Tag Archives: nyc

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image of a large pizza margherita with olives. In the background, the Brooklyn Bridge at sunset.

Most Popular Cuisines in NYC Boroughs

One of the things I love the most about New York City is its diversity, which translates to the incredible variety of restaurants that we’re lucky to have at our disposal. I remember trying Vietnamese, Korean, and Ethiopian food for the first time in the city…Italy has great food, but not a lot of diversity!

I decided to use the dataset for the DOHMH New York City Restaurant Inspection Results, which I had downloaded in Spring 2020 for my Geospatial Humanities class. [1] I was hoping to make a map out of it, but I realized it was a little complicated with ArcGIS on my slow PC…however, this dataset worked very well for Data Visualization!

I was inspired by the work my classmates did for their Mapping assignment and decided to use Tableau for my data visualization. Thanks to a handy tutorial on the Tableau website, I was able to select the dimensions and measures I wanted to portray and explore different styles of visualization. I spent a morning just exploring the data on Tableau and trying to combine different fields to see if anything interesting came out. I experimented with Restaurant Grades and Scores, but then I decided to keep it simple and calculate the top 10 Cuisines for each borough. New Yorkers intuitively know that the best Chinese food is in Chinatown (Manhattan), Flushing (Queens), and Sunset Park (Brookyln). Or that you can find handmade mozzarella in Little Italy (the Bronx one!). And that there is a big Orthodox Jewish Community in South Williamsburg, so that’s the place to go if you are craving bagels, smoked fish, and chocolate babka. I wanted to see if the data reflected this empirical evidence.

I opted for a very simple bar chart showing the Top 10 Cuisines in each borough, which you can find here. I used a tutorial I found on Youtube to display the number of top cuisines for each borough, calculated as a percentage of the total. I decided to differentiate the cuisines by color for easy reference, but also for the aesthetical reason to portray the mosaic of NYC cuisines in my visualization.

As we can see from the visualization, American cuisine is the most prominent in each borough, with a spike in Manhattan. Chinese restaurants are the second most popular establishments in all boroughs except Staten Island, where they predictably get beaten by Italian restaurants. Italian food appears in the top 10 of Manhattan and Staten Island, but surprisingly not in the Bronx – where there is, however, a 7.7% rate for “Pizza” and a 2.7% rate for “Pizza/Italian”. Jewish/Kosher restaurants appear in the top 10 only in Brooklyn, which reflects my initial assumption. A thing I found interesting is that, despite there being a Koreatown in Manhattan, Korean cuisine makes it to the top 10 only in Queens.

In the end, this project left me with more questions than answers.

  1. According to the metadata of the DOHMH New York City Restaurant Inspection Results (the “Data Dictionary” spreadsheet), the “Cuisine Description” field is an “Optional field provided by provided by restaurant owner/manager”. The fact that there is a discreet number of categories makes me think that the owner/manager of a restaurant needs to choose from a list, which means that diversity is necessarily reduced. How do you classify the amazing Chino-Latino cuisine?
  2. What does “American Cuisine” mean? When I saw that field, I immediately assumed it meant burgers, BBQ joints, and steakhouses, but this is just my imagination (and my bias) filling the gap in the data. Soul food is definitely American, but it doesn’t fit neatly in the definition. I would love if there was an ulterior classification for American cuisine, or at least a more extensive description in the metadata.
  3. In the future, I would like to investigate the correlation of predominant cuisine and demographics in a ZIP code: for example, Manhattan’s Little Italy has a lot of touristy Italian restaurants, but I doubt that there are many Italians or Italian Americans living in the neighborhood. I’m looking forward to the release of the 2020 Census Data for this.
  4. How many restaurants have closed due to the Coronavirus crisis? Which cuisine was the most affected? I would need to interrogate the datasets for Spring 2020 and October 2020 to have a comparison.

I would greatly appreciate hearing your feedback on this project and how you would improve it. Stay safe and support your local restaurants!

P.S. The huge pizza in the image is from Juliana’s Pizza in Dumbo, Brooklyn and I highly recommend it!


[1] I’m mentioning the download timeframe because the data gets updated according to new inspections from the Department of Health. Here I’m presenting the data as it appeared in Spring 2020: this means that the dataset probably portrays restaurants that are now closed and doesn’t have data on new restaurants that might have opened since then.

Map of Langston Hughes' poem Harlem Sweeties.

Map of Langston Hughes’ Harlem Sweeties

Map of the Sugar Hill Neighborhood in Harlem. Buildings are colored according to the year they were built and there are indications of NYC landmarks. Langston Hughes' poem, "Harlem Sweeties" is written on the right and left side of the map.
Map of Harlem Sweeties by Langston Hughes. To see the full-size image, click here

In 2018 I had the fortune of visiting Langston Hughes’ house in Harlem during a children’s program organized by the independent bookstore Revolution Books. In the brownstone’s living room, an actor read his favorite poems by Langston Hughes, including one that wasn’t exactly appropriate for children: Harlem Sweeties, an ode to the sensual beauty of the women of Harlem, whom he depicts as delicious desserts and candy. I really loved the poem, not only for its tongue-in-cheek tone, but because it perfectly described the great diversity of Sugar Hill, the neighborhood where I was living at the time.

While working on my map, I asked myself: what did Sugar Hill look like in Langston Hughes’ time? Therefore, I decided to create a map of the neighborhood that depicted the buildings before 1967, the year that Hughes passed. I used ArcGIS because I felt comfortable with the software and its tools. However, this choice came with its sets of problems, as I will describe later in the post.

Step-by-Step Process

  1. I selected a light gray basemap from ArcGIS’s options, to give a simple background to my map.
  2. I imported data from the MapPLUTO database, a dataset of land use and geographic data collected by NYC agencies.
  3. To draw a map of Sugar Hill, I selected the parts of the map that had the neighborhood’s ZIP code, 10031. (Select by Attributes)
  4. From this group, I selected only the buildings built before 1967. (Select by Attributes)
  5. I decided to indicate the age of the buildings with a color scale in the tones of beige and brown. The newer the building, the darker the color. I chose this color palette because it reminded me of the colors that Hughes mentions in Harlem Sweeties
  6. Sugar Hill is a Historic District, so I decided to import the Designated and Calendared Buildings and Sites dataset from NYC Open Data to show which Sugar Hill buildings are landmarks.
  7. I wrote the text of the poem on the map and changed the font to make it look nicer.
  8. I edited the layout of the map, adding a legend, a compass, and a scale indicator.
  9. I created JPEG and TIFF files of the map.

What you don’t see in the map, AKA problems

While working on my mapping assignment, I kept a little journal of my progress and lack thereof. I found this process very helpful when learning how to code in prof. Smyth’s Software design lab. Therefore, here’s a list of what you can’t see in the map, but was a part of the process:

  1. The hour I wasted pursuing other ideas and getting frustrated because I couldn’t get the data to display on the map the way I wanted. I tried to work on:
  2. The hour I wasted trying to georeference an old map of Williamsburg on ArcGIS.
  3. Every time ArcGIS crashed, or my computer crashed. Basically, every time I gave the software a command, it took 5 minutes to complete the work. I took a bunch of coffee breaks.

I decided to share the challenges I encountered while working on this project because, when we look at maps, we don’t usually think about the process behind them. Maps on the internet look great and present the information as a fact, and not as the product of many small and big decisions. I think my map looks pretty nice in its final form, but I think it’s useful to show not only the destination, but also the bumps in the road.