research-article

Whose Walkability?: Challenges in Algorithmically Measuring Subjective Experience

Authors:

Nicholas DiakopoulosAuthors Info & Claims

Proceedings of the ACM on Human-Computer Interaction, Volume 3, Issue CSCW

Article No.: 126, Pages 1 - 22

https://doi.org/10.1145/3359228

Published: 07 November 2019 Publication History

Abstract

The Walk Score is a patented algorithm for measuring the walkability of a given geographic area. In addition to its use in real estate, the accompanying API is used in a range of research in public health and urban development. This study explores how neighborhood residents differently understand the notion of walkability as well as the extent to which their personal definitions of neighborhood walkability are reflected in the Walk Score's underlying algorithm. We find that, while the Walk Score generally aligns with residents' priorities around walkability, significant subjective aspects that influence walking behavior are not reflected in the score, raising the need to consider implications for using algorithmic tools like the Walk Score in certain research contexts. We discuss the challenge of measuring subjective experience and how designers might begin to address it. We call for qualitative evaluations of algorithmic tools to help determine appropriate contexts of use.

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Cited By

Cahill MAnguelov N(2024)Hedonic analysis of willingness to pay for dam removal: evidence from Plymouth, MassachusettsJournal of Environmental Planning and Management10.1080/09640568.2024.2322538(1-21)Online publication date: 20-Mar-2024
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Whose Walkability?: Challenges in Algorithmically Measuring Subjective Experience

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cover image Proceedings of the ACM on Human-Computer Interaction

Proceedings of the ACM on Human-Computer Interaction Volume 3, Issue CSCW

November 2019

5026 pages

EISSN:2573-0142

DOI:10.1145/3371885

Editors:
Airi Lampinen
Stockholm University, Sweden
,
Darren Gergle
Northwestern University, USA
,
David A. Shamma
FXPAL, USA

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Copyright © 2019 ACM.

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Published: 07 November 2019

Published in PACMHCI Volume 3, Issue CSCW

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Cited By

Cahill MAnguelov N(2024)Hedonic analysis of willingness to pay for dam removal: evidence from Plymouth, MassachusettsJournal of Environmental Planning and Management10.1080/09640568.2024.2322538(1-21)Online publication date: 20-Mar-2024
https://doi.org/10.1080/09640568.2024.2322538
Dickinson CManaugh KPathak PSengupta R(2024)Geographic identity and perceptions of walkable spaceTravel Behaviour and Society10.1016/j.tbs.2023.10070334(100703)Online publication date: Jan-2024
https://doi.org/10.1016/j.tbs.2023.100703
Hutchinson BRostamzadeh NGreer CHeller KPrabhakaran V(2022)Evaluation Gaps in Machine Learning PracticeProceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency10.1145/3531146.3533233(1859-1876)Online publication date: 21-Jun-2022
https://dl.acm.org/doi/10.1145/3531146.3533233
Sun YLu WSun P(2021)Optimization of Walk Score Based on Street Greening—A Case Study of Zhongshan Road in QingdaoInternational Journal of Environmental Research and Public Health10.3390/ijerph1803127718:3(1277)Online publication date: 31-Jan-2021
https://doi.org/10.3390/ijerph18031277
Bandy J(2021)Problematic Machine BehaviorProceedings of the ACM on Human-Computer Interaction10.1145/34491485:CSCW1(1-34)Online publication date: 22-Apr-2021
https://dl.acm.org/doi/10.1145/3449148

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