Billions of tweets, satellite data and demand for clean air

Description:

The present research uses Zillow big data to measure the benefits associated with reduced pollution. It compares two hedonic models. One using atmospheric pollution derived from satellite data to analysis the housing market and the second using predicted sentiments from geolocalized tweets as a proxy for atmospheric pollution and its impact on the housing market.

Students

Abdelaziz Lawani
Gwan Seon Kim

Collaborators:

Dr. Tyler Mark (PI)
Prof. Reed Michael (Co-PI)

Software

R, Python, ArcGIS