Combining crowdsourcing and google street view to identify street-level accessibility problems

ABSTRACT Poorly maintained sidewalks, missing curb ramps, and other obstacles pose considerable accessibility challenges; however, there are currently few, if any, mechanisms to determine accessible areas of a city a priori. In this paper, we investigate the feasibility of using untrained crowd workers from Amazon Mechanical Turk (turkers) to find, label, and assess sidewalk accessibility problems in Google Street View imagery. We report on two studies: Study 1 examines the feasibility of this labeling task with six dedicated labelers including three wheelchair users; Study 2 investigates the comparative performance of turkers. In all, we collected 13,379 labels and 19,189 verification labels from a total of 402 turkers. We show that turkers are capable of determining the presence of an accessibility problem with 81% accuracy. With simple quality control methods, this number increases to 93%. Our work demonstrates a promising new, highly scalable method for acquiring knowledge about sidewalk accessibility

A Pilot Study of Sidewalk Equity in Seattle Using Crowdsourced Sidewalk Assessment Data

We examine the potential of using large-scale open crowdsourced sidewalk data from Project Sidewalk to study the distribution and condition of sidewalks in Seattle, WA. While potentially noisier than professionally gathered sidewalk datasets, crowdsourced data enables large, cross-regional studies that would be otherwise expensive and difficult to manage. As an initial case study, we examine spatial patterns of sidewalk quality in Seattle and their relationship to racial diversity, income level, built density, and transit modes. We close with a reflection on our approach, key limitations, and opportunities for future work.Comment: Workshop paper presented at "The 1st ASSETS'22 Workshop on The Future or urban Accessibility (UrbanAccess'22)

Improving public transit accessibility for blind riders by crowdsourcing bus stop landmark locations with Google street view: An extended analysis

Low-vision and blind bus riders often rely on known physical landmarks to help locate and verify bus stop locations (e.g., by searching for an expected shelter, bench, or newspaper bin). However, there are currently few, if any, methods to determine this information a priori via computational tools or services. In this article, we introduce and evaluate a new scalable method for collecting bus stop location and landmark descriptions by combining online crowdsourcing and Google Street View (GSV). We conduct and report on three studies: (i) a formative interview study of 18 people with visual impairments to inform the design of our crowdsourcing tool, (ii) a comparative study examining differences between physical bus stop audit data and audits conducted virtually with GSV, and (iii) an online study of 153 crowd workers on Amazon Mechanical Turk to examine the feasibility of crowdsourcing bus stop audits using our custom tool with GSV. Our findings reemphasize the importance of landmarks in nonvisual navigation, demonstrate that GSV is a viable bus stop audit dataset, and show that minimally trained crowd workers can find and identify bus stop landmarks with 82.5% accuracy across 150 bus stop locations (87.3% with simple quality control). </jats:p

Improving public transit accessibility for blind riders by crowdsourcing bus stop landmark locations with Google street view

Low-vision and blind bus riders often rely on known physical landmarks to help locate and verify bus stop locations (e.g., by searching for a shelter, bench, newspaper bin). However, there are currently few, if any, methods to determine this information a priori via computational tools or services. In this paper, we introduce and evaluate a new scalable method for collecting bus stop location and landmark descriptions by combining online crowdsourcing and Google Street View (GSV). We conduct and report on three studies in particular: (i) a formative interview study of 18 people with visual impairments to inform the design of our crowdsourcing tool; (ii) a comparative study examining differences between physical bus stop audit data and audits conducted virtually with GSV; and (iii) an online study of 153 crowd workers on Amazon Mechanical Turk to examine the feasibility of crowdsourcing bus stop audits using our custom tool with GSV. Our findings reemphasize the importance of landmarks in non-visual navigation, demonstrate that GSV is a viable bus stop audit dataset, and show that minimally trained crowd workers can find and identify bus stop landmarks with 82.5 % accuracy across 150 bus stop locations (87.3 % with simple quality control)

Sensing and Feedback of Everyday Activities to Promote Environmental Behaviors

With population increases, global economic growth, and shifts in climate, the world is facing an unprecedented demand for resources that are becomingly increasingly scarce. Although often overlooked, our everyday activities such as commuting to work, showering, and clothes washing can have significant impact on the environment. The central problem addressed in this dissertation is not that humans negatively impact the environment—indeed, some amount of impact is unavoidable—but rather that we have insufficient means to monitor and understand this impact and to help change our behavior if we so desire. This dissertation focuses on creating new types of sensors to monitor and infer everyday human activity such as driving to work or taking a shower and taking this sensed information and feeding it back to the user in novel, engaging, and informative ways with the goal of increasing awareness and promoting environmentally responsible behavior. We refer to these sensing and feedback systems as eco-feedback technology. Our research takes advantage of a number of technology trends including the increasingly low cost of fast computation, advances in machine learning, and the prevalence and affordability of new types of display mediums (e.g., mobile phones) to design systems never before possible. This dissertation provides a theoretical perspective with which to guide the design of new eco-feedback systems as well as specific formative and technical contributions for eco-feedback in the domains of personal transportation any water usage. Key contributions include the invention of new low-cost sensing systems for monitoring and inferring transit routines and disaggregated water usage in the home along with eco-feedback visualizations that take advantage of this unprecedented data. The approaches, empirical findings and a design space for eco-feedback should be of interest to researchers working on eco-feedback in HCI, Ubicomp and environmental psychology, as well as to practitioners and designers tasked with constructing new types of ecofeedback systems and/or utility bills. More broadly, this dissertation also has implications for the construction of sensing and feedback technology in general, including domains such as persuasive technology, personal informatics, and health behavior change

Disasters in Personal Informatics: The Unpublished Stories of Failure and Lessons Learned ACM Classification Keywords

Abstract Though never a desirable outcome, failure is an inevitable part of research. Too often, however, the tried but failed paths are lost in the translation of work to publication. With the pragmatics of publishing (e.g., page limits) and the academic emphasis on positive outcomes, failed processes, methodologies, study designs, and technologies are frequently not disclosed. This is a missed opportunity, particularly for nascent areas like Personal Informatics (PI) as well as other research areas, more generally, that share high costs in time, development, and recruitment for building and deploying testable systems. Thus, we propose a UbiComp2014 workshop focused on failures in PI research. Through short participant authored papers, breakout sessions, madness talks, and all-group discussions, our overarching workshop goals are to share &quot;disaster&quot; stories, reflect on lessons learned, and articulate promising paths forward