Standing out in a crowd: Big Data to produce new forms of publicness
Carta, Silvio ORCID: https://orcid.org/0000-0002-7586-3121, Onafuye, Rebecca and de Kock, Pieter (2019) Standing out in a crowd: Big Data to produce new forms of publicness. In: Figueiredo, Sergio M., Krishnamurthy, Sukanya and Schroeder, Torsten, (eds.) Architecture and the Smart City. 'Critiques' (Critical Studies in Architectural Humanities) series, 1 . Routledge- Taylor & Francis Group, United Kingdom. ISBN 978-0429324468 (doi:10.4324/9780429324468)
Preview |
PDF (Abstract of book chapter)
44217_CARTA_Standing_out_in_a_crowd_Big_data_to_produce_new_forms_of_publicness.pdf - Other Download (125kB) | Preview |
Abstract
Complex systems are generally approached through various models that are underpinned by the use of approximation and standardisation. The same inductive method is based on the assumption that observations common to several phenomena can be described by the same behaviour. By this logic, societal trends are studied by means of statistical methods, on the assumption that a relevant number of individuals would represent the trend of the totality observed. Generalising at a broader urban scale, Waymo, the independent self-driving technology company that originated as the Google self-driving car project in 2009, represents a case in point and provides an interesting example where individual data is used to reshape the public realm. Space is produced by the dynamic interrelationships between the perceived space, lived space, and the conceived space. Since the environment and people’s daily routines are being mixed in with smart technologies and big data to create smart cities, people as individuals have become a product of that space.
Item Type: | Book Section |
---|---|
Uncontrolled Keywords: | built environment; urban studies |
Subjects: | N Fine Arts > NA Architecture |
Faculty / School / Research Centre / Research Group: | Faculty of Liberal Arts & Sciences Faculty of Liberal Arts & Sciences > School of Design (DES) |
Related URLs: | |
Last Modified: | 10 Nov 2023 07:54 |
URI: | http://gala.gre.ac.uk/id/eprint/44217 |
Actions (login required)
View Item |
Downloads
Downloads per month over past year