NYC COVID-19 GIS App Tracker

How to build an interactive NYC COVID-19 GIS tracker application with RShiny and Leaflet

Justin Morgan Williams


Photo by Alexander Rotker on Unsplash


NYC Department of Mental Health and Hygiene (DOHMOH) has a fantastic open source COVID-19 database on their publicly accessible Github. The data is cleaned and requires minor wrangling to track salient aspects of the COVID-19 pandemic. For the most part the visuals they provide on are adequate, however I wanted to create my own interactive easily updatable tracker to view changes in COVID-19 over time via geospatial analysis.

The Data

There are many files in their repository, however, since I was primarily concerned with geospatial data, I gravitated towards those that were sorted as such. The DOH data was aggregated by Modified Zip Code Tabulation Area (MODACTA). Modified ZCTA combines census blocks with smaller populations to allow more stable estimates of population size for rate calculation. This means they collapse multiple smaller zip codes into one, which works well for tracking NYC neighborhood trends. That being said, here are the following files from the DOH repository, this analysis and subsequent RShiny application will utilize.

Case rate — caserate-by-modzcta.csv contains rate of cases per 100,000 people.

Percent positive percentpositive-by-modzcta.csv contains the percentage of people tested for COVID-19 with a molecular test who tested positive.

Test rate — testrate-by-modacta.csv contains rate of molecular testing by 100,000 people.

Neighborhood nameslast7days-by-modzcta.csv contains person-level information on molecular testing, however in this instance will be used to obtain neighborhood names.

Summary — summary.csv contains cumulative summary information.

Geospatial data MODZCTA_2010.shp geographic files for modified ZIP Code Tabulation Area geographies (MODZCTA)

The Wrangling

First, the data needs to be forked or downloaded, again this is made easy via the DOH Github. This can easily be scheduled to regularly update, which would sync visualizations with latest data. Check out Keith



Justin Morgan Williams

Data scientist passionate about the intersectionality of sustainability and data.