RESUMEN
The increased availability of publicly available data is, in many ways, changing our approach to conducting research. Not only are cloud-based information resources providing supplementary data to bolster traditional scientific activities (e.g., field studies, laboratory experiments), they also serve as the foundation for secondary data research projects such as indicator development. Indicators and indices are a convenient way to synthesize disparate information to address complex scientific questions that are difficult to measure directly (e.g., resilience, sustainability, well-being). In the current literature, there is no shortage of indicator or index examples derived from secondary data with a growing number that are scientifically focused. However, little information is provided describing the management approaches and best practices used to govern the data underpinnings supporting these efforts. From acquisition to storage and maintenance, secondary data research products rely on the availability of relevant, high-quality data, repeatable data handling methods and a multi-faceted data flow process to promote and sustain research transparency and integrity. The U.S. Environmental Protection Agency recently published a report describing the development of a climate resilience screening index which used over one million data points to calculate the final index. The pool of data was derived exclusively from secondary sources such as the U.S. Census Bureau, Bureau of Labor Statistics, Postal Service, Housing and Urban Development, Forestry Services and others. Available data were presented in various forms including portable document format (PDF), delimited ASCII and proprietary format (e.g., Microsoft Excel, ESRI ArcGIS). The strategy employed for managing these data in an indicator research and development effort represented a blend of business practices, information science, and the scientific method. This paper describes the approach, highlighting key points unique for managing the data assets of a smaller scale research project in an era of "big data."
RESUMEN
In terms of natural hazard events, resilience characterizations provide a means of identifying risk profiles, degrees of preparedness, and the ability of communities to respond and recover. While nationally consistent measures of community resilience to natural hazards are needed to address widespread socio-ecological impacts from a broad policy perspective, geographically specific resilience characterizations are needed to target local resources to increase community resilience. The Climate Resilience index (CRSI) was developed to characterize the resilience of socio-ecological systems in the context of governance and risk to natural hazard events for all U.S. counties for the years 2000-2015. Those resilience characterizations were based on the full range of nationwide county domain scores. This paper presents a re-scaled application of CRSI, where county domain scores are limited to the range of scores within a specific set of U.S. coastal and shoreline counties within each of eight coastal regions. The re-scaled CRSI values for selected counties/parishes in the Gulf of Mexico (GOM) region are also presented in conjunction with calculated Location Quotients (LQ) values >1.0, which represent a high employment dependence on ocean economy sectors. Using a combination of re-scaled CRSI and LQ values provides a more holistic picture of vulnerability and resilience in these U.S. coastal shoreline counties. The relative resilience assessments presented for coastal regions are useful in identifying potential strengths and weaknesses in resilience aspects given similar hazard profiles, a signature otherwise diluted in nation-wide county-level assessments. The unique approach of combining CRSI and LQ for characterizing natural hazard resilience described could be transferred to other specific geographies as defined by population groups, hazard profiles and economic dependence.