The National Risk Index developed by the Federal Emergency Management Agency provides a relative measurement of community-level natural hazard risk across 50 US states and Washington, DC. The Index leverages authoritative nationwide datasets and multiplies values for exposure, annualized frequency, and historic loss ratio to derive expected annual loss estimates for 18 hazard types and combines this metric with Social Vulnerability and Community Resilience scores to generate Risk Index scores for every Census tract and county. Scores provide a holistic and comparable measure of risk across the US. Risk scores and underlying data are summarized in a custom web application. Geographical and statistical processing techniques were used to reconcile incompatibilities between the spatial and temporal collection of input datasets. The index was developed using a multidisciplinary and collaborative approach and input from subject matter experts across disciplines and target users. The National Risk Index builds upon previous efforts to develop a multi-hazard risk measurement for a large geography by expanding the number of hazard types considered, applying extensive geoprocessing techniques to combine diverse datasets, and combining traditional risk factors with the community risk factors of social vulnerability and community resilience for an enhanced nationwide picture of risk.
Data analytics and Big Data have revolutionized many industries, especially where minor efficiencies result in significant cost savings. While Big Data is very important, this paper emphasizes use of low cost tools and techniques to provide valuable insight into data that organizations are already collecting. Organizations spend extensive effort collecting and managing data to support operational and business functions. These functions involve specific stakeholders making specific decisions within a specific timeframe. Within highly-mature functions, data is collected and processed to provide information, knowledge, and insight to its stakeholders; however, these functions are often siloed, limiting the utility (and sometimes visibility) of the data across the organization. An organization's data is a valuable asset. For limited additional effort, analysts can leverage this asset to provide unique insight. This paper explains tools and techniques that organizations can use to creatively capitalize on untapped potential. Broadly, the approach includes: Visualizing existing data to improve understanding - In recent years, data visualization software has become much more powerful, inexpensive, and easy to implement for generating and sharing results. Employing intuitive visualization enables analysts to consider multiple dimensions of an issue simultaneously to identify findings and anomalies. Identifying potential applications - By interacting with intuitive data visualizations, analysts can think creatively about use of data beyond its intended uses and posit questions for further exploration. Establishing context within and across datasets – Analysts can define relationships across datasets and analyze them collectively. Correlation analysis across datasets can reveal hidden relationships. Expanding or contracting the analysis time horizon of data can also bring trends to light. Depending on the application, analysts can build data-driven predictive models to simulate various scenarios to improve confidence in decision-making processes. This approach represents a philosophical change from "Is the data sufficient to answer a given question?" to "What questions can the given data answer?" This approach encourages exploration to reduce the opportunity cost of untapped data. This paper will illustrate this approach through a case studies using publicly-available offshore production and platform data.
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