As their final project for a Geographic Information Systems course last Fall semester, GPIA students Laura Guzman and Amanda Smart partnered with Occupy Sandy to create a vulnerability map showing which neighborhoods in the Rockaways were most vulnerable to housing loss or insecurity based on their risk for flooding and and socioeconomic factors. (Please note: Maps are still in the draft stage, and are continual works in progress)

Overall Vulnerability
Overall Vulnerability

 

lauraguzman
Laura

Another group of New School students from in the Design and Urban Ecologies program approached us and asked us if we would like to help them add a data-mapping portion to a larger project involving, among other things, partnership with Occupy Sandy and an investigation of housing vulnerability in New York City.  After meeting with them, we found that their project jibed nicely with our GIS vulnerability mapping course and with various of our personal interests — Amanda has a background in housing in New York City and Laura was involved with Occupy Sandy relief efforts.

We hoped that by taking a look at the existing situation (socioeconomic and housing statuses of those living in Rockaway) we could have a kind of “before” picture that would demonstrate the existing vulnerabilities in the Rockaway area. On top of this, we hope to map data collected by canvassers (from organizations like Occupy Sandy) that highlight the housing vulnerability created or exacerbated by Hurricane Sandy.  We also wanted to start building a GIS mapping framework that we could later extend to the entire city of New York.

amandasmart
Amanda

The information that we have currently mapped is all from the US Census Bureau. The underlying map shape files are from NYC government, and the satellite imagery is from NOAA and NYC government. In order to get the data under control, especially for the maps involving thousands of small census blocks, we spent hours bonding with excel, coaxing the data in to a format that could be used with ArcMap, the GIS software we are using.

When we began the project we expected to be able to isolate a pattern of vulnerability through available census data. What we underestimated, however, was the degree to which data type, scale (e.g. what geography each data point represented), and availability would play a role in patterns that we could identify. The process of then assigning different data points as varying levels of vulnerability was also less direct than we anticipated, and required that we consult similar studies.

Ultimately, we were able to overcome some of the constraints we found, and create a foundational vulnerability framework in order to identify broad patterns in vulnerability.The map demonstrates the areas that face the highest level of combined risk. To construct this representation, we used data on socioeconomic vulnerability (such as income and employment status), housing vulnerability (such as number of tenants), and susceptibility to flooding as measured by flood insurance rate maps.