Nikki DeLuca
GIS Final Project – 12/2012
Climate change has become more and more apparent to Americans in the past decade.
Unfortunately, this has mainly been due to the increase and intensity of severe storms such as
hurricanes. Coupled with sea level rise from the rapidly melting ice caps, hurricane storm surge
is predicted to become increasingly dangerous. Storm surge levels typically associated with a
Category 5 hurricane may become the norm for as small as a Category 1 hurricane, flooding
more land and affecting more of the population on a regular basis than is currently seen. The city
of Charleston, South Carolina is located in a part of the country which frequently sees hurricane
activity. Much of the city lies between marshes and swamp, and much of the population resides
in these low-lying areas or directly on the coast. This project determines the areas likely to be
affected by storm surge in Category 1 through Category 5 hurricanes after sea level rise, and
analyzes the percent of the population affected as well as various demographics.
I began the input process by gathering data on Charleston block groups from the
Census Bureau’s 2000 report. I was not able to find a 2010 report, which would make the study
more relevant to today, but the general concept was still applicable with an earlier repot. The
2000 Census file came as a vector polygon shapefile with a separate table that I needed to join,
which included information regarding the area of the block group, population, ethnicity
population, income, and age brackets. An immediate problem I noticed when I added the
shapefile to ArcMap was that the block groups extend into the ocean, which would throw off
calculations involving the area of the polygons down the road. The block group file was
projected in NAD83 UTM Zone 17N according to the metadata file, so I defined it as such in
ArcCatalog.
I found a DEM file from USGS for the Charleston area, which I had planned on
reclassifying according to the levels of storm surge found later on. However, I was lucky enough
to find a vector polygon shapefile which had already broken Charleston into polygons based on
each Category’s storm surge level according to elevation on the South Carolina Government’s
GIS page (gis.sc.gov), so I was able to put the DEM file aside. This data was determined using a
SLOSH (Sea, Lake, and Overland Surges from Hurricanes) model developed by the National
Weather Service, using MOM (Maximum of Maximum) data. This file was also projected in
NAD83 UTM Zone 17N according to the metadata, but I had to define it in ArcMap once it was
added as a layer so that the units were displayed in meters.
I joined the Block Groups layer with the SLOSH layer, creating more polygons which
refer to each block group’s different areas of storm surge coverage. I was then able to do a Select
by Attribute query and selected all of the blocks whose Zone equaled 0. A Zone which equals
zero indicates that the polygon lies outside of the parameters of the SLOSH layer, either in the
ocean or an inlet. After selecting those polygons, I switched the selection so that only polygons
with Zone numbers were selected and made a new layer using only the selected data. Now that
my map only included land in Charleston, I could begin to analyze the data.
At first, I planned to calculate the area of the new polygons and divide them by the
original area of the block group in order to get a percent area of the block group area that only
included land. I would then apply that percent number to other fields in the attribute, inaccurately
assuming an equal distribution, so that I would be able to get a number of people residing on
land in each block group. I could then take the Calculate Field tool a step further by calculating
the percent of the population in each storm surge zone. However, the actual execution of this
plan turned out to be much more difficult than I had anticipated, so I chose to take the analysis in
a different direction.
After looking through the fields provided by the Census data, I chose to focus on the
elderly in the Charleston area. The older demographic is typically the most effected by natural
disasters and have the highest mortality rate, because they are generally not as equipped to
evacuate quickly and tend to ignore evacuation measures. I used the zones shapefile to export
layers of data for each Category of hurricane. The layer designated as Category 1 includes
information for any block group polygon that lies within Zone 1. The Category 3 layer includes
block group information for polygons that lie within Zones 1, 2, and 3. The Category 5 layer
includes block group information for polygons that lie within Zones 1, 2, 3, 4, and 5. I then
classified each layer by choosing to categorize the “Age 65 and Up” field and normalizing it by
the “2000 Population” field. This gave me numbers that I converted into percentages, and then
defined each of 5 classes by a 20% margin.
Surprisingly, I found that the areas with the densest elderly population were ones within
the Zone 1 layer. If even the smallest Category of storm were to directly hit Charleston, a large
portion of the elderly population would experience severe flooding and life threatening
conditions. The amount of area where the elderly population is affected does increase as the
storm intensity increases, but it appears as though over half would be directly affected by the
Category 1 storm. Because this is Census data from only 2000, I would predict that the elderly
population has increased since, creating even more hazardous conditions for them in the
Charleston area. Although there was more thorough analysis and better methods of gaining
accuracy within my analysis, I had a problem with collecting enough time to further this project
like I would have liked to. However, I certainly reiterated a lot of skills learned within the labs
throughout this project and gained more knowledge about the data available for GIS and ways
GIS is applicable in my fields of interest!