Estimation of mobility and population in Spain during different phases of the COVID-19 pandemic from mobile phone data.

This work aims to find out the effectiveness of sources based on Big Data like mobile phone records to analyze mobility flows and changes in the population of Spain in different scenarios during the period of the pandemic caused by the COVID-19 virus. To this end, we have used mobile phone data provided by the National Institute of Statistics from four days corresponding to different phases of the pandemic. Origin-Destination matrices and population estimation calculations at the spatial level of population cells have been elaborated. The results show different patterns that correspond to the phenomena that have occurred, as the decrease of the population during the periods associated with the confinement measures. The consistency of findings with the reality and the generally good correlation with the population census data indicate that mobile phone records are a useful source of data for the elaboration of demographic and mobility studies during pandemics.

Public Transport Tweets in London, Madrid and Prague in the COVID-19 Period - Temporal and Spatial Differences in Activity Topics

Public transport requires constant feedback to improve and satisfy daily users. Twitter offers monitoring of user messages, discussion and emoticons addressed to official transport provider accounts. This information can be particularly useful in delicate situations such as management of transit operations during the COVID-19 pandemic. The behaviour of Twitter users in Madrid, London and Prague is analysed with the goal of recognising similar patterns and detecting differences in traffic related topics and temporal cycles. Topics in transit tweets were identified using the bag of words approach and pre-processing in R. COVID-19 is a dominant topic for both London and Madrid but a minor one for Prague, where Twitter serves mainly to deliver messages from politicians and stakeholders. COVID-19 interferes with the meaning of other topics, such as overcrowding or staff. Additionally, specific topics were discovered, such as air quality in Victoria Station, London, or racism in Madrid. For all cities, transit-related tweeting activity declines over weekends. However, London shows much less decline than Prague or Madrid. Weekday daily rhythms show major tweeting activity during the morning in all cities but with different start times. The spatial distribution of tweets for the busiest stations shows that the best-balanced tweeting activity is found in Madrid metro stations.