M5 - Applications in GIS - Hazards: Damage Assessment

In this week's lab, we kept focus on Hurricane Sandy, but this time using GIS tools to conduct damage assessments and evaluate the storm's effects on individual properties. 

We began the lab by using previously learned GIS and cartography skills to prepare a map illustrating Hurricane Sandy's track, including the intensity of the storm as it migrated from the Caribbean Sea into the Atlantic Ocean and up the east coast of the United States.  We had some fun with symbology and also learned to add a grid and graticules to our map layout.  This was just a small reminder that I enjoy the cartography aspect of GIS. 

Next, we utilized Survey123, an excellent tool provided by ESRI for creating, managing, and analyzing surveys, to develop a citizen "self-reporting" damage survey. We learned how to construct a comprehensive survey that would allow citizens to report storm damage, including the type of damage (with a text input for details), images, the date and time, and the exact location of the damage. This week’s lecture highlighted how valuable this method of data collection can be immediately after a significant storm like Hurricane Sandy.

We proceeded through the lab by taking historical aerial imagery of the coastline both pre- and post-Hurricane Sandy, and merging them into a mosaic dataset that we could utilize to visualize the before and after.  We learned how to use the Swipe and Flicker tools for comparison, which really aided in damage assessment tasks later in the lab. 

We moved on to learning more about data creation and specifically attribute domains.  Attribute domains in ArcGIS Pro are predefined sets of valid values or ranges that restrict the data entered into a field in a geodatabase. They ensure data integrity by limiting the choices for attribute values, helping to maintain consistency and accuracy in data entry.

This made the task of examining the before and after imagery of a study area, creating point features, and analyzing property damage parcel by parcel much more streamlined and accurate in terms of our data entry.  

In this process, we also used our previously learned skills to create symbology that accurately conveys damage type when viewing our data on the map. 

Finally, multi-ring buffers around the coastline were created at 100 m, 200 m, and 300 m intervals. Using the Tabulate Intersection tool, I summarized the counts of structures within each buffer zone, linking the damage assessments to the corresponding distance ranges.

The analysis shows a strong link between how close buildings are to the coastline and the amount of damage they experience. Structures within 100 m of the shore had the highest damage rates, with fewer issues as you move further out to 200 m and 300 m. This indicates that being near the coast makes buildings more vulnerable. While we can estimate that, say, X% of buildings within 100 m are damaged, we should do more local studies to confirm these numbers before applying them to other areas.