Module 2 - Applications in GIS - Coastal Flooding

Impact of sea level rises up to 6 feet on the population of Honolulu

This week in Applications in GIS we learned a lot about working with raster data.  There is a lot to learn.  I really started to have an appreciation for the elegance and simplicity of raster logic this week.  We used DEM imagery of the coast of Oahu at Honolulu to construct ahypothetical scenarios of 3-foot and 6-foot sea level rises.  This was done by selecting those portions of the raster elevation data below 3 or 6 feet, then converting it into a vector polygon and draping it across the coastal areas of Honolulu.  We then applied US Census data to the map, by tract, and showed the population density compared to sea level rise.

In the second part of the exercise, we took a closer look at the demographics in Honolulu, and compared the percentages of white, home-owner, and older than 65 years in the census blocks to zones of possible inundation.

Here is a table showing some demographics of Honolulu compared to the expected zones of inundation..


The white population is seen to be somewhat more impacted by sea-level rises.  Owner-occupied houses are less impacted.  People over 65 years old are not disproportionately impacted.

In the second part of the lab, we analyzed the effects of a storm surge in Collier County, Florida on buildings in the city of Naples.  We compared the results using a USGS DEM imagery created from photogrammetry, and LIDAR imagery, to get an idea of their relative reliability.  We used the DEM's to construct a zone of 1 meter storm surge inundation and overlaid the resulting polygons on shapefiles of the city buildings' footprints.  Then, by calculating errors of omission and commission, we evaluated the reliability of the USGS DEM against the presumably more accurate LIDAR DEM.

This table shows numbers of different types of buildings impacted by a        1-meter storm surge, as evaluated by the USGS and LIDAR DEM's.
We are assuming the LIDAR data is superior, so errors of omission are those buildings that counted by LIDAR as being impacted, but NOT counted by the USGS DEM.  The errors of commission, on the other hand, are those buildings that are incorrectly counted by the USGS DEM analysis, but do not show up when LIDAR is used.  The percentage error for each is calculated against the total LIDAR building counts.