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Nighttime sidewalk illumination has a significant and unequal influence on where and whether pedestrians walk at night. Despite the importance of pedestrian lighting, there is currently no approach for measuring and communicating how humans experience nighttime sidewalk light levels at scale. We introduce NightLight, a new sensing approach that leverages the ubiquity of smartphones by re-appropriating the built-in light sensor—traditionally used to adapt screen brightness—to sense pedestrian nighttime lighting conditions. We validated our technique through in-lab and street-based evaluations characterizing performance across phone orientation, phone model, and varying light levels demonstrating the ability to aggregate and map pedestrian-oriented light levels with unaltered smartphones. Additionally, to examine the impact of light level data on pedestrian route choice, we conducted a qualitative user study with 13 participants using a standard map vs. one with pedestrian lighting data from NightLight. Our findings demonstrate that people changed their routes in preference of well-light routes during nighttime walking. Our work has implications for improving personalized navigation, understanding pedestrian route choice, and expanding passive urban sensing. 

Data on pedestrian infrastructure is essential for improving the mobility environment and for planning efficiency. Although governmental agencies are responsible for capturing data on pedestrian infrastructure mostly by field audits, most have not completed such audits. In recent years, virtual auditing based on street view imagery (SVI), specifically through geo-crowdsourcing platforms, offers a more inclusive approach to pedestrian movement planning, but concerns about the quality and reliability of opensource geospatial data pose barriers to use by governments. Limited research has compared opensource data in relation to traditional government approaches. In this study, we compare pedestrian infrastructure data from an opensource virtual sidewalk audit platform (Project Sidewalk) with government data. We focus on neighborhoods with diverse walkability and income levels in the city of Seattle, Washington and in DuPage County, Illinois. Our analysis shows that Project Sidewalk data can be a reliable alternative to government data for most pedestrian infrastructure features. The agreement for different features ranges from 75% for pedestrian signals to complete agreement (100%) for missing sidewalks. However, variations in measuring the severity of barriers challenges dataset comparisons. 

Public artwork, from vibrant wall murals to captivating sculptures, can enhance the aesthetic of urban spaces, foster a sense of community and cultural identity, and help attract visitors. Despite its benefits, most public art is visual, making it often inaccessible to blind and low vision (BLV) people. In this workshop paper, we first draw on art literature to help define the space of public art, identify key differences with curated art shown in museums or galleries, and discuss implications for accessibility. We then enumerate how existing art accessibility techniques may (or may not) transfer to urban art spaces. We close by presenting future research directions and reflecting on the growing role of AI in making art accessible. 

Geovisualizations are powerful tools for exploratory spatial analysis, enabling sighted users to discern patterns, trends, and relationships within geographic data. However, these visual tools have remained largely inaccessible to screen-reader users. We introduce AltGeoViz, a new interactive geovisualization approach that dynamically generates alt-text descriptions based on the user’s current map view, providing voiceover summaries of spatial patterns and descriptive statistics. In a remote user study with five screenreader users, we found that participants were able to interact with spatial data in previously infeasible ways, demonstrated a clear understanding of data summaries and their location context, and could synthesize spatial understandings of their explorations. Moreover, we identified key areas for improvement, such as the addition of spatial navigation controls and comparative analysis features