How can you manage spatial data that is too large for memory?

1 Use a spatial database

One of the most common and powerful ways to manage large spatial data is to use a spatial database. A spatial database is a database that supports spatial data types, operations, and indexes, allowing you to store, query, and manipulate spatial data in a structured and scalable way. Spatial databases can handle very large datasets, as they can distribute the data across multiple servers, partitions, or clusters, and use compression and indexing techniques to optimize the storage and retrieval of spatial data. Some examples of spatial databases are PostgreSQL with PostGIS, Oracle Spatial and Graph, and Microsoft SQL Server Spatial.

2 Use a cloud service

Another option to manage large spatial data is to use a cloud service. A cloud service is a service that provides computing resources, such as storage, processing, and networking, over the internet, on demand, and pay-as-you-go. Cloud services can offer many advantages for managing large spatial data, such as scalability, flexibility, accessibility, and security. You can use cloud services to store your spatial data in cloud storage platforms, such as Amazon S3, Google Cloud Storage, or Azure Blob Storage, and access them from anywhere using web services or APIs. You can also use cloud services to process your spatial data in cloud computing platforms, such as Amazon EC2, Google Compute Engine, or Azure Virtual Machines, and leverage the power of parallel, distributed, or serverless computing.

4 Use a data structure

A fourth technique to manage large spatial data is to use a data structure that is appropriate for your data analysis and operations. A data structure is a way of organizing and storing data in memory or disk, using a specific logic and layout. Data structures can influence the efficiency and effectiveness of your spatial data analysis, as they can affect how the data is accessed, manipulated, and searched. You should use a data structure that is optimized for your data size, shape, and complexity, as well as your analytical tasks, functions, and algorithms. Some examples of data structures for spatial data are arrays, trees, graphs, and hashes.

5 Use a data reduction

A fifth method to manage large spatial data is to use a data reduction that is acceptable for your data quality and accuracy. A data reduction is a way of reducing the size or complexity of data, using a specific technique or criterion. Data reduction can help you manage large spatial data, as it can reduce the memory or disk space required, as well as the processing or transmission time. However, data reduction can also introduce some errors or distortions in your data, depending on the level and type of reduction. You should use a data reduction that is consistent with your data objectives, standards, and constraints. Some examples of data reduction for spatial data are sampling, filtering, aggregation, and simplification.

Managing spatial data that is too large for memory can be challenging, but not impossible. By using some of the strategies and tools discussed in this article, you can handle large spatial data more efficiently and effectively, and achieve your GIS goals and outcomes.