When a hazard such as a hurricane, wildfire, or toxic release threatens an area, the public typically has three main options for seeking protection: evacuate, evacuate-to-shelter, or shelter-in-place. A robust literature exists on planning for the first two of these contingencies. Relatively little scientific work, however, has explored the social and behavioral aspects of sheltering-in-place. The central goals of this project are to examine the experience of sheltering-in-place and to formulate a model that accurately predicts behavioral intention to comply with a shelter-in-place order.
This Shelter In Place Project is dedicated to 3 aims:
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Specific Aim #1: To describe the experiences that individuals and households have when placed under a shelter-in-place order. We will seek to illuminate a number of issues with respect to this experience including, but not limited to, the manner in which the order was received, initial reactions, compliance rates, compliance facilitators and barriers, and post-event effects.
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Specific Aim #2: To experimentally model shelter-in-place scenarios evaluating the degree to which compliance is affected by the conditions of the hazard (chemical, radiological), cause (accident, intentional), participant network (home alone or with household), and communication access (enabled, not), taking into consideration a range of individual and social factors.
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Specific Aim #3: To translate findings in three ways through involving a diverse group of stakeholders and research collaborators: create accessible recommendations for emergency planners and other public officials engaged in emergency response, provide guidance for how our findings may be best incorporated into emergency management training materials, and create support for public educational materials instructing on shelter-in-place.
Eventually, we are dedicated to creating a machine learning algorithhm to create predictions on the likelihood of an individual to Shelter in Place given a set of conditions (i.e. a chemical vs radiological incident or if a person is at work vs at home). I currently serve as a Research Assistant to this project and am helping to create descriptive functions in order for the project to make descisions. My hope is to help with the machine learning algorhithms and improve my skillset! Updates to come in the future!