How to motivate participation and improve quality of crowdsourcing when building accessibility maps

Abstract

Crowdsourcing, as one of the most promising techniques for distributed problem-solving, requires sustained human involvement. Therefore, it also brings new challenges to data management, fundamentally data input and its quality. In this paper, we looked at various forms of user motivations and quality control of crowdsourcing when building accessibility maps mobile applications. We discuss how motivations could be used to contribute to our accessibility maps scenarios, and how data can be improved for two types of participants: individual participants and organization participants. We identified three useful techniques for improving data quality: qualification-based, reputation-based, and aggregation-based. In addition, based on our own mobile application (named WEMAP), we evaluated our approaches through focus group discussions and in-depth interviews.

Building a Crowdsourcing based Disabled Pedestrian Level of Service routing application using Computer Vision and Machine Learning

“…Given that crowdsourcing is a centerpiece in this project, some future work must be dedicated to motivation methods, so as to engage citizens to participate in the achievement of the several tasks required by our approach. In the continuation of Liu et al [15], and with the same focus on boosting the motivation of participants and data quality control, we plan to investigate how to use a gamification conceptual framework to help understand how human motivations can be directed by game elements (e.g. Octalysis Framework [22]) and to develop incentives to execute tasks.…”

“…Precisely crowdsourcing techniques offer the ability to reach a wider and more diverse panel of citizens, although targeting strategies may also be required to reach more specific audiences. Concerning the purpose of quality, Liu et al [15] identified three useful techniques for improving the quality of crowdsourced data when building accessibility maps, including qualification tests, reputation systems, and aggregation techniques. Their findings highlighted that the accuracy rate has a significant increase after applying the intervention of quality control methods.…”

“…2) Data acquisition and geolocation: in this step, a second crowdsourcing tool may be relevant to serve as a feeding source for raw images. While applying crowdsourcing motivation methods [15], this tool invites application users to take pictures with their mobile device. These will be used as raw data input for the digitizing stage.…”

SmartWheels: Detecting urban features for wheelchair users’ navigation

“…A well-known problem with crowdsourcing is to motivate users to contribute since it often requires explicit user action. This problem is addressed, among others, by Liu et al [24] while designing the WeMap system [25] that, similarly to Moving Wheels, is aimed at providing accessibility information about routes and places. Other projects share a similar objective; In particular, several services allow the users to rate a Point of Interest (POI) accessibility (e.g., aXs map (www.axsmap.com) and wheelmap (wheelmap.org)).…”

A Two-Level Approach for the Crowd-Based Collection of Vehicles From 3d Point Clouds

“…Quality control is a challenge in paid and voluntary crowdsourcing (Leibovici et al 2017;Liu et al 2018), as the quality of crowdsourced jobs can vary widely (Vaughan 2017). The crowd is composed of people with unknown and diverse skills, abilities, interests, personal goals, and technical resources (Daniel et al 2018).…”