Using Geospatial Property Data to Inform Decision Making During Hazardous Events

The damage, destruction and tragic loss of life caused by the many natural disasters this year is nothing short of heartbreaking. From hurricanes or tropical storms that impacted the Gulf and Atlantic Coasts, to destructive earthquakes in Alaska, to raging Camp and Woolsey California Wildfires (which caused devastating human loss and, according to our recent analysis, caused property losses between $15 and $19 billion dollars), the tragedies seemed to unfold relentlessly before our eyes. After each catastrophic event, I found myself asking the same question repeatedly: how might spatial intelligence help mitigate the loss of lives and property caused by natural disasters and help the organizations we serve, and emergency personnel better respond post-event?

As a geographer and self-proclaimed “map” nerd, when I hear something in the news related to a natural hazard event my instinct is to visualize the geographic location on a map, so I can better understand what occurred and what the impact may be.  As a product manager of spatial data layers, I have access to comprehensive, high-quality property and location data, as well as high-resolution maps and imagery that allows us to assess risk and generate reports that enable both decision making and actionable next steps. While some sort of analysis happens post-event, I am seeing an increase in both governments and businesses taking preventative measures to either avoid or minimize impacts on human lives as well as property.

There are a variety of natural hazard risk and property data one could examine to gain insights into what events occurred or are likely to occur. Prior to an event, this could be anything from historic hazard data to scientifically designed models related to hazard impact, as well as geospatial property information such as parcel boundary data. Post-event, this includes content that identifies flood inundation areas or areas that were consumed during a wildfire. The key to utilization of this type of information is that it should to be analyzed together, on a map. This is what is known as a Geographic Information System (GIS). A GIS enables users to analyze property data by overlaying multiple layers of data or maps on top of each other, providing deep and granular insight into the impact of different disasters. Scientifically-based hazard data, when combined with property level data within a GIS, allows users to identify areas of high risk (pre-event) and loss (post-event). Additionally, it enables users to identify trends in development and changes to the natural environment that allow for improved emergency response and planning both pre- and post-event.

For example, let’s talk through an example of how a geospatial map with data overlays can be used. Earlier this year, the Llano River basin in Central Texas was inundated with rainfall. Due to the intensity of the rainfall, the rate of runoff was so high that the river quickly left its banks. This caused significant flood events across the river basin and to downstream reservoirs. Using granular geospatial property data combined with scientifically-derived flood risk maps from sources such as the Federal Emergency Management Agency (FEMA), first responders and emergency operations centers were able to quickly understand which properties were at the greatest risk of flooding. This detailed level of information and understanding allowed the responders and operation centers to act more quickly and with better precision to help the most vulnerable citizens take preventative action to preserve lives and property.

Additionally, using geospatial tools and analysis prior to natural disasters, such as the Llano River basin flood, undoubtedly helped mitigate severe damage and results. Plans can be proactively put in place to quickly notify property owners of their pending risk and, in the case of some natural disasters, such as wildfire, help first responders take steps to lessen the impact to property. This also allows for preventative action to completely remove endangered property and population from harm’s way and future events through initiatives such as home buyout programs.  Additionally, in the case of wildfire areas, people can implement improvements that make their properties less susceptible to catching fire in the first place. This includes such things as external sprinklers, clearing overgrown brush and utilizing fire-resistant building materials. In some cases, insurance companies are helping fund fire departments to prevent more costly structure loss.

It’s important to remember that when using this type of geospatial data, the same rules apply as when you use any type of data. The results are only as good as the quality and completeness of the data content. If you lack granular real estate boundaries in certain geographic locations, it becomes difficult, if not impossible, to create a strong analytical engine capable of delivering the detailed insights necessary to drive disaster planning, emergency response, and asset management decisions. If the data is incorrectly formatted, lacks key information (or worse yet, key information is incorrect or corrupted), you will inevitably yield poor results.

There is also a positional component to consider. This means that we must evaluate if the data is truly represented in the correct placement on the earth. Essentially, we should ask ourselves, “Is our map accurate?” Those who provide the highest quality data will undoubtedly present data that is validated and based on scientific findings to meet data quality standards.

Geospatial technology and increased availability of high-quality spatial and property datasets have a unique ability to help users proactively create preventative and post-event action plans. These enhanced and comprehensive datasets support a wide variety of use cases across many industries. There are over 150 million properties nationwide and each property can contain over 200 unique attributes and characteristics. With so many variables, it is impossible to place a limit on the potential insight and value one can glean from using an accurate geospatial solution.

In a nutshell, the right geospatial data can help users make more informed decisions before, during, and after a catastrophic event—for improved accuracy of post-event responses, accelerated recovery for those impacted, as well as arm both businesses and planners with the best insights possible to help mitigate the disaster before it happens.

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