In 2013, researchers from the New York City Panel on Climate Change created a document in which they presented their projection on the life in New York in 2020s. According to their research, the sea level in NYC might rise 11 inches. When it happens, during the high flood which happens once in a century, many parts of New York City such as Downtown Manhattan, Coney Island or Rockaway will be severely affected. These areas are very important for many reasons – Downtown Manhattan is the place where New York was originally established. Thousands of New Yorkers enjoy the summer weather on the beaches of Coney Island and Rockaway. These are the places New Yorkers live – sleep, study and if sick, get cured in the hospital. In my visualization, I aimed to present the effect of a combination of changing sea levels and flood on New Yorkers’ well-being. I decided to focus on 3 major categories of buildings: iconic buildings, schools and Health + Hospitals patient care facilities. I aimed to clearly distinguish which are in the risk zones and which might stay unaffected. This map is created for all New Yorkers and city officials who need to prepare the city for the effects of climate change.
Methodology
Datasets and Software
In my work I have used following datasets:
NYC OpenData: Sea Level Rise Maps (the 2020s, 100-year Floodplain). This dataset has been created in July 2013 and updated in September 2018. It is based on FEMA’s Preliminary Work Map Data and the New York Panel on Climate Change’s 90th Percentile Projects for Sea-Level Rise (11 inches).
NYC OpenData: NYC Health + Hospitals patient care locations – 2011. This dataset was created in September 2011 and last updated in July 2019. It contains the data about the NYC Health + Hospitals network facilities (public hospitals, skilled nursing facilities, and some of the community-based health centers).
NYC OpenData: School Point Locations. This dataset was created in September 2011 and updated in April 2019. It is an ESRI shapefile of school point locations.
Curbed New York: New York City’s most iconic buildings, mapped. In this article, updated in August 2019, authors list 30 “biggest best architectural icons”. I have geo-coded these locations using Google Maps. I have decided to exchange following buildings: 56 Leonard Street, The Cooper Union, Seagram Building, Lever House, 432 Park Avenue, New York State Pavilion, TWA Flight Center for Vessel, MoMA, New York Aquarium, BAM, Barclays Center, Queens Museum of Art and Columbia University. I wanted to include more buildings from other boroughs than Manhattan.
The data has been analyzed and mapped in Carto.
Process
As a starting point, I have chosen the Voyager base map offered by Carto. I believe, that it resembles the closest typical Google map. I wanted to create a visualization, which would be easy to use and I thought that as users often are familiar with Google maps, it might be the easiest for them to use something which looks similar to locate interesting for them places. I tried to limit their cognitive workload. On this base map, I added the NYC boroughs map which was provided by Carto. Later on, I have hidden it from the users’ view.
Afterward, I started to upload all other datasets. At the very beginning, I realized that the dataset Sea Level Rise Maps (the 2020s, 100 years Floodplain) created squared boundaries. I was aware of this problem from my colleague – Tian Jing’s work. Tian decided to manage this issue by changing the color of the map. However, this approach would not fit into my general idea – creating a map, which would resemble Google maps. Therefore, manually, one by one, I have either removed additional, square boundaries or made them curvy. To create an effect of immersion, I colored this layer with the same shade of blue as a map already provided by Carto. New York suddenly changed its boundaries.
I quickly realized that uploading all datasets at once was a mistake. Colorful dots didn’t make any visible pattern. I have also realized that there is a mistake in geocoding NYC Iconic buildings. They were dislocated – and for example, dot marking Statue of Liberty was in the middle of the sea. I have manually fixed these points while comparing their locations with Google Maps.
I was inspired by the work of one of my colleagues who took the same topic and managed to separate the data points in the flood zones from the “safe zones”. Following his article “Rising sea level and its impact on the MTA subway system”, I added to my map once again 3 layers: NYC Iconic buildings, NYC Health + Hospitals, and NYC Public Schools. I run the intersect and aggregate analysis. As an effect, the dots representing the buildings exposed to flood risk were overlapped. In my first version of a map, I have used red color for buildings in the red zone and blue in a safer area.
The first round of user feedback
This was also the time when I have gone through the first iteration of the product. I have asked my colleague, student of the IXD program to provide feedback on my map and perform 2 tasks:
- Browse through the map, tell me what you think it is about. What do you see on it?
- Find Statue of Liberty on the map – will it survive flooding?
These tasks were accompanied by the following questions:
- Please tell me 3 things you like, and 3 things you dislike about the map.
- If you had a magic wand, what would you improve on it?
Based on feedback from user testing, I used different types of icons to distinguish different types of buildings: cross for health institutions, monument sign for NYC Iconic buildings and academic cap for school. I believed that these icons follow conventions and will be understood by users.
I also used blue color to mark buildings in danger of flooding. For this participant, it felt more natural. However, when I implemented these suggestions, the map did not look good – the contrast between red and blue points was overwhelming. Therefore I have decided to exchange the color of red dots into grey (symbolizing the grey of buildings). I was also asked to add a filtering option – to filter out the unnecessary data.
The second round of user feedback
After creating my map, I was still not convinced regarding the final effect and I have asked another potential user of my map – New Yorker, 60 years old for feedback regarding colors. She suggested me to use orange instead of blue to signal the risk. Thanks to her feedback, I changed again the colors on my map and added a legend explaining what different icons and different colors mean. I have also created widgets – helping to filter the data by the name of NYC Iconic building, name of school and name of Health + Hospital patient care facility.
The third round of user feedback
Based on the received feedback, I have created another version of the map, which was presented to another UX student, experienced in map-making. I have asked her the same questions as my first participant. This interview and observation were conducted online. This participant appreciated used icons, colors, and legend. However, she pointed out that I use two different icons for schools – dots and academic cap icon. Moreover, she noticed that filters are not dynamic and do not zoom to the place where the searched item is. After receiving this feedback, I have quickly improved the consistency among icons. However, I did not find a way to fix filtering option, therefore I have decided to remove widgets and not to confuse users.
Based on these three rounds of user testing and feedback, I have reached the final version of my map:
Findings
Finding 1: User need Search and Filtering functions
Two of my users were interested in using a filtering option, however, the solution provided by Carto appeared to be not satisfactory. In the beginning, I created 3 different filters: Filter by the name of the Hospital or Health Center, Filter by School Name and Filter by Iconic building. As users intuitively understand the categories, they are surprised to see that while clicking on the interesting results, they are not automatically directed to it.
In fact, widgets work in a different way – user needs to zoom in on the map and they present the schools, hospitals, and health centers and iconic buildings which are located on the zoomed-in part of the map. There was a clear bridge between the Gulf of Evaluation and the Gulf of Execution.
By the Gulf of Evaluation, we understand, what the user thinks is the current state of the system. This is directly linked to the Gulf of Execution, where users execute their actions. In the case of widgets used by me, users thought this is a filtering option that zooms into an interesting area, though in fact, this widgets work slightly different.
For the next iteration of this map, I would create two new options: search function and filtering option. In the search bar, users would enter the name of the location of the place they want to locate e.g. the Statue of Liberty. After clicking <enter> the map would have zoomed into the part of the map with the Statue. Filtering would work in a similar way – users could take away the layers they do not need to see, e.g. hospitals and health centers if they are interested only in public schools and in the iconic buildings.
Finding 2: Color and icons are crucial for map readability
Choosing colors for the map appeared to be the most troublesome design decision I made. As I decided to use 3 different datasets that were uploaded twice to Carto (to create the difference between buildings in flood risk zone and buildings outside of it), I could have easily ended up in a chaotic collage of different colors and icons. My design went through 3 different iterations before reaching its final stage. I tried to investigate the mental models of my users and find the design which will work for the majority of them.
Luckily for me, Carto provided a set of icons, which were accepted and understood by all of the users. They are considered standard ones and users are familiar with them.
I believe, that for the future iterations, the color used by me could be tested among a bigger number of potential users. Based on their feedback, it might be changed to another version. However, in this case, more user research would be required, which might potentially include interviews, observations or A/B testing.
Finding 3: Map clearly presents the areas where the highest number of schools, iconic buildings and Health + Hospitals and patient care facilities will be affected
There are 3 main areas with high public school density which are in the biggest risk of flooding:
- Downtown Manhattan and Alphabet City:
- Downtown Manhattan – especially areas next to West Street, which might be completely under the water. There are two important NYC Iconic Buildings: One World Trade Center and Oculus, there are no facilities belonging to the Health + network.
- Alphabet City – this area, up till Avenue B is the highest danger of being flooded. There are 3 main concentrations of public schools. In this area, there are no iconic buildings and facilities belonging to the Health + network.
East Harlem is another concentration of public schools, which are in risk of flooding. There are no iconic buildings and facilities belonging to the Health + network.
- East part of Brooklyn:
- Coney Island – Coney Island is the area which will be severely impacted by rising sea level. It has many public schools, but also hospitals and health centers. Its Aquarium has already suffered from flooding. This situation does not seem to change within the next few years.
- Rockaway – Rockaway is a peninsula which during extreme flooding might disappear under the water. There are many public schools that might be severely affected.
Currently, the areas with a higher number of public schools are more visibly impacted by the flooding – Health + Hospitals patient care facilities and iconic buildings due to their small number are much less visible. For the next iteration, I would not change the orange color of all buildings in the risk zone, but I would focus on implementing correct filters.
Finding 4: The map does not present the total impact of higher sea levels in New York City
My map focuses on the 3 main categories of buildings: Iconic buildings, Health + Hospitals patient care facilities and public schools. It fulfills its aim, but it misses the total overview of the impact of higher sea levels in New York City. It would be interesting to create a map, with highlighted residential areas. These areas are the places where actually New Yorkers live. Sometimes, there are no special buildings, but it doesn’t mean there are less important. Similarly, it would be interesting to highlight the areas where New Yorkers work – such as Downtown Manhattan and areas next to Battery Park.
It is also interesting to create a map with transitways which will be affected by flooding. Severe flooding will affect highways and bridges. It will create also several islands. Services which are in charge of preparing New York City for the extreme situations need to consider how to maintain the continuity of vital services such as food delivery, emergency help to the areas which will be isolated by the water.
Conclusion
The map aimed to present the effect of changing sea levels on New Yorkers’ well-being. It focused on the Iconic buildings, Health + Hospitals patient care facilities and Public schools. In my visualization, I have used orange color to distinguish buildings in the risk of flooding from grey buildings at a lower risk of flooding.
During the design process, I have tested my map with 3 different participants: two students of IXD program at Pratt Institute and one artist from New York. The map went through 3 iterations, where my design decisions were verified by the independent users.
I have found out, that used colors and icons are extremely important for users. Users expect interactivity and filtering options. This is where my map needs to be further developed.
When looking into the map’s content, there are 3 main areas that are in the risk of flooding: Downtown Manhattan and Alphabet City, East Harlem and East side of Brooklyn – Coney Island and Rockaway Park. For the next map iteration, it would be interesting to highlight the areas New Yorkers live and work.