Beyond Maps—How Cloud Computing Enables the Future of Geospatial Analysis Services

Over the last 5 years, cloud computing has evolved from a promising emergent technology, to an awareness far beyond its information technology roots. Most people even outside of science and technology have heard of “the cloud” and have some perception that it is a computing technology which makes things faster and easier. Cloud computing was originally presented as a way to streamline IT management within an organization and ultimately save money. As the adoption of cloud computing increases by government, industry, and academia, we now see a technology transformation that is enabling deeper understanding, and will lead to new insights and new discoveries.

Early adoption of geospatial cloud computing focused on organizing and sharing data. In atmospheric science, web map services such as WMS have become a common addition from organizations who previously only provided ftp data downloads. Map services (a picture of the data) are symbolized data ready to view, and require fewer specialized skills than working with raw data such as GRIB files. More recently organizations have begun providing weather and climate data services. These are feature services like WFS and image services like WCS. Data services allow customization of the symbology and more flexibility in visually combining with other data, but also can be used for analysis allowing the user to ask new questions with the data. The transition to data services feeding into analysis services will have a profound impact on the utility and growth of geospatial cloud computing. Instead of combining map services of watershed boundaries and 24 hour precipitation to interpret how much water fell in a watershed, with data services and analysis services you can compute the quantity of water that fell in each watershed, or create a custom watershed and summarize rainfall over a user defined time period.

Cloud computing is making powerful computing accessible to a much larger community of scientists and analysts. This combined with easier data integration, more efficient and scalable analytics, and an easier user experience is creating a rich environment for improved understanding.