The raison d'etre of spatial databases is to perform queries inside the database which would ordinarily require desktop GIS functionality. Using PostGIS effectively requires knowing what spatial functions are available, how to use them in queries, and ensuring that appropriate indexes are in place to provide good performance.
Spatial relationships indicate how two geometries interact with one another. They are a fundamental capability for querying geometry.
According to the OpenGIS Simple Features Implementation Specification for SQL, "the basic approach to comparing two geometries is to make pairwise tests of the intersections between the Interiors, Boundaries and Exteriors of the two geometries and to classify the relationship between the two geometries based on the entries in the resulting 'intersection' matrix."
In the theory of pointset topology, the points in a geometry embedded in 2dimensional space are categorized into three sets:
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
is the two endpoints. For POLYGON
s, the boundary is the linework of the exterior and interior rings.
The interior of a geometry are those points of a geometry that are not in the boundary. For POINT
s, the interior is the point itself. The interior of a LINESTRING
is the set of points between the endpoints. For POLYGON
s, the interior is the areal surface inside the polygon.
The exterior of a geometry is the rest of the space in which the geometry is embedded; in other words, all points not in the interior or on the boundary of the geometry. It is a 2dimensional nonclosed surface.
The Dimensionally Extended 9Intersection Model (DE9IM) describes the spatial relationship between two geometries by specifying the dimensions of the 9 intersections between the above sets for each geometry. The intersection dimensions can be formally represented in a 3x3 intersection matrix.
For a geometry g the Interior, Boundary, and Exterior are denoted using the notation I(g), B(g), and E(g). Also, dim(s) denotes the dimension of a set s with the domain of {0,1,2,F}
:
0
=> point
1
=> line
2
=> area
F
=> empty set
Using this notation, the intersection matrix for two geometries a and b 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) ) 
Visually, for two overlapping polygonal geometries, this looks like:




Reading from left to right and top to bottom, the intersection matrix is represented as the text string '212101212'.
For more information, refer to:
To make it easy to determine common spatial relationships, the OGC SFS defines a set of named spatial relationship predicates. PostGIS provides these as the functions ST_Contains, ST_Crosses, ST_Disjoint, ST_Equals, ST_Intersects, ST_Overlaps, ST_Touches, ST_Within. It also defines the nonstandard relationship predicates ST_Covers, ST_CoveredBy, and ST_ContainsProperly.
Spatial predicates are usually used as conditions in SQL WHERE
or JOIN
clauses. The named spatial predicates automatically use a spatial index if one is available, so there is no need to use the bounding box operator &&
as well. For example:
SELECT city.name, state.name, city.geom FROM city JOIN state ON ST_Intersects(city.geom, state.geom);
For more details and illustrations, see the PostGIS Workshop.
In some cases the named spatial relationships are insufficient to provide a desired spatial filter condition.
For example, consider a linear dataset representing a road network. It may be required to identify all road segments that cross each other, not at a point, but in a line (perhaps to validate some business rule). In this case ST_Crosses does not provide the necessary spatial filter, since for linear features it returns A twostep solution would be to first compute the actual intersection (ST_Intersection) of pairs of road lines that spatially intersect (ST_Intersects), and then check if the intersection's ST_GeometryType is ' Clearly, a simpler and faster solution is desirable. 
A second example is locating wharves that intersect a lake's boundary on a line and where one end of the wharf is up on shore. In other words, where a wharf is within but not completely contained by a lake, intersects the boundary of a lake on a line, and where exactly one of the wharf's endpoints is within or on the boundary of the lake. It is possible to use a combination of spatial predicates to find the required features:

These requirements can be met by computing the full DE9IM intersection matrix. PostGIS provides the ST_Relate function to do this:
SELECT ST_Relate( 'LINESTRING (1 1, 5 5)', 'POLYGON ((3 3, 3 7, 7 7, 7 3, 3 3))' ); st_relate  1010F0212
To test a particular spatial relationship, an intersection matrix pattern is used. This is the matrix representation augmented with the additional symbols {T,*}
:
T
=> intersection dimension is nonempty; i.e. is in {0,1,2}
*
=> don't care
Using intersection matrix patterns, specific spatial relationships can be evaluated in a more succinct way. The ST_Relate and the ST_RelateMatch functions can be used to test intersection matrix patterns. For the first example above, the intersection matrix pattern specifying two lines intersecting in a line is '1*1***1**':
 Find road segments that intersect in a line SELECT a.id FROM roads a, roads b WHERE a.id != b.id AND a.geom && b.geom AND ST_Relate(a.geom, b.geom, '1*1***1**');
For the second example, the intersection matrix pattern specifying a line partly inside and partly outside a polygon is '102101FF2':
 Find wharves partly on a lake's shoreline SELECT a.lake_id, b.wharf_id FROM lakes a, wharfs b WHERE a.geom && b.geom AND ST_Relate(a.geom, b.geom, '102101FF2');
When constructing queries using spatial conditions, for best performance it is important to ensure that a spatial index is used, if one exists (see Section 4.9, “인덱스 빌드 작업”). To do this, a spatial operator or indexaware function must be used in a WHERE
or ON
clause of the query.
Spatial operators include the bounding box operators (of which the most commonly used is &&; see Section 7.10.1, “Bounding Box Operators” for the full list) and the distance operators used in nearestneighbor queries (the most common being <>; see Section 7.10.2, “연산자(operator)” for the full list.)
Indexaware functions automatically add a bounding box operator to the spatial condition. Indexaware functions include the named spatial relationship predicates ST_Contains, ST_ContainsProperly, ST_CoveredBy, ST_Covers, ST_Crosses, ST_Intersects, ST_Overlaps, ST_Touches, ST_Within, ST_Within, and ST_3DIntersects, and the distance predicates ST_DWithin, ST_DFullyWithin, ST_3DDFullyWithin, and ST_3DDWithin .)
Functions such as ST_Distance do not use indexes to optimize their operation. For example, the following query would be quite slow on a large table:
SELECT geom FROM geom_table WHERE ST_Distance( geom, 'SRID=312;POINT(100000 200000)' ) < 100
This query selects 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 the specified point, ie. one ST_Distance()
calculation is computed for every row in the table.
The number of rows processed can be reduced substantially by using the indexaware function ST_DWithin:
SELECT geom FROM geom_table WHERE ST_DWithin( geom, 'SRID=312;POINT(100000 200000)', 100 )
This query selects the same geometries, but it does it in a more efficient way. This is enabled by ST_DWithin()
using the &&
operator internally on an expanded bounding box of the query geometry. If there is a spatial index on geom
, the query planner will recognize that it can use the index to reduce the number of rows scanned before calculating the distance. The spatial index allows retrieving only records with geometries whose bounding boxes overlap the expanded extent and hence which might be within the required distance. The actual distance is then computed to confirm whether to include the record in the result set.
For more information and examples see the PostGIS Workshop.
The examples in this section make use of a table of linear roads, and a table of polygonal municipality boundaries. The definition of the bc_roads
table is:
Column  Type  Description ++ gid  integer  Unique ID name  character varying  Road Name geom  geometry  Location Geometry (Linestring)
The definition of the bc_municipality
table is:
Column  Type  Description ++ gid  integer  Unique ID code  integer  Unique ID name  character varying  City / Town Name geom  geometry  Location Geometry (Polygon)
5.3.1. 
What is the total length of all roads, expressed in kilometers? 
You can answer this question with a very simple piece of SQL: SELECT sum(ST_Length(geom))/1000 AS km_roads FROM bc_roads; km_roads  70842.1243039643 

5.3.2. 
How large is the city of Prince George, in hectares? 
This query combines an attribute condition (on the municipality name) with a spatial calculation (of the polygon area): SELECT ST_Area(geom)/10000 AS hectares FROM bc_municipality WHERE name = 'PRINCE GEORGE'; hectares  32657.9103824927 

5.3.3. 
What is the largest municipality in the province, by area? 
This query uses a spatial measurement as an ordering value. There are several ways of approaching this problem, but the most efficient is below: SELECT name, ST_Area(geom)/10000 AS hectares FROM bc_municipality ORDER BY hectares DESC LIMIT 1; name  hectares + TUMBLER RIDGE  155020.02556131 Note that in order to answer this query we have to calculate the area of every polygon. If we were doing this a lot it would make sense to add an area column to the table that could be indexed for performance. By ordering the results in a descending direction, and them using the PostgreSQL "LIMIT" command we can easily select just the largest value without using an aggregate function like MAX(). 

5.3.4. 
What is the length of roads fully contained within each municipality? 
This is an example of a "spatial join", which brings together data from two tables (with a join) using a spatial interaction ("contained") as the join condition (rather than the usual relational approach of joining on a common key): SELECT m.name, sum(ST_Length(r.geom))/1000 as roads_km FROM bc_roads AS r JOIN bc_municipality AS m ON ST_Contains(m.geom, r.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 ... This query takes a while, because every road in the table is summarized into the final result (about 250K roads for the example table). For smaller datsets (several thousand records on several hundred) the response can be very fast. 

5.3.5. 
Create a new table with all the roads within the city of Prince George. 
This is an example of an "overlay", which takes in two tables and outputs a new table that consists of spatially clipped or cut resultants. Unlike the "spatial join" demonstrated above, this query creates new geometries. An overlay is like a turbocharged spatial join, and is useful for more exact analysis work: CREATE TABLE pg_roads as SELECT ST_Intersection(r.geom, m.geom) AS intersection_geom, ST_Length(r.geom) AS rd_orig_length, r.* FROM bc_roads AS r JOIN bc_municipality AS m ON ST_Intersects(r.geom, m.geom) WHERE m.name = 'PRINCE GEORGE'; 

5.3.6. 
What is the length in kilometers of "Douglas St" in Victoria? 
SELECT sum(ST_Length(r.geom))/1000 AS kilometers FROM bc_roads r JOIN bc_municipality m ON ST_Intersects(m.geom, r.geom WHERE r.name = 'Douglas St' AND m.name = 'VICTORIA'; kilometers  4.89151904172838 

5.3.7. 
What is the largest municipality polygon that has a hole? 
SELECT gid, name, ST_Area(geom) AS area FROM bc_municipality WHERE ST_NRings(geom) > 1 ORDER BY area DESC LIMIT 1; gid  name  area ++ 12  SPALLUMCHEEN  257374619.430216 