1 Spatial Concepts

1.1 What Is Oracle Spatial?

Oracle Spatial, often referred to as Spatial, provides a SQL schema and functions that facilitate the storage, retrieval, update, and query of collections of spatial features in an Oracle database. Spatial consists of the following:

• A schema (MDSYS) that prescribes the storage, syntax, and semantics of supported geometric data types

• A spatial indexing mechanism

• Operators, functions, and procedures for performing area-of-interest queries, spatial join queries, and other spatial analysis operations

• Functions and procedures for utility and tuning operations

• Topology data model for working with data about nodes, edges, and faces in a topology (described in Oracle Spatial Topology and Network Data Models Developer's Guide).

• Network data model for representing capabilities or objects that are modeled as nodes and links in a network (described in Oracle Spatial Topology and Network Data Models Developer's Guide).

• GeoRaster, a feature that lets you store, index, query, analyze, and deliver GeoRaster data, that is, raster image and gridded data and its associated metadata (described in Oracle Spatial GeoRaster Developer's Guide).

The spatial component of a spatial feature is the geometric representation of its shape in some coordinate space. This is referred to as its geometry.

1.2 Object-Relational Model

Spatial supports the object-relational model for representing geometries. This model stores an entire geometry in the Oracle native spatial data type for vector data, SDO_GEOMETRY. An Oracle table can contain one or more SDO_GEOMETRY columns. The object-relational model corresponds to a "SQL with Geometry Types" implementation of spatial feature tables in the Open GIS ODBC/SQL specification for geospatial features.

The benefits provided by the object-relational model include:

• Support for many geometry types, including arcs, circles, compound polygons, compound line strings, and optimized rectangles

• Ease of use in creating and maintaining indexes and in performing spatial queries

• Index maintenance by the Oracle database

• Geometries modeled in a single column

• Optimal performance

1.3 Introduction to Spatial Data

Oracle Spatial is designed to make spatial data management easier and more natural to users of location-enabled applications and geographic information system (GIS) applications. Once spatial data is stored in an Oracle database, it can be easily manipulated, retrieved, and related to all other data stored in the database.

A common example of spatial data can be seen in a road map. A road map is a two-dimensional object that contains points, lines, and polygons that can represent cities, roads, and political boundaries such as states or provinces. A road map is a visualization of geographic information. The location of cities, roads, and political boundaries that exist on the surface of the Earth are projected onto a two-dimensional display or piece of paper, preserving the relative positions and relative distances of the rendered objects.

The data that indicates the Earth location (such as longitude and latitude) of these rendered objects is the spatial data. When the map is rendered, this spatial data is used to project the locations of the objects on a two-dimensional piece of paper. A GIS is often used to store, retrieve, and render this Earth-relative spatial data.

Types of spatial data (other than GIS data) that can be stored using Spatial include data from computer-aided design (CAD) and computer-aided manufacturing (CAM) systems. Instead of operating on objects on a geographic scale, CAD/CAM systems work on a smaller scale, such as for an automobile engine or printed circuit boards.

The differences among these systems are in the size and precision of the data, not the data's complexity. The systems might all involve the same number of data points. On a geographic scale, the location of a bridge can vary by a few tenths of an inch without causing any noticeable problems to the road builders, whereas if the diameter of an engine's pistons is off by a few tenths of an inch, the engine will not run.

In addition, the complexity of data is independent of the absolute scale of the area being represented. For example, a printed circuit board is likely to have many thousands of objects etched on its surface, containing in its small area information that may be more complex than the details shown on a road builder's blueprints.

These applications all store, retrieve, update, or query some collection of features that have both nonspatial and spatial attributes. Examples of nonspatial attributes are name, soil_type, landuse_classification, and part_number. The spatial attribute is a coordinate geometry, or vector-based representation of the shape of the feature.

1.4 Geometry Types

A geometry is an ordered sequence of vertices that are connected by straight line segments or circular arcs. The semantics of the geometry are determined by its type. Spatial supports several primitive types, and geometries composed of collections of these types, including two-dimensional:

• Points and point clusters

• Line strings

• n-point polygons

• Arc line strings (All arcs are generated as circular arcs.)

• Arc polygons

• Compound polygons

• Compound line strings

• Circles

• Optimized rectangles

Two-dimensional points are elements composed of two ordinates, X and Y, often corresponding to longitude and latitude. Line strings are composed of one or more pairs of points that define line segments. Polygons are composed of connected line strings that form a closed ring, and the area of the polygon is implied. For example, a point might represent a building location, a line string might represent a road or flight path, and a polygon might represent a state, city, zoning district, or city block.

Self-crossing polygons are not supported, although self-crossing line strings are supported. If a line string crosses itself, it does not become a polygon. A self-crossing line string does not have any implied area.

Figure 1-1 illustrates the geometric types.

Figure 1-1 Geometric Types

Description of "Figure 1-1 Geometric Types"

Spatial also supports the storage and indexing of three-dimensional and four-dimensional geometric types, where three or four coordinates are used to define each vertex of the object being defined. For information about support for three-dimensional geometries, see Section 1.11.

1.5 Data Model

The Spatial data model is a hierarchical structure consisting of elements, geometries, and layers. Layers are composed of geometries, which in turn are made up of elements.

1.6 Query Model

Spatial uses a two-tier query model to resolve spatial queries and spatial joins. The term is used to indicate that two distinct operations are performed to resolve queries. The output of the two combined operations yields the exact result set.

The two operations are referred to as primary and secondary filter operations.

• The primary filter permits fast selection of candidate records to pass along to the secondary filter. The primary filter compares geometry approximations to reduce computation complexity and is considered a lower-cost filter. Because the primary filter compares geometric approximations, it returns a superset of the exact result set.

• The secondary filter applies exact computations to geometries that result from the primary filter. The secondary filter yields an accurate answer to a spatial query. The secondary filter operation is computationally expensive, but it is only applied to the primary filter results, not the entire data set.

Figure 1-2 illustrates the relationship between the primary and secondary filters.

Figure 1-2 Query Model

Description of "Figure 1-2 Query Model"

As shown in Figure 1-2, the primary filter operation on a large input data set produces a smaller candidate set, which contains at least the exact result set and may contain more records. The secondary filter operation on the smaller candidate set produces the exact result set.

Spatial uses a spatial index to implement the primary filter. Spatial does not require the use of both the primary and secondary filters. In some cases, just using the primary filter is sufficient. For example, a zoom feature in a mapping application queries for data that has any interaction with a rectangle representing visible boundaries. The primary filter very quickly returns a superset of the query. The mapping application can then apply clipping routines to display the target area.

The purpose of the primary filter is to quickly create a subset of the data and reduce the processing burden on the secondary filter. The primary filter, therefore, should be as efficient (that is, selective yet fast) as possible. This is determined by the characteristics of the spatial index on the data.

For more information about querying spatial data, see Section 5.2.

1.7 Indexing of Spatial Data

The introduction of spatial indexing capabilities into the Oracle database engine is a key feature of the Spatial product. A spatial index, like any other index, provides a mechanism to limit searches, but in this case the mechanism is based on spatial criteria such as intersection and containment. A spatial index is needed to:

• Find objects within an indexed data space that interact with a given point or area of interest (window query)

• Find pairs of objects from within two indexed data spaces that interact spatially with each other (spatial join)

A spatial index is considered a logical index. The entries in the spatial index are dependent on the location of the geometries in a coordinate space, but the index values are in a different domain. Index entries may be ordered using a linearly ordered domain, and the coordinates for a geometry may be pairs of integer, floating-point, or double-precision numbers.

Testing of spatial indexes with many workloads and operators is ongoing, and results and recommendations will be documented as they become available.

The following sections explain the concepts and options associated with R-tree indexing.

1.7.1 R-Tree Indexing

A spatial R-tree index can index spatial data of up to four dimensions. An R-tree index approximates each geometry by a single rectangle that minimally encloses the geometry (called the minimum bounding rectangle, or MBR), as shown in Figure 1-3.

Figure 1-3 MBR Enclosing a Geometry

Description of "Figure 1-3 MBR Enclosing a Geometry"

For a layer of geometries, an R-tree index consists of a hierarchical index on the MBRs of the geometries in the layer, as shown in Figure 1-4.

Figure 1-4 R-Tree Hierarchical Index on MBRs

Description of "Figure 1-4 R-Tree Hierarchical Index on MBRs"

In Figure 1-4:

• 1 through 9 are geometries in a layer.

• a, b, c, and d are the leaf nodes of the R-tree index, and contain minimum bounding rectangles of geometries, along with pointers to the geometries. For example, a contains the MBR of geometries 1 and 2, b contains the MBR of geometries 3 and 4, and so on.

• A contains the MBR of a and b, and B contains the MBR of c and d.

• The root contains the MBR of A and B (that is, the entire area shown).

An R-tree index is stored in the spatial index table (SDO_INDEX_TABLE in the USER_SDO_INDEX_METADATA view, described in Section 2.9). The R-tree index also maintains a sequence object (SDO_RTREE_SEQ_NAME in the USER_SDO_INDEX_METADATA view) to ensure that simultaneous updates by concurrent users can be made to the index.

1.8 Spatial Relationships and Filtering

Spatial uses secondary filters to determine the spatial relationship between entities in the database. The spatial relationship is based on geometry locations. The most common spatial relationships are based on topology and distance. For example, the boundary of an area consists of a set of curves that separates the area from the rest of the coordinate space. The interior of an area consists of all points in the area that are not on its boundary. Given this, two areas are said to be adjacent if they share part of a boundary but do not share any points in their interior.

The distance between two spatial objects is the minimum distance between any points in them. Two objects are said to be within a given distance of one another if their distance is less than the given distance.

To determine spatial relationships, Spatial has several secondary filter methods:

• The SDO_RELATE operator evaluates topological criteria.

• The SDO_WITHIN_DISTANCE operator determines if two spatial objects are within a specified distance of each other.

• The SDO_NN operator identifies the nearest neighbors for a spatial object.

The syntax of these operators is given in Chapter 19.

The SDO_RELATE operator implements a nine-intersection model for categorizing binary topological relationships between points, lines, and polygons. Each spatial object has an interior, a boundary, and an exterior. The boundary consists of points or lines that separate the interior from the exterior. The boundary of a line string consists of its end points; however, if the end points overlap (that is, if they are the same point), the line string has no boundary. The boundaries of a multiline string are the end points of each of the component line strings; however, if the end points overlap, only the end points that overlap an odd number of times are boundaries. The boundary of a polygon is the line that describes its perimeter. The interior consists of points that are in the object but not on its boundary, and the exterior consists of those points that are not in the object.

Given that an object A has three components (a boundary Ab, an interior Ai, and an exterior Ae), any pair of objects has nine possible interactions between their components. Pairs of components have an empty (0) or not empty (1) set intersection. The set of interactions between two geometries is represented by a nine-intersection matrix that specifies which pairs of components intersect and which do not. Figure 1-5 shows the nine-intersection matrix for two polygons that are adjacent to one another. This matrix yields the following bit mask, generated in row-major form: "101001111".

Figure 1-5 The Nine-Intersection Model

Description of "Figure 1-5 The Nine-Intersection Model"

Some of the topological relationships identified in the seminal work by Professor Max Egenhofer (University of Maine, Orono) and colleagues have names associated with them. Spatial uses the following names:

• DISJOINT: The boundaries and interiors do not intersect.

• TOUCH: The boundaries intersect but the interiors do not intersect.

• OVERLAPBDYDISJOINT: The interior of one object intersects the boundary and interior of the other object, but the two boundaries do not intersect. This relationship occurs, for example, when a line originates outside a polygon and ends inside that polygon.

• OVERLAPBDYINTERSECT: The boundaries and interiors of the two objects intersect.

• EQUAL: The two objects have the same boundary and interior.

• CONTAINS: The interior and boundary of one object is completely contained in the interior of the other object.

• COVERS: The interior of one object is completely contained in the interior or the boundary of the other object and their boundaries intersect.

• INSIDE: The opposite of CONTAINS. A INSIDE B implies B CONTAINS A.

• COVEREDBY: The opposite of COVERS. A COVEREDBY B implies B COVERS A.

• ON: The interior and boundary of one object is on the boundary of the other object (and the second object covers the first object). This relationship occurs, for example, when a line is on the boundary of a polygon.

• ANYINTERACT: The objects are non-disjoint.

Figure 1-6 illustrates these topological relationships.

Figure 1-6 Topological Relationships

Description of "Figure 1-6 Topological Relationships"

The SDO_WITHIN_DISTANCE operator determines if two spatial objects, A and B, are within a specified distance of one another. This operator first constructs a distance buffer, D_b, around the reference object B. It then checks that A and D_b are non-disjoint. The distance buffer of an object consists of all points within the given distance from that object. Figure 1-7 shows the distance buffers for a point, a line, and a polygon.

Figure 1-7 Distance Buffers for Points, Lines, and Polygons

Description of "Figure 1-7 Distance Buffers for Points, Lines, and Polygons"

In the point, line, and polygon geometries shown in Figure 1-7:

• The dashed lines represent distance buffers. Notice how the buffer is rounded near the corners of the objects.

• The geometry on the right is a polygon with a hole: the large rectangle is the exterior polygon ring and the small rectangle is the interior polygon ring (the hole). The dashed line outside the large rectangle is the buffer for the exterior ring, and the dashed line inside the small rectangle is the buffer for the interior ring.

The SDO_NN operator returns a specified number of objects from a geometry column that are closest to a specified geometry (for example, the five closest restaurants to a city park). In determining how close two geometry objects are, the shortest possible distance between any two points on the surface of each object is used.

1.9 Spatial Operators, Procedures, and Functions

The Spatial PL/SQL application programming interface (API) includes several operators and many procedures and functions.

Spatial operators, such as SDO_FILTER and SDO_RELATE, provide optimum performance because they use the spatial index. (Spatial operators require that the geometry column in the first parameter have a spatial index defined on it.) Spatial operators must be used in the WHERE clause of a query. The first parameter of any operator specifies the geometry column to be searched, and the second parameter specifies a query window. If the query window does not have the same coordinate system as the geometry column, Spatial performs an implicit coordinate system transformation. For detailed information about the spatial operators, see Chapter 19.

Spatial procedures and functions are provided as subprograms in PL/SQL packages, such as SDO_GEOM, SDO_CS, and SDO_LRS. These subprograms do not require that a spatial index be defined, and they do not use a spatial index if it is defined. These subprograms can be used in the WHERE clause or in a subquery. If two geometries are input parameters to a Spatial procedure or function, both must have the same coordinate system.

The following performance-related guidelines apply to the use of spatial operators, procedures, and functions:

• If an operator and a procedure or function perform comparable operations, and if the operator satisfies your requirements, use the operator. For example, unless you need to do otherwise, use SDO_RELATE instead of SDO_GEOM.RELATE, and use SDO_WITHIN_DISTANCE instead of SDO_GEOM.WITHIN_DISTANCE.

• With operators, always specify TRUE in uppercase. That is, specify = 'TRUE', and do not specify <> 'FALSE' or = 'true'.

• With operators, use the /*+ ORDERED */ optimizer hint if the query window comes from a table. (You must use this hint if multiple windows come from a table.) See the Usage Notes and Examples for specific operators for more information.

For information about using operators with topologies, see Oracle Spatial Topology and Network Data Models Developer's Guide.

1.10 Spatial Aggregate Functions

SQL has long had aggregate functions, which are used to aggregate the results of a SQL query. The following example uses the SUM aggregate function to aggregate employee salaries by department:

SELECT SUM(salary), dept
   FROM employees
   GROUP BY dept;

Oracle Spatial aggregate functions aggregate the results of SQL queries involving geometry objects. Spatial aggregate functions return a geometry object of type SDO_GEOMETRY. For example, the following statement returns the minimum bounding rectangle of all geometries in a table (using the definitions and data from Section 2.1):

SELECT SDO_AGGR_MBR(shape) FROM cola_markets;

The following example returns the union of all geometries except cola_d:

SELECT SDO_AGGR_UNION(SDOAGGRTYPE(c.shape, 0.005))
  FROM cola_markets c WHERE c.name <> 'cola_d';

For reference information about the spatial aggregate functions and examples of their use, see Chapter 20.

1.10.1 SDOAGGRTYPE Object Type

Many spatial aggregate functions accept an input parameter of type SDOAGGRTYPE. Oracle Spatial defines the object type SDOAGGRTYPE as:

CREATE TYPE sdoaggrtype AS OBJECT (
 geometry SDO_GEOMETRY,
 tolerance NUMBER);

Note:

Do not use SDOAGGRTYPE as the data type for a column in a table. Use this type only in calls to spatial aggregate functions.

The tolerance value in the SDOAGGRTYPE definition should be the same as the SDO_TOLERANCE value specified in the DIMINFO column in the xxx_SDO_GEOM_METADATA views for the geometries, unless you have a specific reason for wanting a different value. For more information about tolerance, see Section 1.5.5; for information about the xxx_SDO_GEOM_METADATA views, see Section 2.8.

The tolerance value in the SDOAGGRTYPE definition can affect the result of a spatial aggregate function. Figure 1-8 shows a spatial aggregate union (SDO_AGGR_UNION) operation of two geometries using two different tolerance values: one smaller and one larger than the distance between the geometries.

Figure 1-8 Tolerance in an Aggregate Union Operation

Description of "Figure 1-8 Tolerance in an Aggregate Union Operation"

In the first aggregate union operation in Figure 1-8, where the tolerance is less than the distance between the rectangles, the result is a compound geometry consisting of two rectangles. In the second aggregate union operation, where the tolerance is greater than the distance between the rectangles, the result is a single geometry.

1.11 Three-Dimensional Spatial Objects

Effective with Oracle Database Release 11.1, Oracle Spatial supports the storage and retrieval of three-dimensional spatial data, which can include points, point clouds (collections of points), lines, polygons, surfaces, and solids. Table 1-1 show the SDO_GTYPE and SDO_ELEM_INFO attributes of the SDO_GEOMETRY type that are relevant to three-dimensional geometries. (The SDO_GEOMETRY type is explained in Section 2.2.)

The following Spatial operators consider all three dimensions in their computations:

• SDO_ANYINTERACT (and SDO_RELATE with the ANYINTERACT mask)

• SDO_FILTER

• SDO_NN

• SDO_WITHIN_DISTANCE

The other operators consider only the first two dimensions. (Spatial operators are described in Chapter 19.)

The SDO_GEOM.SDO_VOLUME function applies only to solid geometries, which are by definition three-dimensional; however, this function cannot be used with geodetic data. (This function is described in Chapter 24.)

For distance computations with three-dimensional geometries:

• If the data is geodetic (geographic 3D), the distance computations are done on the geodetic surface.

• If the data is non-geodetic (projected or local), the distance computations are valid only if the unit of measure is the same for all three dimensions.

To have any functions, procedures, or operators consider all three dimensions, you must specify PARAMETERS ('sdo_indx_dims=3') in the CREATE INDEX statement when you create the spatial index on a spatial table.

For Spatial functions, procedures, and operators that consider all three dimensions, distance and length computations correctly factor in the height or elevation. For example, consider two three-dimensional points, one at the origin of a Cartesian space (0,0,0), and the other at X=3 on the Y axis and a height (Z) of 4 (3,0,4).

• If the operation considers all three dimensions, the distance between the two points is 5. (Think of the hypotenuse of a 3-4-5 right triangle.)

• If the operation considers only two dimensions, the distance between the two points is 3. (That is, the third dimension, or height, is ignored.)

For examples of creating different types of three-dimensional spatial geometries, see Section 2.7.9. That section also includes an example showing how to update the spatial metadata and create spatial indexes for three-dimensional geometries.

For information about support for three-dimensional coordinate reference systems, see Section 6.5.

Three-dimensional support does not apply to many spatial aggregate functions and PL/SQL packages and subprograms. The following are supported for two-dimensional geometries only:

• Spatial aggregate functions, except for SDO_AGGR_MBR, which is supported for both two-dimensional and three-dimensional geometries.

• SDO_GEOM (geometry) subprograms, except for the following, which are supported for both two-dimensional and three-dimensional geometries:

  • SDO_GEOM.RELATE with the ANYINTERACT mask

  • SDO_GEOM.SDO_AREA

  • SDO_GEOM.SDO_DISTANCE

  • SDO_GEOM.SDO_LENGTH

  • SDO_GEOM.SDO_MAX_MBR_ORDINATE

  • SDO_GEOM.SDO_MBR

  • SDO_GEOM.SDO_MIN_MBR_ORDINATE

  • SDO_GEOM.SDO_VOLUME

  • SDO_GEOM.VALIDATE_GEOMETRY_WITH_CONTEXT

  • SDO_GEOM.VALIDATE_LAYER_WITH_CONTEXT

  • SDO_GEOM.WITHIN_DISTANCE

• SDO_SAM (spatial analysis and mining) subprograms

• SDO_MIGRATE.TO_CURRENT procedure

$ORACLE_HOME/md/demo/TIN/examples/java/README.txt
$ORACLE_HOME/md/demo/TIN/examples/java/readTIN.java

1.11.2 Modeling Solids

The simplest types of solids can be represented as cuboids, such as a cube or a brick. A more complex solid is a frustum, which is a pyramid formed by cutting a larger pyramid (with three or more faces) by a plane parallel to the base of that pyramid. Frustums can only be used as query windows to spatial operators. Frustums and cubes are typically modeled as solid-type SDO_GEOMETRY objects. Figure 1-9 shows a frustum as a query window, with two spatial objects at different distances from the view point.

Figure 1-9 Frustum as Query Window for Spatial Objects

Description of "Figure 1-9 Frustum as Query Window for Spatial Objects"

Point clouds, which are large collections of points, can sometimes be used to model the shape or structure of solid and surface geometries. Most applications that use point cloud data contain one of both of the following kinds of spatial queries: queries based on location, and queries based on both location and visibility (that is, visibility queries).

Most applications that use point cloud data seek to minimize data transfer by retrieving objects based on their distance from a view point. For example, in Figure 1-9, object B is farther from the view point than object A, and therefore the application might retrieve object A in great detail (high resolution) and object B in less detail (low resolution). In most scenarios, the number of objects increases significantly as the distance from the view point increases; and if farther objects are retrieved at lower resolutions than nearer objects, the number of bytes returned by the query and the rendering time for the objects decrease significantly.

The general process for working with a point cloud is as follows:

1. Initialize the point cloud, using the SDO_PC_PKG.INIT function.

2. Create the point cloud, using the SDO_PC_PKG.CREATE_PC procedure.

3. As needed for queries, clip the point cloud, using the SDO_PC_PKG.CLIP_PC function.

4. If necessary, use the SDO_PC_PKG.TO_GEOMETRY function (for example, to convert the result of a clip operation into a single SDO_GEOMETRY object).

The PL/SQL subprograms for working with point clouds are described in Chapter 28.

For a Java example of working with point clouds, see the following files:

$ORACLE_HOME/md/demo/PointCloud/examples/java/README.txt
$ORACLE_HOME/md/demo/PointCloud/examples/java/readPointCloud.java

1.12 Geocoding

Geocoding is the process of converting tables of address data into standardized address, location, and possibly other data. The result of a geocoding operation includes the pair of longitude and latitude coordinates that correspond with the input address or location. For example, if the input address is 22 Monument Square, Concord, MA 01742, the longitude and latitude coordinates in the result of the geocoding operation may be (depending on the geocoding data provider) -71.34937 and 42.46101, respectively.

Given a geocoded address, you can perform proximity or location queries using a spatial engine, such as Oracle Spatial, or demographic analysis using tools and data from Oracle's business partners. In addition, you can use geocoded data with other spatial data such as block group, postal code, and county code for association with demographic information. Results of analyses or queries can be presented as maps, in addition to tabular formats, using third-party software integrated with Oracle Spatial.

For conceptual and usage information about the geocoding capabilities of Oracle Spatial, see Chapter 11. For reference information about the MDSYS.SDO_GCDR PL/SQL package, see Chapter 23.

1.13 Spatial Java Application Programming Interface

Oracle Spatial provides a Java application programming interface (API) that includes the following packages:

• oracle.spatial.geometry provides support for the Spatial SQL SDO_GEOMETRY data type, which is documented in this guide.

• oracle.spatial.network provides support for the Oracle Spatial network data model, which is documented in Oracle Spatial Topology and Network Data Models Developer's Guide.

• oracle.spatial.topo provides support for the Oracle Spatial topology data model, which is documented in Oracle Spatial Topology and Network Data Models Developer's Guide.

• oracle.spatial.util provides classes that perform miscellaneous operations.

For detailed reference information about the classes and interfaces in these packages, see Oracle Spatial Java API Reference (Javadoc).

1.14 Predefined User Accounts Created by Spatial

During installation, Spatial creates user accounts that have the minimum privileges needed to perform their jobs. These accounts are created locked and expired; so if you need to use the accounts, you must unlock them. Table 1-2 lists the predefined user accounts created by Spatial.

For information about Oracle Database predefined user accounts, including how to secure these accounts, see Oracle Database 2 Day + Security Guide.

1.15 Performance and Tuning Information

Many factors can affect the performance of Oracle Spatial applications, such as the use of optimizer hints to influence the plan for query execution. This guide contains some information about performance and tuning where it is relevant to a particular topic. For example, Section 1.7.2 discusses R-tree quality and its possible effect on query performance, and Section 1.9 explains why spatial operators provide better performance than procedures and functions.

In addition, more Spatial performance and tuning information is available in one or more white papers through the Oracle Technology Network (OTN). That information is often more detailed than what is in this guide, and it is periodically updated as a result of internal testing and consultations with Spatial users. To find that information on the OTN, go to

http://www.oracle.com/technology/products/spatial/

Look for material relevant to Spatial performance and tuning.

1.16 Open Geospatial Consortium (OGC) Conformance

Oracle Spatial is conformant with Open Geospatial Consortium (OGC) Simple Features Specification 1.1.1 (Document 99-049), starting with Oracle Database release 10g (version 10.1.0.4). Conformance with the SQL92 with Geometry Types Implementation means that Oracle Spatial supports all the types, functions, and language constructs detailed in Section 3.2 of the specification.

Synonyms are created to match all OGC function names except for X(p Point) and Y(p Point). For these functions, you must use the names OGC_X and OGC_Y instead of just X and Y.

1.17 Spatial Release (Version) Number

To check which release of Spatial you are running, use the SDO_VERSION function. For example:

SELECT SDO_VERSION FROM DUAL;

SDO_VERSION
--------------------------------------------------------------------------------
11.1.0.0.0

1.18 Spatial Application Hardware Requirement Considerations

This section discusses some general guidelines that affect the amount of disk storage space and CPU power needed for applications that use Oracle Spatial. These guidelines are intended to supplement, not replace, any other guidelines you use for general application sizing.

The following characteristics of spatial applications can affect the need for storage space and CPU power:

• Data volumes: The amount of storage space needed for spatial objects depends on their complexity (precision of representation and number of points for each object). For example, storing one million point objects takes less space than storing one million road segments or land parcels. Complex natural features such as coastlines, seismic fault lines, rivers, and land types can require significant storage space if they are stored at a high precision.

• Query complexity: The CPU requirements for simple mapping queries, such as Select all features in this rectangle, are lower than for more complex queries, such as Find all seismic fault lines that cross this coastline.

1.19 Spatial Error Messages

Spatial error message numbers are in the range of 13000 to 13499. The messages are documented in Oracle Database Error Messages.

Oracle error message documentation is only available in HTML. If you only have access to the Oracle Documentation DVD, you can browse the error messages by range. Once you find the specific range, use your browser's "find in page" feature to locate the specific message. When connected to the Internet, you can search for a specific error message using the error message search feature of the Oracle online documentation.

1.20 Spatial Examples

Oracle Spatial provides examples that you can use to reinforce your learning and to create models for coding certain operations. If you installed the demo files from the Oracle Database Examples media, several examples are provided in the following directory:

$ORACLE_HOME/md/demo/examples

The following files in that directory are helpful for applications that use the Oracle Call Interface (OCI):

• readgeom.c and readgeom.h

• writegeom.c and writegeom.h

This guide also includes many examples in SQL and PL/SQL. One or more examples are usually provided with the reference information for each function or procedure, and several simplified examples are provided that illustrate table and index creation, combinations of functions and procedures, and advanced features:

• Inserting, indexing, and querying spatial data (Section 2.1)

• Coordinate systems (spatial reference systems) (Section 6.12)

• Linear referencing system (LRS) (Section 7.7)

• SDO_GEOMETRY objects in function-based indexes (Section 9.2)

• Complex queries (Appendix C)

1.21 README File for Spatial and Related Features

A README.txt file supplements the information in the following manuals: Oracle Spatial Developer's Guide (this manual), Oracle Spatial GeoRaster Developer's Guide, and Oracle Spatial Topology and Network Data Models Developer's Guide. This file is located at:

$ORACLE_HOME/md/doc/README.txt