Acquisition, processing, and visualization of big data as applied to robust multivariate impact models

Increased offshore oil exploration and production emphasizes the need for environmental, social, and economic impact models that require big data from disparate sources to conduct thorough multi-scale analyses. The National Energy Technology Laboratory’s  Cumulative Spatial Impact Layers (CSILs) and Spatially Weighted Impact Model (SWIM) are user-driven flexible suites of GIS-based tools that can efficiently process, integrate, visualize, and analyze a wide variety of big datasets that are acquired to better to understand potential impacts for oil spill prevention and response readiness needs. These tools provide solutions to address a range of stakeholder questions and aid in prioritization decisions needed when responding to oil spills. This is particularly true when highlighting ecologically sensitive areas and spatially analyzing which species may be at risk. Model outputs provide unique geospatial visualizations of potential impacts and informational reports based on user preferences. The spatio-temporal capabilities of these tools can be leveraged to a number of anthropogenic and natural disasters enabling decision-makers to be better informed to potential impacts and response needs.