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Abstract
RINX (Raster INformation eXtraction) 2.0 is an advanced solution for efficiently extracting climate data from large raster datasets in a cloud computing environment. Building upon the original RINX 1.0, which utilized high-performance computing clusters, RINX 2.0 leverages cloud technologies such as OpenShift and PostGIS to handle massive datasets and automate the extraction process. The system supports large-scale spatiotemporal raster extractions, processing over 158 million data points from the 15TB PRISM climate dataset. Here, we describe the architecture, methods, and tools used in RINX 2.0, including containerized environments, automated data pipelines, and integration with the New England Research Cloud. The system was deployed for the Environmental influences on Child Health Outcomes (ECHO) project, providing valuable insights into environmental health research. We present performance statistics, data management strategies, and the development of a user interface for real-time querying and visualization of results.
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Details
1 Center for Geographic Analysis, Harvard University, Cambridge, MA, USA; Center for Geographic Analysis, Harvard University, Cambridge, MA, USA
2 Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA; Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA
3 Department of Population Medicine, Harvard Medical School, Boston, MA, USA; Department of Population Medicine, Harvard Medical School, Boston, MA, USA