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Table of contents
What are spatial databases?
Characteristics of Spatial Database:
There are Several Types of Operations like
Need for Spatial Database
Operations of Spatial Database
Spatial Data Types
Spatial Index
Spatial Queries
Difference Between Spatial Databases and Non-Spatial Databases
Some Popular Spatial Databases
Frequently Asked Questions
What are the important characteristics of a spatial database?
What are some popular spatial databases?
What is the difference between spatial and temporal?
What is spatial and temporal database in DBMS?
Last Updated: Apr 19, 2024

Spatial Database in DBMS

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We are already familiar with the terms data and database. In RDBMS, we have learned how the data is stored in the forms of rows and columns in a table, and we use a query language to retrieve information from the table. The question arises if we have all such mechanisms already in place, why do we even need another type of database- a spatial database? 

Spatial Database in DBMS

Before we answer that question, it is important to familiarise ourselves with the term spatial data. In this article, we will learn the reason why we need a spatial database and how the database can fetch the information.

What are spatial databases?

Spatial databases are specialized databases designed to store and manage spatial data, which includes geographic or geometric information associated with objects in space. These databases are commonly used in GIS (Geographic Information Systems), location-based services, and various applications involving spatial analysis.

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Characteristics of Spatial Database:

  • Spatial Data Types: Support for storing and processing spatial data types such as points, lines, polygons, and multi-dimensional geometries.
  • Spatial Indexing: Utilization of spatial indexing techniques to efficiently retrieve and query spatial data based on their spatial relationships.
  • Geometric Operations: Capabilities for performing geometric operations such as intersection, union, buffering, and distance calculations on spatial objects.
  • Coordinate Systems: Support for different coordinate systems and projections to represent spatial data accurately on the Earth's surface.
  • Integration with GIS: Integration with GIS software and tools to enable seamless analysis, visualization, and manipulation of spatial data.
  • Scalability: Scalability to handle large volumes of spatial data and support for distributed computing environments.

There are Several Types of Operations like

  • Spatial Query Operations: Querying spatial data based on spatial relationships (e.g., "find all points within a specified distance of a given point").
  • Geometric Manipulation: Manipulating geometric shapes and objects (e.g., buffering, intersection, union).
  • Spatial Analysis: Performing spatial analysis tasks such as overlay analysis, proximity analysis, and spatial clustering.
  • Topology Operations: Analyzing and manipulating the topological relationships between spatial objects (e.g., connectivity, containment).
  • Network Analysis: Analyzing spatial networks, such as road networks, for routing, shortest path calculation, and network optimization.

Need for Spatial Database

Consider a query that lists the name of the top ten customers with the maximum amount of purchase. This can be very efficiently answered by a DBMS even if the database has to scan through a very large customer database. On the other hand, a relatively simple query like " list the name of the customers that reside within 5 kilometres of the shop" will confuse the database. To process this query, the database will have to transform the shop address and the customer's address into a suitable reference system, possibly latitude and longitude, in which distances can be computed and compared. Then the database will have to scan through the entire customer list, compute the distance between the shop and the customer's residence and if the distance is less than 5 kilometres then save the name of the customer. It will not be able to use an index to narrow down the search because traditional indices are incapable of ordering multidimensional coordinate data. A simple query can thus send a DBMS into a hopeless tailspin. Therefore we need a database tailored for handling spatial data and spatial queries.

Operations of Spatial Database

Spatial databases, in addition to standard SQL queries such as SELECT statements, can perform a wide range of spatial operations. The Open Geospatial Consortium standard specifies the following operations, among many others:

  • Spatial Measurements: Computes line length, polygon area, the distance between geometries, etc.
  • Spatial Functions: Modify existing features to create new ones, for example, by providing a buffer around them, intersecting features, etc.
  • Spatial Predicates: Allows true/false queries about spatial relationships between geometries.
  • Geometry Constructors: Creates new geometries, usually by specifying the vertices (points or nodes) which define the shape.
  • Observer Functions: Queries that return specific information about a feature, such as the location of the centre of a circle.

Spatial Data Types

Spatial data comes in a variety of shapes and sizes. Most databases support points, lines, and polygons, but some support a much broader range of spatial data types. Some databases adhere to the Open Geospatial Consortium's standards.

Spatial Index

A spatial index is a type of extended index that indexes a spatial column. A spatial column is a table column that contains spatial data such as geometry or geography.

Spatial indices are used to optimise spatial queries by spatial databases (databases that store information about objects in space). Traditional index types do not handle spatial queries efficiently, such as how far two points differ or whether points fall within a spatial area of interest.

Some popular spatial index methods are:

  • Binary space partitioning (BSP-Tree)
  • Boundary volume hierarchy
  • Grid
  • HHCode
  • Octree
  • PH-tree
  • Quadtree
  • Geohash
  • R+ tree

Spatial Queries

A spatial query is a type of database query that is supported by spatial databases, including geodatabases.

The spatial queries are mainly of three types:

  • Nearness queries
  • This type of query is used to find when something is to be found near some geographic location.
  • Region queries
  • It deals with spatial regions. It is used to query objects within some geographical boundary.
  • Union/Intersection
  • This type of query is used to fetch the intersection and union of regions.

We will avoid discussing the spatial queries in detail as it is beyond the scope of this article.

Difference Between Spatial Databases and Non-Spatial Databases

Spatial DatabasesNon-Spatial Databases
It can handle spatial data types.It cannot handle spatial data types.
It provides special functions and indexes for querying and manipulating geospatial data.It does not provide anything as such.
It supports spatial queries.It does not support spatial queries.
It can be used to perform queries that can be performed by non-spatial databases.It cannot perform the queries of the spatial databases.


Some Popular Spatial Databases

Some popular spatial databases are:

  • Esri Geodatabases
  • PostGIS
  • Snowflake
  • Oracle
  • Microsoft SQL Server

Also Read - Cardinality In DBMS

Frequently Asked Questions

What are the important characteristics of a spatial database?

The two main characteristics of a spatial database are that it offers spatial data types and spatial indexes to query the information in the spatial database.

What are some popular spatial databases?

Some popular spatial databases are Esri Geodatabases, PostGIS, Snowflake, Oracle, etc.

What is the difference between spatial and temporal?

Spatial refers to the physical location or geometric properties of objects in space, while temporal relates to the time dimension, capturing the chronological order or temporal relationships between events or data points.

What is spatial and temporal database in DBMS?

A spatial database in a DBMS stores and manages spatial data, facilitating spatial analysis and GIS applications. A temporal database handles time-related data, enabling tracking of data changes over time for historical analysis.


In this article, we have extensively discussed the introduction to the spatial database along with its various features of it.

  • Spatial data are relative geographic information about the Earth and its features.
  • A spatial database is designed and optimised to store and query information about objects (spatial data).
  • It provides special functions and indexes for querying and manipulating geospatial data.

We hope that this blog has helped you enhance your knowledge regarding the introduction to spatial databases and if you would like to learn more, check out our articles here. You can also check out the guided path here. Do upvote our blog to help other ninjas grow. Happy Coding!

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