Role of Data Analytics in Disaster Management
First off, data WHAT? Yes, data analytics...simply put, is the process (mainly through computational uses) of investigating raw data (in the case of climate related data this could be water quality, hurricane damage impact, flooding impacts, etc.) to draw conclusions or any correlations on what the data represents.
One of the key challenges in data analytics as it relates to disaster management is identifying the key metrics needed in establishing successful emergency management strategies; as I highlight in the challenges below, these metrics will vary from community to community as each community will have different goals, priorities, and challenges. Publications and guidelines do attempt to identify some resilience strategies according to data collected such as ARUP’s City Resilience Framework and FEMA’s Building Community Resilience with Nature-Base Solutions; however, there is little guidance on how best to identify missing data sets that will translate to metrics that further define and measure resilience within a community.
A good local example that is currently identifying and gathering data sets to inform community-based resiliency solutions is The Resiliency 305 Initiative developed by the Miami-Dade County Office of Resilience. This initiative aims to execute 50 actions that will better prepare the Greater Miami area and beaches for increasing occurrences of shocks such as hurricanes and infrastructure failures as well as mitigate stresses such as sea level rise, sunny day flooding, increased heat index, traffic flow issues and economic inequities. The initiative has been mapping data to develop metrics that will help model resilience within the local community; however, the challenges encountered include how to address missing data sets (such as heat index values, water quality metrics or data illustrating specific impacts on cultural heritage assets) and how to apply this data in a way that is both understandable and impactful for the community to utilize.
Another key challenge is that these data metrics will significantly vary from community to community which is why community stakeholder involvement is so key. A larger community may be concerned with dozens of climate data metrics due to more open financial access; however, the same cannot be said with underrepresented communities. These communities will have a limited set of metrics and will have different priorities than more affluent communities due to access to public and private funding streams. Lastly, another question to consider is how we can identify the key technologies that are consumer friendly that can aid in improving data collection and distributing the outcomes/results in a way that is useful to the community.
Overall, data analytics can certainly play an impactful role in the prevention as well as mitigation stages in the emergency management cycle. When gathered and utilized in a manner that is translatable to a variety of stakeholders, this data can be used to perform prediction and trend models that will aid in the collective decision-making process during external hazards shocks as well as chronic stress; however, going back to my initial point, this data will not deem useful if relationships with stakeholders are not made. The key to identify which data sets are critical is the relationships formed through community engagement initiatives and partnerships with municipal governments.