Big data: A new opportunity for transport geography?

The rise of Web 2.0 and user-generated content has provided the research community with novel sources of unconventional, unstructured data sources, which are widely referred to as big data. These new sources of data have prompted research attention across multiple disciplines. Though they present computational challenges with respect to data cleaning, processing and storage, they also present opportunities to test hypotheses with new data, which have the potential to yield new insights into classic research questions across multiple disciplines. That is true for transportation research, where the availability of new sources of big data, about mobility, transport supply and usage presents opportunities to analyze transportation infrastructure and behavior in new ways. This collection of research papers aims to critically assess and demonstrate how data and methodological developments have influenced transport geography research. Specifically, the issue demonstrates the value of new sources of big data in transport geography by presenting a collection of cutting edge geographical empirical studies, which take advantage of the ‘three Vs’ that characterize these data (variety, volume, and velocity). These papers employ new sources of big data and computationally intensive methodological approaches to solve transportation problems that were not possible with traditional data and methods. In these pieces, a variety of data sources are utilized, which range from train timetables and flows to bike sharing data, to Tweets and to data about individual mobility trajectories from wearable global positioning system (GPS) devices. Thus, one interesting feature is their common interest in exploring how new big data sources can help the transportation community research, explore and test new hypotheses about transportation behavior and variations in this behavior across space. Transport geography has always been a cross-disciplinary community, but it seems that the availability of new sources of big data has drastically enhanced this characteristic of the field

Language

  • English

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  • Accession Number: 01714672
  • Record Type: Publication
  • Files: TRIS
  • Created Date: May 4 2019 3:09PM