In this paper we will describe, using GeoBigData available on the web, the behaviors of people during the various phases of the pandemic. In particular, we will analyze the percentage of increase/decrease of presences in daily life locations, the means of transport used and we investigated whether and to what extent these changes have impacted on air quality.
For this analysis we used various data sources and in particular the data provided by:
• Apple Mobility Trends Reports: which provides data, for each location and day, regarding the changes of percentage in the means of travel used (on foot or by car) in comparison to a standard week pre Covid
• Google Community Mobility Reports: which provides data, for each location and day, regarding changes in presences in daily life locations (Retail & recreation, Grocery & pharmacy, Parks, Transit stations, Workplaces, Residential) in comparison to the median of a period of 5 pre Covid weeks
• EnelX City Analytics - Mobility Map that provides, on a regional basis, the data (variation of movements, variation of KM traveled, distribution of incoming and outgoing flows, variation of incoming and outgoing flows, traffic). Unfortunately daily data are not available and we used the available dashboard to support our research
• Air Quality Historical Data Platform: which provides data, for each location and day, of the measurements of the air quality (PM2.5, PM10, NO2).
With the above data we investigated the changes in people's choices regarding means of travel and preferred places and we also tried to understand if and how these choices/impositions have impacted the air quality taking advantage of this unique opportunity to have entire weeks with zero or reduced mobility.
The research analyzed both the Italy and a specific use case of Milan.
