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Applying and Extending Oracle Spatial
Applying and Extending Oracle Spatial

Applying and Extending Oracle Spatial: This guide takes you straight into the attributes of Oracle Spatial and teaches you to extend, apply, and combine them with other Oracle and open source technologies. A vital manual for solving everyday problems.

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Applying and Extending Oracle Spatial

Chapter 2. Importing and Exporting Spatial Data

Once we have defined a data model, the next step is to load data into it. After the data is loaded, it needs to be checked for "cleanliness" before indexing and using it. There are many methods for loading data of different types and formats. In this chapter, we describe some of the most common formats, and how they can be loaded into the Oracle database using free tools, tools already available with Oracle, and tools from third-party vendors. In addition, we also discuss other issues relating to import performance and organization of data for efficient access by applications.

The goal of this chapter is to give you a complete overview of all aspects of data loading from tools, through physical loading techniques and data organization, data quality checking, and indexing.

  • Extract, transform, and load (ETL) tools: GeoKettle, Oracle Spatial Shapefile loader, and Map Builder

  • Using SQL, Application Express, and Excel

  • Implementing theoretical storage...

Extract, transform, and load (ETL) tools


Spatial data is usually collected from many different sources, such as proprietary file systems, GIS tools, and third-party vendors. One of the biggest challenges for a database administrator (DBA) is to ingest all of these different formats of data, and present them in a uniform data model to their users. There are specific data loading tools (such as Shapefile, KML, and TAB file converters) to load the data from these specific file formats. In traditional database systems, an ETL tool is used to ingest data from different data sources. Now, there are several graphical user interface (GUI) driven ETL tools (both open source and third-party vendor provided) that can support many of the common spatial data formats. We look at one such ETL tool in detail to see how it can be used for the spatial data loading process.

An ETL tool supports extract, transform, and load operations. During the extract process, the tool can extract data from different source...

Storage resolution versus resolution used by functions


When data is collected from external data sources, it is very important to understand different accuracy and storage characteristics of the data. Coordinates of the geometry are stored in VARRAYS of numbers in Oracle Spatial. The Number data type in the Oracle database stores fixed and floating-point numbers. Numbers of virtually any magnitude can be stored, with up to 38 digits of precision. This means() that any coordinate in the Oracle Spatial format can have up to 38 digits in decimal representation. This level of precision is not usually required for many applications, so it is important to understand how the number of digits used for each coordinate affects the storage and performance of the spatial applications.

Precision and accuracy

Oracle Spatial also has the concept of tolerance for geometry data. As described in Chapter 1, Defining a Data Model for Spatial Data Storage, tolerance is used to distinguish unique coordinates in...

Creating spatial autocorrelation via clustering


When spatial data is loaded into a table, data is usually organized depending on the order of the incoming rows. This ordering of the data on disk has a direct impact on the performance of the spatial queries. Consider a simple SDO_ANYINTERACT query that retrieves all the data inside a rectangular box. After the spatial query is performed, the rows that satisfy the result are retrieved from the table. If all of these rows are spread over different data blocks, the cost of the query increases, as many blocks have to be fetched to form the result set. If data in the blocks can be organized in such a way to minimize the number of blocks fetched for each query, the query performance would improve. This improvement will be greater for queries that fetch a large number of rows for each query. For example, in web mapping applications, small scale maps show less detail and large scale maps show more detail. As the scale goes from small to large, more...

Geometry validation and methods to clean imported data


When data is loaded from external data sources, it is often possible that the spatial data is invalid based on the validation rules in Oracle Spatial. Sometimes these errors are due to variations in how the validation rules are defined between different systems. For example, in Oracle Spatial, polygons should be ordered counter-clockwise for exterior rings and clockwise for interior rings. Such errors can be easily fixed using the utility functions provided by Oracle Spatial. Sometimes these errors are due to inherently bad data that has not been fixed or validated. Such errors are harder to fix, as there might not be a way to fix the geometries without drastically modifying the data. In such cases, users need to manually inspect the data and decide on the appropriate fix.

The SDO_GEOM.Validate_Geometry_With_Context function gives very specific information about what is wrong with the invalid geometries. The errors associated with the...

Coordinate system transformation techniques


As we have seen in the first section of this chapter, it is very common to do coordinate system transformations as part of the data loading process. Many of the external data loading tools provide these transformations, but sometimes it is convenient and more efficient to do these transformations in the database. Oracle Spatial provides two distinct methods for doing coordinate system transformations. The SDO_CS.TRANSFORM function works on one row at a time to transform a geometry, while the SDO_CS.TRANSFORM_LAYER function works on a whole table of data and transforms all the geometries in a column of the table. Both methods support different use cases: the transform function is mainly used in dynamic query situations, while the TRANSFORM_LAYER function is mainly used in bulk update or bulk loading situations. As we are describing data loading techniques in this chapter, we will describe how both these methods can be used in bulk data loading situations...

Spatial indexing


Spatial indexing is one of the most important concepts in Oracle Spatial. Using the index can dramatically reduce the query cost for many operations. As Oracle uses an R-tree index, there are actually no maintenance operations required once the index is built. However, there are several important considerations while building the index to get the best possible performance for queries. In the following section, we describe some of the most commonly used parameters in the Create Index statement.

Layer GTYPE for point data

As we mentioned in Chapter 1, Defining a Data Model for Spatial Data Storage, the LAYER_GTYPE parameter can be used to enforce type consistency across all the rows in the table. For point data, this parameter can also be used to improve the performance for spatial index-based queries against these tables. When an operator like SDO_RELATE is evaluated, the R-tree index is used to do the primary filtering. In some cases, the index nodes can be used to perform...

Exporting formats – GML, WKT, and GeoJSON


Oracle Spatial provides different types of converters to convert the geometry data to GML (both Version 2.1 and Version 3.1.1), KML, and the well-known text and binary (WKT and WKB) representations. These converters are provided as PL/SQL functions and Java APIs. In this section, we will show examples of using these converters to generate GML and KML documents. The WKT and WKB examples will be very similar and are left as an exercise for the reader.

We first look at the KML converter using the following SQL example:

Select SDO_UTIL.To_Kmlgeometry(geom) gml_feature
From land_parcels Where fid= 16; 
 
GML_FEATURE
--------------------------------------------------------------------
<Polygon><extrude>0</extrude><tessellate>0</tessellate><altitudeMode>relativeToGround</altitudeMode><outerBoundaryIs><LinearRing> <coordinates>6006491.42346619,2121789.41749893 6006423.35503277,2121779.7587256 6006442...

Summary


Spatial data is usually available in many different file formats as many GIS tools use their own proprietary formats for managing this data, so it is very important to understand how to load all these different formats of spatial data into Oracle Spatial. Many ETL tools and data loaders support Oracle Spatial as both input and output format. In this chapter, we looked at different methods for loading the spatial data into Oracle Spatial including simple CSV files to the most common file formats. We also show how to load and generate data in formats that are very common in web mapping applications. This chapter also described issues related to the storage of geometry data in Oracle Spatial. Sometimes it is beneficial to understand the data layout in Oracle Spatial tables, and this was explained with concepts like spatial clustering.

Once the data is loaded into the database, it is now ready for use with applications. Oracle provides many other database features that can be used to...

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Key benefits

  • Understand how to develop Oracle Spatial data models and applications that use PL/SQL and Java to solve common problems using practical, hands-on examples
  • Design a data model for spatial applications
  • Use standard database technologies for managing Spatial data
  • Learn how to combine spatial and non-spatial data in the database
  • Get to grips with how to use Oracle Spatial's standards compliant geometry data types to develop cross-vendor database solutions to common problems

Description

Spatial applications should be developed in the same way that users develop other database applications: by starting with an integrated data model in which the SDO_GEOMETRY objects are just another attribute describing entities and by using as many of the database features as possible for managing the data. If a task can be done using a database feature like replication, then it should be done using the standard replication technology instead of inventing a new procedure for replicating spatial data. Sometimes solving a business problem using a PL/SQL function can be more powerful, accessible, and easier to use than trying to use external software. Because Oracle Spatial's offerings are standards compliant, this book shows you how Oracle Spatial technology can be used to build cross-vendor database solutions. Applying and Extending Oracle Spatial shows you the clever things that can be done not just with Oracle Spatial on its own, but in combination with other database technologies. This is a great resource book that will convince you to purchase other Oracle technology books on non-spatial specialist technologies because you will finally see that "spatial is not special: it is a small, fun, and clever part of a much larger whole".

Who is this book for?

This book is for existing users of Oracle and Oracle Spatial and so assumes you have a basic knowledge of Oracle in terms of understanding the SDO_GEOMETRY type, creating tables, indexes, and views, executing basic to moderately complex queries, and some idea of PL/SQL programming of triggers and stored procedures.

What you will learn

  • Design a data model for a spatial application
  • ¬ÑBuild and use database triggers that manage data validation and other tasks
  • ¬ÑUse Oracle s materialized view, replication, and PL/SQL technologies with a spatial database
  • ¬ÑUnderstand and use Oracle s raster data management technology to implement solutions within a range of applications
  • ¬ÑExplore advanced queuing for managing spatial data processing
  • ¬ÑApply linear referencing programming to solve business problems
  • ¬ÑDiscover Oracle s OGC and SQLMM-compliant geometry types when implementing solutions capable of cross-vendor database deployment
  • ¬ÑUse partitioning to manage large spatial datasets in the database
  • ¬ÑLoad spatial data (raster and vector) from different data sources in the database
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Table of Contents

11 Chapters
Defining a Data Model for Spatial Data Storage Chevron down icon Chevron up icon
Importing and Exporting Spatial Data Chevron down icon Chevron up icon
Using Database Features in Spatial Applications Chevron down icon Chevron up icon
Replicating Geometries Chevron down icon Chevron up icon
Partitioning of Data Using Spatial Keys Chevron down icon Chevron up icon
Implementing New Functions Chevron down icon Chevron up icon
Editing, Transforming, and Constructing Geometries Chevron down icon Chevron up icon
Using and Imitating Linear Referencing Functions Chevron down icon Chevron up icon
Raster Analysis with GeoRaster Chevron down icon Chevron up icon
Integrating Java Technologies with Oracle Spatial Chevron down icon Chevron up icon
SQL/MM – A Basis for Cross-platform, Inter-operable, and Reusable SQL Chevron down icon Chevron up icon
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