Introduction:

The ability to navigate freely without physical restrictions in an urban city is a comfort that can be overlooked in everyday life. For this lab, I wanted to explore accessibility measures in place for New York City . To do this, I looked into datasets available online that provide useful information, beneficial to those with permanent/short-term disabilities but also to other communities such as the elderly. By examining accessibility in NYC we can get a picture of what needs to be improved. The main questions I had in mind for my visualizations when researching my topic were:

Inspiration:

In my last critique for class, I came across a visualization in an article which pointed out the shortcomings of the map, “Homicide Rates by County“. The map uses shades of red and green which to my surprise are not colorblind safe for those that have some kind of color-blind deficiency. The author of the article goes into detail of how he actually sees the physical and digital world, whilst also advocating for accessibility in design. I was inspired by his writing to attempt to make an color-blind friendly visualization that showcases my research into NYC accessibility.

Methodology

The first part of my process was identifying the datasets I would use for my visualizations. Firstly, I navigated NYC Open Data to begin my research, where I found several datasets I wanted to implement into map visualizations. In addition, data.ny.gov had handy information about NYC subway entrance and exit data which I used for my first map. I also gathered datasets of NYC cartography which would act as a base for my other dataset layers.

The next part of my process was working with the tool called QGIS, which was used to create the final map visualizations. For this project, I did not have to clean up any of the datasets before integrating them with QGIS. I was able to create the maps based on the trustworthy data provided by my resources. The process of creating the visualizations using QGIS was a long process mainly because the software has many options for the visualization to explore. I would eventually come to use only shapefiles and CSV’s as the file formats selected to integrate with QGIS. In addition, I used Adobe Illustrator to create the legends and Microsoft Excel to create supplementary visualizations. Furthermore, the following datasets were explored and the items with asterisks were eventually used in the final visualizations.

Lastly, through my research I learned that there are color schemes that can be used to make visualizations more colorblind friendly. For the most part, the colors selected for the visualizations are from the vibrant color scheme, based on a resource I found by instrument scientist Paul Tol. (Tol, 2021) I also made conscious choice to use different symbols (triangle and circle) in my first map because I had two values I wanted to exemplify. Therefore allowing a person to not only rely on color to distinguish between the two variables.

Visualization

The first visualization came about from a dataset called: NYC Transit Subway Entrance And Exit Data. This dataset has a column which provides information on whether the subway entrance/exit is ADA compliant. The Americans with Disabilities Act (ADA) was passed in 1990 which prohibits discrimination based on disability but also covers public transportation and infrastructure. I have previously heard the NYC subway system is notoriously not very accessible and this data which was last updated on April 11, 2022 showcases this truth. The first image in the slideshow is a screenshot of the overall map, the second image is a zoomed in view of the map showcasing a closer look at which subway stations are accessible, and for the third image I created a pie chart highlighting the percentage of subway stations which are ADA compliant.

The second dataset I utilized was Accessible Pedestrian Signal Locations. According to the NYC Open Data: NYC DOT’s Accessible Pedestrian Signals (APS) are devices affixed to pedestrian signal poles to assist blind or low vision pedestrians in crossing the street. APS are wired to a pedestrian signal and send audible and vibrotactile indications when pedestrians push a button installed at a crosswalk. This particular dataset was last updated April 10, 2023. I used the same base datasets for mapping NYC and integrated the locations for the accessible pedestrian signal locations. I came to the conclusion that there is definitely a robust amount of APS but the goal should be for all pedestrian signal poles to have these devices. I would also have liked to add labels to the symbols, but the dataset did not come with any place labels.

The final dataset I found was for Pedestrian Ramp Locations. I choose to use this dataset for the visualization because I saw a column with the condition of the ramps. Officially called Detectable Warning Surface condition, this data was last updated as of May 22, 2022. I would have wanted to improve this visualization by color coding the conditions and having the symbols on the map reflect the condition of the ramps. However, I could not figure out a way to do so, therefore I added a supplemental chart which tells us that about 40% of the ramps are in good condition and 59% have missing data. Ideally, it would be great if this dataset was updated in order to understand the overall picture of the accessibility of ramps in NYC.

Reflection:

Overall, I enjoyed working with QGIS and learning this new software. However, I found myself reworking the map many times and had some issues such as: Not being able to apply a legend to the map, struggling to create better labels as well as wanting them to hover over a symbol when selected, and also attempting to implement a CSV file [Directory of Accessible Parks Facilities and Programs] that did not have geo locations. On the plus side, I thought the layers were very useful for the various datasets I was able to integrate. I used several shapefiles/csv to create the base NYC map, although I assume there might have been an easier way to create the base map. Even though I liked the interface, I did find it difficult to find things as a beginner. Lastly, I would have wanted to have more than 1 accessibility dataset per map but I thought the design looked very busy so I decided to create 3 separate maps. In terms of the success of the visualizations I created, I think the first map I made with the subway accessibility is the most promising map. There were many things I would have liked to change in the others but I am content with the final outcomes.

References:

Accessible Pedestrian Signal Locations | NYC Open Data. (n.d.). Retrieved April 14, 2023, from https://data.cityofnewyork.us/Transportation/Accessible-Pedestrian-Signal-Locations/de3m-c5p4

Baio, A. (2023, April 7). This is what it looks like to be colorblind. The Verge. https://www.theverge.com/23650428/colorblindness-design-ui-accessibility-wordle

Digital City Map – Shapefile. (n.d.). NYC Open Data. Retrieved April 14, 2023, from https://data.cityofnewyork.us/City-Government/Digital-City-Map-Shapefile/m2vu-mgzw

NYC Transit Subway Entrance And Exit Data | State of New York. (n.d.). Retrieved April 13, 2023, from https://data.ny.gov/Transportation/NYC-Transit-Subway-Entrance-And-Exit-Data/i9wp-a4ja

Parks Properties | NYC Open Data. (n.d.). Retrieved April 14, 2023, from https://data.cityofnewyork.us/Recreation/Parks-Properties/enfh-gkve

Pedestrian Ramp Locations | NYC Open Data. (n.d.). Retrieved April 14, 2023, from https://data.cityofnewyork.us/Transportation/Pedestrian-Ramp-Locations/ufzp-rrqu

Subway Lines. (n.d.). NYC Open Data. Retrieved April 14, 2023, from https://data.cityofnewyork.us/Transportation/Subway-Lines/3qz8-muuu

Tol, P. (2021). Paul Tol’s Notes. Retrieved April 14, 2023, from https://personal.sron.nl/~pault/#sec:qualitative