Introduction

Hurricane Sandy is one of the strongest and most destructive hurricanes to have affected America in the past decade. It is the largest Atlantic hurricane on record as measured by diameter, with tropical-storm-force winds spanning 1,150 miles. One of the majorly inconvenient effects of hurricanes is inundation, or flooding. In order to mitigate the devastating effects of inundation, cities include the identification of potential floodplains in their 500 year development plans so that they can avoid major settlements in those areas, among other reasons. This lab report outlines the process of using QGIS to create a map of New York City displaying the floodplains identified in NYC’s 2050s 500 year plan in relation with the areas inundated by Hurricane Sandy. The resulting visualization shows how effective the identification of floodplains is in different areas of NYC.

Method

Datasets:

Multiple datasets were used to create this visualization. The shape file for the NYC map was taken from the datasets that were used for the demonstration during the class session. Two datasets were utilized to showcase the required data – Sea Level Rise Maps (2050s 500-year Floodplain) and Sandy Inundation Zone. Both datasets were obtained from NYC Open Data, an online platform for free public data published by New York City agencies and other partners.

Software:

This lab session involved the use of QGIS, an open source Geographical Information System (GIS) used to store, manage, analyze, edit and visualize geographic data.

Process:

Both datasets had a lot of extra data that I did not necessarily need for this particular visualization, but since I was able to access the geojson versions of both datasets, I did not mess with them too much. Initially I had a little trouble finding the data that I was importing on my project screen and added the same layer multiple times since I believed that it just wasn’t showing up. I then found about the ‘Zoom to layer’ option, and was able to find the map and delete the additional layers I had added. There were no significant issues that I faced while using the software after this, most functions were easy to use and the way that data is arranged in layers was very helpful. 

Most decisions that I made during the visualization process were related to color, since I was looking for a way to overlay both sets of data on the same map and needed a good amount of contrast without making it too overwhelming. The default colors were definitely not the best, so I decided to experiment with a few different color schemes before settling on one.

Figure 1 shows a version of the map where I tried using the ‘gradient plasma’ fill for the inundated areas against a black map and a solid fill for the floodplain areas. This effect was certainly very eye-catching and attractive, but I felt like it was taking too much attention away from the representation of the floodplain areas. I also felt that this particular fill didn’t really fit the element that it was representing. It seemed to be more indicative of heat than water. However, I did like the black map better than the white.

Figure 2 shows a slight variation of Figure 1, where I tried removing the background to see if that would make things stand out better. It did to an extent, but it didn’t solve the problem of the inundated areas looking more like lava than water. 

In Figure 3, I tried replacing the fill of the inundated areas with a gradient blue since I didn’t want to lose the black map, which made it look more like it was representing water. However I was still facing a bit of a problem with achieving enough contrast to make things pop while keeping it subtle. I also wanted a dark background color, which would not have helped this visualization in the least if the map was black.

Figure 4 shows the next direction I went in, which was to give the background a dark color and lighten the map. I went for shades of green to depict the different boroughs of New York City, but I reduced the opacity of the map so much in an effort to lighten it that this coloring did not really make any clear impact. Additionally, distinguishing between boroughs may also not have contributed very much to data that I was attempting to represent. I changed the color of the inundated areas to dark blue, and changed the colors of the floodplain areas to a lighter shade. This seemed to work well enough, being clear enough to be read while representing the conventional idea of water.

Figure 5 shows the version of the visualization that I settled on, where I increased the opacity of the map to clearly show the distinction between boroughs as well as highlight the map. I decided to keep the distinguishing colors since it could be useful to compare the intensity of inundation across the different boroughs. I used green for the representation of land to stick with universally recognized conventions.

Figure 6 shows the final image of the map, with a legend, north point and title. It took a little time for me to remember how to edit the items of the legend and the title, but I got there in the end.

Results and Reflection

The data shows that all inundations were well within the floodplains identified by the city planners, with a large enough buffer in some places. It is also apparent that Brooklyn and Queens faced the most inundation, which was unsurprising since these areas are along the coast. This lab session marked my very first time working with QGIS, and it was a pretty fun experience. The process to create visualizations is pretty simple, and a wonderful aspect of this software is how it can digest a variety of different file formats making it much simpler to load in data. I had multiple episodes where the application crashed, usually when I was loading geojson files in, though I am not sure if that was a contributing factor. I also had a little trouble getting CSV files to load correctly, which made the availability of geojson versions for all my datasets very convenient. Most of the work involved with using this software went into deciding the color combinations that were good for both my preferences and the effectiveness of the visualization. The feedback from my lab partner was very helpful here, since it also included an assessment of how well the colors were working and possible other combinations that I could try. In the future, I might try to create more complex visualizations and maybe move away from static maps. I also need to work a little bit more with CSV files since I was able to override the process during this session, which did not really fix my understanding of how to load them in correctly. All in all it was a great learning experience, and I look forward to working with QGIS again!