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Best Database for Geospatial Data

Geospatial data is a type of information that describes objects or events with a specific location on or near the surface of the Earth. It combines location information and attribute information with temporal details. This data is critical in transportation, urban planning, and logistics industries, as it provides context for decision-making that traditional data analysis can't. 

Several spatial database systems have been created to meet business needs in geospatial data management. These databases' unique features and capabilities make location-based data technologies more accessible and efficient.  

What is a Geospatial Database?

A geospatial database is optimized for storing and querying data that represents objects defined in a geometric space. Geospatial data or geographic information includes information like coordinates, addresses, and more complex structures like polygons representing areas of interest or lines mapping out routes. 

Geospatial databases are an essential component of GIS (Geographic Information System) technologies. They play a vital role in creating maps, conducting spatial queries and analysis, and powering navigation systems. These databases can handle diverse tasks, including calculating distances between coordinates and identifying geographical features within a specific radius, and executing other location-based queries. 

What to Look for When Choosing a Geospatial Database? 

When choosing a geospatial database, there are several factors to consider. Some of the most important ones are:

  1. Performance: Geospatial databases must be able to handle large volumes of data and queries efficiently. Look for databases that offer high performance and scalability.
  2. Support for Spatial Data Types: The database must support spatial data types and have functions that understand geographic principles. This enables the efficient querying of spatial data.
  3. Ease of Use: Look for a geospatial database that is easy to use and offers SQL interface. This will make it easier to manage and query your data, as there is no need to get familiar with another query language.
  4. Cost: Consider the cost of the database, including licensing fees and maintenance costs.
  5. Open Source: Open source databases are often more flexible and customizable than proprietary ones. They also have large communities of developers who can provide support and contribute to the development of the database. 

CrateDB: A database for storing and analyzing geospatial data

CrateDB is an open source, multi-model, and distributed database that offers high performance, scalability, and flexibility. It is designed to handle large volumes of data and can be used for a wide range of use cases, including geospatial data management. 

CrateDB offers versatile support for geospatial data types. For storing geopoint data, there are two primary formats you can use: Array Format and WKT Format. The Array Format provides a simple and straightforward way to store point locations, while the WKT Format provides a standardized way to represent geospatial data. 

For storing more complex geospatial shapes, CrateDB offers the GeoJSON Format and a variety of Well-Known Text formats. Whether you're storing polygons, lines, or multi-points, WKT offers a standard representation for each of these geospatial structures. 

CrateDB also offers geospatial functions to address various use cases. For instance, the within function can be used to find how many records are located within a specific area, while the distance function can be used to find the closest record. The within and intersects functions can be used to find whether two areas overlap.  

Geospatial databases are critical for managing and analyzing location-based data. When choosing a database for storing and analyzing geospatial data, it is essential to consider factors such as performance, support for spatial data types, ease of use, open-source nature, and cost. CrateDB is a powerful and versatile database that offers high performance, scalability, and flexibility, making it an ideal choice for managing geospatial data.