Bartosz Hawelka

TL;DR: This article analyses geo-located Twitter messages in order to uncover global patterns of human mobility and reveals spatially cohesive regions that follow the regional division of the world.

TL;DR: In this article, the authors analyze geo-located Twitter messages in order to uncover global patterns of human mobility, revealing spatially cohesive regions that follow the regional division of the world.

TL;DR: Examples of the spatiotemporal patterns of collective human dynamics, which are derived from ‘social sensor’ data are shown, which provide additional insights into how collective social activity shape urban systems.

TL;DR: This paper exploits a relatively unexplored source of data–anonymized records of bank card transactions collected in Spain by a big European bank, and proposes a new classification scheme of cities based on the economic behavior of their residents, which exhibits a substantial stability over different city definitions and connects with a meaningful socioeconomic interpretation.

TL;DR: A novel, consistent way of constructing mobility networks using transactional data, a way transposable to a variety of other datasets is proposed, and a surprisingly consistent trend is found between the distance from a given country to Spain, and the mobility characteristics of visitors coming from this country.