Abstract
Geospatial information has been indispensable for many application fields, including traffic planning, urban planning, and energy management. Geospatial data are mainly stored in relational databases that have been developed over several decades, and most geographic information applications are desktop applications. With the arrival of big data, geospatial information applications are also being modified into, e.g., mobile platforms and Geospatial Web Services, which require changeable data schemas, faster query response times, and more flexible scalability than traditional spatial relational databases currently have. To respond to these new requirements, NoSQL (Not only SQL) databases are now being adopted for geospatial data storage, management, and queries. This paper reviews state-of-the-art geospatial data processing in the 10 most popular NoSQL databases. We summarize the supported geometry objects, main geometry functions, spatial indexes, query languages, and data formats of these 10 NoSQL databases. Moreover, the pros and cons of these NoSQL databases are analyzed in terms of geospatial data processing. A literature review and analysis showed that current document databases may be more suitable for massive geospatial data processing than are other NoSQL databases due to their comprehensive support for geometry objects and data formats and their performance, geospatial functions, index methods, and academic development. However, depending on the application scenarios, graph databases, key-value, and wide column databases have their own advantages.
Highlights
The amount of personal location data is forecast to increase by 20% every year, and location-aware information occupies a large proportion of the data generated every day: 2.5 quintillion bytes [1,2]
Neo4j has been applied in agriculture and animal husbandry applications, for example, using web technology and a Neo4j shell to evaluate the condition of the crops on the basis of geospatial data [54] and identifying relations between the members of a cattle herd based on spatial and graph databases [55]
We summarized the state-of-the-art geospatial data processing used in the 10 most popular NoSQL databases and compared their performances based on geometry objects supported, geometry functions, spatial indexes, data formats, query languages, and use in academic research
Summary
The amount of personal location data is forecast to increase by 20% every year, and location-aware information occupies a large proportion of the data generated every day: 2.5 quintillion bytes [1,2]. Examples of relational databases for geographic information include PostGIS [8], WebGIS [9], Oracle 19c [10], the Microsoft Azure SQL Database [11], and the SQL Server [12] These relational databases can define geospatial objects, support the main spatial data types (for geometry), and adopt different indexes for fast spatial queries (Binary Tree in SQL Server, Binary Tree, R-Trees, and Generalized Search Tree in PostGIS). New geospatial applications require more flexible data schema, a relatively fast query response time, and more elastic scalability than traditional spatial relational databases currently have. When the streaming requests from clients to servers suddenly increase, it might cause significant response delays and service unavailability To solve this scalability problem, a scalable framework was proposed based on MongoDB to implement elastic deployment for geospatial information sharing with the client users growing in number [14].
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