Friday, July 25, 2014

My GIS Capstone: Impacts from Superstorm Sandy; Geographic Analysis of Energy Disruption Risks

*The above image is original work. All rights reserved.


   This image represents the culmination of my capstone work in Geographic Information Systems during the pursuit of my graduate degree. I really enjoyed working with the software! The applications in environmental monitoring, public communication, and risk analysis seem endless. My goal was to develop an energy risk map is based on FEMA flood zone data and heads-up digitizing of light emissions in the region of New York City. I used these two datasets to identify areas of relative disruption factors due to extreme weather induced flooding in and around New York City. 

   Precedent has been established as scientists and economists have begun using light emissions as a corollary for economic activity. Thus, this map helps to relate the risk of economic impact through the use of light emission data. While the project focused on energy disruption, many sources indicate that it is safe to assume that disruption of the energy grid is directly correlated to disruption of the local and regional economies. Given that many areas in and around New York City and New Jersey are still recovering from the effects of Sandy, perhaps funding of more comprehensive projects along these lines would be appropriate in the immediate future.

   One of the main highlights of this work is the finding that much of New York City and Long Island are high-risk zones for energy disruption due to extreme weather. While this may seem obvious in the wake of Sandy, this original work is proven in practice because the highest disruption factors match the hardest-hit areas that were most affected when Sandy made landfall. I think that this project is an excellent case study, showing that the information was available before Sandy to help mitigate the storm's damage (as all of my data originated prior to the storm). I was not able to identify any previous work that directly correlates energy disruption due to extreme weather. I think that this final outcome is most useful because the same method could be applied to other areas prone to hurricanes and extreme weather, and used to identify sectors of highest impact to energy infrastructure so that action can be taken before an emergency develops.