Hurricane Sandy Structural Damage Assessment

This week, lab focused on assessing structural damage from Hurricane Sandy in New Jersey. I started by mapping the storm’s track, creating points from the provided Sandy Track dataset and connecting them into a line that showed the hurricane’s path over time. Customizing the point symbols and labels helped clarify the progression of the storm (Figure 1).

Figure 1. Hurricane Sandy Tracking Map

Next, I worked with pre- and post-storm imagery, organizing the raster data into mosaic datasets to facilitate comparison. Using the Flicker and Swipe tools allowed me to visually assess damage by toggling between images taken before and after the storm.

I then created a feature class for damaged structures and assigned attribute domains to standardize categories such as inundation, wind damage, and structural damage. Instead of mapping every building, I took a sample of structures to represent different damage levels within the study area (Figure 2).

Figure 2. Structural Damage Points in the Study Area

To analyze spatial patterns, I digitized the pre-storm coastline and calculated distances from damaged structures to the shoreline, converting the values into meters as required. Grouping damage data by distance bands revealed that damage tends to decrease farther from the coast.

Overall, this approach provided useful insights into the relationship between proximity to the coastline and structural damage. While the method isn’t precise enough to map all damage, it offers a practical way to estimate impacts across larger areas and supports disaster response planning.