Spatio-temporal COVID (GY)
Investigating the spatial-temporal aspects of COVID-19 through touching, transportation and trade
This project-based course will form research teams that analyze spatio-temporal behavior of individuals exiting medical facilities and affiliated socio-economic data. The unique datasets will include 5,000+ records of individuals near 18 NYC based facilities during NYC’s COVID-19 peak 2020 in the spring and an anticipated 14,000+ new records to be collected in 4 cities in the US and 5 overseas locations in the period of Dec. 2020 – Feb. 2021. This will be an opportunity to do cutting edge work on up to the minute data of global importance.
The team will research topics related to (1) how hyper-local, spatio-temporal data can rapidly assess public health intervention success levels, (2) the extent that touch data can be operationalized in a 3D environment to inform public and institutional policies, (3) assessing the relevance of open data GIS layers in predicting behaviors and outbreaks, (4) how cellphone-based footfall data improves destination choices as a function of PAUSE order related closures, and (5) understanding the disproportionate impacts of the PAUSE order on under-resourced communities. Given the multi-site nature of the data, there will be lots of opportunities for students to incorporate local cultural and geographic knowledge.
Students will be encouraged to work in groups of 2 or 3 on a subtopic of their choice in the general 5 areas listed above.
Methods & Technologies
- LiDAR with VR
- Multi-variate regression and/or machine learning to predict behaviors
Areas of Interest
- Remote Sensing
- Computer Science
- Disaster Planning
- Public Health
- Urban Planning
- NYU Tandon School of Engineering
- Center for Urban Science and Progress
- With input from the School of Global Public Health