geometry data type is a plane. The shortest path between two points on the plane is a straight line. That means functions on geometries (areas, distances, lengths, intersections, etc) are calculated using straight line vectors and cartesian mathematics. This makes them simpler to implement and faster to execute, but also makes them inaccurate for data on the spheroidal surface of the earth.

The PostGIS geography data type is based on a spherical model. The shortest path between two points on the sphere is a great circle arc. Functions on geographies (areas, distances, lengths, intersections, etc) are calculated using arcs on the sphere. By taking the spheroidal shape of the world into account, the functions provide more accurate results.

Because the underlying mathematics is more complicated, there are fewer functions defined for the geography type than for the geometry type. Over time, as new algorithms are added the capabilities of the geography type will expand. As a workaround one can convert back and forth between geometry and geography types.

Like the geometry data type, geography data is associated with a spatial reference system via a spatial reference system identifier (SRID). Any geodetic (long/lat based) spatial reference system defined in the spatial_ref_sys table can be used. (Prior to PostGIS 2.2, the geography type supported only WGS 84 geodetic (SRID:4326)). You can add your own custom geodetic spatial reference system as described in Section 4.5.2, “User-Defined Spatial Reference Systems”.

For all spatial reference systems the units returned by measurement functions (e.g. ST_Distance, ST_Length, ST_Perimeter, ST_Area) and for the distance argument of ST_DWithin are in meters.

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):

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:

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.

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.

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.

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).

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.

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.

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 15.11, “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 15.4, “PostGIS Geography Support Functions”

4.3.4.1. Do you calculate on the sphere or the spheroid?

4.3.4.2. What about the date-line and the poles?

4.3.4.3. What is the longest arc you can process?

4.3.4.4. Why is it so slow to calculate the area of Europe / Russia / insert big geographic region here ?

4.4.1. Simple Geometry

4.4.2. Valid Geometry

4.4.3. Managing Validity

PostGIS is compliant with the Open Geospatial Consortium’s (OGC) Simple Features specification. That standard defines the concepts of geometry being simple and valid. These definitions allow the Simple Features geometry model to represent spatial objects in a consistent and unambiguous way that supports efficient computation. (Note: the OGC SF and SQL/MM have the same definitions for simple and valid.)

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.

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. If the endpoints of a simple LineString are identical it is called closed and referred to as a Linear Ring.

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.

`POLYGON`s are formed from linear rings, so valid polygonal geometry is always simple.

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

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 (`POLYGON`s and `MULTIPOLYGON`s) . Validity is defined by rules that allow polygonal geometry to model planar areas unambiguously.

A POLYGON is valid if:

A MULTIPOLYGON is valid if:

These rules mean that valid polygonal geometry is also simple.

For linear geometry the only validity rule is that `LINESTRING`s 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.

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.

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

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

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:

4.5.1. SPATIAL_REF_SYS Table

4.5.2. User-Defined Spatial Reference Systems

A Spatial Reference System (SRS) (also called a Coordinate Reference System (CRS)) defines how geometry is referenced to locations on the Earth’s surface. There are three types of SRS:

There are many different spatial reference systems in use. Common SRSes are standardized in the European Petroleum Survey Group EPSG database. For convenience PostGIS (and many other spatial systems) refers to SRS definitions using an integer identifier called a SRID.

A geometry is associated with a Spatial Reference System by its SRID value, which is accessed by ST_SRID. The SRID for a geometry can be assigned using ST_SetSRID. Some geometry constructor functions allow supplying a SRID (such as ST_Point and ST_MakeEnvelope). The EWKT format supports SRIDs with the SRID=n; prefix.

Spatial functions processing pairs of geometries (such as overlay and relationship functions) require that the input geometries are in the same spatial reference system (have the same SRID). Geometry data can be transformed into a different spatial reference system using ST_Transform. Geometry returned from functions has the same SRS as the input geometries.

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:

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:

4.6.1. Creating a Spatial Table

4.6.2. GEOMETRY_COLUMNS View

4.6.3. Manually Registering Geometry Columns

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):

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:

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:

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.

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.

If we run in psql

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

In geometry_columns, they both register correctly

However — if we were to create a view like this

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

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:

4.7.1. Using SQL to Load Data

4.7.2. Using the Shapefile Loader

Once you have created a spatial table, you are ready to upload spatial data to the database. There are two built-in ways to get spatial data into a PostGIS/PostgreSQL database: using formatted SQL statements or using the Shapefile loader.

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:

The SQL file can be loaded into PostgreSQL using psql:

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:

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

4.8.1. Using SQL to Extract Data

4.8.2. Using the Shapefile Dumper

Spatial data can be extracted from the database using either SQL or the Shapefile dumper. The section on SQL presents some of the functions available to do comparisons and queries on spatial tables.

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:

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:

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:

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:

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:

The commandline options are:

4.9.1. GiST Indexes

4.9.2. BRIN Indexes

4.9.3. SP-GiST Indexes

4.9.4. Tuning Index Usage

Spatial indexes make using a spatial database for large data sets possible. Without indexing, a search for features requires a sequential scan of every record in the database. Indexing speeds up searching by organizing the data into a structure which can be quickly traversed to find matching records.

The B-tree index method commonly used for attribute data is not very useful for spatial data, since it only supports storing and querying data in a single dimension. Data such as geometry (which has 2 or more dimensions) requires an index method that supports range query across all the data dimensions. One of the key advantages of PostgreSQL for spatial data handling is that it offers several kinds of index methods which work well for multi-dimensional data: GiST, BRIN and SP-GiST indexes.

Spatial indexes store only the bounding box of geometries. Spatial queries use the index as a primary filter to quickly determine a set of geometries potentially matching the query condition. Most spatial queries require a secondary filter that uses a spatial predicate function to test a more specific spatial condition. For more information on queying with spatial predicates see Section 5.2, “Using Spatial Indexes”.

See also the PostGIS Workshop section on spatial indexes, and the PostgreSQL manual.

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:

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:

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:

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

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):

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

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

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

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

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

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:

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:

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:

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:

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

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.