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Spatial Data And Spatial Database Systems ((LINK))



A spatial database is a general-purpose database (usually a relational database) that has been enhanced to include spatial data that represents objects defined in a geometric space, along with tools for querying and analyzing such data. Most spatial databases allow the representation of simple geometric objects such as points, lines and polygons. Some spatial databases handle more complex structures such as 3D objects, topological coverages, linear networks, and triangulated irregular networks (TINs). While typical databases have developed to manage various numeric and character types of data, such databases require additional functionality to process spatial data types efficiently, and developers have often added geometry or feature data types. The Open Geospatial Consortium (OGC) developed the Simple Features specification (first released in 1997)[1] and sets standards for adding spatial functionality to database systems.[2] The SQL/MM Spatial ISO/IEC standard is a part the SQL/MM multimedia standard and extends the Simple Features standard with data types that support circular interpolations.[3] Almost all current relational and object-relational database management systems now have spatial extensions, and some GIS software vendors have developed their own spatial extensions to database management systems.




spatial data and spatial database systems



Geographic database (or geodatabase) is a georeferenced spatial database, used for storing and manipulating geographic data (or geodata), i.e., data associated with a location on Earth. The term "geodatabase" may also refer specifically to a set of proprietary spatial database formats, Geodatabase (Esri).


The core functionality add by a spatial extension to a database is one or more spatial datatypes, which allow for the storage of spatial data as attribute values in a table.[4] Most commonly, a single spatial value would be a geometric primitive (point, line, polygon, etc.) based on the vector data model. The datatypes in most spatial databases are based on the OGC Simple Features specification for representing geometric primitives. Some spatial databases also support the storage of raster data. Because all geographic locations must be specified according to a spatial reference system, spatial databases must also allow for the tracking and transformation of coordinate systems. In many systems, when a spatial column is defined in a table, it also includes a choice of coordinate system, chosen from a list of available systems that is stored in a lookup table.


The second major functionality extension in a spatial database is the addition of spatial capabilities to the query language (e.g., SQL); these give the spatial database the same query, analysis, and manipulation operations that are available in traditional GIS software. In most relational database management systems, this functionality is implemented as a set of new functions that can be used in SQL SELECT statements. Several types of operations are specified by the Open Geospatial Consortium standard:


A Spatial index is used by a spatial database to optimize spatial queries. Database systems use indices to quickly look up values by sorting data values in a linear (e.g. alphabetical) order; however, this way of indexing data is not optimal for spatial queries in two- or three-dimensional space. Instead, spatial databases use a spatial index designed specifically for multi-dimensional ordering.[5] Common spatial index methods include:


A spatial query is a special type of database query supported by spatial databases, including geodatabases. The queries differ from non-spatial SQL queries in several important ways. Two of the most important are that they allow for the use of geometry data types such as points, lines and polygons and that these queries consider the spatial relationship between these geometries.


The function names for queries differ across geodatabases. The following are a few of the functions built into PostGIS, a free geodatabase which is a PostgreSQL extension (the term 'geometry' refers to a point, line, box or other two or three dimensional shape):[7]


A spatial database is a database that is optimized to store and query data related to objects in space, including points, lines and polygons. While typical databases can understand various numeric and character types of data, additional functionality needs to be added for databases to process spatial data types. These are typically called geometry or feature. The Open Geospatial Consortium created the Simple Features specification and sets standards for adding spatial functionality to database systems. OGC Homepage.


Database systems use indexes to quickly look up values and the way that most databases index data is not optimal for spatial queries. Instead, spatial databases use a spatial index to speed up database operations. There are different kinds of spatial indexes, which can be adjusted if desired to better fit the data stored.


In addition to typical SQL queries such as SELECT statements, spatial databases can perform a wide variety of spatial operations. The following query types and many more are supported by the Open Geospatial Consortium:


Not all spatial databases support these query types. There are other functions that a spatial database can perform, such as spatial joins, and spatial selection. However, the queries are the most commonly used feature of spatial databases.


The value of spatial data and spatial databases expands beyond maps and visualization. Spatial data is another type of information that drives smart decision-making for your enterprise. With spatial data, you can make better decisions and improve analysis. Spatial data (also known as geospatial or geographic data) is a term used to describe data containing information about a specific location or area on the earth.


Spatial data provides a competitive advantage to your organization. Now and in the near future, spatial data is becoming critical. The decision making systems in generations to come will leverage the vast amount of spatial data coming from 5G, sensors, and the Internet of Things (IoT).


As creators of FME, the only enterprise integration platform with comprehensive spatial data support, we work with spatial data and databases constantly. With our experience and expertise, here is our take on 7 spatial databases that you should consider for your enterprise. Before that, we will also explain why you should embrace and understand the value of spatial data, sooner than later!


Spatial data is diverse. Over the years, spatial data has grown. Now, spatial data covers everything from simple vector data (points lines, or polygons) to imagery, complex 3D scenes, and even indoor locations. Representing real-world objects with accuracy or performing analysis can be quite complex. This is why we need spatial databases (also known as geospatial databases).


Spatial databases are built to store and provide powerful query capabilities for spatial data. Spatial data is often much larger in size than traditional data because of its additional locational component. Spatial databases make the storage of complex spatial data possible. Traditional database management systems are not capable of storing, querying, and indexing spatial data.


You can find spatial databases supported natively through a database (i.e. Microsoft SQL Server), or as an extension to an existing database (i.e. the ever-popular and powerful PostGIS extension for PostgreSQL).


This is where the FME platform reveals some of its strengths. Database barriers no longer matter, as you can move your data wherever you want. With support for over 450 different systems and applications, it can handle all your data tasks, spatial and otherwise.


Spatial queries perform an action on spatial data stored in the database. Some spatial queries can be used to perform simple operations. However, some queries can become much more complex, invoking spatial functions that span multiple tables. A spatial query using SQL allows you to retrieve a specific subset of spatial data. This helps you retrieve only what you need from your database.


This is how data is retrieved in spatial databases. The spatial query capabilities can vary from database to database, both in terms of performance and functionality. This is important to consider when you select your database.


This is why spatial indexes are important. Spatial indexes are created with SQL commands. These are generated from the database management interface or external program (i.e FME) with access to your spatial database. Spatial indexes vary from database to database and are responsible for the database performance necessary for adding spatial to your decision making.


This empowers spatial databases in an Esri-focused package. Geodatabases also utilize the power of versioning, which allows multiple users to edit geometry concurrently without running into conflicts. If you are using ArcGIS or ArcGIS Pro as your primary spatial analysis software, then geodatabase is a clear choice for you.


There is a plethora of documentation available about the PostGIS extension, adding specialty geometry data types. This includes point, line and polygon formats that anyone working with spatial data will be familiar with.


Are you familiar with the Oracle suite of databases and database tools? If you already have your hands on an Oracle database, this might be what your enterprise needs to bring your data to the next level.


Amazon Aurora is a cloud-based spatial database. It can run MySQL or PostgreSQL open-source databases. You can easily migrate your databases to the Amazon Aurora platform and take advantage of their full suite of tools.


This cloud-hosted service means you can have all the functionality of a traditional enterprise database, cost effectively. You can also scale your database for larger capacities, or scale it down, easily with a cloud-based database for your enterprise. This saves you time and money. 041b061a72


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