Lina Witzel, Sophy Chhong, H. Hutter, Alireza Darvishy
{"title":"On-Site and Remote Crowdsourcing of Accessibility Data for People with Mobility\n Impairments: A Case Study in Zurich’s District 1","authors":"Lina Witzel, Sophy Chhong, H. Hutter, Alireza Darvishy","doi":"10.54941/ahfe1002949","DOIUrl":null,"url":null,"abstract":"Collecting accurate accessibility data systematically for pathways is a\n time-consuming task that typically requires expert knowledge. However, it is a\n prerequisite to enable reliable and trustworthy accessible routing. The development of\n Capture & Go, a mobile application to report barriers for people with mobility\n impairments, facilitates the on-site collection of crowdsourced accessibility data.\n Several other mapping tools contain accessibility data, although they have not been\n developed explicitly for this purpose. In contrast to Capture & Go, they allow data\n collection to be performed remotely. Using quantitative and qualitative approaches, we\n analyzed several such applications and examined their efficiency in capturing barriers\n in a case study of District 1 in Zurich. The remotely collected data was compared to the\n data of the barriers captured on-site using Capture & Go. Overall, the remote tools\n were less efficient than Capture & Go in terms of effort, coverage, and accuracy of\n the barriers, as well as usability.","PeriodicalId":383834,"journal":{"name":"Human Interaction and Emerging Technologies (IHIET-AI 2023): Artificial\n Intelligence and Future Applications","volume":"22 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Human Interaction and Emerging Technologies (IHIET-AI 2023): Artificial\n Intelligence and Future Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.54941/ahfe1002949","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Collecting accurate accessibility data systematically for pathways is a
time-consuming task that typically requires expert knowledge. However, it is a
prerequisite to enable reliable and trustworthy accessible routing. The development of
Capture & Go, a mobile application to report barriers for people with mobility
impairments, facilitates the on-site collection of crowdsourced accessibility data.
Several other mapping tools contain accessibility data, although they have not been
developed explicitly for this purpose. In contrast to Capture & Go, they allow data
collection to be performed remotely. Using quantitative and qualitative approaches, we
analyzed several such applications and examined their efficiency in capturing barriers
in a case study of District 1 in Zurich. The remotely collected data was compared to the
data of the barriers captured on-site using Capture & Go. Overall, the remote tools
were less efficient than Capture & Go in terms of effort, coverage, and accuracy of
the barriers, as well as usability.