Module 5: Coastal Flooding

The purpose of this module was to explore procedures for coastal flooding and storm surge analyses using elevation models, overlay analyses for vectors and rasters, and spatial queries.

The first data set was for an area in New Jersey that was impacted by Hurricane Sandy. I converted LAS files from pre- and post- Sandy for the coastline area were to TINs and then rasters. I subtracted the two rasters from each other using the Calculate Raster tool and analyzed the resulting shapefile was for damage with a 2019 building overlay. There are several areas in the study area that show significant erosion (red areas) that have not been rebuilt. The map below shows the overall results of this analysis.
The second data set was also for New Jersey. A DEM was provided and I reclassified it into areas that would flood based on the Hurricane Sandy storm surge of 2 meters. I then converted the raster to a polygon and examined the result for Cape Map County. Based on the analysis, about 52% of Cape May County, New Jersey would be at risk to a storm surge of 2 meters. The screen shot below shows the storm surge area in red from this analysis.



The third and final data set was for Collier County Florida. For this analysis, I compared a traditional USGS DEM and a DEM derived from Lidar assuming a storm surge of 1 meter. I reclassified the rasters based on the storm surge of 1 meter and used the Region Group tool to identify areas connected to open water. I then used the Extract by Attributes tool to extract only those areas in each raster connect to water and converted the rasters to polygons. Each polygon was overlay-ed with a buildings shape file with a spatial join and queried with Select by Attributes to get a count of each of the different building types in each floodzone (either USGS or Lidar). I also determined the agreement between these results under the assumption that the Lidar data was more accurate. I used a combination of spatial query and overlay tools to determine the errors of omission and commission. My analysis resulted in the Lidar storm surge area being contained entirely within the USGS flood areas – probably because the USGS DEM was coarser and more created a wider area, whereas the Lidar data is finer and resulted in a narrower area. This means that if the Lidar is taken as the true scenario, then there were no areas of omission, however the commission errors are very high (because the USGS flood area is so much greater). The map below shows the results of the Storm Surge Analysis for Collier County, Florida, including the analysis results table.



Comments

Post a Comment

Popular posts from this blog

Module 6: Proportional Symbol and Bivariate Choropleth Mapping

Image
The objectives of this week's lab was to explore proportional symbol and bivariate choropleth mapping techniques, as well as customizing legends for each technique. One of the data sets for this lab included positive and negative values. The final map utilizes proportional symbols to show both positive and negative values. This map is essentially two proportional symbol maps overlaid on each other, one for positive job growth and another for negative job growth, with the symbols for both variables sized the same. Utilizing proportional symbols to show both positive and negative values is an effective way to visualize areas that show the greatest and lowest values. These areas are easy to see quickly on the map and give the map reader a simple way to visualize the variable being mapped. Fig 1. Proportional symbol map using positive and negative values.  Another data set for this lab examined contained two data variables: percent of the US population in each state that is obe
Read more

Module 2: Coordinate Systems

Image
In this week's lab I explore different coordinate systems and projections in order to better understand the nature of distortions introduced by the systems. For the last part of the lab, I was tasked with selecting a state in the contiguous US and selecting an appropriate projection for the area. I selected the state of Wyoming as my area of interest. Wyoming has 4 State Plane zones and two UTM zones, so neither a single State Plane nor UTM system would work for this area without distorting the size or shape of the state. Therefore, the best coordinate system for this area is Custom Coordinate system. I chose NAD 1983 WyLam (Meters), a custom system specifically for the state of Wyoming, as the projected coordinate system. This coordinate system is a Lambert secant-case normal-aspect conic type projection which preserves shape and is appropriate for a general reference map. The standard parallels are placed at 41N and 45N, the boundaries of the state of Wyoming, which minimizes
Read more

Module 4: Color Concepts & Choropleth Mapping

Image
In this week's lab, I explored using color and selecting color ramps in ColorBrewer, as well as applying these concepts to create meaningful choropleth maps. I created a map of population change from 2010 to 2014 in Georgia counties. I normalized the data and produced a choropleth map of population change using a diverging color scheme with 6 classes divided around a midpoint of 0. Fig 1. Choropleth map of population change in Georgia counties using a diverging color scheme. For the Georgia population data set, I chose to divide the data set into 6 classes around a midpoint of 0: 3 showing a decrease in population and 3 showing an increase in the population. The classes were grouped based on a manual classification closely based on the Natural Break classification, where the midpoint was adjusted to 0 with the other groups unchanged. Natural Breaks classification creates classes based on similarities in values in the data, thereby revealing patterns in the data. By adjust
Read more