New York City Mayor's Office

This crisis is pushing policy makers to rethink how they can incorporate semi-real-time, data-driven methods to improve decision making. Epidemiological models suggest that infection rate is closely related to contact rate, so the question we are trying to solve for is:

What is the safest and most efficient way to re-open New York City's economy?

In order to infer contact rate, we have been exploring several open data sources to assess the relationship between changes in infection rate and changes in mobility patterns. The goal would be to deliver several data products, dashboards that incorporate time-series visualizations and maps that allow us to track the impact of the reopening of the economy on the population (e.g. mobility, social distancing and shelter in place behavior, health). Some examples I have created over the past weeks include:

NYC's Infection Rate Tracker at the county and Zip Code level: https://covid-growth-ny-metro.herokuapp.com/

NYC's Social Distancing Rank based on 311 Complaint Calls: https://vazquezg.carto.com/builder/5a9930b3-a078-4156-827d-7b7150322594/embed

NYC's People Density at a given Point of Interest based on Safegraph data: https://nycplanning.carto.com/u/dcpadmin/builder/9cb654f6-485c-4320-a270-095f6babfda6/embed

NYC's People Leaving Their Home (breaking shelter in place): https://nycplanning.carto.com/u/dcpadmin/builder/258fdfbb-d3cd-4944-b51d-b1c045545b12/embed

We are currently working in a Subway Ridership Tracker and I'm open to suggestions.

This is the right project for you If you are interested in the analysis, explanation, and prognosis of urban forms, urban social fabric, and economic structures and functions using NYC as your laboratory.

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