Damage Assement

A substantial part of work that GIS professionals do at organizations like FEMA is assessing how areas where damage after a natural disaster.  Damage assessment is useful for evaluating the hardest hit areas and the extent of reconstruction necessary.  This week's lab involved assessing the damage caused by Hurricane Sandy after it made landfall new Atlantic City, New Jersey.  Even though it only struck the area as a category 1 hurricane, not only was the area not accustomed to hurricanes, but the hurricane was also the largest ever recorded.

To assess damage, the first step was to import both pre-storm and post-storm imagery of the area.  I also added in a parcel layer to make it cleared exactly what the boundaries would be for each damage assessment.  Before I could assess the damage, I had to create attribute domains to constrain the data input values so that they could only be from a select set of options and less subject to input error.  The domain particularly useful for this assignment was a structure damage domain set to coded values from zero to four - with zero being no damage and four being completely destroyed.  Following that, I created a new feature class that I set to use the domains that I had just created.

Then came the part where I actually determined the damage.  I found it easiest to first create a point for each of the parcels and then to go through each one and set the level of damage.  I think for someone who did this work regularly it would be useful to have two high resolution monitors so they could see as much of the imagery as they could as well as the tables at the same time.  Judging how much damage there was from only satellite imagery was difficult.  It was easy to see when a building was completely destroyed, but harder to discern between "affected" and "minor damage" in a real world situation it would be ideal to have someone on the ground assess each parcels from up close.

Each of the parcels in the study area were assigned a structure damage value based off of the discernible damage that could be seen from the satellite imagery.
 

The next step was to determine how distance from the coast impacted the extent of the damage.  I created a new feature class for the coast and drew it in as a line.  I then used the multi-ring buffer tool to establish three different ranges of distance.  Next, I used the clip tool to clip out only the parcels in the selected study area.  Then I used spatial join on the clipped parcels with the structure damage data so that the parcels have the structure damage value.  Once again I used the spatial join tool on the layer created in the previous step with the multi-ring buffer.  This gives each parcel in the study area a damage value and a distance value.  Lastly, I got all the numbers by using the select by attribute tool to get all of the counts (ex query: “WHERE distance is equal to 300 AND structure damage is equal to 2”).

The results of the damage assessment for each of the buffer zones.

As is to be expected, areas closer to the coast were generally hit harder by the storm.  This is a fairly small sample size and I would resist the temptation to extrapolate the same data throughout the entire area.  However, this process could be repeated throughout other sections and the results could be used to help form a more complete picture.