Squeaky wheels: Missing data, disability, and power in the smart city

Abstract

Data about the accessibility of United States municipalities is infrastructure in the smart city. What is counted and how, reflects the sociotechnical imaginary (norms and values) of a time or place. In this paper we focus on features identified by people with disabilities as promoting or hindering safe pedestrian travel. We use a regionally stratified sample of 178 cities across the United States. The municipalities were scored on two factors: their open data practices (or lack thereof), and the degree to which they cataloged the environmental features that persons with disabilities deemed critical for safe movement through urban spaces. In contradiction to the dominating narrative of too much data and not enough analyses, we find that when it comes to data points that might be useful to persons with disabilities, data are lacking. This data gap has consequences both politically and materially—on one hand data could help enforce compliance with the Americans with Disabilities Act, on the other they would allow for safe route planning. We find that reading these data formats and collection patterns from the perspective of critical disability studies—particularly those whose work disrupts notions of “normal” —helps answer questions about potential benefits and harms of data practices. This lens has the potential to promote analysis that is as disruptive to injustices as it is practical.

Multiple-Stakeholder Perspectives on Accessibility Data and the Use of Socio-Technical Tools to Improve Sidewalk Accessibility

“…Previous research has documented the lack of data on pedestrian infrastructure accessibility and how such missing data limit municipalities' progress towards barrier removal for people with disabilities [16,21,30]. This unique study examined various stakeholders' perceptions on the use, gathering and application of sidewalks accessibility data.…”

“…The issue for those communities is not only the lack of accessible infrastructure but also the lack of information about what barriers need to be removed and where they are located [20]. For instance, communities often lack reliable data on where sidewalks exist and about their level of accessibility [21]. This lack of data directly limits both planning for and the removal of pedestrian barriers [22].…”

Outlier bias: AI classification of curb ramps, outliers, and context

“…I created a training set based on the open data about curb ramps for nine cities across the United States: Arlington, Boston, Denver, Indianapolis, Nashville, San Francisco, Seattle, Spokane, and Washington DC. Based on work assessing the open data practices across U.S. municipalities (Deitz et al, 2021), these nine cities were the only ones with both adequate data on curb ramp locations (for labeling), LiDAR point cloud data that were open and spatially/temporally proximate to the curb ramp data, and aerial imagery at the time of this analysis. These data were sourced from the USGS (2020) (see Table 1).…”

“…Using triangulation and qualitative methods, I explore algorithmic error to contribute to understanding outlier bias. (Deitz et al, 2021), these nine cities were the only ones with both adequate data on curb ramp locations (for labeling), LiDAR point cloud data that were open and spatially/temporally proximate to the curb ramp data, and aerial imagery at the time of this analysis. These data were sourced from the USGS (2020) (see Table 1).…”

Why government supported smart city initiatives fail: Examining community risk and benefit agreements as a missing link to accountability for equity-seeking groups

“…Loos et al (2020) highlight the issues of accessibility for older people in public transit and mobility planning for smart cities. Describing the lack of data on which smart city decisions are made, Deitz et al (2021) conclude there is a lack of data on infrastructure required by people with disabilities. Wang et al (2021) analyze smart city proposals for the U.S. department of transportation smart city challenge and identify a lack of inclusion of accessibility considerations for people with disabilities.…”