Abstract

This dissertation deals with the need for, and the key role and development of data-driven tools and methodologies for minimizing data complexity in transport geography research. It does so by developing a diverse range of analytical tools that collectively show that this is both feasible and useful. In addition to developing these tools in the strict sense, the dissertation also examines various challenges associated with the process, ranging from acquiring data to data curation, analysis and visualization. The thesis is divided into four chapters: (1) I first introduce SKYNET, a flexible R package that allows generating bespoke air transport statistics for urban studies based on publicly available data from the BTS in the United States. (2) I explore how potential biases in air transport datasets can be revealed and detailed by focusing on the US Origin-Destination Survey (DB1B) and the Air Carrier Statisticsform 41 traffic (T-100) datasets. (3) I explore the spatio-temporal dynamics of airport catchment areas within the New York Multi Airport Region. (4) And finally, I present StationsRadar, a data-driven web-based tool developed to support integrated land use and transport strategy-making at railway station locations in the region of Flanders and the Brussels Capital Region.