GIS 4048 Module 4: Natural Hazards - Hurricanes

Figure 1: Map layout depicting hurricane Sandy's path with storm categorizations and data readings.

Figure 2: Side by side comparison of one street in Toms River Township showing the visible damage extent of the storm.

Continuing the previous modules' focus on GIS applications to natural disasters, this week we focused on some of the GIS tasks that may be utilized in response to hurricanes. Specifically, we looked at Hurricane Sandy and the damage sustained by Toms River Township, New Jersey in October of 2012.

Compared to the previous module, our task this week involved more work in organizing data into two geodatabases. Raster datasets (seen in Figure 2 as the before and after imagery) were imported into two newly created mosaic datasets within one geodatabase. New Jersey reference data layers, such as roads and townships, were imported into one feature dataset. Keeping data well-organized may take time, but doing so will more than likely save time for the original map creator and especially anyone else working on the project. In my internship I have seen examples of poorly organized data that have forced me to spend time looking for data in obscure places. Had some time been invested in keeping data organized and well-labeled, my task would often have been easier.

Figure 1 depicts the path of Hurricane Sandy along with data collected at various points. The points were imported from Excel spreadsheet data and symbolized based on storm category. This layer was used as input for the Points to Line tool to create the polyline depicting Sandy's path.

Figure 2 symbolizes parcel damage based on a visual assessment of the imagery. The structure damage layer was created manually and symbolized according to damage level. A major part of the lab was the creation of attribute domains for the geodatabase in order to restrict the possible values of the structure damage layer's attribute table to a set of coded values. Not only does this make the completion of the attribute table more efficient as each point is digitized, it also helps minimize error when multiple people are entering data.