# Chapter 5. Spatial Queries

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.

## 5.1. Determining Spatial Relationships

Spatial relationships indicate how two geometries interact with one another. They are a fundamental capability for querying geometry.

### 5.1.1. Dimensionally Extended 9-Intersection Model

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

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

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` is the two endpoints. For `POLYGON`s, the boundary is the linework of the exterior and interior rings.

Interior

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.

Exterior

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 2-dimensional non-closed surface.

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

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

• `0` => point

• `1` => line

• `2` => area

• `F` => empty set

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

InteriorBoundaryExterior
Interiordim( I(a) ∩ I(b) )dim( I(a) ∩ B(b) )dim( I(a) ∩ E(b) )
Boundarydim( B(a) ∩ I(b) )dim( B(a) ∩ B(b) )dim( B(a) ∩ E(b) )
Exteriordim( E(a) ∩ I(b) )dim( E(a) ∩ B(b) )dim( E(a) ∩ E(b) )

Visually, for two overlapping polygonal geometries, this looks like:  InteriorBoundaryExterior
Interior dim( I(a) ∩ I(b) ) = 2 dim( I(a) ∩ B(b) = 1 dim( I(a) ∩ E(b) ) = 2

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

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

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

### 5.1.2. Named Spatial Relationships

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

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

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

For more details and illustrations, see the PostGIS Workshop.

### 5.1.3. General Spatial Relationships

In some cases the named spatial relationships are insufficient to provide a desired spatial filter condition. For example, consider a linear dataset representing a road network. It may be required to identify all road segments that cross each other, not at a point, but in a line (perhaps to validate some business rule). In this case ST_Crosses does not provide the necessary spatial filter, since for linear features it returns `true` only where they cross at a point. A two-step solution would be to first compute the actual intersection (ST_Intersection) of pairs of road lines that spatially intersect (ST_Intersects), and then check if the intersection's ST_GeometryType is '`LINESTRING`' (properly dealing with cases that return `GEOMETRYCOLLECTION`s of `[MULTI]POINT`s, `[MULTI]LINESTRING`s, etc.). Clearly, a simpler and faster solution is desirable. A second example is locating wharves that intersect a lake's boundary on a line and where one end of the wharf is up on shore. In other words, where a wharf is within but not completely contained by a lake, intersects the boundary of a lake on a line, and where exactly one of the wharf's endpoints is within or on the boundary of the lake. It is possible to use a combination of spatial predicates to find the required features: ST_Contains(lake, wharf) = TRUEST_ContainsProperly(lake, wharf) = FALSEST_GeometryType(ST_Intersection(wharf, lake)) = 'LINESTRING'ST_NumGeometries(ST_Multi(ST_Intersection(ST_Boundary(wharf), ST_Boundary(lake)))) = 1... but needless to say, this is quite complicated.

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

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

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

• `T` => intersection dimension is non-empty; 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
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');```

## 5.2. Using Spatial Indexes

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, “Spatial Indexes”). To do this, a spatial operator or index-aware 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 nearest-neighbor queries (the most common being <->; see Section 7.10.2, “Distance Operators” for the full list.)

Index-aware functions automatically add a bounding box operator to the spatial condition. Index-aware 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 index-aware 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.

## 5.3. Examples of Spatial SQL

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?
5.3.2. How large is the city of Prince George, in hectares?
5.3.3. What is the largest municipality in the province, by area?
5.3.4. What is the length of roads fully contained within each municipality?
5.3.5. Create a new table with all the roads within the city of Prince George.
5.3.6. What is the length in kilometers of "Douglas St" in Victoria?
5.3.7. What is the largest municipality polygon that has a hole?
 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 turbo-charged 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 ```