Stripe painting: A method of expressing the experience of cycling through 'quantified self' data visualisation

Shaleph O'Neill (Lead / Corresponding author)

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)
94 Downloads (Pure)

Abstract

This paper describes a collection of data-driven aesthetic explorations that investigate the concept of 'cycling as art practice' through the lens of 'quantified self' data-visualization. The explorations draw upon the philosophy of the 'Walking artists' and the concept of 'Deep mapping' and the focus of the work is on trying to visualize the experience of cycling. In doing so it draws attention to what aspects of experience can and cannot be quantified through the kinds of data we capture about ourselves.

Original languageEnglish
Title of host publicationUbiComp 2016 Adjunct - Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing
PublisherAssociation for Computing Machinery
Pages600-601
Number of pages2
ISBN (Electronic)9781450344623
DOIs
Publication statusPublished - 12 Sep 2016
Event2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp 2016 - Heidelberg, Germany
Duration: 12 Sep 201616 Sep 2016

Conference

Conference2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp 2016
CountryGermany
CityHeidelberg
Period12/09/1616/09/16

Keywords

  • Art
  • Cycling
  • Data
  • Quantified Self

Fingerprint Dive into the research topics of 'Stripe painting: A method of expressing the experience of cycling through 'quantified self' data visualisation'. Together they form a unique fingerprint.

Cite this