Hong, Andy;
Baker, Lucy;
Prieto Curiel, Rafael;
Duminy, James;
Buswala, Bhawani;
Guan, ChengHe;
Ravindranath, Divya;
(2022)
Reconciling big data and thick data to advance the new urban science and smart city governance.
Journal of Urban Affairs
10.1080/07352166.2021.2021085.
(In press).
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Abstract
Amid growing enthusiasm for a ”new urban science” and ”smart city” approaches to urban management, ”big data” is expected to create radical new opportunities for urban research and practice. Meanwhile, anthropologists, sociologists, and human geographers, among others, generate highly contextualized and nuanced data, sometimes referred to as ‘thick data,’ that can potentially complement, refine and calibrate big data analytics while generating new interpretations of the city through diverse forms of reasoning. While researchers in a range of fields have begun to consider such questions, scholars of urban affairs have not yet engaged in these discussions. The article explores how ethnographic research could be reconciled with big data-driven inquiry into urban phenomena. We orient our critical reflections around an illustrative example: road safety in Mexico City. We argue that big and thick data can be reconciled in and through three stages of the research process: research formulation, data collection and analysis, and research output and knowledge representation.
| Type: | Article |
|---|---|
| Title: | Reconciling big data and thick data to advance the new urban science and smart city governance |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1080/07352166.2021.2021085 |
| Publisher version: | https://doi.org/10.1080/07352166.2021.2021085 |
| Language: | English |
| Additional information: | © 2022 The Author(s). Published with license by Taylor & Francis Group, LLC. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way. |
| Keywords: | Big data; thick data; urban science; smart cities; road safety; ethnography |
| 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/10145444 |
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