Lab 7 - Coastal Flooding

In GIS Applications this week I used elevation data and ArcMap to model the risk areas for coastal flooding. In the class we did not get in depth with weather effects and I'm glad we held back on that part because some of the tasks this week were already a little difficult.

For the first part of the lab I used a 3m DEM to identify potential flood zones during a 3ft sea level rise and a 6ft sea level rise. The process for the lab began with reclassifying the raster data into two categories, within the flood zone and outside of the flood zone. One of the important things to note during this section was using the correct units of measurement and converting numbers when necessary (the raster was metric but our questions and final graphic were U.S. standard.)  After reclassifying the flood zone rasters they had to be converted to vectors using the Raster to Polygon Tool for analysis later on. This was one of the simple steps. Another simple step I used was inverting the rasters to use them for flood depth instead of height above sea level. This was accomplished by subtracting using the Minus Tool. I thought this was an interesting application for the tool that I probably would have never thought about without this class. The graphic for a 6ft sea level rise in Honolulu, HI is below.

Sea Level Rise in Honolulu

After working through the raster data and flood zones I applied some census data to see how the population in an area could be affected. We were tasked with looking at a couple of specific categories within the census data and that meant some of the attribute tables needed modifying. Using a census tract vector file and a census blockgroup vector file along with different tables containing demographic information for the tracts I was able to compare some of the categories of people affected. Through table joins, adding fields, and the field calculator I was able to copy different attribute fields from all of the layers I was working with into the blockgroup layer to conduct the comparison. The results of my work are in the table below.

Variable	Entire District	3ft Scenario		6ft Scenario
		Flooded	Not-Flooded	Flooded	Not-flooded
Total Population	1,360,301	8,544	1,351,757	60,005	1,300,296
% White	23.8	36.8	63.4	29.6	70.4
% Owner Occupied	57.2	32.2	67.8	38.1	61.9
% 65 and over	13.4	17.1	82.9	17.0	83.0

To me it looks like a lot of the area at risk for flooding is rental property and that would mean bad news for owners and renters if this area floods.