Walkie-Talkie Maps – A Novel Method to Conduct and Visualize Remote Ethnography

Author:

Yavo-Ayalon Sharon1ORCID,Gong Cheng1ORCID,Yu Harrison1,Mandel Ilan1,Ju Wendy1

Affiliation:

1. Jacobs Technion-Cornell Institute at Cornell Tech, New York, NY, USA

Abstract

The COVID-19 pandemic, travel restrictions, and social distancing measures have made it difficult to observe, monitor, or manage urban life. To capture the experience of being in New York City during the first year of the COVID-19 pandemic, we used a novel method of remote ethnography to interview people who were walking the city. We developed the Walkie-Talkie Map to collect and present these interviews, enabling website visitors to see what the subject saw as they walked the route of their choice. Visitors can interactively scroll through the interview and have access to additional visualizations and imagery that contextualize the main narrative. Visitors are thus able to vicariously experience what it was like to be in New York City at the outset of the COVID-19 epidemic. This work provides a case study on how to perform observational research when geographic and bodily distance has become the norm. We discuss the advantages and limitations of our method and conclude with its contributions to the study of cities and for others looking to conduct remote observational research in different fields of knowledge. The Walkie-Talkie maps can be found on this url: https://www.socialdistancing.tech.cornell.edu/what-is-a-walike-talkie

Publisher

SAGE Publications

Subject

Education

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Towards Instrumented Fingerprinting of Urban Traffic: A Novel Methodology using Distributed Mobile Point-of-View Cameras;Proceedings of the 16th International Conference on Automotive User Interfaces and Interactive Vehicular Applications;2024-09-11

2. Participant Directed Mobile Interviews: A Data Collection Method for Conducting In-Situ Field Research at a Distance;International Journal of Qualitative Methods;2023-01

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