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Big Data: The Engine to Future Cities—A Reflective Case Study in Urban Transport

Pettit, CJ; Leao, SZ; Lock, O; Ng, M; Reades, J; (2022) Big Data: The Engine to Future Cities—A Reflective Case Study in Urban Transport. Sustainability , 14 (3) , Article 1727. 10.3390/su14031727. Green open access

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Abstract

In an era of smart cities, artificial intelligence and machine learning, data is purported to be the ‘new oil’, fuelling increasingly complex analytics and assisting us to craft and invent future cities. This paper outlines the role of what we know today as big data in understanding the city and includes a summary of its evolution. Through a critical reflective case study approach, the research examines the application of urban transport big data for informing planning of the city of Sydney. Specifically, transport smart card data, with its diverse constraints, was used to understand mobility patterns through the lens of the 30 min city concept. The paper concludes by offering reflections on the opportunities and challenges of big data and the promise it holds in supporting data-driven approaches to planning future cities.

Type: Article
Title: Big Data: The Engine to Future Cities—A Reflective Case Study in Urban Transport
Open access status: An open access version is available from UCL Discovery
DOI: 10.3390/su14031727
Publisher version: https://doi.org/10.3390/su14031727
Language: English
Additional information: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
Keywords: big data; data analytics smart cities; visualisation; dashboards; urban modelling
UCL classification: UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment > Centre for Advanced Spatial Analysis
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL
URI: https://discovery.ucl.ac.uk/id/eprint/10143916
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