Project Sidewalk: A Web-based crowdsourcing tool for collecting sidewalk accessibility data at scale

• Main Authors: SAHA, Manaswi, SAUGSTAD, Michael, MADDALI, Hanuma, ZENG, Aileen, HOLLAND, Ryan, BOWER, Steven, DASH, Aditya, CHEN, Sage, Li, Anthony, HARA, Kotaro, FROEHLICH, Jon
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2019
• Subjects: GIS; Mobility impairments; Accessibility; Crowdsourcing; Databases and Information Systems; Geographic Information Sciences
• Online Access: https://ink.library.smu.edu.sg/sis_research/4399; https://ink.library.smu.edu.sg/context/sis_research/article/5402/viewcontent/Saha_ProjectSidewalkAWebBasedCrowdsourcingToolForCollectingSidewalkAccessibilityDataAtScale_2019.pdf
• Institution: Singapore Management University

We introduce Project Sidewalk, a new web-based tool that enables online crowdworkers to remotely label pedestrian-related accessibility problems by virtually walking through city streets in Google Street View. To train, engage, and sustain users, we apply basic game design principles such as interactive onboarding, mission-based tasks, and progress dashboards. In an 18-month deployment study, 797 online users contributed 205,385 labels and audited 2,941 miles of Washington DC streets. We compare behavioral and labeling quality differences between paid crowdworkers and volunteers, investigate the effects of label type, label severity, and majority vote on accuracy, and analyze common labeling errors. To complement these findings, we report on an interview study with three key stakeholder groups (N=14) soliciting reactions to our tool and methods. Our findings demonstrate the potential of virtually auditing urban accessibility and highlight tradeoffs between scalability and quality compared to traditional approaches.