The Uneven Spread of Covid-19 In New York City



In the early months of 2020, Covid-19 changed the world in a dramatic way. When the virus reached the United States, nowhere was hit harder than New York City. Peaking at several hundred deaths per day, the city went from business-as-usual, to a state of emergency in a matter of weeks. Now, many months after the first case arrived in New York City, the data reveals some interesting patterns in how the virus spread around the city. Before diving into the data, I wanted to know if there were certain areas of the city that were hit noticeably harder than others. If this was true, it could likely lead to further conclusions about how the virus spreads, and potentially how that could be linked to the socio-economic structure of the city.

Design References

Before beginning this visualization, I watched a brief YouTube tutorial about creating maps using Carto. In the tutorial, the narrator creates a simple point map (pictured below). As she went through each of the basic features in Carto, I started to get ideas about what I could potentially do with my dataset.


I created my visualization using Carto – a free web-based mapping tool.

The dataset that I used came from the NYC Department of Health, and is the most up-to-date Covid-19 data available for New York City.


My first step in creating this visualization was to find a dataset that had a geographic component. I knew that I wanted to look at Covid-19 data in New York City, so I found a dataset that broke down the data by zip code. Once I imported the dataset into Carto, I ran the Geocode analysis so that the tool would recognize the Zip Code column and place dots in the appropriate locations on the map.

Once I had dots on each of the NYC zip codes, my next step was to re-size and color the dots in a way that reflected the rate of Covid-19 in each. When re-sizing and coloring points on a map in Carto, it allows you to select any column from your dataset upon which to base these style items. My dataset included both the raw number of Covid-19 cases in each zip code, as well as the case rate in each zip code (cases per 100,000). Given that the population can vary dramatically in each zip code, I didn’t want this to obscure the point that I was trying to get across to the user. Because of this, I based the size and color of the dots on the case rate, as this provides a more consistent way of comparing data between zip codes of varying population. Pictured below are the settings I used to achieve the desired look of the map.

Once I had the dots appropriately sized and colored, I decided to create a pop-up box that would display certain data when the user hovered over a selected zip code. I decided that the most pertinent information to be displayed were the borough name, neighborhood, zip code, Covid case rate, and death rate.

Finally, I decided to use the Dark Matter base map, as I felt that it made the colors on my map points stand out. I also picked the option that listed the city names below the data on the map, so as not to obscure the data itself.

The interactive map can be viewed here.


The map that I created serves the purpose that I set out to accomplish. Using both the color and the size of the dots, a user of this map can quickly identify the areas of the city that have been hit the hardest by the Covid-19 pandemic.

Moving forward, I believe this project offers a lot of opportunity for future studies. Now that we can easily identify the disparities in how this virus has affected different parts of the city, the next question is, “why?”. Do income differences between zip codes play a role in the spread of the virus? Does access to health care have an effect? Perhaps household size could be a factor contributing to the disparity that we see here. I think future studies could go further into this data and identify some different variables that are correlated to the spread of Covid-19.