Our project integrates socio-economic data with high-resolution multispectral images from Planet Explorer and Google Earth to view and understand the areas where most frontline workers in New York live.
This project was inspired by our father, who passed away from COVID-19. RPV were his initials. He contracted the virus in Manila, Philippines, a densely populated city and where he lived at the time. We always wondered where and how he got the virus.
For the hackathon, our team decided to focus on New York City due to our proximity to this area and because it was the epicenter of the pandemic in the US. We hypothesized that Manhattan, a great tourist attraction where major public transportations were concentrated, would have the highest rates of confirmed cases and mortality. Quite to our surprise, Kings county located in Brooklyn, Queens and the Bronx, respectively, were leading in these numbers [1].
We reviewed the socio-economic background of these locations. We found out that people residing in the Bronx, Queens, and Brooklyn tended to have lower salaries than those in Manhattan [2]. Moreover, most NYC frontliners resided in the boroughs reported to be heavily struck by COVID-19.
With this information, we examined high resolution multispectral images covering the 5 boroughs using Planet Explorer and Google Earth Pro. We noticed that Brooklyn, Bronx, and Queens are more highly residential than Manhattan. Most houses in these boroughs are apartment-type buildings and are built so close together. Next, we used the primary residential zones spatial vector data to confirm our observation from satellite images. We integrated these two geospatial data in QGIS for assessment.
Please see a demo of our project in this link. Note that we saved it to two formats PPT and PDF. Thanks!
References:
[1] Johns Hopkins COVID-19 Map https://coronavirus.jhu.edu/
[2] New York City's Frontline Workers https://comptroller.nyc.gov/reports/new-york-citys-frontline-workers/
[3] NYC Open Data: https://data.cityofnewyork.us/Housing-Development/Primary-Residential-Zoning-by-lot/ieyi-rqsn
Data used: PlanetScope 4-BandScene, Google Earth Screenshots, and New York Open data - Primary Residential Zoning
Tools/Software used: QGIS, Planet Explorer, and Google Earth Pro