Analysis of Italian mobility patterns using institutional and networked data

Abstract Traditional data sources, such as population censuses, have several limitations, including long update times, huge costs, and measurement bias, which can lead to gaps in understanding population dynamics. The aim of this article is to assess the quality of new technologies and the usefulness of the Web as a source of massive amount of geospatial data for mobility analysis. To this end, we would like to compare data obtained through web scraping with available population censuses to identify similarities or possible new trends. We will explore the travel routes offered on the carpooling site BlaBlaCar in Italy and compare them with data coming from the commuting matrix for study or work purposes elaborated by ISTAT(Istituto Nazionale di Statistica), a public research organization that produces and disseminates official statistics in Italy. Finally, we will use a gravity model by combining other open data on the web to try to estimate internal migration flows based on demographic and economic variables. While institutional data capture dense, routine commuting, BlaBlaCar data reflect longer, voluntary travel behaviours. Despite structural differences, both sources offer valuable insights into population movements.