Geographically large climatic hazard events are occurring more frequently, and with this increase, more research emphasis is being placed on their impact. However, a metrology for selecting which communities to survey following an event is not frequently discussed and as a result does not effectively incorporate all relevant disciplines in disaster research. This article provides a method for selecting communities for inclusion in field studies not only based on anticipated damage but also community-level social factors that show predictive power in long-term analyses. Due to the perishable nature of disaster data, this social metric and the field study decision tool were developed with the intent of being as rapidly implementable as possible while still providing insight regarding long-term post-event community trends. The community-level social metric was developed using a hindcasting technique for hazard events in the years 2011, 2012, and 2013. Thresholds for stability and decline were established for both the predicted and the actual community outcomes. Of the communities that the model predicted would recover, 73% recovered using the definition provided. Meanwhile 74% of the communities predicted to decline did indeed decline, again using the definition provided. To enhance operability of this social metric, a decision-making tool for community selection was also formalized and implemented in a field study conducted following the December 2021 quad-state tornado outbreak in the United States. The lessons learned from this first implementation were used to inform a refined version of the decision-making tool that is presented herein.
Keywords: field study; interdisciplinary disaster research; longitudinal; natural hazards; social vulnerability.
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