SQL in GIS: The Emergence of Spatial SQL

GIS (Geographic Information Systems) is the technology that brings together maps and geospatial data. Spatially-enabled databases store this geographic information, making location data easily accessible and searchable. The backbone for querying geometry data such as vectors (points, lines, and polygons) or raster grids is spatial SQL.

This article will explore what spatial SQL is, and how it can be used in GIS to store, query, and analyze data. It also covers common uses for Geographic Information Systems and how they relate to spatial SQL. Finally, it’ll provide some examples from the various industries that have implemented it successfully.

What is Spatial SQL?

Structured Query Language (SQL) is a standard computer language for storing and retrieving data in tables. SQL is widely used for managing and analyzing large volumes of data. But it also has a variety of uses, such as data mining and business intelligence.

If you’re familiar with SQL, then you’ll find that spatial SQL is nothing new. Spatial SQL is the combination of SQL and geospatial technologies. It allows users to store, query, and analyze geospatial data. Spatial SQL enables GIS users to control how data is displayed and organized on a map, such as how many features are shown or how they are organized.

Why Use Spatial SQL in GIS?

On the data management side of things, GIS works with a database of locations. Data is linked to a specific location, and GIS software can overlay different layers of information with each other to perform geospatial analysis. With a GIS, organizations can collect, store, analyze, visualize, and understand their data in new ways.

GIS platforms store geospatial data in a database which can be accessed and manipulated using SQL statements. Spatial SQL adds a level of sophistication to GIS because it allows geospatial data to be queried based on location. This was not possible before because standard SQL statements do not include geocoded information. 

Types of Spatial Databases 

As you already know, spatial databases can be used to store geospatial data in a way that allows for efficient retrieval and processing

But what are some examples? Here is a list of the most common spatial databases that exist today.

PostgreSQL & PostGIS

PostgreSQL is a powerful open source database that supports a wide range of features including spatial data. PostGIS is a more commonly used extension package because it is better suited to working with geospatial data.

First, you have to install the PostgreSQL Geospatial Extension which can provide support for spatial data types and functionality.

With PostGIS, you can define points, lines, polygons, and other geometric shapes as tables in your database. You can then use these shapes to store and manipulate spatial data.

SQLite & SpatialLite

SQLite is an open-source relational database management system and is one of the most popular database engines because it has low overhead, fast performance, and supports a wide range of basic features needed by most applications.

SpatialLite provides added benefits like the ability to build a spatial index that allows you to quickly query against the stored points in your database.

Microsoft SQL Server

SQL Server is Microsoft’s flagship database product (alongside Azure SQL Database for the cloud). It provides a wide range of features, including data storage, reporting, and analytics. Spatial data types enable users to store geographic information in SQL Server without the requirement to install it.

Industries with Potential Uses for Spatial SQL

Spatial SQL has widespread applications in a variety of industries. Here are a few examples of how organizations can leverage the use of GIS and spatial SQL:

• Businesses – Businesses use geospatial data to understand the location of customers. This can be helpful when deciding where to open new stores or hire new employees. It can also be helpful when deciding where to send marketing materials and SQL integration with Power BI.
• Insurance – Insurance companies use geospatial data to determine the risk of insuring a home based on its location. They also use it to determine how to respond to events such as hurricanes.
• Government – Government agencies use geospatial data in many ways, from collecting census information to monitoring natural disasters.
• Healthcare – Healthcare organizations use geospatial data to track epidemics. They can also use it to track the location of doctors and nurses.
• Transportation – Transportation companies use geospatial data to improve their routes, such as the routes used by package delivery companies.

Learning Spatial SQL in GIS

Before you begin working with spatial data, it’s important to know how SQL works in order to be able to use databases efficiently. There are a number of different ways you can learn SQL, including self-study, online tutorials, and in-person training courses.

Some courses are offered as part of a paid membership while others may include a certificate at the completion of training. Before embarking on any type of training, it’s important to research the right resources and find one that aligns with your needs and goals.

As you become more proficient in SQL, there are a lot of programs that will provide students with the necessary training and experience to work in the field of spatial SQL. For example, two of the best places to stay are familiarizing yourself with the two main GIS software systems QGIS 3 and ArcGIS Pro.

Conclusion

Spatial SQL allows users to store, query, and analyze geospatial data. The use of geospatial data has increased significantly over the last decade. This is especially true in the Internet of Things (IoT), where physical locations are now more frequently being used as part of data analytics and machine learning models.

This article explored what spatial SQL is. It also covered why to use spatial SQL in GIS, how to implement Spatial SQL in GIS, and the benefits of Spatial SQL for GIS. Now, it’s time to explore how you can use spatial SQL in GIS.