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Master of Science (MS)
Miles, Scott B.
Medler, Michael J.
Waldo, Tyson Z.
Currently there is no comprehensive source of community resilience data. Geographic data is collected by multiple agents and stored using different schemas. In most cases the schemas that store the data do not relate them to concepts of community resilience, or the disasters the data could be associated with. So this begs the question, how can decentralized geographic data be leveraged to facilitate data-driven decision-making about community disaster resilience? This question was answered by completing three related objectives. First a data aggregation was performed, second a schema was created to organize data with respect to components of disaster resilience, and third a data system called WISCkey was developed for storing, managing, and disseminating data over the web.
A data aggregation was performed for two case studies and was developed specifically for the variety of data related to disaster recovery. Subsequently, a schema was developed to organize aggregated data based on attributes of resilience and aggregation outcomes. Technical infrastructure was selected and configured to store, manage and disseminate the organized data.
The result of this research is a web-based application called WISCkey. WISCkey was built using MongoDB, Python, and Bottle and organizes aggregated data. It was developed to store, mange, disseminate, and provide the means to centralize a variety of resilience data. Ultimately these completed objectives permit applying community resilience theory to facilitate data-driven decision-making, and research, in a user friendly way.
Western Washington University
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Kemp, Jonathan A. Jr, "Investigating Effective Methods for Aggregating, Organizing, Storing, Managing, and Disseminating Community Resilience Data" (2015). WWU Masters Thesis Collection. 434.