Damage Assessment - Module 5

 This week's lab was not only engaging but rather fun. We were given two tasks: the first of which we were instructed with how to manipulate point symbology to illustrate the track of Hurricane Sandy in 2012 and the second we had to perform a damage assessment of the storm.

To accomplish the first task we simply uploaded the XY data for the monitoring of hurricane Sandy. We turned this data into a point feature class and then manipulated the symbology through ESRI's meteorolgoical symbols to illustrate the hurricane. With the symbols representing the tropical storm and post-tropical cyclone, we had to customize the symbols in order to create ones that were up to international meteorlogical standards. The map is below:

For the second task, we were given a small study area in Ocean County, New Jersey. Were provided with several raster images and combined them to create a raster mosiac in our geodatabase. Several of the images were from before the hurricane and the others were directly after. The latter showed the scope of the damage which occured along the coastline. 

We then created domains in our geodatabase so we would be able to apply those to an attribute table in a new point feature class. A county parcel grid was applied to the map and an outline for our study area which was of several coastal blocks in the county. A point was added to each structure residing within each polygon representing a parcel and the attribute table for the point was classified in regard to structure damage choosing from one of the domains we had earlier applied. These classificatiosn ranged through the descriptions of no damage, impacted, minor damage, major damage, and destroyed. I classified structures that were completely demolished as destroyed and ones where only the structure or part of it remained as major damage. Minor damage was considered where the structure was still standing but the area aroudn the strucutre was in dissaray. No damage or impacted were for strucutres that appeared to be in great shape but had some minor damge to the area surrounding it. 
Using the swipe tool, we swiped between the before and after images and classified them visual adding the appropriate point. 
I clipped the parcels from our study area to clean up the map. Below are our results. I provided images of both pre and post storm. 

Next, we had to determine whether proximity to the coastline has an imact on the severity of damage. Naturally, we determined that it did. We created a line feature class alond the coastline parallel to our study area. I then created a buffer of 100m, 200m, and 300m. I joined the attributes of the parcel feature class to the damage point feature class. Using select attributes by location, I then delineated which structures resided within each 100m buffer and used the summary function to determine the severities of these points. 
Based on our data, we can see that 100% of buildings within 100m from the coast were either completely destroyed or experienced major damage. 38% of structures between 100-200m from the coast experienced major damage or were completely destroyed. 15% of of structures within 200-300m from the coast either experienced major damage or were completely destroyed. Conversely, 85% of structures 200-300m from the coast only experienced minor damage or less. 61% of structures 100-200m from the coast experienced damage that was minor or less. 0% of structures within 100m from the coast experienced minor damage or less. There is a definite trend which I believe would be appropriate to apply to the remaining, heavily-impacted shoreline.
Below is the buffer image of the post-Sandy damage and a copy of the table of our buffer/damage rankings.