Module 5: Coastal Flooding

 In this week's module, I had the opportunity to conduct a damage assessment of the impact Hurricane Sandy had on states in the northeast United States. I started off by running a query to export the 11 impacted states and converting them into a single feature. These points would then be converted into a polyline to display the path Sandy took in 2012 using the Point to Line tool. I never used this tool before, but I can see myself definitely using it in during wildlife surveys. After completing the map, I had the opportunity to create a Survey123 survey form. I have used Collector and Fieldmaps, but had no prior experience with Survey123. This survey form would be a great way to get community members involved in large scale damage assessment projects after a storm. It was pretty simple to setup and I hope I can provide services during times of need for my community if such damage assessment surveys are needed. I then created mosaic datasets that could be used to compare post and pre storm damage. I even learned how to use the Swipe and Flicker tools. I have used GIS for a while, but had no clue these tools even existed. I could have used the Swipe tool in multiple situations in the past. 

I eventually had to create a new point feature class to assess the damage of Sandy on buildings in a small study area in Ocean County, New Jersey. I then marked each property with how much structural damage I observed on the aerial. To differentiate between building types, I used a topographic base map to get any clues for buildings that were anything other than residential. I then zoomed in at a scale of 1:600 in order to pick out the intensity of the damage. I found it difficult to label empty properties and properties that appeared to have no structural damage. For the empty properties, sand still inundated them and I assumed that there was still some type of damage. For the properties that seemed undamaged, they still experienced what seemed to be sand inundation on the property. This could potentially cause minor structural damage to both types of properties that would be difficult to to see from an aerial. It would be helpful to have a layer with information of insurance claims or damage reported from the properties. This could help with identifying foundation damage, electrical damage, driveway damage, and other property damage difficult to document from an aerial.

To conduct the analysis of structural damage points within a certain distance from the coastline, I used the multi-ring buffer tool to create a 100 m, 200 m, and 300 m buffer around the coastline line feature I created where the water meets the beach. After doing so, I used the Spatial Join tool and included the buffer layer as my target feature and my structure damage point feature as my joined feature. After that, I used the select by attribute tool and selected points by structure damage and the length field to get my results for the table I filled out. 

After assessing the data and reviewing the table, it can be said that the closer to the coastline a property was, the higher the chances of the property having severe damage. Those buildings that were either destroyed or received major damage were all within 200 meters of the coastline. All of the properties within 100 meters of the coastline endured major damage or complete destruction. No property within 100 meters endured minor to no damage. The final results are reliable enough when showing which buildings sustained damage. 100% of buildings within 100 meters of the coastline sustained at least major damage.